discharges against medical advice: associations with

DISCHARGES AGAINST MEDICAL ADVICE: ASSOCIATIONS WITH SELECTED OUTCOMES AND THE ROLE OF HOSPITAL-LEVEL CHARACTERISTICS

by Hoon Byun

A dissertation submitted to Johns Hopkins University in conformity with the requirements for the degree of Doctor of Public Health

Baltimore, Maryland

March, 2016

ii

Abstract

Discharges Against Medical Advice or ‘DAMA’ occur when patients decide to withdraw

consent and leave care against the advice of the treating physician. Studies have found this

phenomenon to account for 1-2% of all US hospital discharges. It is a problematic subject,

where the rights of the patient can and do clash with the responsibilities of the physician,

which can result not only in conflict, but also worse health outcomes, wasted resources, and

legal liability. By using both anonymized and publicly available administrative healthcare

data, this study investigated the factors associated with DAMA in two different sets of data

by matching DAMA observations with similar non-DAMA observations and comparing

outcomes. Finally, the study investigated the existence of characteristics at the hospital level

that are associated with high levels of DAMA.

The study found associations between discharges against medical advice and selected

outcomes, such as a higher likelihood of 30-day readmission, longer lengths of stay, higher

costs, and increased severity of illness on the readmission visit in comparison to the index

visit at one prominent hospital. Also, the study found associations between discharges

against medical advice and decreased lengths of stay, total costs, and lower severity of illness

on the index visit at both the study hospital, as well as nationally. Finally, the study found

evidence that there are patient level and hospital level characteristics that are associated with

higher than expected levels of discharges against medical advice, and that certain hospitals

are at increased risk for experiencing DAMA.

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This study confirms the notion that DAMA is an issue worth addressing in order to improve

the conditions of the patient, provider, and hospital.

Advisor: Laura Morlock, PhD

Readers: Elizabeth Stuart PhD (Chair), Greg de Lissovoy, PhD, Conan Dickson PhD,

Sydney Dy MD, Kenneth Shermock PhD, and Scott Levin PhD.

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Acknowledgements

As I write this, I think back to what I have been through; surely, I am not the same

person as when I began many years ago. This has been a long but rewarding journey,

personally, professionally, and academically. I still ponder how I will spend the next chapter,

with new found hours, but this is a good problem to have.

I am honored to have had such an experience within these hallowed halls, under the

guidance of persons for whom I have the utmost respect and admiration; persons of

character, experience, and patience. I make a promise to myself that I shall emulate them as

best I can, but I will surely fall short.

Persons like Conan Dickson, who not only gave me the idea to study this topic of

patients who leave against medical advice, but also showed me what it means to be a

knowledgeable and capable practitioner, liked and respected among those at the university

and the Hospital, but ever humble and jovial. Or like Ken Shermock, who was generous

with his time to talk with me throughout my journey, dispensing advice and sharing the

many experiences gleaned from his own long journey, and sometimes giving sorely needed

encouragement. Or Greg de Lissovoy, who was kind and patient enough to take on this

graduate student, and use his skills and long-experience to help carve and shape a rough-

hewn work-in-progress into something more elaborate and contoured. Or Liz Stuart, whose

deep knowledge of advanced techniques, diligent email responses, and easy willingness to

share of this knowledge was and is still striking to me. Or Sydney Dy, who always, in her

soft-spoken manner and ready smile, had sharp insight into my work, but also words of

empathy and understanding.

And of course, my dear advisor, Laura Morlock, who despite her many other

obligations, took the time to guide me throughout the whole journey since step one. I will

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forever be indebted to her kindness and good cheer, her patience with me, and her tireless

efforts on my behalf. For me, she will always represent the model professor and mentor.

There are many others who contributed in their own ways to this effort. Though I

am grateful and obliged, I cannot possibly list them all for you, gentle reader, so I keep them

close in my thoughts and prayers. And so with deep gratitude and acknowledgement, I now

take this next step.

Hoon Byun.

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Table of Contents Abstract................................................................................................................................................. ii

Acknowledgements .......................................................................................................................... iv

Chapter 1: DAMA: Introduction and Statement of the Problem ......................................... 1

Problem Statement ....................................................................................................................... 1

Aims of the Study .......................................................................................................................... 2

Significance .................................................................................................................................... 3

A Roadmap ..................................................................................................................................... 3

Chapter 2: DAMA Background and Literature Review ......................................................... 5

Informed Consent and Autonomy ........................................................................................... 6

Reasons for DAMA ....................................................................................................................... 7

An Emerging Profile .................................................................................................................... 9

DAMA and the Community Hospital ..................................................................................... 9

DAMA and the ER ..................................................................................................................... 10

DAMA and Psychiatric Care.................................................................................................... 11

Legal Liability .............................................................................................................................. 13

Patient Characteristics Associated with DAMA ................................................................ 14

Substance Abuse and DAMA .................................................................................................. 14

DAMA, Anxiety and Depression ............................................................................................ 16

Role of the Provider .................................................................................................................... 17

Providers’ Perspectives on DAMA ......................................................................................... 17

Importance of Communication in DAMA ........................................................................... 19

Outcomes Associated with DAMA ........................................................................................ 21

All-Cause 30-day Readmissions .............................................................................................. 21

Maryland and Readmissions ................................................................................................... 24

DAMA and The Johns Hopkins Hospital ........................................................................... 24

Literature Review Summary .................................................................................................... 26

Chapter 3: Methods ......................................................................................................................... 29

The Research Question: ........................................................................................................... 29

Research Aims ............................................................................................................................. 31

Research Aim1: .......................................................................................................................... 31

Research Aim 2: ......................................................................................................................... 39

Research Aim 3: ......................................................................................................................... 44

Ethical Considerations .............................................................................................................. 50

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Chapter 4: Results ........................................................................................................................... 51

Research Aim 1: ............................................................................................................................. 51

Analysis, Research Aim 1 ..................................................................................................... 51

Results for Research Aim 1 .................................................................................................. 53

Research Aim 2: ............................................................................................................................. 64



Research Aim 3: ............................................................................................................................. 71



Chapter 5: Discussion and Policy Implications ..................................................................... 78

Key Findings ................................................................................................................................ 78

Research Aim 1 ........................................................................................................................ 78

Research Aim 2 ....................................................................................................................... 80

Research Aim 3 ....................................................................................................................... 81

Policy Implications ..................................................................................................................... 83

Research Aim 1 ........................................................................................................................ 83

Research Aim 2 ....................................................................................................................... 84

Research Aim 3 ....................................................................................................................... 85

Weaknesses and Limitations ................................................................................................... 85

Future directions ......................................................................................................................... 87

Conclusions .................................................................................................................................. 89

References ......................................................................................................................................... 91

Appendix A. Assessing covariate balance before and after greedy matching of DAMA and non-DAMA on propensity score, JHH Data ................................................................................. 96

Appendix B: Assessing covariate balance before and after greedy matching of DAMA and non-DAMA on propensity score, HCUP Data ............................................................................. 99

Appendix C: Code used in the Identification of Specific ICD9 codes for Alcohol Abuse, Psychoses, HIV/AIDs, Depression, and Active Drug Use ...................................................... 103

Appendix D: IRB Approval Letter ............................................................................................... 105

Appendix E: Curriculum Vitae ...................................................................................................... 107

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List of Tables

Table 1: Variables in the Aim1 DAMA Propensity Score Model ............................................... 37 Table 2: Variables in the Aim2 DAMA Propensity Score Model ............................................... 43 Table 3: Variables used to calculate expected DAMA for each hospital ................................... 47 Table 4: Variables used to regress High O/E ratios ..................................................................... 47 Table 5: Aim1 Sample Characteristics ............................................................................................. 53 Table 6: Resulting Odds Ratios from the DAMA Propensity Score Regression ..................... 56 Table 7: Comparing Index Visit Outcomes between DAMA and matched non-DAMA ...... 57 Table 8: Likelihood of All-Cause 30-day Readmissions between DAMA and Matched non-DAMA observations .......................................................................................................................... 60 Table 9: Likelihood that Readmit LOS > Index LOS for DAMA in comparison to non-DAMA ................................................................................................................................................. 61 Table 10: Likelihood that Readmit Charges > Index Charges for DAMA in comparison to non-DAMA ......................................................................................................................................... 62 Table 11: Likelihood that Readmit Severity > Index Severity for DAMA in comparison to non-DAMA ......................................................................................................................................... 63 Table 12: Aim 2 Resulting Odds Ratios from the DAMA Propensity Score Regression ....... 64 Table 13 Aim 2 Sample Characteristics ......................................................................................... 66 Table 14: Comparing Index Visit Outcomes between DAMA and matched non-DAMA by Major Diagnostic Category ............................................................................................................... 68 Table 15: Aim 3 Sample Characteristics .......................................................................................... 73 Table 16: Modeling High Observed to Expected Ratios, all hospitals ....................................... 74 Table 17: Modeling High Observed to Expected Ratios on hospitals with any DAMA experience ............................................................................................................................................ 75

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List of Figures

Figure 1: The General Conceptual Framework for DAMA ........................................................ 30 Figure 2: Conceptual Framework for Aim1: DAMA and Readmissions ................................... 33 Figure 3: Conceptual Framework for Aim2: DAMA and Index Admission Outcomes ......... 40 Figure 4: Conceptual Framework for Aim3: Hospital and Patient Characteristics that are associated with DAMA ..................................................................................................................... 45 Figure 5: HCUP Hospitals by DAMA Observed to Expected Ratios where DAMA>0 ....... 72

1

Chapter 1: DAMA: Introduction and Statement of the Problem

Problem Statement Providing high-quality medical care is difficult, especially to patients who may not wish to

adhere to the treatments and protocols prescribed by their care-team. Discharge against medical

advice (DAMA) is a major form of non-adherence affecting one out of every 50 hospital visits, and

can be defined as either patients actively leaving their care in an unscheduled fashion; or by patients

signing release forms that they are leaving in spite of their doctor's orders (Hwang, Li, Gupta, Chien,

& Martin, 2003; Hwang, May, 2005). It is a form of noncompliance by the patient that limits the

effectiveness of appropriate and comprehensive treatment given at the hospital. Such patients may

face many personal, economic and social pressures, but may also be subject to pressures that are

provider and environment-related (Onukwugha et al., 2010). Thus, an informed patient choosing to

leave may not be making a fully voluntary decision (Berger, 2008). This phenomenon is especially

challenging because of the competing interests involved -- the clinician’s duty to provide care and

the patient’s right to refuse care, thus giving rise to ethical, economic, and legal implications.

Physicians and other members of the care teams that treat such patients often report distress and

feelings of being powerless when confronted with a patient choosing to leave in such a manner,

which may create conflict and friction not only between the patient and provider, but also between

members of the care team (Alfandre & H. Schumann, 2013).

A discharge against medical advice can place the patient at risk for adverse health outcomes

by disrupting the normal course of therapy. It thwarts the relationship between the patient and the

care-team and can affect their morale, is a waste of healthcare resources, and can expose the hospital

to legal liability (Ernest Moy & Barbara A. Bartman, 1996). Various studies have shown that leaving

against medical advice increases the likelihood for further health-complications and re-admission to

2

the hospital, which can place their care-team and hospital at legal liability and financial risk (Brook,

Hilty, Liu, Hu, & Frye, 2006; Devitt, Devitt, & Mantosh, 2000; Pages et al., 1998). These prior

studies also attempted to assess effects, more generally, but were focused on specific types of

patients, and/or restricted to smaller samples and particular care-settings (Garland et al., 2013).

One can argue that when a patient is admitted into inpatient care, there is an agreement,

implicit and explicit, that the providers of care will perform their duties, and that the patient will

undergo the prescribed treatment until the recommended discharge. A discharge against medical

advice, whenever it occurs, is a break in the contract by the patient, and is motivated by different

reasons and factors. Such an action can have harmful consequences for both the patient and

provider; as well as others. This study aims to add new knowledge by exploring the associations

between DAMA and selected outcomes. Doing so would then confirm that DAMA is an important

phenomenon in US healthcare, and help inform further study of this charged issue.

Aims of the Study There are three primary aims of the study:

1) How are discharges against medical advice associated with the likelihood of 30-day

readmissions and outcomes of the index visits, and is it also associated with outcomes

tied to the readmission visits? This question will be addressed using the experience of

the Johns Hopkins Hospital, and the outcomes for both the index admission and

readmission will be length of stay, charges, and severity of illness.

2) Building on these analysis results, the second aim of the study is to establish a broader,

more national picture of the associations of DAMA, if any, with the same index visit

outcomes using HCUP data.

3

3) The final aim is to investigate if there are person level and hospital level characteristics

that differentiate hospitals according to higher versus lower levels of DAMA.

Addressing these three aims will help address the question of whether this phenomenon does have

measureable associations with well-known outcomes and measures, and perhaps set the stage for

study of possible causality in the future.

Significance The study of discharges against medical advice is addressing an important and timely issue

because it not only involves quality of care, patient safety, and engagement of clinical human

resources, but also the hospital care of a vulnerable population that is oftentimes associated with

drug abuse, mental health comorbidities, and lower socio-economic status. DAMA are also

occurring in a variety of treatment settings, as reflected in the increasing number of studies of

DAMA among patients in General Medicine (Onukwugha et al., 2010). It is not apparent that there

has been a study of this phenomenon at The Johns Hopkins Hospital in terms of 30-day

readmissions. It is also not apparent that there has been a study of the associations of DAMA in

terms of well-known outcomes such as Lengths of Stay and Total Costs using national data. Finally,

to our knowledge, there has not been a study identifying hospital-level factors that may be associated

with a higher- than-expected likelihood of DAMA. This study aims to fill those research voids and

by doing so, provide a more informed picture of DAMA within the US healthcare system.

A Roadmap The next chapter will be devoted to the review of literature relating to discharges against

medical advice, delving not only into the background and some of the issues that help define the

phenomenon and its importance, but also going into detail about the current research and what has

yet to be addressed. Chapter 3 will be the Methods chapter, where we first frame the main research

4

question and primary conceptual framework that motivates the whole study, and then each of the

three component research aims that address different aspects of the initial question. For each

research aim, we describe the respective framework, the associated hypotheses, study population,

sources of data, and the approach for addressing each hypothesis. Chapter 4 is devoted to

describing the findings of analyses for each of the research aims by including tables summarizing

findings and providing some discussion of the key findings by hypothesis. The following Chapter 5

will include a discussion of the findings in light of the research aims, and then will tie these findings

back to the original research question posed at the beginning of the Methods chapter. Also, there

will be a discussion of some policy implications regarding discharges against medical advice, as well

as a review of some of the limitations of the study, and possible future directions to pursue from this

initial inquiry.

5

Chapter 2: DAMA Background and Literature Review

As stated earlier, Discharges Against Medical Advice are instances where patients who are

deemed competent, decide to withdraw consent and leave care against the advice of the treating

physician. Implicit in hospital care is the notion of a patient consenting to treatment, initially given

at admission and also re-iterated throughout the course of the hospital stay. Leaving care against

medical advice is predicated upon a given patient’s decision to forego further treatment after having

been informed of the associated risks. Indeed, patients who are deemed competent, and who leave

their care against medical advice, present an issue in which medical, legal and ethical concerns arise.

Discharges against medical advice occur at the intersection of patient autonomy and the

physicians' duty to provide treatment; resulting in a tension that places a patient's rights against a

physician's responsibility for the well-being of that patient, and even of third parties (Gerbasi &

Simon, 2003). It can be an emotionally charged situation for both patients and providers, and many

doctors and nurses have ready examples and vignettes of patients, sometimes in the midst of

psychological crises, or financial concerns, or outside obligations, who decide to withdraw consent

and participation in their course of hospital care (Hwang, May, 2005; Stern, Silverman, Smith, &

Stern, 2011; Taquetti, 2007). A discharge from the hospital against medical advice can involve a

heightened level of risk for the patient and liability for the physician, and can represent a failure in

the relationship between patient and caregiver, with direct implications for the well-being of the

patient.

Approximately 1%-2% of all hospital discharges in acute-care hospitals in the US are against

medical advice (Alfandre, 2009; Hwang, May, 2005). This rate may vary depending on the type and

location of the hospital, the population from which the patient came, and underlying comorbid

conditions of the patient, and the course of treatment. It has been estimated that 6%-35% of

psychiatric patients are discharged against medical advice (Devitt et al., 2000), and that certain

6

patient subgroups, like those from urban areas that are injection drug users, can have rates that are

upwards of 13% to 54% (Anis et al., 2002).

These types of encounters are more often seen as a problem at general hospitals, often

leading to both frustration and resentment from doctors and nurses, and poor health-outcomes for

the patients (Stern et al., 2011). The trend has been going upward over the last few decades, in part

because of the recent emphasis and importance placed on patient autonomy and patient

empowerment by legislative bodies (Devitt et al., 2000). Moreover, there has been increasing

awareness as evidenced by the number of studies over the past few years of patients admitted to

General Medicine and Psychiatric wards who had left against medical advice (Onukwugha et al.,

2010). The issue is of particular importance for those units of the hospital treating patients with

substance abuse and co-morbid psychiatric conditions because they are more likely to have AMA

discharges (Pages et al., 1998).

Informed Consent and Autonomy The main idea of informed consent is the disclosure of risks, and thus the presiding clinician

should have a documented discussion with the patient or surrogate so that the patient comprehends

the inherent risks and consequences of leaving before medically ready, and is informed about

possible alternatives to leaving. Necessarily, patients making known their intentions to leave care

against medical advice pit their interests against those of their physicians and care teams. A tension

arises from the competing rights of the patient in regards to autonomy and privacy, and the

clinician’s right to provide sound and adequate care, making the best attempt at protecting the

patient from harm. The clinician’s obligation may also extend to those third parties not immediately

present who would also be affected by the patient’s decision to leave prematurely.

7

Informed consent not only serves as the basis of treatment acceptance, it is also the

foundation of ethical medical practice and patient autonomy. If adult patients (or their surrogates)

are cognitively and mentally capable, then they have the right to determine what treatment they will

accept or decline. A patient must provide informed consent to non-emergency and psychiatric care,

and may refuse treatment even though such refusal would put his or her life at risk (Gerbasi &

Simon, 2003). Thus, the decision to discharge oneself from care against medical advice is predicated

on informed consent. However, this is all predicated on the notion that not only is the patient at

hand informed of the risks and consequences, but also competent to make decisions (Janofsky,

2012). There can be reason to believe that patients’ capacity to fully understand their decisions to

forego the benefits of further hospitalization, as well as comprehend the resultant consequences may

be impaired due to some underlying mental illness (Gerbasi & Simon, 2003). It is not difficult to

imagine that this diminished cognitive ability of the patient may also directly correspond with a

heightened risk of harm to self and others, which, set against a backdrop of individual rights and

self-determination, could cause tension and risk to the treating care team. This topic of patient

competency and capacity and how it can affect a patient’s ability to leave against medical advice will

be further discussed below with regards to DAMA and psychiatric care.

