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Using a Transitional Care Program to Prepare Patients to Take Care of Themselves after Leaving the Hospital
Jeffrey Schnipper1 ;Nyryan Nolido1 ;Michelle Potter1 ;Cherlie Magny‐Normilus1 ;Hilary Heyison1 ; Catherine Yoon1 ;Asaf Bitton 1,2;David Bates 1,2; Elyse Park 2,3; Eric Weil 3
; Anuj Dalal 1,2;Gwen Crevensten 2,3;Ryan Thompson 2,3;Jacquelyn Minahan1;Molly O’Reilly1;Deborah Williams1;Natasha Isaac4 ;Stephanie Labonville1;E. John Orav 1,2
1 Brigham and Women’s Hospital, Boston, MA 2 Harvard Medical School, Boston, MA 3 Massachusetts General Hospital, Boston, MA 4 Dana Farber Cancer Institute, Boston, MA
Original Project Title: Relative Patient Benefits of a Hospital‐PCMH Collaboration within an ACO to Improve Care Transitions PCORI ID: 811 HSRProj ID: 20142231 ClinicalTrials.gov ID: NCT02130570
_______________________________ To cite this document, please use: Schnipper J, Nolido N, Potter M, et al. 2019. Using a Transitional Care Program to Prepare Patients to Take Care of Themselves after Leaving the Hospital. Washington, DC: Patient‐Centered Outcomes Research Institute (PCORI). https://doi.org/10.25302/5.2019.CER.811
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Table of Contents Abstract .............................................................................................................................................3 Background .......................................................................................................................................4 Participation of Patients and Other Stakeholders in the Design and Conduct of Research and Dissemination of Findings ..................................................................................................................7 Methods ............................................................................................................................................9
Formal Study Protocol .............................................................................................................................. 9 Study Population ....................................................................................................................................... 9 Study Design ........................................................................................................................................... 12 Subject Enrollment .................................................................................................................................. 12 Study Setting ........................................................................................................................................... 14 Intervention ............................................................................................................................................ 14 Usual Care ............................................................................................................................................... 22 Clinician Surveys...................................................................................................................................... 22 Inpatient and Outpatient Inventories of Transitional Care Tasks ........................................................... 22 Outcomes ................................................................................................................................................ 23 Preventable Readmissions ...................................................................................................................... 27 Analysis ................................................................................................................................................... 28
Results ............................................................................................................................................. 10 Evolution of the Intervention .................................................................................................................. 10 Enrollment and Study Flow ..................................................................................................................... 14 Patient Characteristics ............................................................................................................................ 15 Baseline Clinician Surveys ....................................................................................................................... 15 Inpatient and Outpatient Inventories of Transitional Care Tasks ........................................................... 19 Intervention Fidelity ................................................................................................................................ 19 Quantitative Outcomes ........................................................................................................................... 21 Subgroup Analyses .................................................................................................................................. 21 Patient Survey Results ............................................................................................................................ 31 Qualitative Analysis ................................................................................................................................. 31
Discussion ........................................................................................................................................ 34 Context for Study Results ........................................................................................................................ 34 Generalizability of the Findings .............................................................................................................. 38 Implementation of Study Results............................................................................................................ 38 Subpopulation Considerations ................................................................................................................ 42 Study Limitations .................................................................................................................................... 42 Future Research ...................................................................................................................................... 45
Conclusions ..................................................................................................................................... 46 References ....................................................................................................................................... 47
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Abstract
Background: Transitions from hospitals to the ambulatory setting are high‐risk periods for patients. The
advent of the patient‐centered medical home (PCMH) and accountable care organizations (ACOs) has
provided an opportunity for true collaboration in which both inpatient and outpatient providers
contribute to improving transitions in care. Few studies have rigorously evaluated real‐world, patient‐
centered interventions that take advantage of these new developments in health care.
Objectives: The goal of this study was to develop, implement, refine, and evaluate a multifaceted,
multidisciplinary transition intervention across 2 hospitals and 18 PCMHs within a Pioneer ACO.
Methods: The study population included adult patients admitted to medical or surgical services at 2
hospitals within the Partners ACO, with primary care physicians at the 18 participating PCMHs, and with
a plan to be discharged home. We developed an intervention with the following components: inpatient
pharmacist‐led medication reconciliation and patient counseling, coordination of care and patient
education from an inpatient discharge advocate and the PCMH responsible outpatient clinician, a
structured visiting nurse intervention, structured postdischarge phone calls, timely follow‐up visits, tools
to improve communication among care team members, and home pharmacist visits for selected
patients. The study used a stepped‐wedge design in which each PCMH practice started in the usual care
arm and then, at a randomly selected point in time, changed to the intervention. Outcomes included 30‐
day hospital readmissions using administrative data and telephone follow‐up, and new or worsening
symptoms in the 30 days after discharge based on telephone follow‐up and medical record review. We
analyzed the 2 outcomes by multivariable logistic and Poisson regression, respectively, adjusted for
study month, season, patient demographics, risk for postdischarge adverse events, inpatient unit, and
primary care practice.
Results: We enrolled 1657 patients, including 679 assigned to usual care and 978 to the intervention.
Receipt of different components of the intervention varied by component and in some cases by hospital,
unit, and practice. Thirty‐day nonelective readmission rates were 10.9% in the intervention arm and
11.5% in usual care (adjusted odds ratio = 1.08; 95% CI, 0.64‐1.85, p = 0.77). The number of new or
worsening symptoms was 0.90 per patient in the intervention arm and 0.92 per patient in usual care
(adjusted OR = 0.78; 95% CI, 0.64‐0.95; p = 0.01). The intervention was also associated with a 48%
relative reduction in postdischarge adverse events (adjusted IRR = 0.52, 95% CI, 0.30‐0.91, p = 0.02). A
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priori subgroup analysis found no evidence for effect modification of the intervention on readmission
rates, new or worsening symptoms, or adverse events.
Conclusions: Results showed no difference in adjusted 30‐day readmission rates among patients in the 2
study arms, likely owing to lower than expected intervention fidelity and the low proportion of
readmissions that are truly preventable in this patient population. However, the intervention was
associated with a reduced rate of new or worsening symptoms in the postdischarge period and on
postdischarge adverse events—outcomes that are more sensitive to change than readmissions. As with
readmissions, efficacy was likely limited by intervention fidelity. Limitations include confounding by
indication for some of the intervention components. Further study is needed to explore the causes and
effects of low intervention fidelity, to determine the most important components of the intervention,
and to explore variation in care by hospital, inpatient unit, and primary care practice.
Background Each year, more than 32 million adult hospitalizations are recorded in the United
States.1 Many of these hospitalized patients suffer from chronic conditions, including 61% with
3 or more chronic conditions.2 Several studies have shown that an estimated 20% of
hospitalized patients suffer an adverse event within 30 days of discharge.3,4 Approximately two‐
thirds of these events might have been preventable or ameliorable (ie, reduced in severity or
duration) had care been better. Moreover, it is estimated that almost 20% of Medicare patients
are readmitted within 30 days of hospital discharge; the cost to Medicare of unplanned
hospitalizations in 2004 was estimated at $17.4 billion.5
Multiple studies have shown that processes of care during transitions between sites of
care are suboptimal and lead to risks to patient safety. For example, Forster and colleagues
estimated that 59% of preventable or ameliorable postdischarge adverse events were the result
of poor communication between inpatient providers and either patients or ambulatory
providers.3,4 Other studies have shown generally poor quality and timeliness of discharge
documentation,6 low patient understanding of postdischarge plans of care or ability to
carry out these plans,7 medication discrepancies and nonadherence after discharge,8 failure to
follow up on test results pending at the time of discharge,9 failure to schedule needed follow‐up
appointments and tests,10 and lack of timely follow‐up appointments with outpatient
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providers.5 Even assuming that only 25% of hospital readmissions are truly preventable (a
number that is vigorously debated),11 more than 1 million readmissions per year are
unnecessary, at a cost of over $1 billion dollars (including non‐Medicare patients), in addition to
more than 4 million preventable or ameliorable postdischarge adverse events as a result of
suboptimal transitional care. Suboptimal transitions between sites of care also lead to other
risks to patient safety besides readmissions, including new or worsening signs or symptoms,
reductions in functional status, and physical or psychological distress, not to mention out‐of‐
pocket costs, time away from work, and caregiver burden.
Health care organizations lack sufficient information to know what actions to take to
reduce readmissions and postdischarge adverse events and to improve patient outcomes after
discharge. A recent systematic review of interventions hospitals could employ to reduce
readmissions identified several positive studies (ie, statistically significant improvements in
readmission rates) but also many negative studies, and significant barriers exist to
understanding what works to reduce readmissions.12 Most of the interventions described in
both positive and negative studies were multifaceted, and the authors were unable to
determine which components of the interventions were most effective. Also, while several
studies have identified risk factors for readmission,13 few have been able to determine which
subgroups of patients benefit most from specific interventions.
One promising development in health care reform efforts is the advent of accountable
care organizations (ACOs), “groups of doctors, hospitals, and other health care providers who
come together voluntarily to give coordinated high‐quality care to their patients.”14 Another
development is the patient‐centered medical home (PCMH), which consists of patient‐oriented,
comprehensive, team‐based care enhanced by health information technology and
population‐based disease management tools.15 More hospitals and PCMH clinics are joining
ACOs, and both have a vested interest in improving transitions and preventing
readmissions.16 To date, few care transition initiatives have leveraged this alignment of
incentives. It is likely that hospital–PCMH collaboration can improve the efficacy of
transitional interventions, because (1) optimal communication and collaboration on a discharge
plan are more likely when inpatient clinicians and clinicians in PCMHs are similarly motivated,
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and (2) continuity of care is improved when PCMH personnel are able to contact the patient in
the hospital and see the patient shortly after discharge.
As ACOs and PCMHs become more common, interventions that include both hospital
and PCMH personnel can promote optimal transitions for patients. Such interventions are
novel; most interventions studied to date derive either from hospitals or from ambulatory
clinics but rarely from both.12 The evidence gap that this study addresses is the efficacy of
collaborative interventions from hospitals and primary care clinics within an ACO to improve
postdischarge outcomes, as well as the barriers to and facilitators of such interventions.12
Rigorous evidence that quantifies the effects of this type of intervention on important patient
outcomes should influence the adoption of such interventions among health care leaders. If the
interventions are beneficial, their widespread adoption would have a large effect on patient
outcomes and health care performance.