Reasons for DAMA Patients will choose to withdraw consent and leave their inpatient care for individualized and

myriad reasons. Many of the reasons often cited by patients are that they must attend to their

families or personal obligations, that they must maintain their employment or are worried about

their finances resulting from hospitalization; others may feel that they are dissatisfied with the

treatment provided, disagree with their care team about an aspect of care or have differences with

hospital staff, feel well enough to leave without further delay, or suffer some sort of psychological

8

agitation or anxiety emanating from the hospitalization itself (Brook et al., 2006; Dubow, Propp, &

Narasimhan, 1992; Gerbasi & Simon, 2003; Hwang et al., 2003; Hwang, May, 2005; Stern et al.,

2011).

Indeed, Onukwugha and colleagues conducted a qualitative study based at a large, urban

teaching hospital, using focus group interviews separately of patients and providers from the general

medicine service of an urban medical center in order to identify reasons for DAMA among those

who have experienced it firsthand. From their sample of 18 patients and 15 providers (nurses,

physicians and social workers), they identified seven major reasons patients left early:

1) Drug-seeking behavior,

2) Pain management,

3) Personal obligations,

4) Wait times,

5) Bedside manner of the provider,

6) Confusion of the hospital setting, and

7) Lack of communication (Onukwugha et al., 2010).

Although this study was performed at one medical center, and from the interviews of a few

persons, the finding that both patients and providers seem to identify common reasons would

support the idea that not only is there sufficient opportunity for finding an intervention that takes

into consideration the joint input and perspectives of both patients and providers to reduce DAMA,

but also that there are actions that could be implemented rather quickly, such as speaking in an

empathetic tone with lay terminology, and including nurses in patient-physician consultations

(Onukwugha et al., 2010).

9

An Emerging Profile As mentioned earlier, a review of the DAMA literature yields common themes regarding

characteristics associated with the likelihood of leaving against medical advice such that there is

general agreement on a profile of DAMA. These characteristics include being male, not elderly,

either having Medicaid coverage or having no insurance coverage, lacking a regular primary care

physician, being admitted to the hospital through the emergency room, being admitted for a

diagnosis related to alcoholism and substance abuse, suffering from a mental health condition, and

having experienced a previous instance of DAMA (Ernest Moy & Barbara A. Bartman, 1996;

Hwang, May, 2005; Saitz, 2002). Additionally, it has been noted that rates of AMA discharges are

higher at urban hospitals than hospitals located in suburban or rural areas, and higher at community

hospitals than at teaching hospitals (Brook et al., 2006; Hwang, May, 2005). We now explore the

following characteristics in more detail, given their associations with DAMA.

DAMA and the Community Hospital Despite the fact that this phenomenon takes place both in rural and urban settings, prior

studies of, and interest in, discharges against medical advice have primarily focused on large, urban

hospitals and their inpatients. Seaborn-Moyse and Osmun conducted a study of attributes of

patients who discharged against medical advice from a rural community hospital in Canada to

compare them against the profile of patients leaving against medical advice in the broader literature

(Seaborn-Moyse & Osmun, 2004). From a series of chart-reviews covering a two year period, they

found that the profile of patients who left against medical advice from the rural hospital primarily

matched what others’ research had found: male and middle-aged, had substance abuse and

psychiatric comorbidities with shorter lengths of stay; but that this hospital had an overall lower rate

of DAMA than their larger, urban counterparts (Seaborn-Moyse & Osmun, 2004). The authors

attribute this to certain possible factors, such as the familiarity that is part of being in a smaller

10

community, limited options for alternative hospitals or physicians, and the increased likelihood that

the attending physician is already the patient’s family physician (Seaborn-Moyse & Osmun, 2004).

Unfortunately, there was no other comparisons done with other like-hospitals in order to obtain a

sense of whether DAMA was indeed lower for this class of hospitals, which the authors conceded

(Seaborn-Moyse & Osmun, 2004).

DAMA and the ER In addition to the general medical, psychiatric, and surgical settings at a hospital, discharges

against medical advice can also take place in the emergency room setting. Ding, Jung, Kirsch, et al.

conducted a retrospective cohort study of adult patients of an urban teaching hospital. They

compared the characteristics and short-term outcomes of patients leaving the emergency room

against medical advice with the other patients admitted to the emergency room who either left

without being seen, were admitted for care, or were discharged after being seen (Ding, Jung, Kirsch,

Levy, & McCarthy, 2007). They found that patients leaving against medical advice had significantly

higher rates of readmission within 30 days than patients of the other three ED-disposition groups,

and had correspondingly higher rates of emergent hospitalizations, without having had a higher level

of illness burden (Ding et al., 2007). These suggest that such patients, like DAMA patients in

general, did not stay long enough to benefit from having completed the course of medical care,

and/or were still symptomatic (Ding et al., 2007). In addition, these DAMA patients were much

more likely to be enrolled under Medicaid or have no coverage at all; they also tended to report

having conditions, like chest pain or abdominal pain, that required a degree of diagnostic ‘work-up’

and testing that they may have felt was not worth the trouble or inconvenience (Ding et al., 2007).

In light of the findings, DAMA as a phenomenon is evident in hospital emergency rooms, and ER

physicians would also do well to prevent DAMA by discussing the consequences of leaving early,

11

providing documentation, involving other associates of the patient in the decision, and negotiating

alternate treatments.

In their retrospective review of AMA discharges from an ED at a suburban level-1 trauma

center, Dubow, Propp, and Narasimhan found that the documentation for the ED patients was

often poor and of low quality, and that standards for reasonable care for patients leaving against

medical advice--such as ascertaining that patient understands the diagnosis, that no patient signs out

of care unless assessed for competency, or accorded proper medical follow-up after leaving the

emergency room--were not routinely followed (Dubow et al., 1992). The study also showed the

inherent difficulty that emergency room doctors face in quickly establishing trusting relationships

with patients (Dubow et al., 1992). Findings from these studies indicate that although DAMA

accounts for a larger portion of ER admissions, it is still relevant and reflective of the broader

DAMA phenomenon, and that the emergency room is also vulnerable to the kinds of liabilities and

legal risks that other departments of the hospital face when dealing with patients who leave against

medical advice.

DAMA and Psychiatric Care There can be significant clinical overlap between psychiatric patients and medical patients,

given the high incidence of psychiatric comorbidities in patients admitted to medicine units

(Alfandre, 2009). The tension between patients’ rights and the physicians’ duty to ensure the health

and safety of the patient and others is particularly relevant to the psychiatrists and psychiatric units

because patients with psychiatric conditions or comorbidities are more likely to leave against medical

advice than those patients on general medical or surgical units (Alfandre, 2009; Pages et al., 1998).

These psychiatric patients are likely to be ill and have symptoms at discharge (Gerbasi & Simon,

12

2003). Broadly, psychiatric admissions can either be one of two types: ‘formal’ and ‘informal’

voluntary admissions.

Let us first consider the term ‘voluntary admissions’, which at its core, implies an admission

to care based on a person’s own accord or an ability to choose. These types of admissions and

associated processes can vary by State, but in general, can be divided into two main kinds of

voluntary admissions: ‘formal’ and ‘informal’. Formal voluntary admissions to psychiatric care are

also known as ‘conditional voluntary admissions’, in which the admitted patients must give advance

written notice of their intentions and desire to leave against medical advice. Apart from patients

admitted to General Medicine, such patients may indeed be held against their will for a limited

period of days, while an assessment is made to see if the patient meets the standard for an

involuntary hospitalization. Formal voluntary admissions have been used often in the US because

adult patients are presumed to be competent to both give consent and enter into contracts; one type

of contract being hospitalization that may involve being held later against one’s will (Gerbasi &

Simon, 2003).

Most State statutes encourage voluntary commitment when addressing patient treatment.

The reasons for this bias are threefold: this type of admission holds less stigma for the patient;

involves less coercion and thus a lower likelihood of opposition or conflict between patient and

care-team; and allows patients to retain a level of autonomy and control in their care (Gerbasi &

Simon, 2003). Voluntary admissions comprise 73% of all admissions to psychiatric facilities in the

US (Gerbasi & Simon, 2003). Though policies on voluntary admissions may vary by State, the

common thread that runs across these is the fact that the ‘voluntary’ nature of the patient’s

admission is not truly voluntary, nor do patients have the absolute freedom to leave voluntarily

(Janofsky, 2012).

13

In contrast to formal voluntary admissions, let us also consider the notion of informal

voluntary admissions to care, which center on the patient’s right to leave. In the purest sense, an

informal voluntary admission may be admitted by simple request, oral or written, and patients may

just as easily request to leave care when they wish, even if the care-team feels that the patient meets

the requirements of civil commitment (Gerbasi & Simon, 2003). The other type of informal

voluntary commitment is the quasi-pure informal voluntary admission, where unlike the pure

informal case, patients may be kept against their will for up to 72 hours if they are deemed to be a

risk to themselves or others, thus meeting emergency-hold criteria (Gerbasi Simon 334). This, in

effect, turns their informal voluntary admission into an involuntary admission in the course of their

hospitalization.

Legal Liability Hospitals and health systems have had standardized release forms for use in such instances

when the patient, deemed competent, can request early discharge by signing paperwork absolving

the provider and organization of liability after an AMA discharge. However, physicians may have

mistaken notions that discharging a given patient against medical advice through the usual hospital

protocols will absolve them of any future liability (Devitt et al., 2000; Hwang, May, 2005). In their

review and examination of case-law and precedents regarding DAMA, Devitt and Dewan could not

find support that such precaution is actually protective in a rigorous legal challenge. They generally

found that the defendant-physicians were not held liable on the grounds that the plaintiffs could not

prove negligence, with some of these legal decisions being made on appeal, and that sometimes the

patients were also found to be at fault through ‘contributory negligence’ (Devitt et al., 2000). Thus,

legal action is still possible against providers, and liability for malpractice and liability in failing to

provide sufficient information still exists for the doctor (Devitt et al., 2000). Given the uncertain

14

nature and adverse possibilities after discharge, it is clear that both the care team and hospital have a

clear obligation to themselves to conduct a thorough assessment of a given patient’s capacity to

make informed decisions, whether he or she meets the criteria for involuntary commitment, and to

thoroughly document clinical practice as a good, but not failsafe, measure against possible lawsuits.

Patient Characteristics Associated with DAMA The role of race as a factor in discharges against medical advice has had more controversy

than others. Indeed, there have been many retrospective studies that have found African American

patients to be consistently at a higher risk of leaving AMA, but that other intervening influences

relating to socio-demographic factors may also be at play (Alfandre, 2009). Using three years of

hospital discharge data from California, Florida, and New York to study the determinants of

DAMA, Franks, Meldrum and Fiscella found that African American patients are more likely to leave

their care than other groups, but also found that increased likelihood decreasing after adjusting for

other patient characteristics (Franks, Meldrum, & Fiscella, 2006). They do acknowledge that there

are likely other confounding factors that may help explain some of that difference, such as certain

hospital characteristics, and socioeconomic factors such as Medicaid coverage and income (Franks et

al., 2006). Another mediating factor affecting associations between race and DAMA may likely be

the differences and the (lower) quality of doctor-patient relationships, since non-white rates of

DAMA were associated with lower-quality hospitals (Stern et al., 2011). Hence, the role that race

and ethnicity play in discharges against medical advice may not be fully understood.

Substance Abuse and DAMA There is generally a high prevalence of discharges against medical advice for patients afflicted

with alcohol and substance abuse (Alfandre, 2009; Saitz, 2002). Many of the retrospective studies on

DAMA since the 1980s have cited a consistent association between a patient’s decision to leave

15

against medical advice and the presence of alcoholic abuse or a drug problem (Alfandre, 2009). It

may understandably be the case that these decisions represent underlying addictive and drug-seeking

behaviors, signaling that supporting a habit may be a higher, more immediate priority for the patient

than his or her medical treatment (Onukwugha et al., 2010). Many patients afflicted with active

substance abuse and addiction may leave against medical advice because of inadequate treatment of

pain associated with withdrawal, perceived lack of empathy and respect, and the inconvenience of

being hospitalized while addicted (Saitz, 2002).

Noting the high prevalence of injection drug users within the HIV-positive patients at a

Vancouver hospital, Anis and colleagues conducted a retrospective cohort study of HIV-positive

inpatients to understand those factors that led to DAMA. Using the discharge records of this urban

hospital, they followed HIV patients for 2 years based on an index admission, and any subsequent

readmissions. What they found was that, after adjusting for person-level factors such as age, gender,

and illness-burden, the HIV-positive patients were more likely to leave against medical advice than

standard discharge patients (Anis et al., 2002). Furthermore, these HIV-positive patients leaving

prematurely were more likely to be injection-drug users, and be readmitted more frequently, with a

longer length of stay in the subsequent episodes, which is consistent with the broader literature that

substance abuse is a factor in DAMA (Anis et al., 2002). Hence, HIV-AIDs may well be a condition

associated with DAMA, and worthwhile for further study.

Despite all of the reasons that patients may give, few patients cite their decision to leave to

drug addiction or substance abuse (Hwang, May, 2005). These studies have discussed the need for

early identification, along with communication with the patient about counseling as necessary

components for strategies to reduce rates of DAMA among patients with a history of either alcohol

or drug abuse (Alfandre, 2009). Indeed, substance abuse and the associated addiction-behaviors

cannot be downplayed in discussing DAMA.

16

DAMA, Anxiety and Depression It is widely accepted that anxiety and depression are risk factors for non-compliance in

medical treatment. Patients commonly react to hospitalization with anxiety and/or depression; and

when taken together, these can affect their ability to comprehend and reason (Alfandre, 2009). It is

also well known that these two affective disorders may complicate treatment of underlying medical

conditions, either directly by manifesting physiologically, or indirectly by manifesting behaviorally,

which altogether affect the given patient’s health outcomes (DiMatteo, Lepper, & Croghan, 2000).

Indeed, one of these behavioral manifestations may be non-adherence to prescribed treatment

recommendations, thereby limiting the efficacy of medical treatment. DiMatteo, Lepper, and

Croghan conducted a meta-analysis of quantitative studies from 1968 to 1998 that measured patient

adherence in the presence of anxiety or depression, where the patients were not drawn from a

special population, not already involved in treatment for depression or anxiety, or taking part in an

interventional study (DiMatteo et al., 2000). Their analysis found that there was a 3-fold increase in

the likelihood that depressed patients would be non-compliant than non-depressed patients, but that

anxiety did not have any significant effect (DiMatteo et al., 2000).

The authors state that depression may deter adherence to care because it may bring about

feelings of hopelessness and lack of general optimism, and may reduce cognitive functioning

necessary for following directions. The increase in isolation from social and familial support that

may come about for afflicted patients may also be a factor (DiMatteo et al., 2000). Their study

shows an association as opposed to causation, which the authors acknowledge as a limitation, but

they still offer the finding as an important marker for noncompliance, including DAMA.

17

Role of the Provider Administering medical treatment is a significant responsibility, and for those who are given this

mandate, providing quality care is a larger challenge when confronted with patients who do not adhere or

agree with the course of care prescribed to them. The values, beliefs, and attitudes of such providers that

help comprise a culture of safety and quality care may be at odds with those of the patients who find

themselves leaving care prematurely, causing further lack of connection and affecting perceptions of all

parties involved. The challenge of DAMA can lead to strong reactions from providers, such as concern for

the patient’s safety, feelings of ineffectiveness, and frustration.

The importance of the provider’s role in DAMA cannot be downplayed. In a thoughtful

and early piece on this topic, Schorer examined reasons and associated factors regarding why

patients decided to leave against medical advice by investigating the DAMA experiences of four

major hospitals in the Detroit area with respect to 1-5 year outcomes. DAMA patients were matched

with non-DAMA patients on demographics, diagnoses, comorbidities, and even outlooks of the

physicians (Schorer, 1965). He found that the only significant differences between the two groups

emanated from their physicians -- those physicians who exhibited certain traits relating to goal-

achievement and a more aggressive style and outlook had higher rates of AMA discharges, than did

doctors who exhibited traits relating to nurturance, introspection, and deference (Schorer, 1965).

The core of his findings was that the patients were able to discern their low likelihood of treatment

success from their doctor, leading them to leave care (Schorer, 1965). This would lead to the notion

that the provider and his or her manner in dealing with the patient does have a large influence on

the patient’s determination to leave against medical advice.

Providers’ Perspectives on DAMA To that end, Windish and Ratanawongsa conducted a qualitative study of providers at The

Johns Hopkins Hospital and their perceptions of patients who leave against medical advice by

interviewing physicians attendant at recent AMA discharges, focusing on the actions they took when

18

learning of their patient’s wish to leave, their resultant feelings of the experience, and any lessons

learned. Their analyses found four primary themes that the surveyed physicians seemed to have in

common:

1) Providers believe that their patients lacked insight into their medical conditions: they

perceived that these patients did not or could not appreciate the import of their illness or

the proffered medical treatment at hand, and downplayed their sickness.

2) The interaction was characterized by mistrust and conflict: the providers felt resistance

from the patients, and sometimes felt that patients withheld information or would not

articulate reasons (possibly activities related to substance abuse) for wanting to leave care.

3) Some physicians still expressed a level of empathy with their patients: they could

understand how such patients who leave against their advice may have competing

priorities outside the hospital, and that these patients may feel anger, frustration, and fear

about their medical care, thereby leading some physicians to be open minded about

exploring ways to connect.

4) Reflection on their own professional role and obligations towards challenging patients: the

doctors acknowledged the inherent conflict between maintaining professional

responsibilities for care while recognizing the importance of patient autonomy and right

to make decisions, however poor (Windish & Ratanawongsa, 2008).

It is interesting to note that in order to help balance their study, they did try to contact the

corresponding patients who had left AMA, but they were only able to find four patients of a total

possible 34, and thus excluded that portion of their study (Windish & Ratanawongsa, 2008).

Though their study focuses on a limited sample of providers from one urban, teaching institution,

they do bring light to the issue that providers may need to address the need for better connection

19

and understanding with their patients, and that they be more aware of their own assumptions and

attitudes regarding DAMA.

It has even been noted that providers may have incorrect notions about who may bear

financial responsibility for discharges against medical advice. In such instances, physicians often

inform their patient they will be financially responsible for charges incurred during their

hospitalization (Schaefer et al., 2012). In an interesting study of the topic of financial responsibility,

Schaefer et al. conducted a survey of medical residents and attending physicians, as well as a review

of a decade’s worth of medical inpatient discharge records and AMA release forms at an academic

medical center in Chicago (Schaefer et al., 2012). They subsequently found that among the 1.1% rate

of DAMA during the review period, payment was indeed refused, but not for the reasons that may

have been expected, such as administrative and paperwork delays (Schaefer et al., 2012). Indeed,

payments--whether by Medicare or private insurance--is based upon medical necessity, irrespective

of how the patient was finally discharged (Schaefer et al., 2012). Their study found no evidence for

the notion of insurance denying payment for patients leaving against medical advice. These

experiences not only indicate a need to promote effective communication, but also to ensure that

physicians have accurate information when negotiating with patients who have given notice of their

intention to leave.