Increasingly, patients will have to decide which health care organizations to join or
affiliate with (for example, PCMHs). While the effectiveness of this intervention on
postdischarge outcomes might be only one of several factors patients consider in making this
decision, it could be a deciding factor for certain patients, such as frequently hospitalized
patients, who are shown in our subgroup analyses to benefit most from the intervention.
The following are the specific aims of this study:
1. To develop, implement, and refine a multifaceted, multidisciplinary transition
intervention with contributions from hospital and primary care personnel across several
PCMHs within the Partners Healthcare Pioneer ACO.
Hypothesis: A collaborative transition intervention can be designed and implemented within an
ACO that reliably provides the components of an ideal transition in care.
2. To evaluate the effects of this intervention on the safety of care transitions as determined
by postdischarge adverse events, functional status, patient satisfaction, and
emergency department and hospital use within 30 days of discharge.
Hypothesis: Compared with usual care, a collaborative transition intervention will decrease
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postdischarge adverse events, improve postdischarge functional status, increase patient
satisfaction, and reduce emergency department and hospital use in the postdischarge
period.
3. To understand barriers to and facilitators of successful implementation of this intervention
across practices.
Hypothesis: Several barriers to and facilitators of implementation can be identified and used to
create lessons learned to enable other health systems to successfully implement this type of
intervention.
Participation of Patients and Other Stakeholders in the Design and Conduct of Research and Dissemination of Findings Patient‐Family Advisory Council (PFAC)
Our PFAC included patients and caregivers of recently hospitalized patients, thus
representing those likely to benefit from the intervention. They were identified and recruited
through the BWH Office of Patients and Families and the MGH Institute for Patient Care.
Through their own monthly meetings and our quarterly steering committee meetings, they
participated in every aspect of the study, including formulating research questions, finalizing
study outcomes, developing and refining the interventions, monitoring study progress, and
making plans for dissemination, including reporting results in a manner that would be
understood by the public.
We worked with our steering committee on the clinical and policy implications of the
study through quarterly meetings throughout the study period (PCORI Methodology Standard
PC‐4). In addition to PFAC representatives, the steering committee consisted of physician,
nursing, pharmacy, information technology, and administrative leadership representing
primary care, inpatient care, and transitional care at BWH and MGH, and within the Partners
ACO. The committee consisted of approximately 20 members, including 1 or 2 designated
PFAC members who participated on a rotating basis for each meeting.
While we based the initial concept for the intervention on the literature and on a
model designed by the principal investigator and others, the PFAC members met monthly
during the design phase and shaped the design of most of the intervention components,
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such as the following. (See the Results section for additional details regarding how
stakeholders influenced the intervention.)
1. The sicker and more complicated a patient is, the faster the clinical team wants him
or her to be seen in follow‐up, but the less likely it is that the patient will feel well enough to
get to an appointment. Scheduling follow‐up appointments must be a negotiation among the
inpatient, the patient, and the caregiver within the framework of the outpatient provider’s
availability.
2. Patients have different kinds of relationships with their primary care physicians
(PCPs). Those with close relationships will likely want a quick follow‐up appointment with the
PCP soon after discharge, regardless of the reason for the hospitalization (eg, even if it was a
surgical admission). But this may not be true for everyone. The types and timing of follow‐up
appointments should be customized for each patient. Patients generally want one person to
be the point person during a complicated transition such as hospital discharge. Depending on
the patient, this could be the PCP, a nurse from the primary care practice, the discharge
advocate, or a pharmacist. This person should be identified early on; if possible, the other
members of the intervention should defer to the point person and communicate with and
through him or her.
The PFAC confirmed the relevance of the research question and study outcomes,
while the steering committee ensured the transparency of the research process. The
stakeholders played a smaller role in influencing the overall design, rigor, and quality of the
study, including participant recruitment, but they provided valuable input into the design of
all the patient‐facing quantitative survey instruments, and they designed and conducted the
qualitative interviews of patient participants under the leadership of the director of the BWH
Center for Patients and Families. Perhaps most important, the PFAC and members of the
steering committee played (and will continue to play) a major role in translating the research
evidence into practice, from helping to convey these results to the public to assisting with
manuscript preparation and other dissemination activities to directly applying the results to
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ongoing improvement activities in care transitions at BWH, MGH, and Partners.
One challenge of this research was balancing various stakeholder perspectives while
adhering to the specific aims and research approach as originally proposed and funded. In
the end, the principal investigator, with input from the other study investigators, had the
final say on how the study was conducted. However (in a process that was built into the
design of the study), he gave broad latitude to how the intervention was ultimately designed,
implemented, and refined at each study site to best simulate how it would be implemented
in the real world, to maximize stakeholder buy‐in, and to maximize the generalizability of the
findings. This customization of the intervention required constant stakeholder engagement.
Methods
Formal Study Protocol
The research question was “What is the impact of a collaborative transition
intervention involving hospital and PCMH personnel within an ACO on postdischarge patient
outcomes compared with usual care?”
We collected data for the purpose of determining the impact of our intervention on
patient safety outcomes. The study population is representative of the population for whom
the research question is pertinent. The results of the study will inform several groups of
stakeholders, including patients and caregivers (eg, should I belong to an ACO?), hospital
and health care executives (eg, how should we set up our health care system to improve
transitions of care?), health policy personnel (eg, how should health care be organized and
financed to facilitate adoption of transitional care interventions?), and direct care providers
(which services should I offer to which patients to optimize patient outcomes?). We used
the Standards for Quality Improvement Reporting Excellence guidelines for studies of quality
improvement wherever applicable, except that we did not include a discussion of costs.
Study Population Potential subjects were adult patients admitted to medical and surgical services at
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Brigham and Women’s Hospital and Massachusetts General Hospital (both academic medical
centers within the Partners ACO) who were likely to be discharged back into the community
(based on input from each patient’s nurse) and whose PCP belonged to one of 18 Partners
primary care practices that admitted at least 2 patients per month to BWH or MGH, agreed to
participate in the study, and met Primed criteria for being a PCMH. (Primed criteria are a
standard set of requirements that cover 6 essential building blocks of PCMH practices: (1)
electronic health record, (2) patient portal, (3) team‐based care, (4) practice redesign, (5)
care management, and (6) identification of high‐risk patients.) Patients were required to be
fluent in either English or Spanish and to be cognitively intact or to have a health care proxy
who was willing to provide consent, was planning to live with the patient after discharge, and
was willing and able to answer study questions on behalf of the patient. Exclusion criteria
included homelessness, police custody, or lack of a telephone.
Figure 1. Stepped‐Wedge Study Design.
The intervention is rolled out to individuals or clusters sequentially over time, from blank cells (usual care) to shaded cells (intervention). (Reproduced with permission from BMJ, Brown C and Lilford R, 337, a2764, 2008, with permission from BMJ Publishing Group Ltd.)
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Study Design
The study employed a stepped‐wedge design—an observational study design in which an
intervention is sequentially rolled out to different groups (in this case, different primary care
practices) at different times (see Figures 1 and 2).17 For example, if the patient’s PCP worked at
BWH Primary Care Associates of Brookline and the patient was enrolled in the study in May
2014 (ie, before June 6, 2014, the date of the transition from control to intervention for that
practice, see Figure 2), he or she would be assigned to usual care. If a patient with the same
PCP was enrolled in July 2014 (ie, after June 6, 2014), he or she would be assigned to the
intervention. Thus, each practice had a different amount of time in the usual care and
intervention arms, and each one served as its own control. We randomized the order of the
rollout to avoid confounding; that is, the primary care practices that were most ready for the
intervention (and which may have had other characteristics associated with better
implementation or better patient outcomes) did not necessarily get it first (and thus contribute
more patients to the intervention arm).
Subject Enrollment
We took several steps to reduce selection bias and maximize the representativeness
of the study population. Each day, a trained research assistant received a list of potentially
eligible patients (ie, those admitted to medical or surgical services and with a PCP from one of
the participating practices) from an automated report. We randomized the order in which
these patients were approached for enrollment in the study; patients at the top of the order
were likely to be enrolled in the study, while patients at the bottom of the order were
unlikely to be enrolled because the daily and weekly quotas for enrollment (about 11 patients
a week) would likely have been met before the research assistant got to the bottom of the
list. If we had not taken this approach (eg, if we had started at the top floor of the hospital
and worked our way down), enrollment in the study would have favored patients on a
particular service or floor, leading to a nonrepresentative study population.
Figure 2. Stepped Wedge Study Design.
Control InterventionNote: Patients were followed for 30 days after discharge. Therefore, the follow‐up period for BWH patients extended into June 2015, and into November 2015 for MGH and PCHI patients.
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Once we selected a patient for possible enrollment, we made multiple attempts to
enroll that patient until he or she enrolled, declined, or was discharged from the hospital
without enrolling or declining. If we had not taken this approach (eg, if we had tried to enroll
a patient once and then moved on to the next patient on the list), we would have biased our
enrollment toward patients most likely to be in their rooms at the beginning of each
enrollment day (eg, patients who were stable, not in need of diagnostic tests or procedures,
unlikely to be walking around the hospital for exercise or leaving their room to smoke). By
randomizing the order in which we approached patients and committing to enroll patients
once we selected them, we preserved the randomization of patients for enrollment to the
extent possible, maximizing the chance that our enrolled patient cohort was indeed a random
sampling of medical/surgical patients with Partners PCPs. Note that this individual‐level
randomization had nothing to do with treatment assignment, only enrollment in the study.
Once we selected a patient, the research assistant reviewed the medical record of the
potential subject and asked his or her nurse for additional details to confirm eligibility and for
permission to approach the patient regarding informed written consent to participate in the
study. Patients did not learn of their allocation to the usual care or intervention arm (based
on the practice of the patient’s PCP and the date of patient enrollment) until after they
agreed to participate.
Study Setting
The study took place on the medical and surgical non‐intensive‐care units of BWH and
MGH (where patients were enrolled in the study, completed baseline data collection, and
began the intervention) and in patients’ homes, communities, and primary care practices or
other health care settings (where additional intervention components were provided and
where patients completed data collection by phone call 30 days after discharge).
Intervention
We based our intervention on a conceptual model we developed of the ideal
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transition in care (Figure 3). The model incorporates work by Naylor et al18 and by Coleman
and Berenson,7 best practices in medication reconciliation and information transfer based on
our own research,19‐21 the best examples of interventions to improve the discharge
process,18,22,23 and a recent systematic review of discharge interventions.12 Some of the
factors necessary for a high‐quality transition in care are complete documentation of clinical
information regarding the patient’s hospitalization and postdischarge plan of care, clear
organization and timely transmission of that information, effective discharge planning,
coordination of care among the patient’s providers, methods to ensure medication safety,
advanced care planning for appropriate patients, and education and coaching of patients and
their caregivers to help them learn how to manage their conditions. Successful interventions
likely require several of these factors working in concert.