Importance of Communication in DAMA A lack of effective communication between patient and provider (and even among

providers) may complicate an already complex situation between parties, and lead to subsequent

problems with the accurate diagnosis and appropriate treatment of the patient. A patient’s decision

to leave against medical advice may stem from a breakdown in communication with her or his care

provider (Windish & Ratanawongsa, 2008). It has even been said that a threat to leave can be

20

interpreted as an entreaty or a method to express frustration by the patient in a desire for better

communication and respect (Albert & Kornfield, 1973; Devitt et al., 2000). It has been found that

there can be discordant views of the importance and justification of a hospital stay between parties

due to poor communication between patient and provider, which could further deteriorate trust

placed upon the provider, stressing an already tenuous relationship (Windish & Ratanawongsa,

2008).

In their meta-review of the communication between patients and providers and its impact

on patient adherence and patient satisfaction, Roter and Hall found that physician communication

and training do positively matter in the patient’s view of the encounter, which in turn influences and

impacts their adherence behavior (Roter & Hall, 2009). They found that when measured, discrete

behaviors such as asking questions, exchanging information, and positive coaching led patients to

feel trusted and empowered, and that they were emotionally supported. This led to higher measured

rates of patient satisfaction as well as higher rates of patient adherence to a given regimen (Roter &

Hall, 2009).

When the patient has decided to withdraw consent and leave, communication may also have

import on the patient’s well-being, as it may allow the provider to give the exiting patient timely

information for care outside the hospital (Hwang, May, 2005). Also, effective and open

communication between the provider and an at-risk patient has been shown to facilitate counseling-

based interventions for medication adherence and addiction -- which are issues that feature

prominently for those at risk of DAMA (Saitz, 2002). Finally, good communication between the

providers themselves will facilitate the sharing of information about a patient’s ability to provide

informed consent, perhaps leading to an involuntary hold due to the patient’s diminished capacity,

thereby preventing a larger problem (Hwang, May, 2005). Although there are many researchers who

actively study patient characteristics and behavior in the context of leaving hospital care prematurely,

21

there has yet to be rigorous study of how physician factors may contribute to this phenomenon, but

several studies suggest that physicians indeed have an indispensable role in addressing and managing

DAMA.

Outcomes Associated with DAMA Partnerships between the physician and patient are essential when trying to improve

adherence to prescribed care, and realizing better health-outcomes. Given that the patient is leaving

before the presiding physician recommends discharge and the course of treatment completed, it is

likely that he or she will be at an increased risk of adverse health outcomes. Typically, the lengths of

stay for such patients wishing to leave against medical advice last only for several days, which may

imply that their underlying conditions were not fully treated, thereby increasing the risk that they

may be dangerous to themselves, or to others once they leave the structure and confines of the

hospital setting (Gerbasi & Simon, 2003; Saitz, Ghall, & Moskowitz, 2000). Moreover, the nature of

such decisions by patients to leave care may leave little time or opportunity for the care team to fully

assess the patient’s condition, as well as his or her capacity to make such a choice, and to understand

its possible consequences.

All-Cause 30-day Readmissions A significant outcome of a discharge against medical advice is a readmission to the hospital,

and higher rates of readmission as a result (Brook et al., 2006). Different studies have shown that

patients who leave AMA are at a higher risk of readmissions than patients who leave by standard

discharge (Alfandre & H. Schumann, 2013; Hwang, May, 2005; Stern et al., 2011).

Unplanned re-hospitalizations are an expensive and frequent phenomenon in US healthcare,

with implications for the well-being of patients who experience the event. Also commonly known

as ‘readmissions’, this event can be associated with inferior quality of care, or with gaps in the

22

structure or processes of care. Unplanned readmissions to the hospital after a prior inpatient stay

have been a focus for hospitals and policy-makers as a gauge for the quality of hospital care, as well

as overall health-system performance (Jencks, Williams, & Coleman, 2009). The issue of avoidable

readmissions has been identified by policy makers as a relevant issue and policy-lever, with the idea

that reimbursement-based incentives could be used to reduce rates of re-hospitalizations, thereby

improving both quality of care and health outcomes. Reducing hospital readmissions is an important

element in the Affordable Care Act, with the aim of making hospitals change how accountable they

are for patients’ health outcomes. Previously, the Centers for Medicare and Medicaid Services

(CMS) paid for all Medicare re-hospitalizations, unless the patient is re-admitted to care for the same

conditions within a 24-hour window (Jencks et al., 2009).

However, in 2012 the Medicare program began implementing payment-based incentives as

part of the Affordable Care Act in order to reduce hospital readmissions for selected conditions

common to the Medicare populations, such as COPD or knee replacement; as part of this program,

hospitals with readmission rates higher than the national average faced reduced Medicare

reimbursements of between 1% - 3% (Boccuti & Casillas, 2015). This initiative, called ‘Hospital

Readmissions Reduction Program’ (HRRP) led to a decrease in readmissions soon after the

enactment of the program, suggesting that care providers and health systems have begun to focus on

preventable hospital readmissions as an area for improvement (Boccuti & Casillas, 2015). Previously

some may have argued that having hospital readmissions could lead to more revenue for the

hospital, however this possible ‘benefit’ may not be realized for those hospitals where capacity is

well-managed (Jencks et al., 2009). Although readmissions are a major area of current inquiry in

health services and policy research, it is not the focus of this study, but rather we seek to draw

linkages between discharges against medical advice and the likelihood of unplanned hospital

readmissions.

23

In a study of Canadian general hospitals, it was found that general medical inpatients who

had left against medical advice were more likely to be readmitted to care within 15 days of discharge

by a factor of seven, compared to their standard discharge counterparts after controlling for

demographic and diagnostic groupings (Hwang et al., 2003). However, their study did not address

the question of whether these readmitted patients had worse health outcomes or higher cost as a

result. Also, in analyses of unscheduled readmission rates and mortality associated with DAMA in

the Canadian province of Manitoba, Garland et al. used a 20-year retrospective cohort study to show

a 2-fold higher likelihood of readmission from an index admission ending with a DAMA, as well as a

2.5-fold higher odds of death (Garland et al., 2013). However, these authors do acknowledge that

one weakness or drawback of their study was the inability to adjust for severity of illness, as that

measure was unavailable in their administrative data (Garland et al., 2013).

Glasgow, Vaughn-Sarazzin and Kaboli looked at the rates of hospital readmission and 30-

day mortality using a 5-year cohort study of general medicine patients across the Veterans

Administration. Their study demonstrated that general medical patients leaving against medical

advice were subject to worse health outcomes than their standard discharge counterparts, and that

they were 1.35 times more likely to have a 30-day readmission. However, the study was unable to

show a clear increase in likelihood of mortality within 30-days (Glasgow, Vaughn-Sarrazin, &

Kaboli, 2012). These findings regarding VA patients would suggest that DAMA patients are at a

higher risk of readmission due to the continued worsening of the patient’s condition after not being

fully addressed during the initial hospitalization. Moreover, the authors found that the DAMA

patients at highest risk for readmission were likely to have suffered from an acute event, or had a

psychological comorbidity that may have affected their ability to realize the seriousness of their

condition (Glasgow et al., 2012).

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Maryland and Readmissions Maryland has higher readmission rates relative to the rest of the nation according to an

analysis of CMS data; Maryland hospitals either rank below the national average for Medicare

hospital readmissions, or are in the worst-performing quartile of hospitals (HSCRC, 2014). To help

address this, Maryland, through its Health Services Cost Review Commission, began exploring ways

with CMS to incentivize better quality of care, and to control healthcare spending within the state.

To that end, the state has designed and implemented the ‘Readmissions Reduction Incentive

Program' (RRIP) starting in 2014, which defines what a readmission is, specifies the criteria that

excludes certain types of admissions from the program, tracks readmissions across acute-care

settings and across all payors, and sets targets for Maryland's hospitals (HSCRC, 2014).

The main tenets of the initiative are such that measurement is fair and accurate to the

hospitals and consistent with CMS's definitions, and that resultant data are used to link measured

performance vis-à-vis re-hospitalizations of all patients, regardless of payor, and that the established

risk-adjusted targets for lower readmissions are in line with matching the national Medicare

readmissions rates by 2018 (HSCRC, 2014). To that end, the readmissions reduction program

measures each hospital’s readmission performance over time while providing at-risk financial

incentives as a portion (.5%) of inpatient revenues for hospitals to meet mandated reduction targets.

It should be noted however, that in Maryland, discharges against medical advice do count towards

the readmissions calculations, whereas CMS excludes DAMA in calculating hospital readmission

measures (HSCRC, 2014).

DAMA and The Johns Hopkins Hospital In order to better understand the current protocols at The Johns Hopkins Hospital, their

current policies on DAMA (MEL020) and Voluntary Admissions for psychiatric patients (PAL404)

were reviewed. The DAMA policy outlines what is to be done by the care team once non-

25

psychiatric adult patients have made it known to their care teams that they intend to leave against

medical advice. This policy makes the distinction between DAMA and those cases where the

departure is unannounced, which are referred to as ‘elopements’, and entail a different set of

protocols. The policy written for psychiatric patients or their guardians is based on the JHH

psychiatry practice manual, and directs patients who wish to leave AMA to discuss their intentions

with their attending physicians or residents. Their conditions and level of competence will be

assessed within a 72 hour period, and unless they are deemed a safety-risk and involuntarily

committed, patients must be released within that window.

These policies touch on the different departments that play a role--from the floor or unit, to

Legal, Admitting, Security, and Social Work--and outline responsibilities and procedures of each that

are in line with the current thinking: upon realizing that the patient in question intends to leave

before being medically ready, and is deemed to be competent enough to make such a decision, the

care team (most likely the reporting nurse) will notify the attending physician and ask the patient to

stay long enough to receive discharge instructions, prescriptions, and any follow-up arrangements.

Before the patient leaves, the care team will try to explain to the patient that the payor, if any, may

not pay for part or all the care given his or her non-compliance, and the patient will be asked to sign

a waiver of liability and an acknowledgement of the risks of leaving earlier than recommended.

Coding and data abstraction for discharges against medical advice is also an important

process at Johns Hopkins Hospital. The process begins when the presiding physician fills out the

patient’s discharge summary. Regulations stipulate that coding and input of health data into the

enterprise data system is to be completed within 30 days; along with any rolling changes or updates

(Milby, 06/16/2014). There are two teams of coders, the first group to do the abstraction of codes

from the medical record and nursing notes, where the second team ensures accuracy, adequate detail

and depth though audits of the first team’s work. According to Heidi Milby, the coder who looks

26

through the patient’s medical record makes the determination for what goes in the Disposition field,

i.e., whether to place a code representing a discharge against medical advice in the data field. These

determinations are based on existing documentation in the patient’s record and coding guidelines,

and very little is left to their judgment or interpretation. This implies that the quality of coding is

dependent upon how thorough the clinician was in documenting in the encounter (Milby,

06/16/2014).

Reimbursement for discharges against medical advice were also investigated at The Johns

Hopkins Hospital as part of this study in order to get a clearer picture of how such events are

handled. Once a patient is admitted, he or she is assigned a barcode for billing purposes which then

follows the patient throughout his or her stay at JHH. All services assigned are billed using the

barcode and thus recorded into the billing system. It is untrue that if a patient leaves against medical

advice, they are necessarily responsible for the accrued charges. The services that took place during

the patient’s stay, however brief, are of course recorded and billed for under the patient’s name.

Claims will be generated by the billing department, which will try and recoup some costs from the

third party payor such as Medicare or Medicaid, so that the accrued charges from the abbreviated

visit are not all written off as bad debt to the hospital. (Milby, 06/16/2014).

Literature Review Summary In doing a review of the literature, we have tried to address whether our questions related to

DAMA have been asked before, and if so, how they have been answered. From what has been

gathered in reviewing prior studies, it can be said that leaving the hospital against medical advice can

have negative consequences for all parties involved. Such discharges affect a relatively small share of

the overall hospital discharges in the US, but tend to affect a vulnerable segment of the patient

27

population, where informed consent, patient autonomy, psychiatric comorbidities, and risks to

patient, provider and hospital abound.

We have learned from previous qualitative studies and accounts of patients and care teams

that there are many reasons that can cause a patient to withdraw consent and leave care

unexpectedly, from anxiety, perceptions of disrespect and a lack of good communication, to

personal matters and drug abuse. We see that patients with psychiatric illness and comorbidities

were very much associated with leaving care earlier than expected, in addition to the myriad legal

and ethical issues that surround the treatment of mental illness. Also, many studies have noted that

inpatients that are male, are not elderly or frail, have no insurance coverage or are covered by

Medicaid, and receive care, albeit incompletely, from larger-sized hospitals that are located in urban

areas are more likely to leave against medical advice. Another general finding coming from the

research suggests that patients with substance abuse disorders may be more susceptible to DAMA,

given their apparent propensity for drug-seeking behavior. It was also noted that many patients who

leave against medical advice are first admitted to a hospital’s emergency room, which may also speak

to their socio-economic conditions. Others have tried to model the phenomenon and have

discussed those risk factors or predictors of discharges against medical advice, although these studies

were likely to include small sample sizes, or were restricted to subsets of patients and thus limited

generalizability (Pages et al., 1998).

We have also learned from the various studies that discharges against medical advice are

associated with worse health outcomes, such as increased likelihood of being readmitted to care, and

even 30-day mortality (Garland et al., 2013; Glasgow et al., 2012). Researchers have tended to focus

on strategies and policies for risk-management, improving communication between parties in order

to reduce likelihood of sub-optimal health outcomes as well as liability for providers, and careful and

thorough documentation of the encounter leading up to the discharge against medical advice.

28

However, the studies that were reviewed focused on DAMA on a limited scale, as in

studying rates of DAMA for a specific hospital, or limited geographic area, or a subset of patients.

There has not been a study of DAMA and associations on lengths of stay, illness severity, or costs at

a broader, national level. Also, there has not been a study that explores the question of whether

there are any hospital-level characteristics associated with higher levels of DAMA; the phenomenon

has only been seen as a function of patient-level characteristics. Indeed, some studies have alluded

to the fact that most discharges against medical advice do take place in urban hospitals, however this

may indicate need for more exploration of other factors that could help explain DAMA (Said,

Kwoh, & Krishnan, 2007). Finally, when it comes to one particular institution, The Johns Hopkins

Hospital, there has not been to date a study on DAMA and its association with the likelihood of 30-

day all-cause readmissions back to the hospital.

To begin a study of discharges against medical advice that is motivated by the interest in

associations of DAMA with lengths of stay, costs or charges, and severity of illness while taking into

account the conjectured roles of patient and hospital level characteristics in a simple and effective

framework would not only add to what others have stated regarding DAMA and readmissions, but

also explore to what extent this is an area on which JHH should focus attention and resources.

29

Chapter 3: Methods

This chapter begins with the main research question and, based on the literature review, a

conceptual framework and general theory to help explain themes and influences relating to

discharges against medical advice, and how they are associated with selected outcomes. As noted

from the review of literature pertaining to DAMA, we have come to understand that there are

certain factors that have been tied to this phenomenon; many inferring that for certain constructs,

there are indeed significant associations with DAMA. For instance, we have learned that socio-

demographic characteristics of the patients are associated with DAMA. Also, it has been found that

certain diagnostic conditions are also tied to the likelihood that patients may leave care prematurely.

Given that there seem to be relationships between this exposure of sorts, and outcomes we wish to

look further at these constructs and quantify these associations.

The Research Question: We first posit the main research question to motivate our study: are discharges against

medical advice associated with certain outcomes, and are hospital and patient-level characteristics

associated with high-levels of discharges against medical advice?

We try to describe the occurrence of discharges against medical advice, and to capture these

connections in the following overarching framework:

30

Figure 1: The General Conceptual Framework for DAMA

First, let us consider a population of US adults, aged 18 and over, who did not die during

their hospital visits, and did not transfer to another facility, and are eligible for readmission

(excluding planned readmissions). Pediatric DAMA is a subset issue, and although reflects many of

the same dynamics of adult DAMA, has slightly different dynamics than in the adult cases primarily

because of age and the child-protection issues, and the necessity of a parent or guardian making the

decision to withdraw a child from care (Macrohon, 2012). Broadly, we see that leaving against

medical advice is a decision that is a function of characteristics, both at the level of the patient, and

at the level of the hospital, which we take as being given. In our posited theory, the patient is

admitted to the hospital for any reason, and it is then determined if the patient decides to leave care

against medical advice. If not, the patient experiences what we could consider to be a standard

discharge, and realizes the requisite index-visit outcomes. Otherwise, the patient is discharged

against medical advice, and in turn, realizes the requisite outcomes. From that juncture, the patient,

regardless of how he or she was discharged from the initial visit, is subject to the possibility of

31

experiencing a 30-day all-cause re-hospitalization. If indeed they are readmitted back to hospital

care in that window, the patient then realizes outcomes associated with that readmission. For our

study, this is the over-arching general model of discharges against medical advice.

In order to go about addressing this overall research question, we posit three aims for our

research, each analyzing different aspects of our overall conceptual framework:

Research Aims 1) To examine the association between DAMA and A) index visit outcomes B) Readmissions, and

C) Readmission visit outcomes.

2) To examine, for a national sample of hospitals, the association between DAMA and index-visit

outcomes in the overall population and for selected diagnosis groups.

3) To examine patient and hospital characteristics associated with higher levels of DAMA for a

national sample of hospitals.

In the following sections of this chapter, we delve further into the reasoning and

methodology for each research aim, describing the relevant conceptual framework, the posited ideas

within each aim we wish to test using a series of hypotheses, the population on which the analyses

will focus, the sources of data that will be used for the analyses, and the empirical variables that will

be used in the different models and tests.

Research Aim1: To examine the association between DAMA and A) index visit outcomes B)

Readmissions, and C) Readmission visit outcomes.

32

Conceptual Framework From what we had learned from the literature review, inpatient stays that end in a discharge

against medical advice are associated with a higher likelihood of a 30-day readmission than visits that

ended with a standard discharge. We would thus like to see if this finding also holds in the case of

The Johns Hopkins Hospital (JHH), an urban and academic medical center located in Baltimore,

Maryland. Building upon that, we would like to see the association that discharge against medical

advice may have with three index-visit outcomes of interest: the length of stay (LOS), the total

charges, and the severity of illness (SOI) as recorded during the index-visit. Understandably, a

patient deciding to leave prematurely against the advice of the attending doctor would have lesser

LOS and associated charges, than might have been initially anticipated. The literature on DAMA

and readmissions also found that there was an association of DAMA with a subsequent, increased

risk of mortality. To augment this line of inquiry, we also want to test for any association DAMA

may have with other subsequent outcomes tied to the readmission -- namely LOS, total charges and

SOI.