Figure 3. Conceptual Model of the Ideal Transition of Care. Burke, Kripilani, Vasilevskis, and Schnipper. J Hosp Med 2013.
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Before the start of the study, we approached each primary care practice associated
with Partners, described the study, and invited them to participate. Before a practice moved
from usual care to the intervention arm as part of the stepped‐wedge methodology, we
revisited the practice and engaged in an in‐depth discussion of the intervention, including the
components that would be conducted by others and those that would be conducted by the
practice. We helped them adapt the intervention components they would conduct (eg, nurse‐
to‐nurse communication, postdischarge phone calls, postdischarge visits) and address
potential staffing needs and workflows in the following areas:
1. Who would communicate with the inpatient discharge advocates, who would
initiate the contact, when would it occur during the hospitalization, and how (eg, phone,
email)?
2. Who would make the postdischarge calls, what changes might need to be made to
the call script and documentation template, and what actions should be taken in response
to certain answers from patients for each question?
3. What actions should be taken during the postdischarge visit and by whom, and what
changes might need to be made to the postdischarge note template?
Components of the proposed intervention, as originally designed, included the
following (see Figure 3 for how each of these corresponds to a component in the ideal
transition in care conceptual model):
1. Inpatient medication safety interventions. An inpatient pharmacist ensured accurate
medication reconciliation, including confirming the accuracy of the preadmission medication
history, conducting in‐depth medication reconciliation at admission and discharge, and
communicating with postdischarge providers regarding the discharge medication regimen
and reasons for changes. The pharmacist worked with the patients’ primary inpatient nurse
to educate patients and caregivers about the discharge medication regimen, including
indications, special instructions for administration, and reasons for changes from the
preadmission regimen. Lastly, prepared patients (and their caregivers) to use medications
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correctly and safely after discharge, including maximizing adherence and minimizing adverse
drug events. This included reviewing barriers to adherence (including cost, side effects,
forgetting, lack of access, inconvenience, skepticism about their efficacy) and addressing the
barriers or communicating with the primary care practice regarding the need to address
them. It also included identifying potential side effects of medications, especially any new
medications, and providing education on what to do if these side effects occur. The intensity
of the intervention was designed to vary depending on the complexity of the patient’s
medication regimen, the patient’s health literacy and understanding of his or her
medications, and the patient’s previous medication problems, such as nonadherence or side
effects.
2. Inpatient discharge advocate. This component (based on the discharge advocate role in
Project Re‐engineered Discharge23) called for a nurse at each of the 2 hospitals to form a
therapeutic alliance with the patient, facilitate communication between inpatient and
outpatient care teams, ensure the creation of a high‐quality discharge plan, facilitate
education and preparation of the patient and caregivers for discharge based on their level of
health literacy, work with patients to determine what optional interventions they might need,
and teach primary inpatient nurses to incorporate these tasks into usual care. The discharge
advocate initiated an ongoing dialogue between inpatient and outpatient teams, and
facilitated collaborative creation of a discharge plan and scheduling of follow‐up
appointments and tests within an appropriate time frame. As part of these tasks, the
discharge advocate provided and went over with patients and caregivers a predischarge
checklist and asked patients and caregivers to identify their most important goals for the
postdischarge period, documented them, and took steps to maximize achievement of these
goals.
3. Visiting Nurse Association (VNA) appointments. In the week after discharge, Partners
Healthcare at Home provided VNA services to qualifying patients (ie, those who were at
least temporarily homebound and in need of nursing services). Unlike routine VNA visits,
these included a structured template to assess the patient’s home situation, current
services, and level of support, and to ensure that patients could manage their conditions at
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home (eg, take their medications, modify health‐related behaviors, manage follow‐up
appointments including transportation to clinic visits, and carry out other aspects of their
postdischarge care plan, such as changing dressings). The visiting nurses also verified that
patients who could not manage on their own had caregivers who could help them with
these tasks. The nurses were encouraged to contact either the inpatient or primary care
team with questions or concerns, by phone or email as appropriate. They also wrote
structured notes that were placed in the ambulatory electronic medical record (EMR) used
by all Partners practices.
4. Responsible outpatient clinician. The responsible outpatient clinician (ROC)—usually a
registered nurse (RN) from the patient’s PCMH previously assigned to patients of one or more
PCPs—carried out several tasks: to communicate with the discharge advocate to exchange
information and prepare for postdischarge care, to videoconference with the patient while he
or she was still in the hospital, to make a postdischarge phone call within 2 days of discharge,
to see the patient during the postdischarge clinic visit, and to make 2 additional phone calls
during the month after the clinic visit. ROCs were expected to conduct coaching based on the
Care Transitions Intervention model,24 to help patients identify and overcome barriers to self‐
management and help them effectively interact with the health care system at their level of
health literacy, to communicate with the PCP, to make plans and arrange follow‐up as
needed, and to document findings in the EMR.
5. Videoconference. Using mobile technology provided by the study team, the ROC was
encouraged to videoconference with the patient prior to discharge; ask the patient about
his or her most important goals for the recovery period; discuss the reasons for and the
importance of keeping follow‐up appointments; remind patients to bring the discharge
instructions, personal medical record, follow‐up calendar, medication list, and pill bottles
to the postdischarge follow‐up appointment; discuss the use of the personal medical
record; and discuss barriers to keeping follow‐up appointments and explore ways to
overcome them.
6. Postdischarge phone call. ROCs were given phone call scripts and documentation
templates and were encouraged to call all patients within 2 days of discharge. The goals of
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these calls were to screen for new or worsening symptoms; assure themselves that patients
could perform activities of daily living independently or with available help, ensure that
patients could carry out the postdischarge care plan, verify the patient’s understanding of and
adherence to the medication regimen, review danger signs and tell the patient what to do if
they occurred, encourage the use of the personal medical record, review follow‐up
appointments and transportation plans, and identify and manage barriers to keeping the
appointments.
7. Multidisciplinary postdischarge PCMH clinic visit. The ROC, PCP, PCMH pharmacist, and
other personnel as needed (eg, social worker) were encouraged to work as a team to see all
patients within 7 to 14 days of discharge, depending on the acuity of the patient. Following a
standardized algorithm, each team member was trained to play a specific role in evaluating
the patient’s progress along the plan of care, ensuring patient safety, and optimizing
postdischarge outcomes, including helping patients meet their postdischarge goals. PCPs and
ROCs were encouraged to review the discharge summary, patient instructions, follow‐up
appointment calendar, patient’s personal medical record, and any VNA notes in the EMR; to
complete postdischarge medication reconciliation; to review test results finalized after
discharge; to communicate with other members of the care team; to continue patient
coaching activities; and to arrange additional needed services, appointments, tests, home
monitoring, and community resources.
8. High‐risk patients received additional interventions as deemed necessary by the care
team. For each intervention patient, the inpatient attending and PCP received a menu of
additional interventions and were asked to consider them on a case‐by‐case basis on the
basis of perceived risk. Additional interventions included the following:
• A home visit by Dovetail (a home pharmacist company) with several goals: confirm that
inpatient medication reconciliation was done correctly; identify and resolve any
discrepancies between the discharge regimen and what medications the patient
believed he or she should be taking; screen for barriers to adherence and early side
effects, and address them as needed; and work on strategies to maximize medication
adherence, identify medication red flags, and provide contingencies when problems
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arise. Dovetail pharmacists communicated with PCMH practices as needed, provided
additional outreach to patients as needed, and documented their findings in the EMR.
o Enrollment in telemedicine programs; for example, daily monitoring and
electronic transmission of weights and diuretic dose adjustment by a
nurse.
o Specialist (communicating with the PCP or cardiologist as needed) for patients
with chronic heart failure who were enrolled in Partners Healthcare at Home.
o Advance care planning: We created a tool to automatically flag patients with a
15% 30‐day mortality, using an algorithm created by the MGH Lab of Computer
Science based on empirical data obtainable from the EMR. At BWH, the flag
triggered a recommendation for an inpatient palliative care consultation with
patients, caregivers, and providers regarding goals of care; communication
with outpatient providers so this discussion could be continued; and
documentation in the EMR. At MGH, the flag triggered a recommendation for
inpatient or outpatient palliative care referral as appropriate. We also
provided education to MGH residents regarding appropriate inpatient
referrals. A home‐based palliative care program was available for a limited
number of homebound patients.
o Enrollment in the Partners integrated Care Management Program (iCMP),
which included intensive and individualized case management.
9. Novel health information technology
a. A web‐based discharge‐ordering module to help ensure the quality of
discharge documentation by auto‐importing certain information from the EMR and
requiring completion of structured data fields.
b. An automated notification system that emailed inpatient attendings and PCPs
the results of tests pending at discharge as they became available (BWH only).25
c. Tools to identify and group text messages and email to all inpatient and
ambulatory care team members, facilitated by the Partners Enterprise Patient List
(PEPL) application, to improve multidisciplinary communication across care settings.26
Page 22 of 53
Usual Care
Once primary care practices become a PCMH, they usually take several steps on their
own to improve transitional care. The question is whether patient outcomes can be improved
even further with a standard set of intervention components—informed by the medical
literature and a conceptual model of the ideal transition in care—that leverages the PCMH’s
new capabilities and can be combined with interventions on the hospital side to facilitate
coordinated, patient‐centered care. This choice of comparator mimics as closely as possible
the choices faced by any practice that has become a PCMH and that works within an ACO or
other similar integrated delivery system.
Clinician Surveys
To provide information on environmental context at baseline, we surveyed clinicians
involved in the transition process (physicians, nurses, pharmacists, and care coordinators)
before implementing any interventions. These surveys contained several types of questions:
the clinician’s assessment of preparedness to deliver transition interventions (eg, role clarity,
training, feedback, use of tools and technology), assessment of the quality of the transition
process, teamwork climate, and patient safety climate. We assessed the latter 2 factors using
Agency for Healthcare Research and Quality (AHRQ) survey instruments,27 and derived the
former from surveys used to assess other safety processes, such as medication reconciliation.
We surveyed clinicians from each of several roles in the transition process (eg, residents,
inpatient attending physicians, nurses, pharmacists, care coordinators, therapists, and
physician assistants; outpatient PCPs, nurses, care coordinators, and medical assistants) and
from each inpatient unit and primary care practice participating in the study.
Inpatient and Outpatient Inventories of Transitional Care Tasks
We surveyed physician and nurse leadership from all inpatient units and outpatient
practices in the study regarding their general staffing levels (eg, number and clinician types),
how often they carried out various transitional care tasks, and who usually performed those
tasks (Tables 4A, 4B, and 4C, and Results).
Page 23 of 53
Outcomes
Study outcomes included the following:
1. New or Worsening Signs or Symptoms Within 30 Days of Discharge.
A trained research assistant contacted patients 30 (+/– 5) days after discharge and
administered a questionnaire to identify any new or worsening symptoms since discharge,
any health care utilization since discharge, functional status in the previous week, and patient
experience, including participation in, understanding of, and ability to carry out the
postdischarge care plan. Follow‐up questions used branching logic to determine the
relationship of any new or worsening symptoms in response to medications or other aspects
of medical care, as well as the consequences of these symptoms, including additional health
care utilization, functional decline, worry and anxiety, time lost from work, out‐of‐pocket
expenses, and additional caregiver time. This patient‐reported outcome (new or worsening
symptoms since discharge) has been used in other studies of patient safety, is meaningful to
patients, and relates to health decisions patients would need to make. For patients not
reachable by phone despite 5 attempts (about 47% of the study cohort), the research
assistant reviewed the outpatient medical record for provider reports of any new or
worsening symptoms noted during follow‐up within the 30‐day postdischarge period.
Research assistants also reviewed laboratory test results for evidence of renal failure,
elevated liver function tests, or new or worsening anemia in the postdischarge period.
Table 4A. Inpatient Inventory Results: Summary by Unit (BWH)
Activitya Proportion of Elements in Each Activity Usually or Always Performed, by Inpatient Unit A B C D E F G H I J K L M N O P Q R S Discharge planning (out of 6 elements)
17%
100%
100%
17%
50%
33%
50%
17%
33%
17%
50%
83%
67%
50%
50%
50%
83%
50%
33%
Discharge documentation (out of 4)
75%
25%
100%
25%
75%
100%
25%
100%
75%
50%
50%
100%
25%
25%
25%
0%
100%
100%
100%
Patient goals of care for recovery period (out of 2)
50%
0%
100%
0%
0%
100%
0%
50%
50%
0%
0%
100%
0%
0%
0%
0%
50%
50%
50%
Discharge medication reconciliation and medication education (out of 4)
75%
25%
100%
25%
75%
57%
25%
50%
100%
75%
50%
100%
25%
25%
25%
25%
0%
75%
75%
Patient education at discharge (out of 9)
22%
0%
100%
11%
44%
44%
22%
44%
56%
33%
33%
89%
0%
0%
0%
0%
78%
33%
44%
Perceived barriers and facilitators of effective communication /collaboration (1‐5 scale, mean of 6 elements)
1.0
4.6
1.0
1.3
2.0
1.5
1.3
3.3
1.3
1.5
2.5
1.0
0.0
1.5
1.5
1.5
1.3
1.3
1.2
a Based on a survey of physician and nurse leadership from each unit. See attached inpatient inventory data collection tool for activity details.
Table 4B. Inpatient Inventory Results: Summary by Unit (MGH)
Activitya A B C D E F G Discharge planning (out of 6 elements) 33% 100% 17% 67% 50% 50% 33%
Discharge documentation (out of 4)
100%
100%
75%
100%
75%
75%
75%
Patient goals of care for recovery period (out of 2)
100%
100%
100%
100%
50%
100%
0%
Discharge medication reconciliation and medication education (out of 4)
100%
75%
75%
100%
75%
75%
50%
Patient education at discharge (out of 9) 56% 78% 33% 44% 33% 33% 33%
Perceived barriers and facilitators of effective communication/collaboration (1‐5 scale, mean of 6 elements)
1.2
1.3
1.3
1.2
2.7
1.7
1.2
a Based on a survey of physician and nurse leadership from each unit. See attached inpatient inventory data collection tool for activity details.
Table 4C. Outpatient Inventory Results: Summary by Practice
Activitya Proportion of Elements in Each Activity Usually or Always Performed, by Outpatient Practiceb
A B C D E F G H I J K L M N O Contact with patients and providers while patient still hospitalized (out of 2 elements)
0%
0%
0%
0%
0%
0%
0%
0%
50%
0%
0%
0%
0%
0%
0%
Postdischarge call (out of 11 elements)
73% 64% 64% 0% 81% 0% 73% 18% 45% 82% 100% 100% 73% 82% 91%
Postdischarge visit (out of 14) 71% 79% 71% 0% 93% 0% 64% 0% 86% 71% 100% 100% 29% 86% 100%
Inpatient contact score: percent PCP, NP, PA, RN, pharmacist out of all providers contacting patients or inpatient providers while patient still in hospital
83%
75%
50%
100%
100%
75%
50%
50%
100%
71%
100%
80%
50%
100%
100%
Call score: percent PCP, NP, PA, RN, pharmacist out of all providers calling patients after discharge
50%
50%
0%
0%
0%
50%
0%
67%
100%
50%
100%
25%
100%
100%
100%
FU score: percent PCP, NP, PA, RN, pharmacist out of all providers assessing and educating patient after discharge
83%
67%
75%
58%
58%
67%
67%
100%
88%
50%
88%
55%
30%
30%
63%
% of nurses making postdischarge calls who are
0% 0% 0% 0% 0% 0% 0% 50% 100% 50% 100% 50% 100% 100% 100%
Subjective assessment of quality of transitional care (1‐ 10 scale, mean of 3 elements)
7.7
6.8
8.0
0.0
7.7
0.0
7.8
6.3
7.8
8.6
8.7
6.3
7.7
8.7
9.7
a See attached outpatient inventory data collection tool for activity details. b Sites A‐H are BWH affiliates; Sites I, K, M, N, and O are MGH affiliates; J and L are Partners Community Healthcare, Inc. (PCHI) affiliates Note: IMA was consolidated into 1 survey for this analysis; 2 practice sites of NSPG completed separate surveys; and 1 MGH practice did not complete the survey.
Page 27 of 53
2. Hospital Readmissions
We measured nonelective hospital readmissions within 30 days of discharge using a
combination of administrative data for BWH and MGH plus patient report during the 30‐day
phone call for all other readmissions. Previous studies have shown that this method is
effective in capturing almost all admissions for patients discharged from BWH.28
3. Adjudicated Outcomes
Adverse Events and Preventable Adverse Events
All cases of new or worsening symptoms, along with all supporting documentation,
were presented to teams of 2 trained, blinded physician adjudicators. Each adjudicator
independently reviewed the information, along with the medical record, and completed a
standardized form to confirm or deny the presence of any adverse events (ie, patient injury
owing to medical care rather than to underlying medical conditions). If applicable, they
documented the type of event (eg, adverse drug event, hospital‐acquired infection,
procedural complication, diagnostic or management error), the severity and duration of the
event, other consequences of the event (eg, on functional status, health care utilization, out‐
of‐pocket costs), and whether the event was preventable or ameliorable. The 2 adjudicators
then met to resolve any differences in their findings and to reach consensus.
We did not give the adjudicators the stepped‐wedge schedule and we purposely
delayed adjudication until 2 months after the first practice implemented the intervention, but
they could not be fully blinded to intervention status: We could not remove dates from
medical records, they might have come across documentation of interventions while
adjudicating, and they might have been vaguely aware that later cases were more likely to
involve intervention patients.
Preventable Readmissions
If patients were readmitted to BWH or MGH, we conducted a thorough evaluation to
determine whether and how the readmission could have been prevented. Based on the
HOMERUN study of 1000 patients at 12 academic medical centers,29 this process included (1)
Page 28 of 53
a standardized patient and caregiver interview to identify possible problems with the
transition process and the patient’s preparedness to manage the postdischarge care plan,
and (2) an email questionnaire to the inpatient teams that cared for the patient during the
index admission and the readmission and to the patient’s PCP regarding possible deficiencies
in the transition process. Using that information and medical record review, teams of 2
physician adjudicators completed a form to determine the preventability of the readmission;
which, if any, deficiencies in the transition process contributed to the readmission; and what
interventions might have prevented it. As with adverse event adjudications, these physicians
worked independently and then met to resolve their differences and reach consensus.
To improve the reliability of adjudicated outcomes, we trained all adjudicators using
standardized cases and held monthly group meetings at which particularly difficult cases
could be discussed. We compiled lessons learned from this process in a Frequently Asked
Questions document, which we updated and distributed to all adjudicators as appropriate.
4. Other Outcomes
Patient Satisfaction and Opinions of the Discharge Process During the 30‐day follow‐up phone call, we asked patients about their participation in,
understanding of, and ability to carry out the postdischarge plan. These questions included
some selected from Care Transitions Measure 3 (CTM‐3),30 the Interpersonal Processes of
Care survey,31 and the HOMERUN study of readmitted patients. On the basis of input from
our Patient and Family Advisory Council during monthly meetings, our population of interest
cared about the following outcomes and believed that certain adjustments would help inform
health care decisions.
Analysis
To evaluate the effects of the intervention (independent variable) on the number of
new or worsening postdischarge signs and symptoms per patient, the number of adverse
events per patient, and the number of preventable adverse events per patient (dependent
variables in the form of number of events per patient), we used multivariable Poisson
Page 29 of 53
regression, with the study arm as the main predictor (independent variable). We used an
intention‐to‐treat analysis: If a practice did not start the intervention when it was supposed
to according to our randomization, we counted all patients in that practice who were
admitted after that point as intervention patients. Covariates in this model—all collected at
baseline—included study month (to adjust for temporal trends); season (to adjust for the
learning curve of residents and also for seasonal changes in hospital census and patient mix);
and patient age, sex, primary language, and race and ethnicity. Research assistants
administered several measures at the time of patient enrollment, including of cognitive
status (mini‐Cog score),32 health literacy (Short Test of Functional Health Literacy in Adults [s‐
TOFHLA] score),33 and functional status 1 month before admission (Medical Outcomes Study
12‐Item Short Form Health Survey [SF12]) score per patient self‐report), caregiver status,
Elixhauser comorbidity score,34 median income by zip code, inpatient unit, primary care
practice, number of emergency department (ED) visits in the previous 6 months, and
HOSPITAL risk score for potentially avoidable readmissions (hemoglobin level at discharge,
oncology service, sodium level at discharge, procedure during the hospitalization, index
admission type [elective versus nonelective], admissions in the previous year, and hospital
length of stay).35,36 See Table 1B for details of how we categorized each variable in the final
model and the data source for each variable. To summarize, we used administrative data
sources when we considered them reliable, and we used patient sources when administrative
sources were unavailable (eg, health literacy) or unreliable (eg, race).