33

Figure 2: Conceptual Framework for Aim1: DAMA and Readmissions

Thus, by revisiting the overall model of DAMA as expressed earlier, we begin by noting

whether or not there is a discharge against medical advice. Once the index-admission is realized, the

possibility of a readmission within 30 days back to the same hospital exists. Based on research aim

1, we first explore the association DAMA has with index-visit LOS, total charges, and SOI

(hypotheses H1A, H1B, H1C). We then test to see if the likelihood of readmission for DAMA

observations is indeed higher than the likelihood of readmissions from non-DAMA observations

(hypothesis H1D). Finally, we will test the association between an index-visit discharge against

medical advice and the same three outcomes – LOS, total charges, SOI -- where there is a

subsequent readmission-visit by again comparing the DAMA and non-DAMA observations

(hypotheses H1E, H1F, H1G).

In order to test whether there is an association with DAMA and the selected outcomes, we

will compare as a group, JHH observations that had experienced DAMA during the index visit with

those JHH observations that did not experience DAMA during the index visit. In a sense, this is

34

similar to identifying a treatment effect by comparing groups on a given measure or outcome, where

one has an exposure of interest, such as a discharge against medical advice, and the other does not

have the exposure of interest. Given that there are visit-level factors that may affect both the

outcomes of interest and the likelihood of leaving against medical advice, propensity scores will be

used to match DAMA-exposed observations with comparable non-exposed observations to reduce

the effects of confounding. This topic will be addressed later in the chapter.

Hypotheses H1A0) Patients who leave against medical advice will have no measureable differences in

index-visit mean LOS relative to their standard discharge counterparts.

H1A1) Patients who leave against medical advice will have decreased mean index-visit LOS

relative to their standard discharge counterparts.

H1B0) Patients who leave against medical advice will have no measureable differences in

index-visit mean total charges relative to their standard discharge counterparts.

H1B1) Patients who leave against medical advice will have lower index-visit mean total

charges relative to their standard discharge counterparts.

H1C0) Patients who leave against medical advice will have no measureable differences in

index-visit mean Severity of Illness relative to their standard discharge counterparts.

H1C1) Patients who leave against medical advice will have lower index-visit mean Severity of

Illness relative to their standard discharge counterparts.

35

H1D0) Patients who leave against medical advice will have no measurable differences in the

likelihood of 30-day readmissions relative to their standard discharge counterparts.

H1D1) Patients who leave against medical advice will have a higher likelihood of 30-day

readmissions relative to their standard discharge counterparts.

H1E0) Patients who left against medical advice and were then readmitted will have a length

of stay no different than that of their index visit.


of stay longer than that of their index visit.

H1F0) Patients who left against medical advice and were then readmitted will have total

charges no different than that of their index visit.


charges greater than that of their index visit.

H1G0) Patients who left against medical advice and were then readmitted will have a Severity

of Illness no different than that of their index visit.


of Illness greater than that of their index visit.

Population and Study Design The study population for this first research aim1 will be encounters from adult patients of

the Johns Hopkins Hospital that had at least one inpatient visit within the period of January 2013 to

December 2014, and were also involved in at least one readmission in that time period. This equates

36

to 69,551 records that encompass their inpatient stay. To note, this hospital is an urban, academic

medical center with 951 beds in Baltimore, Maryland and affiliated with the Johns Hopkins Health

System and the JHU School of Medicine. This can be considered a retrospective cohort design,

given that there is an ‘exposure’ of interest, and the study contains data involving different time

periods. The readmission-visits that are found in these data were deemed to be unplanned

readmissions; exclusions were made for planned-readmissions, deaths, and same-day transfers. The

study will utilize propensity score matching between exposed cases (DAMA) and controls in order

to make comparisons on the outcomes length of stay, total charges, and severity of illness. The data

source is the Johns Hopkins Hospital Readmissions data, which is described below.

Data Sources To examine the first research hypothesis, it was necessary to have access to Johns Hopkins

Hospital data that not only has information in regards to patient demographics, diagnoses, and other

information collected during the inpatient visit, but most importantly, has information indicating

whether patients had readmissions to care within 30 days of discharge from JHH. The data used for

calculating hospital readmissions (any re-hospitalizations within 30 days of a discharge across a given

calendar year) are taken from inpatient abstracts and case-mix data from the hospital. The study

excluded planned readmissions, deaths and same-day transfers. These readmissions data are

maintained by the JHH Care Coordination group, which routinely collects data in order to make

determinations regarding which admissions are, in fact, 30-day readmissions from an earlier inpatient

visit. These data are also shared with the state of Maryland. These data were then linked, using a

masked patient identifier, to the administrative data that are maintained by the Johns Hopkins

Health System. The data are at the level of an inpatient visit, along with information related to the

patient and the visit itself, such as patient demographics, dates of admission and discharge, along

37

with any dates of readmissions, unit and department of the hospital, insurance provider, lengths of

stay, disposition at discharge, and APR-related information such as major diagnostic categories

(‘MDCs’, diagnoses-related grouping (‘DRGs’), Severity of Illness, total charges, and whether the

discharge is eligible to have a subsequent 30-day readmission visit.

Table 1: Variables in the Aim1 DAMA Propensity Score Model

Variable Status Type Source Label DAMA Dependent Binary JHH: Disposition Indicates AMA discharge Active Independent Binary JHH: ICD9 Has Comorbidity Alcohol Independent Binary JHH: ICD9 Has Comorbidity BLACK Independent Binary JHH: Race code Indicates African-American Depression Independent Binary JHH: ICD9 Has Comorbidity Drug Independent Binary JHH: ICD9 Has Comorbidity

ER Independent Binary JHH: ER Admission Indicates ER Services During Stay

HIVAIDS Independent Binary JHH: ICD9 Has Comorbidity Male Independent Binary JHH: Gender Code Male Gender Medicaid Independent Binary JHH: Payor Codes Medicaid MIDAGE Independent Binary JHH: Age field Between 30-59 Years Old

NoProc Independent Binary JHH: Procedure Code No Procedures Done

Psychosis Independent Binary JHH: ICD9 Has Comorbidity Self Pay Independent Binary JHH: Payor Codes Self-Pay or No Coverage

Analysis Approach In order to compare the outcomes between DAMA and non-DAMA patients, it was

necessary to create propensity scores using logistic regression to be used in matching. These scores

help create comparable, balanced samples in order to facilitate comparison and thus better isolate

the effect of a given exposure, in this case DAMA. As will be explained later in the chapter, these

scores are basically the conditional probabilities of being treated, given a set of covariate-attributes.

These attributes may differ between the exposed and unexposed groups being compared, and so

propensity scoring can be used to simultaneously balance the covariates to reduce any selection bias,

38

and aid in comparability between groups. To get these propensity scores for matching, it was

necessary to obtain the variables in the JHH data that were identified as being important

determinants of DAMA (or likelihood of exposure), and may also have an effect on the selected

outcomes.

Once identified, the information was transformed into binary variables representing patient

factors for use in the logistic model. As identified in the literature review, men who were within a

certain age-range, and are either covered by Medicaid or are self-pay were more likely to leave

against medical advice. We note that patients who were afflicted with addiction and active substance

abuse, alcohol abuse, and/or were injection drug users, were often associated with leaving against

medical advice. We also note from the literature that patients who suffered from depression and

psychoses were likely to be noncompliant in their medical care, and so these were also included. It

should be noted that having these conditions may indicate admission for psychiatric care, and this

may be protective against DAMA, as such patients may be held from leaving. It was also

understood that patients who did not have a procedure planned were associated with leaving against

medical advice. Finally, many patients who leave are initially admitted in the hospital’s ER for

treatment. Again, these covariates were likely to be not only associated with DAMA, but also

readmission and LOS, total cost and severity-of-illness (SOI). A logistic regression with those

factors was run using data based on the index-admission to compute the probability of DAMA per

observation. For the matched analysis, the greedy-match algorithm was applied to the propensity

scores to link one exposed (DAMA) observation to two, independent non-exposed observations. It

was then possible to better identify an association, if any, between DAMA and index-visit outcomes,

the likelihood of readmission, and finally, the readmission-visit outcomes. For this study, SAS Base

version 9.3 (SAS Institute, Inc. Carey, NC) was used to manage and analyze the data.

39

Research Aim 2: To examine, for a national sample of hospitals, the association between

DAMA and index-visit outcomes in the overall population and for selected diagnosis groups

Conceptual Framework To further address the overall research question, we now wish to build on what we have

already described for the first research aim. However, unlike the first research aim, we cannot

directly address the question of DAMA’s association with readmissions using these national data.

As will be explained below, the 2012 HCUP National Inpatient Sample has observations at the

index-visit level only, and it will not be possible to tie index-visits and associated information to any

subsequent readmissions; therefor this is a gap in our knowledge of patterns at the national level.

However, as part of this second research aim, it is still be possible to confirm if in fact

discharges against medical advice, using such national data, are associated with decreased lengths of

stay, total costs, and lower levels of illness-severity. Moreover, it is also possible to test the existence

of such associations in selected diagnosis groups within the overall population -- diagnoses that are

most common to those who leave care against medical advice. This research aim will make it

possible to confirm these associations on a national scale.

Thus, we refine our initial conceptual framework so as to accommodate this second research

aim by focusing on those aspects we can directly address with the available data.

40

Figure 3: Conceptual Framework for Aim2: DAMA and Index Admission Outcomes

Here we see the constructs and themes being parallel to those found in the first aim: a patient is first

admitted to the hospital, and based on both patient-level and hospital-level characteristics, the

patient will either leave by standard discharge or against medical advice. We can thus explore the

association between DAMA and the outcomes of that index-admission: LOS, Total Costs, and SOI;

thus motivating the second set of hypotheses (H2A, H2B, H2C) in a similar manner to what was

done in the first research aim. Because of the data limitation, we note that the portion of the

framework relating to readmissions is not relevant in this context.

Also as in the first aim, comparisons will be made between propensity-score matched

DAMA and non-DAMA groups on each outcome at a ratio of 1 DAMA to 2 non-DAMA

observations. Even on a wider scale, we would expect to see that there are factors at the visit-level

that may affect both outcomes and the likelihood of DAMA, and that there would be need to

address this possible source of bias in resulting estimates on treatment effects.

41

Hypotheses H2A0) Patients who leave against medical advice will have no measureable differences in

index-visit mean Length of Stay, relative to their standard discharge counterparts.

H2A1) Patients who leave against medical advice will have lower index-visit mean Length of

Stay, relative to their standard discharge counterparts.


index-visit mean Total Costs, relative to their standard discharge counterparts.

H2B1) Patients who leave against medical advice will have lower index-visit mean Total

Costs, relative to their standard discharge counterparts.





Sample and Study Design For the second set of hypotheses that aims to address associations of DAMA with index-

visit outcomes, the study will include a representative sample of all inpatient hospital encounters as

found in the 2012 HCUP National Inpatient Sample dataset, which can be characterized as a cross-

sectional dataset. The observations used for this analysis will be comprised of hospital visits by

persons aged 18 and over who did not die during their hospitalizations.

42

Data Sources The Healthcare Cost and Utilization Project or ‘HCUP’ is a national database published by a

federal and state partnership sponsored by the Agency for Healthcare Research and Quality

(AHRQ). The Agency for Healthcare Research and Quality publishes a collection of US healthcare

datasets that is a result of its collaboration between States and their resident healthcare and health-

system organizations. It is the largest collection of administrative encounter data for inpatient and

outpatient visits, as well as emergency care, and includes data beginning in 1988. The datasets are

constructed so that both national and state-level privacy restrictions are in place (Agency for

Healthcare Research and Quality, 2014). There are six forms of HCUP datasets that encompass

national and regional data, as well as State-level data. For our analyses here, we have focused on the

2012 ‘National Inpatient Sample’ or NIS.

The National Inpatient Sample represents all-payor inpatient data from those States that

participate in AHRQ’s Healthcare Cost and Utilization Project, and is a stratified systematic sample

of inpatient discharges from those community hospitals (all non-federal, general or specialty, and

short-term hospitals) in HCUP, which captures 20% of all discharges from US hospitals, exclusive

of rehabilitation facilities and acute, long term care hospitals (Agency for Healthcare Research and

Quality, 2014). This sample includes roughly 7 million inpatient visits and discharges in a given year.

These data contain demographic, clinical and resource usage that are found on the discharge

abstracts and the usual billing data submitted by care facilities to states and statewide data

organizations.

Beginning with the 2012 NIS data, there was a redesign by AHRQ to improve accuracy in

national estimates. The sampling design was changed to create a sample of discharge records from

the participant-hospitals, instead of containing all discharge records from a subset of hospitals within

HCUP, the rationale being that it would be more representative of the overall population, and thus

reduce margins of error and increase accuracy of estimates for both patient factors and hospital

43

factors (Agency for Healthcare Research and Quality, 2014). Hence, what we have is a sample of

discharges from all hospitals that participate in the HCUP, whereas in prior years, the NIS data

consisted of a sample of hospitals with all of their discharges included. These data will provide the

same types of data as were found in the JHH readmission data, and also have much more detail,

including hospital-level information which will be useful for addressing the third and final research

aim.

Table 2: Variables in the Aim2 DAMA Propensity Score Model

Variable Status Type Source Label

DAMA Dependent Binary HCUP: Disposition Indicates AMA discharge Active Independent Binary HCUP: ICD9 Has Comorbidity Alcohol Independent Binary HCUP: ICD9 Has Comorbidity BLACK Independent Binary HCUP: Race code Indicates African-American Depression Independent Binary HCUP: ICD9 Has Comorbidity Drug Independent Binary HCUP: ICD9 Has Comorbidity ELECTIVE Independent Binary HCUP: Elective Ind. Indicates Elective Proc. ER Independent Binary HCUP: ER Svcs. Ind. Indicates ER Services During Stay HIVAIDS Independent Binary HCUP: ICD9 Has Comorbidity INCOME1 Independent Binary HCUP: Zip Income Median Income [$0-$39K) INCOME2 Independent Binary HCUP: Zip Income Median Income [$40K-$48K) INCOME3 Independent Binary HCUP: Zip Income Median Income [$49K-$63K) MALE Independent Binary HCUP: Gender Code Male Gender Medicaid Independent Binary HCUP: Payor Codes Medicaid MIDAGE Independent Binary HCUP: Age field Between 30-59 Years Old

NoProc Independent Binary HCUP: Procedure Code No Procedures Done

Psychosis Independent Binary HCUP: ICD9 Has Comorbidity Self Pay Independent Binary HCUP: Payor Codes Self-Pay or no coverage

URBAN Independent Binary HCUP: Population code Pt from a large Metro Area (1M+)

Analysis Approach The HCUP data also contain many of the same variables as the JHH dataset, plus others that

are not present, such as a categorical variable indicating median income in a patient’s zip code, a

variable indicating if the hospital admission was elective in nature, and an Urban residence indicator.

44

These were deemed to be of interest because of their possible associations with both DAMA-

likelihood and the outcomes, and thus included. To be consistent, it was decided to try and mirror

the same list of variables that were included in the first research aim. As before, the analysis focused

on US adults aged 18 and over with valid values for gender who did not die during their

hospitalizations, nor transfer to another facility. The ICD9 diagnoses data associated with each visit

were used to identify the same disease conditions (Depression, Alcohol Abuse, Drug Abuse,

Psychoses, HIV/AIDs, and Active Substance Abuse, as these are associated in the literature with

discharges against medical advice. Also as before, the risk factors that are associated with DAMA

could also be associated with a decreased length of stay as well as cost of care, regardless of DAMA,

and so this would have to be accounted for to prevent undue bias.

In running the logistic regression, the main priority was to obtain robust propensity scores

for use in matching. After obtaining predicted values for each observation, the Parsons matching

algorithm was applied to match pairs of cases and controls at a ratio of 1:2, where a DAMA

observation is matched to two standard discharge observations based on similarity of the propensity

scores. It was then possible to make comparisons within these matches according to selected

variables, such as Lengths of Stay, Total Charges, and even Severity of Illness. The intention was to

see if indeed, the ‘exposure’ of interest is somehow linked to differences in outcome within the

matched pairs using national data.

Research Aim 3: To examine patient and hospital characteristics associated with higher levels

of DAMA for a national sample of hospitals.

45

Conceptual Framework This aim is a departure from the other previous research aims that focused primarily on the

visit-level associations of DAMA and selected outcomes, but nonetheless, still relevant to our overall

inquiry into discharges against medical advice in the US. If we refer to our original conceptual

framework, we see that a patient’s likelihood of leaving care against medical advice is a function of

two, primary sets of influences: factors that are at the level of the visit, and those that are

characteristic of the hospital itself (Franks et al., 2006). We now focus at a level of the hospital;

where some information from the visit-level (such as risk of patient mortality) is aggregated to the

hospital-level; all in order to see if there are such factors that are not only associated with DAMA,

but also higher-than-expected levels of DAMA, where the ‘expected level’ is the average level of

DAMA given underlying hospital attributes, such as nonprofit status, size by number of hospital

beds, wage-index, teaching status, or urban locale.

Figure 4: Conceptual Framework for Aim3: Hospital and Patient Characteristics that are associated with DAMA

46

With our first two research aims, we wanted to see if DAMA is associated with certain

outcomes, such as the increased risk of readmissions, along with decreased lengths of stay and total

costs. However, it may be the case that there are characteristics intrinsic to hospitals and the

hospital environment that may be associated with increased risk of DAMA. Despite the fact that

DAMA seems to be more common in larger, urban hospitals, there may be an aspect of these

hospitals that may serve as proxies for unseen or unmeasured factors that increases the likelihood of

having higher rates of DAMA compared to similar hospitals.

Hypotheses H30) There are no characteristics that can differentiate hospitals with higher than expected

levels of DAMA from hospitals with lower than expected levels of DAMA.

H31) There are characteristics that can differentiate hospitals with higher than expected

levels of DAMA from hospitals with lower than expected levels of DAMA.

Population and Study Design The population under study was the hospitals included in the 2012 HCUP data. These

hospitals are US community hospitals that are general, short term, specialty hospitals that are also

non-Federal, not long-term acute care hospitals, and have over 25 beds. This population of

community hospitals is segmented into five primary characteristics: number of beds, teaching status,

urban or rural location, hospital ownership, and census region (Agency for Healthcare Research and

Quality, 2014).

Data Source In conjunction with the visit-level file mentioned in Aim2, HCUP also provides a 2012

hospital-level file with these characteristics, and with the unique hospital identifiers common to both

47

files, it is possible to link aggregated visit-information with existing hospital-information. There are

over 4,300 hospital-observations in this file.

Variable List Table 3: Variables used to calculate expected DAMA for each hospital

Variable Status Type Source Label DAMA Dependent Binary HCUP: Disposition Indicates AMA discharge Alcohol Independent Binary HCUP: Has Comorbidity Black Independent Binary HCUP: Race code Indicates African-American Depression Independent Binary HCUP: Has Comorbidity Drug Independent Binary HCUP: Has Comorbidity Elective Independent Binary HCUP: Elective Ind. Indicates Elective Proc.

ER Independent Binary HCUP: ER Svcs. Ind.Indicates ER Services During Stay

INCOME1 Independent Binary HCUP: Zip Income Median Income [$0-$39K) INCOME2 Independent Binary HCUP: Zip Income Median Income [$40K-$48K) INCOME3 Independent Binary HCUP: Zip Income Median Income [$49K-$63K) Male Independent Binary HCUP: Gender Code Male Gender Medicaid Independent Binary HCUP: Payor Codes Medicaid or Self-Pay Midage Independent Binary HCUP: Age field Between 30-59 Years Old

NoProc Independent Binary HCUP: Procedure Code No Procedures Done

Psychosis Independent Binary HCUP: Has Comorbidity Self Pay Independent Binary HCUP: Payor Codes Medicaid or Self-Pay

Severity Independent Binary HCUP: MSDRG SOIMajor or extreme loss of function

Urban Independent Binary HCUP: Population code

Pt from a large Metro Area (1M+)

Table 4: Variables used to regress High O/E ratios

Variable Status Type Source Label OE High Dependent Binary HCUP Indicates O/E Ratio >1.5

HighMarkup* Independent Binary HCUP: Hosp CCR File

Indicates Markup over Costs > Strata Avg

HighMort* Independent Binary HCUP: visit file Shows Higher Pt. Deaths > Strata Avg

HighRisk Independent Binary HCUP: visit file Shows Pt. Death Risk > Strata Avg

48

Variable Status Type Source Label

HighWage Independent Binary HCUP: Hosp CCR File Indicates WageIndex > Strata Avg

Large Independent Binary HCUP: Hosp File Indicates Large bedcount NonProfit Independent Binary HCUP: Hosp File Non-Profit Hospital Private Independent Binary HCUP: Hosp File Privately-Owned Hospital Rural Independent Binary HCUP: Hosp File Rural Hospital Teaching Independent Binary HCUP: Hosp File Teaching Hospital * Dropped

Analysis Approach The method for this final Aim is devoted to creating a hospital-level analysis file for use in a

logistic regression modeling observed to expected ratios (‘O/E’) of DAMA, with the theory that

characteristics at the hospital-level may be associated with higher-than-expected levels of DAMA.

To obtain O/E ratios for each hospital, all actual instances of discharges against medical advice at

the visit-level during the 2012 study period were aggregated to the hospital-level to arrive at the

numerator for this ratio. The denominator, or expected level of DAMA, was constructed by

calculating the predicted probability of a discharge against medical advice for each visit-level

observation using logistic regression model with same types of regressors as used in the earlier

research aims. As in the case of aggregating instances of observed DAMA, these predicted values

could also be summed to the hospital level to arrive at the denominator of the ratio. These two

elements thus comprised a given hospital’s O/E ratio, providing an adjusted measure of their

DAMA.

This metric makes apparent which hospitals have higher or lower levels of expected

discharge against medical advice, after adjusting for those characteristics identified in the logistic

regression model. In order to create a basis for deciding what constituted a ‘high’ level of discharges

against medical advice, it was decided that an O/E =1.5 would be a reasonable criterion for

49

determining whether a hospital had truly an expectedly high level of DAMA, since this number was

larger than over 80% of the distribution, after excluding hospitals with no observed DAMA. Any

hospitals with a ratio over 1.5 were coded as having higher than expected levels of DAMA, and this

indicator served as the dependent variable (‘OE_High’).

To create the analytic file, data from the HCUP hospital-level file and the HCUP person-

level files were aggregated to the hospital level, made possible by the fact that each hospital visit

record within the 2012 HCUP data also has an associated hospital identifier. Indeed, it would be

necessary to account for certain aspects of the population as found in the visit-level file in order to

fully explain a given hospital’s level of discharges against medical advice, such as the underlying

morbidity-burden and actual mortality of the served population.

The hospital file contains information about each individual institution, from which it was

possible to create indicator variables describing hospital ownership, nonprofit status, size measured

by the number of beds, teaching status, and whether the hospital is located in a rural area or not.

HCUP also provides a wage-index, which measures a given hospital staff’s wage-level relative to the

national hospital-wage average; and a cost-to-charge ratio which is a ratio of all-payor inpatient costs

to charges, unique to each hospital. These factors were made into binary variables and included as

explanatory variables in a logistic regression model with DAMA as the dependent variable. The

resulting predicted values for each observation were then retained and aggregated to the level of

each hospital, so as to have a predicted count of DAMA. For certain regressors, it was surmised

that using strata-adjusted means would create more meaningful information for use in the

regression.

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Ethical Considerations The HCUP-NIS 2012 datasets are constructed such that confidentiality of both patients and

hospitals are maintained by using constructed or synthetic identifiers, and also by excluding any state

identification. These data are made publicly available for general health services research. The

Johns Hopkins Hospital data, before being made available for DAMA research, had undergone de-

identification of patient-identifiers and other patient-specific information to help ensure

confidentiality and anonymity. As per the Johns Hopkins Bloomberg School Institutional Review

Board, the proposed research was not deemed to be human subjects research, and therefore not

needing IRB oversight (IRB#00006197).

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Chapter 4: Results

In this chapter, we review the main findings of the research, organized by each of the three

aims and their corresponding hypotheses.

Research Aim 1: To examine the association between DAMA and A) index visit outcomes

B) Readmissions, and C) Readmission visit outcomes

This part of the inquiry into discharges against medical advice examines the relationships

between DAMA and sequential outcomes (index visit LOS, total charges, SOI), readmissions, and

readmission visit outcomes at The Johns Hopkins Hospital, by comparing similarly matched DAMA

and non-DAMA groups.

Analysis, Research Aim 1 In the JHH dataset, there were a total of 69,551 observations that met the criteria for

inclusion (adult, and eligible for readmission during the 2 year study period). In our sample we see

that 929 observations associated with a discharge against medical advice, accounting for 1.3% of the

sample, which is in line with prior estimates of DAMA. We also note that in terms of

demographics, DAMA observations when compared to the non-DAMA observations, tends to

occur more commonly in Men(63% vs. 48%) who are between the ages of 30 and 59 years of age

(69% vs. 49%), and tend to be African American (65% vs. 38%). Also, according to Table 5, those

observations tied to DAMA are associated with higher levels of Medicaid coverage (62% vs. 23%),

admittance via the ER (86% vs. 42%), and tended to have comorbidities relating to substance abuse

and mental health. In terms of LOS and total charges, we note that the DAMA group skews

52

towards lower initial LOS and initial total charges, whereas the rate of readmission is higher than

that of the non-DAMA group (19% vs. 14%).

The logistic regression was run without stepwise selection, and predicted values for

propensity score matching were created. Using the covariates contained in table 1, matching was

done between DAMA and non-DAMA groups, and balance was assessed in order to ascertain that

the match had worked as intended. Before the match, the differences in balance between the groups

across the covariates ranged from 1.7% to 44%. After the match, the differences in distributions of

the given covariate across the common support ranged from 0% to 1.2%, which suggest the groups

were more nearly comparable by the distributions of the covariates. (Please refer to the JHH balance

table in the appendix.) The following are resulting estimates of the Odds Ratios for each of the

binary regressors are displayed in Table 6. Even though parsimony was not the goal of the model, it

is still interesting to note that the estimates tend to follow what theory would suggest, regarding

discharges against medical advice.

With propensity scores assigned to each observation by the logistic regression, the greedy-

match algorithm was applied at a 1:2 exposed/non-exposed ratio; which resulted in linking 928

DAMA cases and 1,856 corresponding non-DAMA controls based on the proximity or nearness of

the propensity scores between groups. It was then possible to compare means of the selected

outcomes (rate of readmission, lengths of stay, total charges, Severity of Illness) between DAMA

and pooled non-DAMA groups, along with the associated t-tests for differences in means. For the

other remaining hypotheses that relate to DAMA and the subsequent likelihood of readmission, and

DAMA and readmission-visit outcomes, we will employ a series of 2x2 tables of matched pairs,

where we assess the agreement between the DAMA cases and their pooled non-DAMA

counterparts, using McNemar's Test for paired observations to see whether discharges against

medical advice is significantly associated with the outcomes.

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Results for Research Aim 1

Table 5: Aim1 Sample Characteristics

DAMA non-DAMA Characteristics N Percent N Percent Gender

Female 339 36.5 35,440 51.6 Male 590 63.5 33,182 48.4 Total 929 100.0 68,622 100.0

Age Aged 18-29 167 18.0 9,694 14.1

Aged 30-59 645 69.4 33,457 48.8 Aged 60-100 117 12.6 25,472 37.1 Total 929 100.0 68,623 100.0

Race American Indian 1 0.1 102 0.1

Asian 4 0.4 1,467 2.1 Black 601 64.7 26,025 37.9 Other 28 3.0 3,871 5.6 Declined to Answer 57 0.1 57 0.1 Unknown 2 0.2 681 1.0 White 293 31.5 36,420 53.1 Total 986 100.1 68,623 100.0

Coverage Commercial/Blue Cross 65 7.0 16,842 24.5

HMO 30 3.2 10,384 15.1 International 5 0.5 518 0.8 Medicaid 574 61.8 15,615 22.8 Medicare 225 24.2 22,497 32.8 Other 6 0.6 2,128 3.1 Self Pay & No Charge 24 2.6 639 0.9 Total 929 100.0 68,623 100.0

Other Covariates ER Admitted 800 86.1 28,627 41.7

HIV/AIDs 72 7.8 1,558 2.3 No Procedures Done 516 55.5 14,959 21.8 Depression 109 11.7 6,886 10.0 Alcohol Abuse 246 26.5 4,864 7.1 Substance Abuse 373 40.2 7,316 10.7 Psychoses 214 23.0 6,114 8.9

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DAMA non-DAMA Characteristics N Percent N Percent

Active Drug User 609 65.6 14,716 21.4

Outcomes Index Length of Stay 0-3 Days 733 78.9 34,129 49.7

4-7 Days 122 13.1 19,532 28.5 8-14 Days 53 5.7 9,462 13.8 15 Days to 1 Month 18 1.9 4,095 6.0 31 Days to 2 Months 3 0.3 1,177 1.7 Two to Four Months 0 0.0 207 0.3 Over 4 Months 0 0.0 21 0.0 Total 929 100.0 68,623 100.0 Index Severity of Illness

Minor loss of function 219 23.6 14,228 20.7 Moderate loss of function 376 40.5 25,830 37.6 Major loss of function 283 30.5 22,928 33.4 Extreme loss of function 51 5.5 5,637 8.2 Total 929 100.0 68,623 100.0 Index Total Charges

$100- $1,000 2 0.2 8 0.0 $1,001- $5,000 287 30.9 3,415 5.0 $5,001- $10,000 378 40.7 15,219 22.2 $10,001- $25,000 189 20.3 28,581 41.7 $25,001- $75,000 66 7.1 17,387 25.3 $75,001 - $100,000 5 0.5 1,525 2.2 $100,001- $200,000 2 0.2 1,939 2.8 $200,001- $400,000 0 0.0 468 0.7 $400,001- $500,000 0 0.0 39 0.1 $500,001-$1,000,000 0 0.0 41 0.1 Over $1,000,000 0 0.0 1 0.0 Total 929 100.0 68,623 100.0

Readmission to JHH 176 18.9 9,468 13.8

Readmitted Length of Stay 0-3 Days 114 65.1 4,083 43.2

4-7 Days 40 22.9 2,984 31.6 8-14 Days 13 7.4 1,547 16.4 15 Days to 1 Month 7 4.0 628 6.6 31 Days to 2 Months 1 0.6 171 1.8 Two to Four Months 0 0.0 36 0.4

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Over 4 Months 0 0.0 4 0.0 Total 175 100.0 9,453 100.0

Readmitted Severity of Illness

Minor loss of function 27 15.3 842 8.9 Moderate loss of function 71 40.3 2922 30.87 Major loss of function 64 36.4 3958 41.82 Extreme loss of function 13 7.4 1193 12.61 Total 175 99.4 8,915 94.2 Readmitted Total Charges

$0- $99 0 0.0 22 0.2 $100- $1,000 0 0.0 2 0.0 $1,001- $5,000 33 18.9 715 7.6 $5,001- $10,000 71 40.6 2,257 23.9 $10,001- $25,000 48 27.4 3,909 41.4 $25,001- $75,000 22 12.6 2,079 22.0 $75,001 - $100,000 1 0.6 182 1.9 $100,001- $200,000 0 0.0 203 2.2 $200,001- $400,000 0 0.0 63 0.7 $400,001- $500,000 0 0.0 13 0.1 $500,001-$1,000,000 0 0.0 7 0.1 Over $1,000,000 0 0.0 1 0.0 Total 175 100.0 9,453 100.0

56

Table 6: Resulting Odds Ratios from the DAMA Propensity Score Regression

95% CI

DAMA Covariates Odds Ratio LB UB

P-Value

Middle Aged 1.432 1.233 1.664 <.0001Black 1.117 0.963 1.297 0.1437Medicaid 2.126 1.815 2.491 <.0001Self-Pay 3.208 2.075 4.958 <.0001ER Admitted 3.245 2.641 3.987 <.0001Male 1.51 1.312 1.738 <.0001HIV-AIDS 1.241 0.96 1.604 0.099No Procedures Done 2.523 2.2 2.894 <.0001Depression 0.911 0.738 1.124 0.3849Alcohol Abuse 1.135 0.957 1.346 0.1453Drug Abuse 1.139 0.956 1.357 0.1442Psychoses 1.204 1.012 1.432 0.0358Active User 2.39 1.976 2.892 <.0001* FYI: Concordance statistic: .851

57

Table 7: Comparing Index Visit Outcomes between DAMA and matched non-DAMA

Outcome Measures N 25th Pctl. Mean 75th Pctl. Std.Dev P > |t|

LOS - Index (DAMA) 928 1.0 2.9 3.0 4.4 <.0001

LOS - Index (non-DAMA) 1,856 2.5 5.3 6.0 5.9

Total Charges - Index (DAMA) 928 $4,556 $10,485 $10,809 $12,315 <.0001

Total Charges - Index (non-DAMA) 1,856 $8,340 $17,023 $18,690 $16,404

Severity of Illness - Index (DAMA) 928 2.0 2.18 3.0 0.85 <.0001

Severity of Illness - Index (non DAMA) 1,856 2.0 2.32 2.5 0.59

58

We revisit each of the earlier posited hypotheses related to this first research aim and address

them in the context of the results displayed in Table 7.

H1A0) Patients who leave against medical advice will have no measureable differences in

index-visit mean LOS relative to their standard discharge counterparts.

H1A1) Patients who leave against medical advice will have decreased mean index-visit LOS

relative to their standard discharge counterparts.

In the preceding table, we see each of the outcomes listed by group: DAMA cases, followed

by the corresponding non-DAMA controls. We consider the first set of lines that relate to the

Length of Stay for the index visit. Between the DAMA and non-DAMA groups, we see a marked

difference between index lengths of stay, where the standard discharges have a LOS of 5.3 days, and

the DAMA observations are over half of that, with 2.9 days. The t-test indicates that differences

between the groups were significant beyond the 99% level of significance. As stated before, one

would expect to see a shorter LOS for visits that end with a discharge against medical advice, and

these findings help quantify by just how much. Hence, we reject the null hypothesis in favor of the

alternative hypothesis.


index-visit mean total charges relative to their standard discharge counterparts.

H1B1) Patients who leave against medical advice will have lower index-visit mean total

charges relative to their standard discharge counterparts.

59

The second set of lines relate to the total charges of the index visit, where we see that the

mean total charges for the DAMA group is $10,485, compared to the mean of the matched controls,

which is $17,023. According to the t-test, these differences were beyond the 99% level of statistical

significance. As in the case with index LOS, this finding suggests that inpatient visits that end in a

discharge against medical advice have less total charges, and by extension, less total costs compared

to visits that end with a standard discharge, and we can now see by how much less. Hence, we reject

the null hypothesis of no measureable difference in index total charges between DAMA and non-

DAMA in favor of the alternative.





The final set of lines in this table relate to the third outcome; severity of illness of the index

visit. This is a calculated and ordinal metric that varies between 1 and 4, where the higher the

integer-number, the lower the health status associated with the visit. The analysis finds that the

mean SOI of the DAMA group to be 2.18, whereas the corresponding non-DAMA observations

have a mean SOI of over 2.3. Though the findings are not as statistically significant as the prior two

findings), this still suggests that DAMA is associated with lower severity of illness during the index

visit than the severity of illness associated with standard-discharges. Hence, we reject the null

hypothesis in favor of the alternative.

60

The following analyses relate to both B and C of the first research aim, where we examine

both readmission likelihood and readmission-outcomes of the DAMA observations as compared to

the matched, pooled non-DAMA observations.

Table 8: Likelihood of All-Cause 30-day Readmissions between DAMA and Matched non-DAMA observations

non-DAMA

Not

Readmitted Readmitted Totals

DAMA

Not Readmitted

Observed 643 109 752

Expected 637.3 114.7

Readmitted Observed 143 33 176

Expected 149.2 26.8

Totals 786 142 928

McNemar's Test

Statistic (S) 23.4 DF 1 Pr > S <.0001

H1D0) Patients who leave against medical advice will have no measurable differences in the

likelihood of 30-day readmissions relative to their standard discharge counterparts.

H1D1) Patients who leave against medical advice will have a higher likelihood of 30-day

readmissions relative to their standard discharge counterparts.

In regards to the likelihood of readmissions back to JHH, we see from the tables that those

who left against medical advice have a readmissions rate of 18.9% (=176/928), compared to 15.7%

(=142/928); with corresponding p-value of <.0001. With this, we can say that these results suggest

that leaving against medical advice does have the effect of increasing the likelihood of being

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readmitted, if only at the same hospital. Hence, we reject the null hypothesis in favor of the

alternative.

Table 9: Likelihood that Readmit LOS > Index LOS for DAMA in comparison to non-DAMA

non-DAMA

Readmit LOS <

Index Los Readmit LOS >

Index Los Totals

DAMA

Readmit LOS <

Index Los

Observed 748 49 797

Expected 741.2 55.8

Readmit LOS >

Index Los

Observed 115 16 131

Expected 121.8 9.2

Totals 863 65 928

McNemar's Test



of stay no different than that of their index visit.


of stay longer than that of their index visit.