Table 1A. Enrollment Rates and Key Dates of Study Period, by Practice
Practice Name Usual Care Start Date
Randomized Intervention Start Date
Actual Intervention Start Date
Enrollment End Date
Number of Patients Enrolled per Month
Total Number of Patients Enrolled
Brigham and Women's Hospital (BWH) Primary Care Practices
Phyllis Jen Center for Primary Care 11/6/13 4/5/14 4/5/14 5/15/15 13.8 249
Southern Jamaica Plain Health Center 11/13/13 5/13/14 5/12/14a 5/15/15 3.7 67
Brigham and Women's Faulkner Community Physicians
11/13/13 5/13/14 5/12/14 a 5/15/15 2.7 48
Primary Care Associates of Brookline 11/6/13 6/6/14 6/16/14 5/15/15 5.3 95
Brookside Community Health Center 2/4/14 7/4/14 7/25/14 5/15/15 3.7 56
Primary Care Associates of Foxboro 2/11/14 8/11/14 8/25/14 5/15/15 7.7 115
Brigham and Women's at Newton Corner 11/6/13 9/6/14 10/14/14 5/15/15 2.9 52
850 Boylston—Brigham and Women's Physician Group
11/6/13 10/6/14 10/14/14 5/15/15 9.1 163
Massachusetts General Hospital (MGH) Primary Care Practices
Bulfinch Medical Group 2/7/14 7/7/14 7/23/14 10/13/15 7.4 148
MGH Charlestown Healthcare Center 2/7/14 8/7/14 9/29/14 10/13/15 4.7 93
Internal Medicine Associates Pods 2, 3, and 4C 1/23/14 9/23/14 10/14/2014 (4C) 2/17/15 (IMA 2,3)
10/13/15 4.2 84
MGH Revere Primary Care—Broadway 5/21/14 10/18/14 12/15/14 10/13/15 3.2 51
MGH Beacon Hill Primary Care 5/21/14 11/21/14 1/12/15 10/13/15 4.9 78
Internal Medicine Associates Pods 4A, 4B, 5, and 6
7/7/14 1/7/15 2/17/2015 (4A/B Only) 3/23/2015 (IMA 5 and 6)
10/13/15 5.2 78
MGH Chelsea Healthcare Center 2/7/14 2/7/15 3/30/15 10/13/15 4.7 94
Internal Medicine Associates Pods 7, 8, 9, and 10 7/7/14 3/7/15 3/23/15 10/13/15 4.7 71
Partners Community Healthcare, Inc. (PCHI) Network Practice
NSMC North Shore Physicians Group 12/2/13 11/2/14 11/18/14 10/13/15 5.7 126
a The intervention was started 1 day early in these 2 practices because May 12, 2014, was a Monday and the practices preferred to start the intervention at the beginning of the work week.
Table 1B. Patient Characteristics
Characteristic Data Source Usual Care N = 679
Intervention N = 978
P value
Age in years, mean (SD) Hospital administrative data 157 (23%)
209 (21%)
0.71 18‐49 50‐59 152 (22%) 229 (24%) 60‐69 168 (25%) 258 (26%) 70 or greater 202 (30%) 278 (29%) Female, N (%) Hospital administrative data 364 (54%) 540 (55%) 0.46 Service, N (%) Hospital administrative data
403 (59%) 556 (57%)
0.31 Medicine Surgery 276 (41%) 422 (43%) Elixhauser Comorbidity Score, N (%) Calculated from hospital
219 (32%) 331 (34%)
0.77 0 or less billing data 1‐5 159 (23%) 232 (24%) 6‐10 105 (16%) 155 (16%) 11 or more 196 (29%) 260 (26%) Language, N (%) Hospital administrative data
637 (94%) 947 (97%)
0.003 English Spanish 42 (6%) 31 (3%) Race/ethnicity, N (%) White non‐Hispanic
Patient survey administered at time of enrollment
500 (74%)
728 (74%)
0.71
Dementia, N (%)a Mini‐Cog administered at time of enrollment
66 (10%) 96 (10%) 0.62
Health literacyb s‐TOFHLA administered at 463 (68%)
665 (68%)
0.90 Adequate time of enrollment Inadequate or marginal 59 (9%) 91 (9%) N/A 157 (23%) 222 (23%) Caregiver Patient survey administered
486 (72%) 746 (76%)
0.03 No at time of enrollment Yes, helps with IADLs only 128 (19%) 171 (18%) Yes, helps with basic ADLs 64 (9%) 61 (6%) Emergency department visits in past 6 months Hospital administrative data 0.81
0 433 (64%) 639 (65%) 1 90 (13%) 124 (13%) 2 more 156 (23%) 215 (22%) HOSPITAL Potentially Avoidable Readmission Risk Score,c median (IQR)
Calculated from hospital administrative and electronically available clinical data
3.0 (2.0, 4.0) 3.0 (2.0, 4.0) 0.08
Baseline SF‐12 score (1 month prior to admission), median (IQR)
SF12 administered at time of enrollment
45.0 (20.0, 59.5) 46.1 (22.0, 59.5) 0.01
Median income by zip code, median (IQR) Address from hospital $71 809 (51 723, $72 461 0.25 administrative data crossed $88 829) ($53 795, with national database $88 829)
Education Patient survey administered 53 (8%)
44 (4%)
0.02 Less than HS graduate at time of enrollment HS graduate or GED 155 (23%) 241 (25%) At least some college 361 (53%) 510 (52%) Other/not reported 110 (16%) 183 (19%) Marital status Patient survey administered
177 (27%) 292 (31%)
0.03 Single at time of enrollment Married or domestic partner 369 (56%) 514 (54%) Separated, widowed, or divorced 114 (17%) 138 (15%)
Abbreviations: a Based on mini‐Cog score (< 3 considered consistent with dementia) b s‐TOFHLA score: > 23 adequate, 17‐22 marginal, < 17 inadequate. N/A: various reasons (eg, patient is blind, patient declined/refused to take test, patient unable to take test because of medical condition/surgery). c HOSPITAL Score for 30‐Day Potentially Avoidable Readmissions. Low hemoglobin level at discharge (< 12 g/dL) (1 point); discharge from an oncology service (2 points); low sodium level at discharge (< 135 mEq/L) (1 point); procedure during hospital stay (any ICD‐9‐CM coded procedure) (1 point); index admission type: nonelective (1 point); number of hospital admissions during the previous year (0 = 0 points, 1‐5 = 2 points, > 5 = 5 points); length of stay ≥ 5 d (2 points). Donzé J, Aujesky D, Williams D, Schnipper JL. Potentially avoidable 30‐day hospital readmissions in medical patients: derivation and validation of a prediction model. JAMA Intern Med. 2013;173(8):632‐638. doi:10.1001/jamainternmed.2013.3023.
To evaluate the effects of the intervention (independent variable) on nonelective
readmissions and preventable readmissions (dependent variables that are dichotomous), we
used a similar approach, using multivariable logistic regression. In that model, we were able
to cluster by inpatient unit and use primary care practice as a random effect. We used the
general linear mixed model (GLIMMIX) procedure in the Statistical Analysis System (SAS) 9.3
statistical package (Cary, NC) to carry out all analyses.
Subgroup Analyses
One of the goals of this research was to determine whether any patient subgroups
preferentially benefited from this type of intervention (PCORI Methodology Standard HT‐1).
Therefore, we conducted several subgroup analyses and used interaction terms (ie,
subgroup*intervention) to determine effect modification (PCORI Methodology Standard HT‐
2‐3) on our quantitative outcomes. Chosen subgroups included the following:
• Elderly (over age 65)
• Patients with inadequate or marginal health literacy using the s‐TOFHLA score
• Patients with multiple chronic conditions based on Elixhauser comorbidity scores34
• Patients at high risk for potentially avoidable readmissions using the HOSPITAL score35
Missing Data
We addressed issues of loss to follow‐up for new or worsening symptoms by
conducting manual medical record review for any patient we could not reach by phone 30
days after discharge. For most covariates, missing data were not sufficiently prevalent (< 2%)
to warrant special statistical methods (eg, multiple imputation) or were not in the final
multivariable model (eg, marital status). Where missing data were prevalent (> 5%) (eg,
sTOFHLA, education), we created a separate category of “missing” for that variable. Patient
withdrawals from the study are listed under Enrollment and Study Flow; only 9 patients
(0.5%) withdrew consent to use their data.
Qualitative Analysis of Barriers to and Facilitators of Implementation
The principal investigator facilitated focus groups with assistance from a qualitative
researcher (Elyse Park) during the postintervention period. Focus groups were conducted
with 2 to 8 individuals for each clinician type (inpatient and outpatient physicians, nurses,
pharmacists, and care coordinators; n = 21 participants total). We piloted and used a
semistructured qualitative interview guide to standardize data collection across all groups.
Domains included the following:
• Description, expectations, and satisfaction with the intervention
• Perceptions of the intervention’s effects on workflow and on patient care
• Aspects of the intervention believed to be most and least beneficial and explanations as to why
• Barriers to and facilitators of implementation
• Co‐interventions
• Recommendations for improvements to the intervention
Each focus group session lasted approximately 60 minutes. They were audio‐recorded and
transcribed. The transcribed data were then uploaded into NVivo 11 (QSR, Melbourne, 2012),
a computer‐assisted qualitative data analysis application. Preliminary themes were identified
using the Systems Engineering Initiative for Patient Safety model of work‐system design for
patient safety. Two coders (Hilary Heyison, a research assistant, and Cherlie Magny‐Normilus,
the BWH discharge advocate) did preliminary coding of themes, iteratively refined them with
input from the principal investigator and Dr. Park until consensus was reached (which
occurred after 4 transcripts were reviewed), and then independently double‐coded all
transcripts. Discrepancies in themes and codes were resolved through discussions of
interpretations and comparisons to raw data. Coding continued until coder reliability (kappa >
0.80) was achieved. Each category was then examined for salient quotes. Differences in
themes by clinician type were also analyzed.