Using the same pair-wise approach to look at this first outcome relating to the readmissions

visit, we note instances where the readmission LOS is longer than the index LOS, and cross-

reference that with the occurrence of DAMA and matched non-DAMA observations using 2x2

tables. We find evidence that when patients leave against medical advice from JHH and are then

readmitted within 30 days, they are more likely at 14.1% (=131/928) to have a resulting length of

stay that is greater in duration than for their preceding index visit than the matched controls 7.0%.

62

Noting the differences and the associated levels of significance beyond 99% from McNemar’s test

would suggest that they may not have completed the prescribed regimen during the index visit, and

have thus returned to do so. Hence, we reject the null hypothesis in favor of the alternative.

Table 10: Likelihood that Readmit Charges > Index Charges for DAMA in comparison to non-DAMA

non-DAMA

Readmit Charges <

Index Charges Readmit Charges>

Index Charges Totals

DAMA

Readmit < Index

Observed 784 39 823

Expected 779.5 43.5

Readmit > Index

Observed 95 10 105

Expected 99.5 5.5

Totals 879 49 928

McNemar's Test



charges no different than that of their index visit.


charges greater than that of their index visit.

By noting the cases for which total charges from the readmission are greater than the total

charges from the index visit, and comparing across our matched DAMA and non-DAMA pairs, we

see that visits that end with a discharge against medical advice with readmission are more likely to

experience higher costs of care during their return visit (11.3% = 105/928) than from their index

visit, as compared to the corresponding non-DAMA counterparts (5.3%). This difference were

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found to be well beyond the 99% level of statistical significance. This finding is also in line with

analysis results for the preceding hypothesis regarding increased likelihood for a longer readmission

LOS for those with DAMA, it is possible that a longer return stay to address unresolved medical

issues, may well result in more charges. Hence, we reject the null hypothesis of no difference in

total charges in favor of the alternative.

Table 11: Likelihood that Readmit Severity > Index Severity for DAMA in comparison to non-DAMA

non-DAMA

Readmit SOI <

Index SOI Readmit SOI >

Index SOI Totals

DAMA

Readmit SOI <

Index SOI

Observed 6 132 138

Expected 6.4 131.6

Readmit SOI >

Index SOI

Observed 37 753 790

Expected 36.6 753.4

Totals 43 885 928

McNemar's Test



of Illness no different than that of their index visit.


of Illness greater than that of their index visit.

In our examination of the last readmission-outcome, we see another instance where one

comparison between the matched pairs yields a statistically meaningful result well beyond the .01

level. This finding suggests that the calculated severity of illness for patients who leave AMA and

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are then readmitted back to the same hospital, may experience a decline in health status between

their index and readmission visits; more so than their non-DAMA counterparts. Hence, we would

find reason to reject the null hypothesis of no difference in severity of illness, in favor of the

alternative that those who had previously left against medical advice are likely to have a greater

severity of illness on readmission than their non-DAMA counterparts.

Research Aim 2: To examine, for a national sample of hospitals, the association between

DAMA and index-visit outcomes in the overall population of the U.S and for selected

diagnosis groups.

The objective for this second research aim is to repeat the analysis that was performed for

the first research aim relating to the association of DAMA and the index-visit outcomes: LOS, total

costs, and SOI, but on a wider scale. Doing so would help confirm the findings from one hospital’s

experience, and also provide a picture of these associations on a national scale.

Analysis, Research Aim 2 Table 12: Aim 2 Resulting Odds Ratios from the DAMA Propensity Score Regression

95% CI


P-Value

Middle Aged 1.401 1.358 1.446 <.0001Black 1.264 1.22 1.309 <.0001URBAN 1.37 1.324 1.416 <.0001Medicaid 2.167 2.096 2.24 <.0001Self-Pay 2.041 1.944 2.144 <.0001ER Admitted 1.539 1.474 1.607 <.0001Elective Admission 0.605 0.567 0.645 <.0001Inc. [$0-$39K) 1.111 1.069 1.153 <.0001Inc. [$40K-$48K) 1.018 0.975 1.063 0.413

65

95% CI


P-Value

Inc. [$49K-$63K) 0.918 0.88 0.957 <.0001Male 1.639 1.589 1.691 <.0001No Procedures 1.496 1.452 1.542 <.0001Depression 0.856 0.819 0.895 <.0001Alcohol Abuse 1.564 1.507 1.624 <.0001Drug Abuse 1.959 1.885 2.035 <.0001Psychoses 0.664 0.635 0.693 <.0001HIV-AIDS 1.675 1.518 1.849 <.0001Active User 2.834 2.725 2.947 <.0001* FYI: Concordance statistic: .828

Similar to what was done with the JHH data, propensity scores were calculated and used for

matching between exposed and non-exposed groups at a ratio of 1:2, and the match was assessed by

comparing balance of covariates before and after matching. Prior to matching, differences between

DAMA and non-DAMA groups ranged from 1.3% to 43% across the covariates. However, after

the match, they ranged from 0% to .6%, which suggested the match improved comparability (please

refer to the appendix for the full table.) Table 13 below will provide an overview of the HCUP data

used to address this second research aim, and table 14 will address hypotheses A, B, and C relating

to the second research aim. The data are based on 973,758 visit-observations of adults from the

2012 HCUP. We see many similarities and patterns in this national population between the DAMA

and non-DAMA as was noted in the JHH population used in the prior research aim. Note that the

overall rate of DAMA using the HCUP sample is 2%. Unlike the JHH data, we now have some

insight into income and location; and can note that more observations associated with DAMA come

from large urban areas (47% vs. 32%), that they come from the lowest median income group (32%

vs. 22%).

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Results for Research Aim 2 Table 13 Aim 2 Sample Characteristics

DAMA non-DAMA Characteristics N Percent N Percent Gender

Female 7,272 35.8 562,038 58.9 Male 13,025 64.2 391,423 41.1 Total 20,297 100.0 953,461 100.0

Age Aged 18-29 3,540 17.4 137,669 14.4

Aged 30-59 13,101 64.5 406,881 42.7 Aged 60-100 3,656 18.0 408,911 42.9 Total 20,297 100.0 953,461 100.0

Race White 10,606 52.3 625,852 65.6

Black 5,306 26.1 138,320 14.5 Hispanic 2,562 12.6 90,034 9.4 Asian/Pacific Islander 230 1.1 22,860 2.4 Native American 39 0.2 1,933 0.2 Other 1,363 6.7 55,625 5.8 Total 20,106 99.1 934,624 98.0

Patient Locale Large Central Metro 9,545 47.0 302,498 31.7

Large Fringe Metro 5,443 26.8 328,426 34.4 Medium Metro 2,653 13.1 181,990 19.1 Small Metro 725 3.6 44,758 4.7 Micropolitan 918 4.5 66,388 7.0 Noncore 255 1.3 21,591 2.3 Total 19,539 96.3 945,651 99.2

Coverage Medicare 5,098 25.2 371,536 39.0

Medicaid 8,713 43.0 185,776 19.5 Private insurance 3,571 17.6 330,219 34.7 Self-pay 2,396 11.8 39,809 4.2 No charge 53 0.3 1,137 0.1 Other 437 2.2 23,092 2.4 Total 20,268 100.0 951,569 100.0

Other Covariates ER Admitted 16,415 80.9 568,264 59.6

HIV/AIDs 504 2.5 5,082 0.5 No Procedures Done 9,813 48.3 312,472 32.8 Depression 2,599 12.8 100,446 10.5

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Alcohol Abuse 5,996 29.5 60,658 6.4 Substance Abuse 6,513 32.1 58,631 6.1 Psychoses 3,030 14.9 79,025 8.3 Active Drug User 12,786 63.0 185,963 19.5 Median Income [$0-$39K) 6,436 31.7 205,649 21.6

Median Income [$40K-$48K) 3,704 18.2 186,349 19.5

Median Income [$49K-$63K) 3,791 18.7 226,066 23.7

Outcomes Index Length of Stay 0-3 Days 16,424 80.9 569,937 59.8

4-7 Days 2,748 13.5 268,591 28.2 8-14 Days 807 4.0 84,181 8.8 15 Days to 1 Month 274 1.4 25,621 2.7 31 Days to 2 Months 37 0.2 4,163 0.4 Two to Four Months 7 0.0 802 0.1 Over 4 Months 0 0.0 156 0.0 Total 20,297 100.0 953,451 100.0 Index Total Charges

$100- $1,000 40 0.2 229 0.0 $1,001- $5,000 3,586 17.7 37,525 4.0 $5,001- $10,000 5,894 29.1 149,011 15.7 $10,001- $25,000 6,915 34.2 356,503 37.6 $25,001- $75,000 3,178 15.7 318,077 33.5 $75,001 - $100,000 265 1.3 36,462 3.8 $100,001- $200,000 279 1.4 39,682 4.2 $200,001- $400,000 60 0.3 9,520 1.0 $400,001- $500,000 4 0.0 884 0.1 $500,001-$1,000,000 9 0.0 851 0.1 Over $1,000,000 2 0.0 149 0.0 Total 20,232 100.0 948,893 100.0 Index Severity of Illness

Minor loss of function 7,261 35.8 342,915 36.0 Moderate loss of function 9,301 45.8 407,237 42.7 Major loss of function 3,247 16.0 180,061 18.9 Extreme loss of function 483 2.4 23,109 2.4 Total 20,292 100.0 953,322 100.0

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Table 14: Comparing Index Visit Outcomes between DAMA and matched non-DAMA by Major Diagnostic Category

Number of Discharges Averages T-Test

non-DAMA DAMA

95% Confidence

Intervals

MDC Category Outcome Measure

Projected National Matched Actual

Without DAMA

With DAMA Difference Lower Upper

Pr > |t|

Overall LOS (Days) 4,767,299 40,586 20,293 4.83 2.54 2.29 2.21 2.37 <.0001 Total Costs 4,767,299 40,456 20,228 $8,346 $5,223 $3,128 $2,986 $3,271 <.0001 Illness Severity 4,767,299 40,586 20,293 1.93 1.85 0.08 0.07 0.09 <.0001

Alcohol/Drug Abuse Induced

Mental Disorders

LOS (Days) 100,210 8,804 4,402 6.03 2.57 3.45 3.29 3.61 <.0001 Total Costs 100,210 8,796 4,398 $5,954 $3,178 $2,776 $2,633 $2,919 <.0001 Illness Severity 100,210 8,804 4,402 1.79 1.62 0.17 0.15 0.19 <.0001

Diseases and Disorders,

Circulatory System



Respiratory System



Digestive System


69

Table 15 displays analysis results to address the hypotheses relating to Research Aim 2. Note that

the results are organized by major diagnostic category (MDC), including the combined, overall

population. This was done in order to show differences in each outcome by condition and thus

suggest that discharges against medical advice may affect these outcomes differently, depending on

the underlying medical condition. Each outcome is listed separately within these categories, showing

the number of discharges of the matched DAMA and non-DAMA groups, the projected national

counts, and the mean differences in outcomes with the associated confidence bounds on these

differentials. As in the case of the first research aim, we revisit each of the earlier hypotheses related

to this research aim and address them in turn within the context of the above table of results. Let us

first address the findings from the ‘Overall’ category. Given the somewhat parallel structure of this

second research aim to the first, we address each hypothesis in turn.

H2A0) Patients who leave against medical advice will have no measureable differences in

index-visit mean Length of Stay, relative to their standard discharge counterparts.

H2A1) Patients who leave against medical advice will have lower index-visit mean Length of

Stay, relative to their standard discharge counterparts.

We note that for the index LOS, the mean for the matched DAMA observations is 2.54

days, whereas the matched non-DAMA observations have a mean of 4.83 days. As in the first aim,

this is an understandable association, but it is also worth noting that the relative difference between

the two groups is roughly the same as with the first aim’s LOS findings. As we can see throughout

the table, the 95% confidence bounds for any differences between groups is nonzero, and each t-test

suggests that the differences are not by chance. These suggest that there are significant underlying

differences between DAMA and non-DAMA groups across outcomes, and diagnostic categories.

70

Hence, we reject the null hypothesis in favor of the alternative hypothesis of an association between

DAMA and lower mean LOS.


index-visit mean Total Costs, relative to their standard discharge counterparts.

H2B1) Patients who leave against medical advice will have lower index-visit mean Total

Costs, relative to their standard discharge counterparts.

Again, looking at the total costs for the ‘Overall’ category, we note that for the DAMA

group, the total costs associated with the index-visit is $5,223; compared to $8,346 for the matched

non-DAMA group. This is also in line with the general findings from JHH under research aim 1; a

visit ending earlier than expected with a discharge against medical advice is associated with lower

overall charges. Hence, we reject the null hypothesis in favor of the alternative hypothesis of an

association between DAMA and lower mean total costs.





In terms of the SOI of the index visit, the DAMA group based on the national sample had

an average of 1.85, whereas the non-DAMA group had a mean SOI of 1.93. Given the difference in

means, this may suggest that DAMA is indeed associated with a lower severity of illness, relative to

71

non-DAMA counterparts. We reject the null hypothesis that there are no measureable differences in

index-visit mean SOI in favor of the alternate hypothesis.

From looking at the overall group ‘All’, we see that for each of the outcomes, the differences

between DAMA and non-DAMA groups are statistically significant; and so we can reject a

composite null statement in favor of the composite alternative statement; that there indeed is an

association between DAMA and LOS, total costs, and illness severity. The largest costs and volume

of visits are associated with Diseases and Disorders of the Circulatory System, with Alcohol/SA

induced Mental Disorders having the least dollars and visits associated. It is worth noting that

overall, the difference between groups in terms of cost as a proportion of non-DAMA was 37%;

with the largest difference being the one associated with Alcohol/SA induced Mental Disorders at

47%, which may represent lost opportunities for providing care.

Research Aim 3: To examine patient and hospital characteristics associated with higher

levels of DAMA for a national sample of hospitals.

The objective for this last research aim is to use observed to expected ratios and logistic

regression analysis to investigate the existence of characteristics at the level of the hospital which are

associated with higher than expected levels of DAMA. Indeed, preliminary hospital-level models

did show associations of certain hospital-level factors with occurrences of discharges against medical

advice. These are meant to address the latter half of our overall research question posited in the

beginning of Chapter 3 regarding hospital characteristics associated with high-levels of discharges

against medical advice.

72

Analysis, Research Aim 3 Observed-to-expected ratios were calculated per hospital, where predicted rates of DAMA

were obtained from modeling data from the visit-level file. To create the binary dependent variable

for the logistic regression, hospitals with an O/E > 1.5 were marked as higher than expected

DAMA hospitals. This cutoff roughly split the distribution of hospitals into 80% and 20% in terms

of these ratios. Regressors at the hospital level were taken from the HCUP file, such as ownership

status, teaching status, urban/rural locale, and used to create binary regressors.

Figure 5: HCUP Hospitals by DAMA Observed to Expected Ratios where DAMA>0

Correlation matrices were created to assess the level of collinearity among the explanatory

variables. In so doing, it was found that the indicator for high markup was highly correlated with

the indicator for private hospital ownership, and that the High-Mortality indicator was closely

correlated with High Risk. As a result, those regressors were dropped from the logit model to

prevent any undue confounding with the remaining regressors. Other measures were explored to

see if collinearity was still an issue, such as variance inflation factors (VIF) as used in linear

regression models, as well as factor analysis. The VIF did not point to major collinearity among the

0

50

100

150

200

250

300

0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1 1.1 1.2 1.3 1.4 1.5 1.6 1.7 1.8 1.9 2 2.1 2.2 2.3 2.4 2.5 2.6

73

binary regressors, and factor analysis was considered of questionable value, given that it can be most

effective when scores of regressors need to be ‘reduced’. For our purposes here, it was easier to

drop correlated regressors from the logistic model.

Hence, we arrived at a set regressors (‘best model’) used in the two logistic regressions

below, the first being a model predicting high ratios of observed DAMA to expected DAMA (O/E

ratios) with the resulting set of estimated Odds Ratios. The second approach also models high O/E

ratios of DAMA as before, but conditioning on only those hospitals that have experienced DAMA

in the 2012 analysis period. Apart from assessing the estimated odds ratios per model, we also

assess the generally accepted goodness-of-fit measures.

Results for Research Aim 3 Table 15: Aim 3 Sample Characteristics

High DAMA

O/E Non-DAMA Hospital Characteristics N Percent N Percent HCUP-Provided Categories Hospital Bed count Small 414 49.9 1,486 42.0

Medium 230 27.7 901 25.5 Large 185 22.3 1,147 32.5 Total 829 100.0 3,534 100.0 Location/Teaching Status of Hospital

Rural 357 43.1 1,308 37.0 Urban non-Teaching 342 41.3 1,377 39.0 Urban Teaching 130 15.7 849 24.0 Total 829 100.0 3,534 100.0 Hospital Region

Northeast 163 19.7 403 11.4 Midwest 217 26.2 1,150 32.5 South 266 32.1 1,315 37.2 West 183 22.1 666 18.8 Total 829 100.0 3,534 100.0 Hospital Ownership

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High DAMA

O/E Non-DAMA Hospital Characteristics N Percent N Percent Government, non-federal 180 21.7 654 18.5

Private, non-profit 452 54.5 2,299 65.1 Private, Investor-owned 197 23.8 581 16.4 Total 829 100.0 3,534 100.0

Derived Covariates Hospital located in a Rural Area 357 43.1 1,308 37.0

Teaching Hospital 130 15.7 849 24.0 Hospital is Privately Owned 649 78.3 2,880 81.5 Hospital's Wage Index is Higher than Mean 382 46.1 1,269 35.9

Hospital's Mark-up over Costs Higher than Mean 494 59.6 2,092 59.2

Large Hospital in terms of Beds 185 22.3 1,147 32.5 Hospital is Not-for-Profit 632 76.2 2,953 83.6

Hospital has Higher Risks of pt. Death than Mean 392 47.3 1,709 48.4

For this final research aim, we begin with the associated hypothesis, and follow with our findings.

H30) Hospital characteristics are not statistically associated with higher-than-expected or

lower-than-expected levels of DAMA.

H31) There are hospital characteristics that can differentiate hospitals with higher than

expected levels of DAMA from hospitals with lower than expected levels of DAMA.

Table 16: Modeling High Observed to Expected Ratios, all hospitals

Dependent Variable: High DAMA Observed to Expected Ratios Best Model (n=4363) 95% CL Regressor Odds Ratio Lower Upper Pr>ChiSqr. RURAL 1.14 0.96 1.36 0.147 TEACHING 0.67 0.53 0.84 0.000 PRIVATE 0.81 0.66 0.99 0.041 HIGHWAGE 1.56 1.34 1.83 <.0001

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LARGE 0.61 0.51 0.74 <.0001 NONPROFIT 0.61 0.50 0.74 <.0001 HIGHRISK 1.01 0.86 1.18 0.921

-2 LL, Naïve Model: 4,242.80 -2 LL, Full Model: 4,124.80 Logit R-Square: 0.03 Chi-Square DoF Pr>ChiSqr. Whole Model Test (Wald): 111.50 7 <.0001 Hosmer-Lemeshow GoF: 32.69 8 <.0001

As we see, there are factors that seem to have significant associations with the dependent

variable of higher-than-expected O/E ratios, wherein some have protective or downward

associations on the likelihood of being high O/E. These factors include teaching status, private

ownership, being large and nonprofit, as their predicted odds ratios are less than 1.0. The indicators

for rural hospitals and hospitals with a patient population having a higher than expected risk of

death were shown to not have any association with high DAMA O/E as shown by their fitted odds

ratios. On the other hand, we have one factor -- the indicator for a hospital’s wage index being

greater than average (HIGHWAGE) -- that is positively associated with high observed-to-expected

DAMA ratios.

Table 17: Modeling High Observed to Expected Ratios on hospitals with any DAMA experience

Dependent Variable: High DAMA Observed to Expected Ratios Best Model (n=3414) 95% CL Regressor Odds Ratio Lower Upper Pr>ChiSqr. RURAL 1.72 1.42 2.07 <.0001 TEACHING 0.57 0.45 0.72 <.0001 PRIVATE 0.57 0.46 0.71 <.0001 HIGHWAGE 1.71 1.45 2.02 <.0001 LARGE 0.37 0.31 0.45 <.0001 NONPROFIT 0.56 0.46 0.69 <.0001 HIGHRISK 0.72 0.61 0.85 0.000

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-2 Log Likelihood, Naïve: 3,784.30 -2 Log Likelihood, Full: 3,468.90 Logit R-Square: 0.08 Chi-Square DoF Pr>ChiSqr. Whole Model Test (Wald): 272.20 7 <.0001 Hosmer-Lemeshow GoF: 16.56 8 0.04

When we condition the model to include only those hospitals that have any DAMA

experience in the study period, we find the results only strengthened, and that the indicators for

Rural hospital (increased log-odds) and HIGHRISK (decreased log-odds), do become statistically

significant; Rural hospitals are associated with levels of DAMA higher than expected, whereas

hospitals with populations at high risk of mortality are associated with lower levels of DAMA.

Although these sets of results may seem at first counter-intuitive, it should be considered

that larger, urban, teaching, and nonprofit hospitals likely have both experience and protocols in

place that help them manage patients who leave or threaten to leave against medical advice, thus

making it likely that such hospitals do not have higher than expected levels of DAMA. In contrast,

their rural counterparts will tend to be smaller, with perhaps less established procedures in place to

deal with DAMA patients, and may not have the controls or experience in place to adequately

recognize and prevent DAMA from occurring.

Note that the Wald statistic, which is the equivalent of the Fisher-test for model significance,

is indeed highly significant for both scenarios, meaning that changes in the included regressors do

have an association on whether a hospital is considered high DAMA. This would lend support to

the alternative hypothesis of having hospital factors that are associated, versus none at all. With

these collective results on factors that have significant associations with higher-than expected levels

77

of DAMA, we reject the null hypotheses in favor of the alternative that there are indeed factors at

the hospital-level that are significantly associated with both higher-levels of DAMA

However, it is also worth noting that the logistic version of the R-square statistic for both

models as generally low, and that the Hosmer-Lemeshow goodness of fit test suggests that the

models could be better-fitted.

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Chapter 5: Discussion and Policy Implications Key Findings Research Aim 1

In the first research aim, the goal was to examine the association between DAMA and index

visit outcomes, Readmissions likelihood, and Readmission visit outcomes. The data used to address

this research aim came from The Johns Hopkins Hospital (JHH). Using propensity-scores for

paired matches were created between DAMA-cases and standard-discharge controls at a ratio of 1:2.

Looking at the overall likelihood of readmission at JHH between the DAMA-exposed and

non-DAMA groups, we note a marked difference in terms of the level of readmissions of the

DAMA-exposed, versus both groups of non-DAMA controls. Using McNemar's test for paired

observations, we noted that the differences were significant between groups at the .05% level. These

results suggest that more readmissions will be experienced by those leaving against medical advice

than those who leave by standard discharge. This would then suggest that the null hypothesis of no

association of DAMA on the likelihood of readmissions would be rejected in favor of the alternative

-- that indeed, leaving care earlier than expected or recommended may thwart medical care

objectives and not fully address the underlying illness, leading the patient to return within 30 days.

Part of this research aim was to assess any association of DAMA to the following outcomes:

LOS, Total Charges, and Severity of Illness. In a manner similar to the analysis on readmissions,

each outcome was analyzed in turn by the same approach, where binary indicators were created for

the outcomes of interest comparing the readmission-visit to the corresponding index-visit in terms

of the selected outcome. These flags indicate when a readmission-visit LOS is greater than the

preceding index-visit LOS; or for the total charges, indicating instances where the readmission-visit

total charges were greater than the preceding index-visit total charges; and for Severity of Illness,

indicating instances when illness severity recorded on the readmission had increased since the index-

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admission. Averages were taken to show proportions, and these proportions were then cross-

compared between the DAMA cases and the pooled matched non-DAMA controls.

What was generally found was that there were indeed overall differences between DAMA

and non-DAMA groups; that when it came to lengths of stay, those that were DAMA on their index

visit typically had a readmission LOS that was longer than the index LOS. Correspondingly, we see

that total charges associated with the readmission were also higher than on the index-visit, and

finally, that the severity of illness score was higher for the DAMA observations than for their

matched control counterparts. In all, these results were strong enough to reject what could be

considered a composite null hypothesis that given a DAMA on the index visit, there are no

discernable associations with the readmission LOS, total charges, and health status, in favor of the

alternative hypotheses. As noted before, others have also examined discharges against medical

advice and 30-day all-cause readmissions: Glasgow et al., Hwang et al., and Garland et al. all note

that those who do leave hospitals against medical advice are more likely to be readmitted to inpatient

care and also are more likely to experience 30-day mortality than those who experience a standard

discharge (Garland et al., 2013; Glasgow et al., 2012; Hwang et al., 2003).

Our study corroborates the findings that DAMA is associated with an increased likelihood

of readmissions within 30 days of the index visit, but it also adds that the severity of illness worsens

between the index visit and readmission visit. This may suggest that because full treatment was not

received as a result of a premature exit, the underlying condition was not adequately addressed and

thus led to further decreased health, apart from that which had caused the readmission itself. This

finding may have been implied by research done by others regarding DAMA and the increased risk

of mortality, but our findings herein help confirm the notion, and address an earlier research need

cited by Hwang, Li, and Gupta to see if DAMA is associated with worse health outcomes and

increased healthcare costs (Hwang et al., 2003).

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Research Aim 2 The second research aim was to establish a broader, more national picture of the association

of DAMA, if any, with Lengths of Stay and Total Charges of the index visit. The data used to

address this aim come from the 2012 HCUP National Inpatient Sample, which are administrative

data at the visit level taken from a representative sample of all discharges in the US. Similar to the

JHH data used in the first research aim, these data also had myriad information about the inpatient

visit; the major difference being that there is no information relating to readmissions. Propensity

score matching on the likelihood for a discharge against medical advice was again used to match

DAMA-cases to non-DAMA controls at a ratio of 1:2, using covariates associated with DAMA and

the respective outcomes to account for confounding.

From the analysis on the matched pairs of DAMA and non-DAMA observations, it was

possible to discern that there were differences between the DAMA group and the control groups;

understandably, lengths of stay were comparatively shorter when DAMA was present, with

correspondingly lesser total charges as a result. Also noted is the reasonable finding that the mean

severity of illness attached to a DAMA-visit at 1.85 is lower than those attached to the matched non-

DAMA. Visits at 1.93. This finding suggests that with all else equal, those who leave against medical

advice are somewhat healthier than their standard discharge counterparts. These differences in

health status also hold to varying degrees within each of the four diagnostic groups, the largest

difference being found for those under the major diagnostic cluster of ‘Alcohol/Drug Abuse

Induced Mental Disorders’. These analysis results allowed not only the ability to refute the null

hypotheses of DAMA having no association with these selected measures, but also the ability to

quantify the differences between DAMA-exposed and non-DAMA groups in terms of LOS, Total

Costs, and Illness Severity.

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The comparisons of costs, LOS, and Severity of Illness between DAMA and non-DAMA

observations was also done on selected diagnostic conditions that were found to be most common

among DAMA in order to see if these overall findings still hold, and if so, to what degree. What

these results indicate is that leaving against medical advice is associated with decreased utilization:

overall, leaving prematurely results in a mean cost of $5223 with a LOS of 2.5 days, whereas a

corresponding standard discharge accounts for a mean cost of $8346 at a LOS of 4.8 days. These

differences in LOS and costs ranged from $2324 and 1.5 days for those visits associated with

Diseases and Disorders of the Digestive System; to $3385 and 1.3 days for those observations

associated with Diseases and Disorders of the Circulatory System. It is again interesting to note

that the largest mean difference in LOS between DAMA and non-DAMA groups was found among

those afflicted with ‘Alcohol/Drug Abuse Induced Mental Disorders’, at 3.45 days. This finding

lends support to the notion that many patients may leave against medical advice due to underlying

addictive behaviors.

Research Aim 3 This final component to the study was to investigate if there are characteristics at the level of

the hospital that are associated with higher than expected levels of DAMA. As mentioned

previously, this was done by utilizing observed-to-expected (O/E) ratios of DAMA. This was

comprised of two measures - the actual number of DAMA for a hospital in a given time period as

the numerator, and the expected number of DAMA based on a set of variables as the denominator -

this resulting rate is a risk-adjusted measure of a given hospitals DAMA rate. Data used to address

this final research aim were also from the 2012 HCUP dataset, using the hospital-level files in

addition to the visit-level files.

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All hospitals that had DAMA that were 50% or more than expected (an O/E ratio at or

above 1.5) were deemed high DAMA hospitals, and flagged as such with an indicator variable. This

value was then regressed against hospital-level characteristics using a logistic regression in order to

obtain adjusted odds ratios corresponding to each included regressor, thereby telling us of any

statistically relevant increase or decrease in probability of our selected event-outcome. After

addressing possible collinearity among certain regressors, a set of independent regressors that were

available and thought to have relevance to DAMA were selected for the model.

The first two models suggest there are indeed factors associated with a hospital's

characterization of high-DAMA – namely the indicators for rural locations and for high-wage

indices. The other variables in the hospital-level model seemed to have a protective effect, meaning

that their presence reduces the likelihood that a given hospital would be considered high-DAMA.

Being a teaching hospital, privately-owned, large, and a non-profit hospital all have odds ratios less

than 1.0. Higher proportions of inpatients in rural hospitals may leave against medical advice than

the inpatients in large, urban hospitals because the rural facilities may not have the controls or

protocols in place to better manage or altogether prevent patients from leaving against medical

advice. It is also very likely that their larger, urban counterparts have much more institutional

experience and history in dealing with such patients.

Other authors have noted the relationships between hospital-level characteristics and

DAMA. For instance, Ibrahim et al. found that larger, urban hospitals were associated with higher

risk of DAMA (Said et al., 2007). Also, Franks et al. in their analysis of race among other risk factors

for DAMA, found that for-profit hospitals and teaching hospitals were more likely to be at risk for

high levels of DAMA (Franks et al., 2006). These findings, however, do not directly speak to

whether these factors contribute to the likelihood of higher than expected levels of DAMA given

the hospital patient-mix; our study seems to be the only one addressing this notion.

83

As noted, having a high hospital wage-index was also listed as a significant risk factor for

experiencing higher-than-expected DAMA. As Bai and Anderson found, hospitals that have high

charges relative to their costs are typically private, for-profit hospitals, or are members of for-profit

health systems, and that this type of arrangement will affect the more vulnerable segments of the

population disproportionately (Bai & Anderson, 2015). This patient group would include those who

are not insured, have insufficient coverage, or are not covered by Medicare or Medicaid or third-

party payors, and are thus asked to pay full charges (Bai & Anderson, 2015). As a result, such

hospitals may have patients who either forego treatment, or decide to leave care prematurely to

avoid further charges (Bai & Anderson, 2015). Having a higher than average wage index may be a

proxy for the hospital also having high costs, leading to high charges for patients. According to the

findings of Bai and Anderson, higher charges may put the more vulnerable segments of the

hospital’s patient population under financial pressure, resulting in higher levels of discharges against

medical advice.

Policy Implications Research Aim 1

An important finding form this study is that visits ending with a discharge against medical

advice at Johns Hopkins have a higher likelihood of readmission within 30 days. We also noted that

such visits have higher utilization and severity of illness on this re-hospitalization relative to the

index visit. These findings should add further urgency for finding ways to prevent patients from

leaving before they are medically ready – not only are there immediate consequences and risks to the

patient, care team and hospital, but there are also consequences tied to the likely return visit. Unlike

other states, Maryland does account for DAMA when assessing hospitals’ performance relative to

readmissions, and so there exists an incentive for a given hospital under the Readmissions Reduction

84

Incentive Program (‘RRIP’) within the All-Payor, Global Budget Model to minimize such discharges

in order to maximize its earned readmissions-reduction incentive. This may also provide an

opportunity for recognizing DAMA as a formal category for readmissions-risk, and may lead policy-

makers and hospitals across the state to focus more attention and resources to identify and intervene

in visits involving high-risk patients in order to reduce the likelihood of a premature discharge. A

pilot program to identify DAMA and track associated readmissions and outcomes over time,

especially for certain subsets of the population identified by certain demographic and diagnostic

characteristics to be at high-risk, could indeed be implemented at a large, urban hospital such as

Johns Hopkins.

Research Aim 2 Using a national sample, we see that lengths of stay and total costs for patients discharged

against medical advice are understandably less than those of standard discharges. There are similarly

significant differences in lengths of stay and total costs of care across the different diagnostic groups

found to be most common among those discharged against medical advice. Indeed, these abridged

dollar amounts and lengths of stay may not be actual savings in any sense, but rather, lost

opportunities for hospitals, especially in a fee-for-service environment, to provide the necessary care

that is normally foregone as a result of an unexpectedly short hospital stay. As suggested by the

findings at JHH, there may likely be downstream costs and utilization by such patients who could

not complete the initial course of care. Also, there are likely downstream hospital and societal costs,

and health-related consequences resulting from these premature discharges. These possibilities

provide more reason and impetus to identify and prevent occurrences of DAMA when possible, and

to make this a higher priority for providers and hospitals.

85

Research Aim 3 It was also found rural hospitals and those hospitals with high-wage indices are likely to have

higher than expected DAMA. Such hospitals would be well advised to assess the matter of

discharges against medical advice within their respective patient populations to become more aware

of its causes and consequences, and be encouraged to become familiar with or adopt the protocols

and lessons for managing DAMA from their larger, more urban counterparts. It may also behoove

the for-profit hospitals and health systems to know how having high charges or markups over cost

may translate to their patients, especially those with no insurance or limited coverage, who may

decide to leave inpatient care against medical advice to avoid any ensuing financial burden.

After seeing the implications of DAMA, most US hospitals would do well to better track

their performance in regards to DAMA. By being good stewards of their own DAMA data, local

governments that can access all-hospital data across their jurisdictions or boundaries could then

facilitate linkage and accurate tracking of DAMA across providers and communities for a more

comprehensive picture of the phenomenon (Jencks et al., 2009). Indeed, the rate of DAMA can be

used as a measure of provider or hospital-level performance. With wider and more-available linked

data spanning multiple hospitals and health systems, tracking DAMA may become not only a key

performance metric for integrated, patient-specific care, but population health as well. In the

interests of informed consent to all stakeholders, these findings may bolster the notion that risks to

providers, hospitals, and patients and their families should be known and disclosed.

Weaknesses and Limitations This study has limitations and weaknesses. Administrative healthcare data are generated as a

result of the processing of claims for billing and eventual reimbursement from third-party payors.

However, given that they also contain important details regarding the encounter and of the patient,

such as demographics, disposition, and diagnoses, they are often used for health services research, in

86

addition to the original purpose of billing and revenue. Also, these administrative records were

created and handled by persons and hospitals, and as such, are subject to miscoding, human error,

and may contain systematic biases or inaccuracies. Despite these possible issues, administrative

databases will be the primary source of information for these kinds of studies until more accurate

and detailed information as found in electronic medical records become widely and safely available

(deLissovoy et al., 2009).

The observational study presented herein is descriptive and assesses associations, and cannot

determine causality between the identified variables. Conclusions of cause and effect would require

an experimental design that allows for assessment of an intervention or treatment. Exploration of

this would require longitudinal datasets in which DAMA and other relevant variables are assessed

over time. The use of propensity scoring to compare DAMA-exposed and non-exposed groups

assists in trying to identify differences in our selected outcomes, but this feature does not allow us to

establish clear causality.

In addressing the question of DAMA’s association with the likelihood of readmissions, it is

the case that only data from The Johns Hopkins Hospital were used. It would have been preferable

to have had the data of other hospitals in order to conduct a broader study of DAMA and

readmissions, as not all hospitals share the same characteristics and experiences as this large, well-

known, inner-city, academic hospital in the mid-Atlantic. A limitation of this study is that only one

hospital’s experience was used to look at DAMA and all-cause readmissions, and that the population

treated at JHH may not be representative or generalizable to more of the US population; hence

external validity may be of issue for these models.

Moreover, if the HCUP dataset used in the second research aim also had readmissions

information akin to those found in the JHH dataset, it would have been possible to obtain a national

estimate of the overall DAMA association with re-hospitalization, inclusive of the associated

87

outcomes at both the index-visit and readmission-visit. Though one could surmise that the national

data would likely have confirmed the findings from JHH, we are still left to conjecture and

speculation without usable data. This limitation can serve as the basis and impetus for further

research on DAMA and readmissions.

In addressing the third aim, the models were found to be significant; at least some of the

regressors included therein influenced the dependent variable. However the goodness-of-fit tests

suggested they could be more rigorously fitted. More work would be needed to explore the use of

other regressors derived from the patient-level data, as well as the use of interaction terms to

account for possible non-linearity or interactions between regressors. However, the fitted regressors

and the resultant odds-ratios were still intuitive, and suggest that the models were representing the

intended constructs. More study would be necessary to build upon the models presented herein to

further strengthen their construct validity, but an argument can already be made that hospital-level

analyses of discharges against medical advice, especially in regards to high levels of DAMA, can and

should be conducted.