Treatment Analysis
Because we did not expect implementation of the intervention to be perfect, we
planned a number of secondary analyses to evaluate the efficacy of the various intervention
components under perfect conditions. First we used documentation in the EMR to determine
whether each patient received each of the major components of the intervention (see Table
5 and Results). Then we used the receipt of each intervention as a covariate, one at a time, in
an otherwise fully adjusted model (except for intervention arm) for each of the major
outcomes described above, thus simulating the effects of each intervention component
under conditions of perfect adherence. We also created an intervention fidelity score, with
the numerator being the number of components received and the denominator being the
number of components for which each patient was eligible (eg, if the patient was not
referred to the home pharmacist program, that component was removed from the
denominator).
Mixed Methods
We complemented the qualitative analysis of barriers to implementation with exploratory
quantitative analyses to further explore the relationships among environmental context,
intervention fidelity, processes of care, and patient outcomes. For example, in qualitative
analyses we learned about the effect of the microculture (eg, of a particular unit in the
hospital or outpatient practice) on the success of implementation. To explore this relationship
more thoroughly, we quantitatively measured patient safety culture (using the AHRQ survey
instruments) for providers of various types in each hospital unit and outpatient practice. We
could then attribute a mean patient safety culture score to each patient based on his or her
location in the hospital, medical team, and PCP. Finally, we used these scores as independent
variables in adjusted models to predict each outcome (dependent variable) and the
intervention fidelity (ie, receipt of interventions). A positive correlation would provide
evidence that a strong patient safety culture can lead to more successful implementation of
the intervention and therefore to better patient safety outcomes during transitions in care.
For this analysis, we assumed that survey respondents (eg, internal medicine residents or
nurses from a particular unit) represented all the clinicians in that particular category.
Table 5. Intervention Fidelity
Intervention Componenta BWH (N
= 531)
MGH (N
= 447)
Inpatient pharmacist, n (%) 195 (36.7%) 152 (34.0%)
Discharge advocate note in EMR for BWH;
reminder email for nurse‐to‐nurse
communication at MGH
208 (39.2%) 385 (86.1%)
Postdischarge phone call by ROC within
0‐14 business days
206 (38.8%) 235 (52.6%)
Postdischarge clinic visit 156 (29.4%) 197 (44.1%)
Visiting nurse 88 (16.6%)
87 (19.5%) Network VNA
Non‐network VNA 59 (11.1%) 93 (20.8%)
Not sent home with VNA services 384 (72.3%) 267 (59.7%)
Outpatient pharmacist visit 55 (10.4%)
30 (6.7%) Patient accepted service
Patient declined service 48 (9.0%) 27 (6.0%)
Not referred 428 (80.6%) 390 (87.3%)
a Based on electronic medical record review documenting receipt of each intervention component.
In a second set of mixed methods analyses, we analyzed the results of “inventories”
for each inpatient unit and outpatient practice—surveys completed by leaders of their unit’s
or practice’s resources and ability to carry out several aspects of high‐quality transitional
care. We attributed these scores to each patient based on the inpatient unit on which he or
she was hospitalized and the primary care practice to which his or her PCP belonged. These
scores were used as independent variables in models to predict outcomes (dependent
variables) and intervention fidelity. Positive correlations, if present, would provide evidence
for the link between environmental context (eg, factors such as staffing or prioritization of
transitional care tasks at baseline) with successful implementation and patient safety
outcomes.
Sample Size
On the basis of previous data, we assumed a baseline rate of postdischarge adverse
events of 0.30 per patient. We conservatively assumed an effect size from 0.30 in the control
group to 0.23 in the intervention group—a relative reduction of 22%, based on studies of
preventability rates and of interventions to prevent postdischarge adverse events and close
to the minimum clinically important difference. Based on our previous studies of patient
safety,8,28 we assumed an intraclass correlation coefficient of 0.01 with an average cluster size
of 7 patients per PCP. Assuming a 10% loss to follow‐up rate and an alpha of 0.05, we
targeted a sample size of 1800 patients to achieve 80% power, with one‐third of the patients
in the usual care arm and two‐thirds in the intervention arm.
Results
Evolution of the Intervention
During the pilot phase of the study (March through September 2013), during which
the intervention was implemented at one practice, and throughout the intervention study
period (October 2013 through September 2015), the intervention was iteratively refined in
response to input from the Patient Family Advisory Council, the steering committee, and
members of the intervention team; cases of adverse events and readmissions from patients
in the intervention arm; exit interviews (conducted by PFAC members and the director of the
BWH Center for Patients and Families) of patients who had recently completed the
intervention; and informal feedback from clinicians, including each primary care practice and
several inpatient units at both hospitals. At the same time, the intervention components
evolved as a result of factors outside our control, including other efforts to improve
transitions at each hospital and at Partners (eg, use of Medicare’s Transition Care
Management billing codes, which required a phone call within 2 business days of discharge)
and limitations in personnel number and type. In general, we tried to standardize the
intervention by function across hospitals, units, and practices, using checklists and other tools
to maximize reliability and consistency while still allowing for local adaptation. In other
words, rather than specify exactly how a task (such as medication counseling) needed to be
performed, we gave the sites flexibility regarding how they would implement the task, given
their available personnel and institutional culture. One goal of the study was for the PCORI
contract to pay for as little of the intervention as possible. The point was for the ACO and
PCMH practices to invest in these interventions and increase their sense of ownership of
transitional care systems now that incentives are more aligned to improve transitions of care.
Also, we did not want the intervention to end once the contract period was over.
Following are brief descriptions of how each intervention component evolved over the course
of the study.
1. Inpatient medication safety intervention. The intervention was implemented as
designed, but adequately staffing it with inpatient pharmacists was a
constant challenge at both hospitals. The hospital pharmacy departments were short‐staffed,
and intervention pharmacists were often required to cover other responsibilities. At MGH,
only certain units agreed to the use of inpatient pharmacists for much of the study period.
And because these pharmacists were centrally deployed and not unit‐based, they often had
difficulty identifying and getting to patients to perform discharge tasks before the patients left
the hospital. Over the course of the study, we developed semimanual systems (such as
routinely calling unit coordinators where enrolled patients were hospitalized) to proactively
identify patients soon to be discharged and in need of patient counseling.
2. Discharge advocate. This component was implemented differently at the 2 hospitals. At
BWH, we hired a dedicated nurse practitioner, which led to a high degree of quality control
and the ability to perform more tasks, such as using the predischarge preparation checklist to
identify and take steps to address barriers to patient self‐management at home. The
disadvantages included a reduced ability to see every intervention patient because of limited
bandwidth. Plans for the nurse practitioner to teach frontline nurses to incorporate these
tasks into usual care never came to fruition, in part because of the preparation needed to
launch the new electronic medical record at BWH following the end of the intervention
period. At MGH, the discharge advocate (DA) role was assumed by attending nurses, an
existing special role assumed by one frontline nurse each shift, with a focus on patient
education and postdischarge planning. This approach enabled us to reach more patients and
offered a built‐in plan for scale‐up, at least in theory, but it led to greater variability in the role
and a more limited intervention, focused mainly on communication with the outpatient
nurses (ROCs) about each patient.
3. Structured VNA visit. The intervention was implemented as designed, with support from
2 leaders in Partners Healthcare at Home (the network VNA) who served as liaisons between
the study and the visiting nurses. However, because the intervention involved only a few
patients per nurse, it was difficult for them to develop proficiency. In addition, most patients
were not discharged with VNA support, and of those who were, only half had Partners
Healthcare at Home as their VNA agency.
4. Responsible outpatient clinician. Each practice identified personnel to assume this role,
and each ROC was assigned to a small group of PCPs. The professional certification of the
ROCs varied by practice; they included RNs, licensed practical nurses (LPNs), and physician
assistants (PAs). MGH used RNs almost exclusively, while BWH used mostly LPNs and some
RNs and PAs. The variation in certification influenced how independently the ROCs could
manage patients’ problems (eg, in response to postdischarge phone calls). Because no
practices hired new staff to fill these roles, ROCs were often pulled from other tasks. One
practice, the Phyllis Jen Center at BWH, reallocated RNs to conduct postdischarge calls from
intervention patients, while calls for nonintervention patients were conducted by LPNs.
5. DA–ROC communication. At BWH, communication was fairly easy because one person
assumed the DA role. MGH experienced challenges with regard to availability and different
communication styles between inpatient and outpatient nurses (ie, outpatient nurses usually
sit at a desk and use email, while inpatient nurses move among patient rooms and use phone
and pager to communicate, often not checking email until the end of the day). The study
project manager at MGH sent an email reminder to both the DA and the ROC to communicate
with each other, but this led to diffusion of responsibility. Over time, we refined the
procedure, encouraging the ROC to initiate communication on admission by calling the
inpatient unit, and encouraging the attending nurse to call or email the ROC before discharge.
6. Postdischarge phone call. Almost all practices at MGH were already making these calls,
but they were not always using a standard template to guide the conversation and were
sometimes calling only medical patients. We encouraged practices to use a template and to
call surgical patients as well, especially if they were part of the study (we alerted practices to
study patients by email and an electronic study registry). At BWH, fewer practices were
making postdischarge calls, but the hospital’s physician organization starting incentivizing
practices to do so. As with MGH, we worked to standardize the content of the calls. Transition
care management billing codes initiated by the Centers for Medicare and Medicaid Services
became the major driver of this initiative, and we synchronized our efforts with those
requirements.
7. Postdischarge clinic visits. For almost all practices, PCPs conducted these visits with little
help from other personnel, such as ROCs or pharmacists (ie, visits were less team‐based than
designed). We tried to standardize visit note templates, again using transition care
management billing as the driver of standardization. In reality, there was substantial variation
in how PCPs conducted and documented these visits.
8. Interventions for high‐risk patients.
a. Home visits by Dovetail pharmacist. This is one area in which Partners provided
additional resources, in this case, Partners Population Management. We spent several
months working on the logistics of referrals, documenting eligibility, documenting findings,
and communicating with providers. Referral rates for the program by inpatient attendings
and PCPs was lower than expected, and even when they were referred, many patients
declined the program or could not be reached by phone to schedule a home visit. On the
other hand, we heard many stories of successful interventions that likely had a profound
impact on the patient’s postdischarge course.
b. Congestive heart failure telemedicine program. As with Dovetail, Partners
Population Management agreed to subsidize the cost of enrolling any additional patients
(who would not otherwise have been eligible) in this program. In reality, very few patients
were enrolled—many patients were not eligible for Partners Healthcare at Home (or already
had a different agency) or were enrolled in a different congestive heart failure program. The
BWH heart failure service was hesitant to enroll patients in our study because of concerns
about conflicts with studies they were conducting.
c. Advance care planning. The automated trigger tool identified very few patients,
so we shifted to a referral system initiated by an email to inpatient attendings and PCPs
(“Would you be surprised if this patient passed away in the next 6 months?”). Very few
patients were identified this way either, even when we subsequently changed the time
horizon of the question to 2 years.
d. integrated Care Management Program. The iCMP, funded by Partners Population
Management, was the most robust program already in place to improve transitions of care.
We enrolled iCMP patients in our study so we could evaluate the efficacy of that program. To
avoid redundancy and patient confusion, we did not provide too many additional services, but
we did offer the Dovetail program (iCMP did not provide this service) and the inpatient
pharmacist program for MGH patients (not provided at MGH).
9. Information technology.
a. The web‐based discharge‐ordering module was implemented as planned.
b. The automated system to alert providers of the results of tests pending at
discharge was also successfully deployed at BWH. The system facilitated closed‐loop
communication, information transfer, acknowledgment, and transfer of responsibility for
the results of tests pending at discharge among responsible inpatient and ambulatory
providers during the postdischarge period. The system was never adopted at MGH.
c. Improvements were made to the Partners Enterprise Patient List application
around the time the intervention started. These included functionality to automatically or
manually add all members of a patient’s care team. We conducted a quality improvement
initiative to improve care team identification using PEPL; however, all team members were
not entered into the system owing to workflow constraints and cultural issues at both
hospitals.26 We set up working groups as part of the quality improvement initiative at both
sites to address these issues, with limited success. Use of the group email functionality was
modest.
10. Home‐based coaching. Despite the fact that this intervention is more evidence‐based
than most (eg, from the Coleman Care Transitions Intervention), it never received the
internal funding it needed to be deployed, despite several efforts (eg, providing it as an
additional per diem service of Partners Healthcare at Home, linking it to local community‐
based organizations, proposing the use of community health workers).
Enrollment and Study Flow
We enrolled 18 PCMH primary care practices to participate in the study, including 8
from BWH (out of 13 approached), 8 from MGH (out of 11), and 2 from non‐BWH‐MGH
Partners practices (out of 9). We also enrolled 2 pilot practices, 1 each from BWH and MGH.
Reasons given for not participating included not having dedicated personnel to assume the
role of the ROC, recent turnover in practice leadership, and not enough patients admitted.
Figure 4 shows the flow diagram for patient screening and enrollment. Reasons given
for not enrolling patients included being unable to complete the screen before discharge, not
meeting inclusion criteria or meeting exclusion criteria, assignment to a pilot practice, and
patients declining informed written consent.
Table 1A shows the enrollment rates (number of patients enrolled per month) and
dates of starting enrollment, moving from usual care to the intervention (as randomized and
in actuality), and ending enrollment, by practice.
Patient Characteristics
Table 1B shows the characteristics of the patients in the 2 arms of the study.
Compared with usual care, patients in the intervention arm were less likely to be non‐English‐
speaking (ie, Spanish‐speaking, given the inclusion criteria) or to have a caregiver to help them
with basic or instrumental activities of daily living. Also, technically, patients in the
intervention arm had a statistically significantly higher baseline functional score, but the
magnitude of these differences was so small as to be clinically meaningless; statistically
significant differences in education level were also seen, again with differences so small as to
be clinically meaningless. Overall, the arms were well balanced, especially considering that
different primary care practices had different patient populations and spent different
amounts of time in the 2 arms of the study.
Baseline Clinician Surveys
Response rate of the baseline clinician survey was 58%. Selected results are shown in
Table 3. Many—if not most—clinicians believed that they did not have sufficient time to
perform transitional care tasks and that they had received insufficient training or feedback
on their performance in this area, including education of patients. They also gave mediocre
assessments of the quality of transitions of care from their vantage point. Their scores on
the patient safety culture survey were also mediocre.
Figure 4. Study Flow Diagram.
Table 3. Selected Results of Provider Surveys
Question Response %
Did you receive training in how to do your role in the transition process? No 48%
Do you feel that you are given sufficient time to do your role in the transitions process? No 58%
Have you ever received feedback on the quality of your job with respect to transitions? No 66%
Were you ever trained in the process of teach‐back to confirm patients’ understanding of what
they have been taught?
No 43%
Have you ever received training in how to effectively communicate with patients with low health
literacy?
No 60%
Question Meana
The transitions process leads to high‐quality instructions for patients. 4.75
The transitions process leads to patients and caregivers understanding what to do after discharge. 4.82
The transitions process leads to providers understanding what to do after discharge. 4.95
The transitions process leads to accurate orders (eg, medications). 5.05
The transitions process leads to effective follow‐up plans. 4.98
The transitions process is efficient. 4.13
The transitions process fits into my workflow. 4.17
The transitions process improves the quality of patient care. 5.15
Question Response %
The actions of management show that patient safety is a top priority. Disagree 26%
When a lot of work needs to be done quickly, we work together as a team to get the work done. Disagree 26%
When one area gets really busy, others help out. Disagree 34%
Staff will freely speak up if they see something that may negatively affect patient care. Disagree 26%
Staff feel free to question decisions and actions of those with more authority. Disagree 45%
We have enough staff to handle the workload. Disagree 71%
I have enough time to complete patient care tasks safely. Disagree 52%
a 1 = never, 7 = always.
Inpatient and Outpatient Inventories of Transitional Care Tasks
In general, we found inconsistent performance of most transitional care tasks in both
settings. We also found tremendous variation by practice and by unit, based on the surveys
of physician and nurse leadership (Tables 4A, 4B, and 4C). For example, for most tasks in the
inpatient setting, some units never performed the task while others performed it most or all
of the time. In most of those cases, there was a lack of role clarity in terms of who was
supposed to perform that task. The tasks are listed in Box 1.
Intervention Fidelity
Table 5 shows the frequency with which different interventions were delivered to
patients in the intervention arm, based on review of documentation in the electronic medical
record. In general, most patients did not receive most intervention components, even those
that were supposed to be delivered to all intervention patients. Not surprisingly, the rate of
receipt of interventions was even lower for components reserved for selected patients. Most
patients were not discharged with VNA services (75% of BWH patients and 72% of MGH
patients), and of those who were, many (almost half) received services from a different
agency than Partners Healthcare at Home (because of location, need for certain services,
insurance issues, past relationship with a different agency, or patient or case manager
preference). Only a small percentage of patients (20% of BWH patients and 15% of MGH
patients) were referred for the Dovetail home pharmacy program by their inpatient or
outpatient providers, even with prompting from our project manager, and of those who
were referred, only a little over half accepted (this proportion did increase somewhat over
the course of the study with changes to our logistics and to patient messaging, ie, to remove
any stigmatization of referral to the program).
Box 1. Inpatient Inventory Tasks Inconsistently Performed
Inpatient Inventory: Tasks With Unclear Role Responsibility and Those Inconsistently Completed • Confirming that a patient knows the location of his or her follow‐up appointments and has transportation. • Talking with a clinician from the patient’s primary care practice; learning more about the patient and communicating that information to the team. • Scheduling follow‐up appointments according to an agreed‐upon time frame, availability of the practice, and patient/family preferences. • Documenting the most important behavioral changes in patient instructions. • Documenting what to do if a problem arises (eg, when to call the practice, when to go to the emergency department). • Identifying the active listener and making sure he or she is present for discharge instructions.
Quantitative Outcomes
Table 6 shows the results of the main quantitative outcomes. The intervention had no
effect on readmission rates or on preventable readmission rates in either unadjusted or
adjusted models. However, for new or worsening signs or symptoms within 30 days of
discharge, while we saw no effect in unadjusted analysis, we found a 22% relative reduction
in fully adjusted models; this effect was robust to a number of different models (results not
shown). In addition, the intervention was associated with a significant 48% reduction in
postdischarge adverse events and a significant 63% reduction in preventable postdischarge
adverse events in fully adjusted and clustered models, and similar effects in the unadjusted
models.
Regarding postdischarge functional status (adjusted for preadmission functional
status), we found a small and borderline significant difference in the intervention arm
compared with usual care in the unadjusted analysis, but this difference essentially
disappeared in the adjusted analysis.
Table 7 shows the rates of postdischarge adverse events in the control and
intervention arms by type of event. Significant reductions were noted in 2 types: adverse drug
events and procedural complications. Table 8 shows examples of adverse events noted in the
control arm that were judged to be preventable by the adjudicators and thought to be
potentially addressable by the intervention, thus demonstrating how the intervention might
theoretically have achieved the outcomes noted above.
Subgroup Analyses We ran a number of prespecified subgroup analyses to determine the effects of the
intervention on various patient populations. Table 9A shows the subgroup analyses on
readmissions; Table 9B shows the results on new or worsening signs and symptoms, Table 9C
for postdischarge adverse events, and Table 9D on preventable postdischarge adverse
events. To determine the statistical significance of this effect modification, we ran interaction
terms in multivariable models (subgroup*arm); these models required fewer covariates in
order to converge. No effect modification was seen in the subgroup analyses of readmissions,
new or worsening symptoms, or postdischarge adverse events. In the analysis of preventable
postdischarge adverse events, we found borderline evidence of effect modification, with
patients on the surgical service, at BWH, and with an Elixhauser score less than 5 seeming to
benefit more than other patients from the intervention.