Future directions It is acknowledged that only readmissions data from The Johns Hopkins Hospital were used

to address the first research aim. Though informative, the analysis could have been strengthened

and more generalizable if there were readmissions information from more and more varied

hospitals. More research is needed to confirm that what was found herein between readmissions

and DAMA can also hold on a national scale. Future analyses could explore how diagnostic groups

common to patients who leave against medical advice are also affected by DAMA and readmissions.

Indeed, patients who suffer from different diagnostic conditions may be more or less likely to be re-

admitted. Finally, in regards to the hospital-level analysis, an avenue of future work would be to

88

delve further into what other factors may also be tied to high-levels of DAMA, to explore other

variables and interactions to build a more detailed and robust model.

Discharges against medical advice may be very difficult to predict accurately with a statistical

model, because this is a relatively uncommon phenomenon. However, we also know that with

today’s computing power and storage capabilities, there are vast amounts of data being collected and

stored as thousands of inpatient encounters occur each day over time, and that this bank of

experience can be searched and analyzed for patterns in disease and health-behavior at a broad level

(Wachter, 2008). There have been retrospective studies that look at the medical record, as well as

retrospective analyses of factors that are associated with DAMA, and best practices for physicians in

managing these problematic encounters.

It does not seem that there are any large-scale studies that have accessed or used detailed

clinical information in the patient’s medical record, apart from those fields found in standard

administrative or claims datasets (Saitz, 2002). The (electronic) patient medical records, beyond

what is found in administrative data, can be a rich source of available clinical information about the

condition of the patient, and may even contain some notes and observations about patients and their

behavior from the provider’s perspective. These may provide an avenue of research inquiry to see if

analyses of the notes and nursing assessments contained in the medical record could provide further

insight or indications for an impending discharge against medical advice. As Wachter suggests,

given the querying and text-search technologies available today, certain analyses would now be

possible to do. For instance, it would not be hard to scan textual data in these records to find input

for a model to assess DAMA-risk. Subsequent findings of this type of analysis could inform staff of

warning signs of an untimely discharge, or assist in the identification and formulation of interview

questions for staff to ask the patient that may serve as a quick risk-assessment tool to predict a

89

possible premature discharge. Identifying patients at risk for DAMA creates an opportunity for

prevention or at least buys time for preparation, and a chance for a better outcome.

Other future work could entail prospective studies that focus on the causes and

circumstances of DAMA across The Johns Hopkins Hospital in order to address questions such as

what are the causes and circumstances of a given DAMA-episode, and what the outcomes were as a

result of that DAMA. This could involve primary data collection through a survey-instrument or

questionnaire for the care-team present for the discharge in question, allowing insight when existing,

secondary data-sources are unable to do so. This may even provide opportunity to gain further

insight into the role the provider has (communication and manner, differential health literacy, biases)

with discharges against medical advice. The collected data and the resultant concepts and insights,

could be summarized and standardized so as to be used to form a taxonomy of DAMA at Johns

Hopkins. This may also form the basis for a DAMA intervention with an appropriate experimental

design, using the collected knowledge to account for patient factors and even physician factors; and

their joint effects.

Conclusions It goes without saying that patients, regardless of their diagnosis or associated therapy, may

hold different values and expectations than their providers, especially when it comes to their health.

It is not necessarily the case that everyone would consider his or her health as the highest priority in

the face of other competing priorities.

As we have come to know, discharges against medical advice are generally known as an

instance whereby a patient has exercised their choice to leave the hospital care before the attendant

physician has recommended a discharge. It signifies a failure to reach consensus between the

attendant physician and the patient regarding the need for further inpatient care and attention, as

90

well as a breakdown in the implicit therapeutic alliance between patient and physician. Such patients

withdraw consent for myriad reasons, whether medical, social, economic, or inter-personal. Though

it represents a small proportion of all inpatient discharges, discharges against medical advice are

nonetheless an increasingly well-known and persistent issue among the doctors and nurses that treat

such patients, and among the general hospitals where they are treated. Patients who leave against

medical advice are a diverse group, and suffer from a host of maladies, which can make certain

generalizations difficult. However, they are a subset of patients who by their actions, will experience

less-effective medical care and are thus at increased risk for worse health outcomes. From the work

we have done here using visit-level and hospital-level data, we have a better and more informed

narrative of who these patients are, and how their decisions to leave against medical advice can have

measurable associations that involve their well-being, and the well-being of the institutions that treat

them.

91

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Windish, D., & Ratanawongsa, N. (2008). Providers' perceptions of relationships and professional

roles when caring for patients who leave the hospital against medical advice. Journal of General

Internal Medicine, 23(10), 1698-1707.

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Appendix A. Assessing covariate balance before and after greedy matching of DAMA and non-DAMA on propensity score, JHH Data

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JHH Covariate Balance Prior to Matching 1:2

DAMA non‐DAMA Differences

Regressors N Proportion N Proportionin

Proportion

Black 328 35.3% 42,598 62.1% ‐26.8%

601 64.7% 26,024 37.9% 26.8%

Alcohol 683 73.5% 63,758 92.9% ‐19.4%

246 26.5% 4,864 7.1% 19.4%

ER 129 13.9% 39,995 58.3% ‐44.4%

800 86.1% 28,627 41.7% 44.4%

Middle Age 284 30.6% 35,166 51.2% ‐20.7%

645 69.4% 33,456 48.8% 20.7%

Depression 820 88.3% 61,736 90.0% ‐1.7%

109 11.7% 6,886 10.0% 1.7%

HIVAIDS 857 92.2% 67,064 97.7% ‐5.5%

72 7.8% 1,558 2.3% 5.5%

Drug Abuse 556 59.8% 61,306 89.3% ‐29.5%

373 40.2% 7,316 10.7% 29.5%

Male 339 36.5% 35,441 51.6% ‐15.2%

590 63.5% 33,181 48.4% 15.2%

Medicaid 355 38.2% 53,008 77.2% ‐39.0%

574 61.8% 15,614 22.8% 39.0%

Self‐Pay 905 97.4% 67,984 99.1% ‐1.7%

24 2.6% 638 0.9% 1.7%

NoProcs 413 44.5% 53,663 78.2% ‐33.7%

516 55.5% 14,959 21.8% 33.7%

Psychoses 715 77.0% 62,508 91.1% ‐14.1%

214 23.0% 6,114 8.9% 14.1%

Active 320 34.4% 53,906 78.6% ‐44.1%

609 65.6% 14,716 21.4% 44.1%

N(Overall) = 69,551 N(DAMA) = 929

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JHH Covariate Balance After Matching 1:2



Proportion

Black 328 35.3% 655 35.3% 0.1%

600 64.7% 1,201 64.7% ‐0.1%

Alcohol 683 73.6% 1,368 73.7% ‐0.1%

245 26.4% 488 26.3% 0.1%

ER 129 13.9% 252 13.6% 0.3%

799 86.1% 1,604 86.4% ‐0.3%

Middle Age 284 30.6% 563 30.3% 0.3%

644 69.4% 1,293 69.7% ‐0.3%

Depression 819 88.3% 1,647 88.7% ‐0.5%

109 11.7% 209 11.3% 0.5%

HIVAIDS 857 92.3% 1,708 92.0% 0.3%

71 7.7% 148 8.0% ‐0.3%

Drug Abuse 556 59.9% 1,113 60.0% ‐0.1%

372 40.1% 743 40.0% 0.1%

Male 339 36.5% 672 36.2% 0.3%

589 63.5% 1,184 63.8% ‐0.3%

Medicaid 354 38.1% 686 37.0% 1.2%

574 61.9% 1,170 63.0% ‐1.2%

Self‐Pay 905 97.5% 1,820 98.1% ‐0.5%

23 2.5% 36 1.9% 0.5%

NoProcs 413 44.5% 845 45.5% ‐1.0%

515 55.5% 1,011 54.5% 1.0%

Psychoses 715 77.0% 1,435 77.3% ‐0.3%

213 23.0% 421 22.7% 0.3%

Active 320 34.5% 640 34.5% 0.0%

608 65.5% 1,216 65.5% 0.0%

N(Overall) = 2784 N(DAMA) = 928

99

Appendix B: Assessing covariate balance before and after greedy matching of DAMA and non-DAMA on propensity score, HCUP Data

100

HCUP Covariate Balance Prior to Matching 1:2



Proportion

Middle Age 7,196 35.5% 546,580 57.3% ‐21.9%

13,101 64.5% 406,881 42.7% 21.9%

Black 14,991 73.9% 815,141 85.5% ‐11.6%

5,306 26.1% 138,320 14.5% 11.6%

Urban 5,309 26.2% 322,537 33.8% ‐7.7%

14,988 73.8% 630,924 66.2% 7.7%

Medicaid 11,584 57.1% 767,685 80.5% ‐23.4%

8,713 42.9% 185,776 19.5% 23.4%

Self‐Pay 17,901 88.2% 913,652 95.8% ‐7.6%

2,396 11.8% 39,809 4.2% 7.6%

ER 3,882 19.1% 385,197 40.4% ‐21.3%

16,415 80.9% 568,264 59.6% 21.3%

Elective 18,786 92.6% 720,639 75.6% 16.9%

1,507 7.4% 232,177 24.4% ‐16.9%

Male 13,861 68.3% 747,812 78.4% ‐10.1%

6,436 31.7% 205,649 21.6% 10.1%

NoProcs 16,593 81.8% 767,112 80.5% 1.3%

3,704 18.2% 186,349 19.5% ‐1.3%

Income1 16,506 81.3% 727,395 76.3% 5.0%

3,791 18.7% 226,066 23.7% ‐5.0%

Income2 7,272 35.8% 562,038 58.9% ‐23.1%

13,025 64.2% 391,423 41.1% 23.1%

Income3 10,484 51.7% 640,989 67.2% ‐15.6%

9,813 48.3% 312,472 32.8% 15.6%

Depression 17,696 87.2% 852,971 89.5% ‐2.3%

2,599 12.8% 100,446 10.5% 2.3%

Alcohol 14,299 70.5% 892,759 93.6% ‐23.2%

5,996 29.5% 60,658 6.4% 23.2%

Drug Abuse 13,782 67.9% 894,786 93.9% ‐25.9%

6,513 32.1% 58,631 6.1% 25.9%

Psychoses 17,265 85.1% 874,392 91.7% ‐6.6%

3,030 14.9% 79,025 8.3% 6.6%

HIVAIDS 19,791 97.5% 948,335 99.5% ‐2.0%

504 2.5% 5,082 0.5% 2.0%

Active 7,509 37.0% 767,454 80.5% ‐43.5%

12,786 63.0% 185,963 19.5% 43.5%

N(Overall) = 973,712

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N(DAMA) = 20,295

HCUP Covariate Balance After Matching 1:2



Proportion

Middle Age 7,194 35.5% 14,335 35.3% 0.1%

13,099 64.5% 26,251 64.7% ‐0.1%

Black 14,988 73.9% 30,055 74.1% ‐0.2%

5,305 26.1% 10,531 25.9% 0.2%

Urban 5,309 26.2% 10,544 26.0% 0.2%

14,984 73.8% 30,042 74.0% ‐0.2%

Medicaid 11,581 57.1% 23,209 57.2% ‐0.1%

8,712 42.9% 17,377 42.8% 0.1%

Self‐Pay 17,897 88.2% 35,775 88.1% 0.0%

2,396 11.8% 4,811 11.9% 0.0%

ER 3,881 19.1% 7,543 18.6% 0.5%

16,412 80.9% 33,043 81.4% ‐0.5%

Elective 18,786 92.6% 37,811 93.2% ‐0.6%

1,507 7.4% 2,775 6.8% 0.6%

Male 13,859 68.3% 27,642 68.1% 0.2%

6,434 31.7% 12,944 31.9% ‐0.2%

NoProcs 16,589 81.7% 33,140 81.7% 0.1%

3,704 18.3% 7,446 18.3% ‐0.1%

Income1 16,502 81.3% 33,021 81.4% 0.0%

3,791 18.7% 7,565 18.6% 0.0%

Income2 7,270 35.8% 14,598 36.0% ‐0.1%

13,023 64.2% 25,988 64.0% 0.1%

Income3 10,481 51.6% 20,884 51.5% 0.2%

9,812 48.4% 19,702 48.5% ‐0.2%

Depression 17,692 87.2% 35,436 87.3% ‐0.1%

2,599 12.8% 5,140 12.7% 0.1%

Alcohol 14,295 70.4% 28,717 70.8% ‐0.3%

5,996 29.6% 11,859 29.2% 0.3%

Drug Abuse 13,781 67.9% 27,687 68.2% ‐0.3%

6,510 32.1% 12,889 31.8% 0.3%

Psychoses 17,262 85.1% 34,554 85.2% ‐0.1%

3,029 14.9% 6,022 14.8% 0.1%

HIVAIDS 19,788 97.5% 39,693 97.8% ‐0.3%

503 2.5% 883 2.2% 0.3%

Active 7,509 37.0% 15,077 37.2% ‐0.2%

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12,782 63.0% 25,499 62.8% 0.2%

N(Overall) = 60,867 N(DAMA) = 20,291

103

Appendix C: Code used in the Identification of Specific ICD9 codes for Alcohol Abuse, Psychoses, HIV/AIDs, Depression, and Active Drug Use

104

IF '2910' <=: I9DIAG <=: '2913' OR '30300' <=: I9DIAG <=: '30393' OR '2910' <=: I9DIAG <=: '2913' OR '30500' <=: I9DIAG <=: '30503' THEN ALCOHOL = 1; ELSE ALCOHOL =0; IF '29282' <=: I9DIAG <=: '29289' OR '30400' <=: I9DIAG <=: '30493' OR '30520' <=: I9DIAG <=: '30593' OR '64830' <=: I9DIAG <=: '64834' OR I9DIAG in ('2920','2929') THEN DRUG = 1; ELSE DRUG = 0; IF '29500' <=: I9DIAG <=: '2989' OR I9DIAG IN ('29910', '29911') then PSYCHOSES = 1; ELSE PSYCHOSES = 0; IF i9diag in ('3004','30112','3090','3091','311') THEN DEPRESSION = 1 ; ELSE DEPRESSION = 0; IF '042' <=: I9DIAG <=: '0449' THEN HIVAIDS =1 ; ELSE HIVAIDS = 0; IF I9DIAG IN ('2910','2911','2913','2914','2915','2918','2919','2920','2922','2929', '3051','3575','4255','5353','5710','5711','5712','5713','7903', '9670','9800','9808','9809','29181','29182','29189','29211','29212', '29281','29283','29284','29285','29289','30300','30301','30302','30390', '30391','30392','30400','30401','30402','30410','30411','30412','30420', '30421','30422','30430','30431','30432','30440','30441','30442','30450', '30451','30452','30460','30461','30462','30470','30471','30472','30480', '30481','30482','30490','30491','30492','30500','30501','30502','30510', '30511','30512','30520','30521','30522','30530','30531','30532','30540', '30541','30542','30550','30551','30552','30560','30561','30562','30570', '30571','30572','30580','30581','30582','30590','30591','30592','53530', '53531','64830','64831','64832','64833','64834','65550','65551','65553', '76071','76072','76073','76075','96500','96501','96502','96509','V6542' ) THEN ACTIVE = 1; ELSE ACTIVE =0 ; IF SUM(DEPRESSION, ALCOHOL, DRUG, PSYCHOSES) GE 1 THEN DAMACOMO = 1; ELSE DAMACOMO = 0;

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Appendix D: IRB Approval Letter

107

Appendix E: Curriculum Vitae

108

Hoon Byun

Permanent Address Work Address 14 East Mt. Vernon Place B3 Billings Admin. rm.311 Baltimore, Md. 21202 Baltimore, Md. 21287 [email protected] Education

DrPH Public Health Johns Hopkins University (2016) M.A. Economics The University of Virginia (1997) B.A. Economics The College of William and Mary (1995)

Doctoral Dissertation "Discharges Against Medical Advice: Associations With Selected Outcomes and the Role of Hospital-Level Characteristics”

A study of how leaving inpatient care against medical advice (AMA) can result in worse outcomes (lengths of stay, costs, severity of illness) as compared with outcomes of comparable standard discharges, and an exploration into the association of hospital-level characteristics with higher-than expected levels of AMA discharges.

Publications

Provided research assistance on 'The Costs of Decedents in the Medicare Program: Implications for Payments to Medicare + Choice Plans' Buntin MB, Garber AM, McClellan M, Newhouse JP. Health Services Research. 2004 Feb;39(1): 111-30.

Provided research assistance on 'Persistence of Medicare Expenditures among Elderly Beneficiaries' by Alan M. Garber, Thomas E. MaCurdy, Mark B. McClellan Volume URL: http://www.nber.org/books/garb98-1 Jan.1998, ISBN: 0-262-57120-X.

Provided research assistance on “Area Differences in Utilization of Medical Care and Mortality among U.S. Elderly," Victor R. Fuchs, Mark McClellan, and Jonathan Skinner, Perspectives on the Economics of Aging, David A. Wise, ed., Chicago: University of Chicago Press, 2003.

Panels

Brookings-AAFA Roundtable on Asthma Payment and Delivery Innovation March 6, 2015 The goal of the event was to identify specific community interventions that could be implemented in the near-term to improve asthma care.

Teaching Experience

Part-time Teaching Assistant, JHSPH Patient Safety, Spring terms 2011, 2012

109

Relevant Experience Business Manager, The Johns Hopkins Health System 2007-Present

Facilitating and overseeing the administrative and fiscal operations for Johns Hopkins Health System and Hospital departments: involving the planning of annual operating budgets worth over $37.5 million, creating regular departmental operations reports for senior leadership, ensuring accuracy of human resources data for over 300 staff, facilitating the finance of major institution-wide projects worth over $7.3 million over 6 years, managing vendor relations, submitting annual community benefits reports and institution-wide philanthropy, and assisting with institution-wide compliance initiatives on behalf of senior leadership.

Senior Programmer Analyst, Johns Hopkins University 2002-2007 Provided analytic assistance in the design, application, and maintenance of the ACG risk-adjustment software product, as well as provide analytic assistance on the ongoing HIV Research Network study at the Department of Medicine.

Senior Research Analyst, University of Maryland, Baltimore County 1999-2001 Constructed the Maryland FFS Medicaid Database for use in risk-adjusted capitation analyses and MCO rate-setting for the Medicaid Population in fiscal years ’00 and ’01 on behalf of the Maryland Department of Health and Mental Hygiene.

Research Analyst, National Bureau of Economic Research 1997-1999 Provided analytic assistance on concurrent, grant funded research initiatives at the Stanford branch of the NBER, as well as provide assistance on the management of national Medicare databases.

Organizations

American College of Healthcare Executives 2011-Present Johns Hopkins University Diversity Leadership Council 2011-Present

References

Conan Dickson, PhD. Laura Morlock, PhD. Kenneth Shermock PhD. Ashley Llorens Jonathan Weiner, DrPH.

discharges against medical advice: associations with

Documents