Table 6. Quantitative Outcomes
Outcome Usual Care
N = 679
Intervention
N = 978
Unadjusted Rate (95% CI), p value
Adjusted Rate (95% CI), p value
Nonelective 30‐ 78 (11.5%) 107 (10.9%) OR 0.95 (0.69‐ OR 1.08 (0.64‐ day readmission 1.29), p = 0.73 1.85), p = 0.77a
Preventable 12 (1.8%) 22 (2.3%) OR 1.31 (0.64‐ OR 1.86 (0.61‐ readmissions 2.67), p = 0.46 5.62), p = 0.27
New or worsening postdischarge signs/symptoms
92 per 100 patients
90 per 100 patients
IRR 0.98 (0.88‐1.08), p = 0.64
IRR 0.78 (0.64-0.95), p = 0.01b
Postdischarge adverse events
21 per 100 patients
12 per 100 patients
IRR 0.59 (0.45-0.78), p = 0.0002
IRR 0.52 (0.30-0.91), p = 0.02
Preventable postdischarge adverse events
9 per 100 patients 3 per 100 patients IRR 0.34 (0.21- 0.57), p < 0.0001
IRR 0.37 (0.13-0.99), p = 0.046
30‐day postdischarge SF‐12 score
46.14 47.25 Difference 1.11 (p = 0.05)
Difference 0.08 (p = 0.91)c
Abbreviations: ED, emergency department; IRR = incidence rate ratio; OR = odds ratio. a Adjusted for study month, Elixhauser comorbidity score, patient age, sex, language, race/ethnicity, cognitive status, health literacy, functional status 1 month before admission, caregiver status, median income by zip code, ED visits in the previous 6 months, HOSPITAL readmission risk score; clustered by inpatient unit; primary care practice as a random effect. b Adjusted for study month, Elixhauser comorbidity score, patient age, sex, language, race/ethnicity, cognitive status, health literacy, functional status 1 month before admission, caregiver status, median income by zip code, ED visits in the previous 6 months, HOSPITAL readmission risk score, inpatient unit, and primary care practice. c Adjusted for study month, Elixhauser comorbidity score, patient age, sex, language, race/ethnicity, cognitive status, health literacy, functional status 1 month before admission, caregiver status, median income by zip code, ED visits in the previous 6 months, HOSPITAL readmission risk score; clustered by inpatient unit; primary care practice as a random effect.
Table 7. Rates of Adverse Event Type, by Arm
Adverse Event Type Rate per 100 Patients IRR (95% CI) P value Intervention Control
Adverse drug event 7.5 12.2 0.61 (0.44‐0.85) 0.004 Hospital‐acquired infection
0.4 0.6 0.55 (0.12‐2.47) 0.44
Procedural complication 0.8 3.4 0.23 (0.10‐0.55) 0.0008 Surgical complication 3.3 2.5 1.33 (0.72‐2.46) 0.35 Diagnostic error 0.2 0.3 0.74 (0.10‐5.24) 0.76 Management error 3.6 4.5 0.79 (0.47‐1.31) 0.35
I
Table 8. Examples of Adverse Events in the Control Arm That Might Have Been Prevented by the Intervention
Clinical History Category of Adverse Event
How It Might Have Been Prevented
Patient with history of recurrent kidney stones and urinary infections admitted for urinary infection, discharged on trimethoprim/sulfamethoxazole (for methicillin‐resistant staph aureus) and amoxicillin/clavulanic acid (for enterococcus). Patient did not take the former antibiotic because she thought she was supposed to take it after completing the course of the latter antibiotic. Readmitted for worsening urinary infection.
Adverse drug event—patient nonadherence
Better education and counseling at discharge regarding discharge medication regimen; postdischarge phone call to confirm taking the correct medication regimen
Patient with bipolar disorder and hemophilia. Admitted after mechanical fall and trauma to left knee, found to have hemarthrosis, treated with analgesics and physical therapy. Given crutches to use at discharge, but patient decided not to take them home. Seen in follow‐up, noted to have increased pain, owing in part to full weight bearing. Given crutches at that appointment.
Management error Better coaching at discharge regarding need for partial weight bearing and use of crutches; follow‐up phone call to ensure receipt of durable medical equipment and ability to carry out discharge plan
Patient with metastatic uterine sarcoma admitted for debulking surgery. Was discharged on stool softeners, regular diet (“advance as tolerated”). Called her providers a few days after discharge with increased abdominal pain and bloating. Instructed to scale back to a clear liquid diet and take laxatives, which relieved her symptoms.
Management error Better coaching at discharge regarding what to expect after surgery, danger signs to watch for, need to slowly advance diet, use of a bowel regimen
Patient admitted for a myocardial infarction, underwent cardiac catheterization with placement of a bare metal stent, discharged on several new medications, including high‐dose atorvastatin. Patient informed at the pharmacy that medication required a prior authorization by his insurance, which was eventually obtained, but patient was without a statin for approximately 5 days.
Management error Pharmacist‐assisted medication reconciliation at discharge to ensure that all medications are covered
Patient admitted with chest pain, found to have acute renal failure due to ANCA‐ positive vasculitis, treated with steroids, cyclophosphamide, rituximab, and plasmapheresis. Discharged to continue plasmapheresis. A few days after discharge, patient developed fatigue and weakness, contacted providers, who thought it was likely owing to poor oral intake in the setting of plasmapheresis.
Procedural complication
Better education and coaching before discharge regarding potential side effects of plasmapheresis and the need for good oral intake
Patient with severe osteoarthritis admitted for elective total knee arthroplasty. Discharged with home health services. Developed cellulitis below the knee 1½ weeks after discharge, possibly from poor wound care at home.
Surgical complication Better coaching on self‐management at discharge and after discharge regarding wound care
Patient with severe osteoarthritis admitted for total knee arthroplasty. Discharged on opioids and docusate, activity as tolerated. Developed constipation (no bowel movement for 5 days), noted during follow‐up, treated with polyethylene glycol laxative. Also developed bleeding from wound, likely from excessive flexion of the knee. Placed in an immobilizer for 1 week.
Adverse drug event—surgical complication
Counseling at discharge about possible constipation while on opioids and what to do if it occurs; postdischarge call regarding development of any medication side effects; counseling at discharge and after discharge regarding avoiding excessive knee flexion
Table 9A. Subgroup Analysis for 30-Day Readmissions
Subgroup
Intervention Versus Usual Care Adjusted Odds Ratio (95% CI)a
p Value for Effect of Intervention
p Value for Interaction Term (subgroup*arm)
Service Medicine 1.10 (0.64‐1.91) 0.73 0.93 Surgery 1.07 (0.49‐2.35) 0.87
Study hospital BWH 1.11 (0.59‐2.11) 0.74 0.97 MGH 1.23 (0.59‐2.16) 0.72
Age 65 years and older 1.13 (0.61‐2.10) 0.70 0.82 Under 65 years 1.05 (0.57‐1.91) 0.88 Hospital score 5 or more 0.95 (0.45‐2.00) 0.89 0.68 Below 5 1.11 (0.64‐1.91) 0.71 s-TOFHA literacy score Adequate 0.97 (0.54‐1.74) 0.91 0.73
Inadequate or marginal 1.43 (0.52‐3.93) 0.49 Not assessed 1.15 (0.55‐2.39) 0.71 Elixhauser comorbidity score 5 or more 0.90 (0.51‐1.57) 0.71 0.31 Below 5 1.31 (0.64‐2.66) 0.46
a Adjusted for arm, HOSPITAL readmission risk score, Elixhauser comorbidity score, ED visits in the previous 6 months, study month, season, SF‐12 score; primary care practice and inpatient unit as random effects.
Table 9B. Subgroup Analysis for New or Worsening Symptoms and Signs
Intervention Versus Usual Care Adjusted Incidence Rate Ratio
p Value for Effect of
Intervention
p Value for Interaction Term (subgroup*arm)
Service Medicine 0.77 (0.62‐0.96) 0.02 0.47 Surgery 0.83 (0.66‐1.05) 0.12
Study hospital BWH 0.92 (0.76‐1.12) 0.41 0.22 MGH 0.81 (0.66‐0.99) 0.04
Age 65 years and older 0.81 (0.64‐1.02) 0.08 0.85 Under 65 years 0.79(0.64‐0.98) 0.03 Hospital score 5 or more 0.89 (0.66‐1.20) 0.46 0.30 Below 5 0.77 (0.63‐0.95) 0.01 s-TOFHLA literacy score Adequate 0.76 (0.53 1.10) 0.15 0.77 Inadequate to marginal 0.82 (0.66‐1.02) 0.07 Not assessed (eg, patient declined) 0.75 (0.58‐0.99) 0.04 Elixhauser comorbidity score 5 or more 0.83 (0.66‐1.04) 0.10 0.43 Below 5 0.76 (0.61‐0.95) 0.02
a Adjusted for arm, HOSPITAL readmission risk score, Elixhauser comorbidity score, ED visits in the previous 6 months, study month, season, SF‐12 score; primary care practice and inpatient unit as random effects.
Table 9C. Subgroup Analysis for Postdischarge Adverse Events
Intervention Versus Usual Care Adjusted Incidence Rate Ratio
p Value for Effect of
Intervention
p Value for Interaction Term (subgroup*arm)
Service Medicine 0.48 (0.25‐0.91) 0.03 0.90 Surgery 0.50 (0.26‐0.94) 0.03
Study hospital BWH 0.69 (0.40‐1.20) 0.19 0.85 MGH 0.73 (0.42‐1.27) 0.26
Age 65 years and older 0.56 (0.28‐1.11) 0.10 0.49 Under 65 years 0.45 (0.24‐0.83) 0.01 Hospital score 5 or more 0.69 (0.30‐1.58) 0.38 0.23 Below 5 0.45 (0.25‐0.80) 0.007 s-TOFHLA literacy score Adequate 0.54 (0.19‐1.56) 0.26 0.95 Inadequate to marginal 0.49(0.27‐0.90) 0.02 Not assessed 0.46 (0.21‐0.99) 0.05 Elixhauser comorbidity score 5 or more 0.47 (0.25‐0.89) 0.02 0.78 Below 5 0.51 (0.51‐0.97) 0.04
a Adjusted for arm, HOSPITAL readmission risk score, Elixhauser comorbidity score, ED visits in the previous 6 months, study month, season, SF‐12 score; primary care practice and inpatient unit as random effects.
Table 9D. Subgroup Analysis for Preventable Postdischarge Adverse Events
Intervention Versus Usual Care Adjusted Incidence Rate Ratio
p Value for Effect of
Intervention
p Value for Interaction Term (subgroup*arm)
Service Medicine 0.59 (0.20‐1.71) 0.33 0.05 Surgery 0.16 (0.04‐0.63) 0.009
Study hospital BWH 0.21 (0.07‐0.68) 0.009 0.10 MGH 0.56 (0.23‐1.37) 0.20 Age 65 years and older 0.64 (0.20‐2.17) 0.48 0.12 Under 65 years 0.26 (0.09‐0.79) 0.02 Hospital score 5 or more 0.57 (0.14‐2.32) 0.43 0.41 Below 5 0.33 (0.12‐0.93) 0.04 s-TOFHLA literacy score Adequate 0.79 (0.15‐4.14) 0.78 0.52 Inadequate to marginal 0.32(0.11‐0.96) 0.04 Not assessed 0.37 (0.10‐1.35) 0.13 Elixhauser comorbidity score 5 or more 0.56 (0.19‐1.64) 0.28 0.07 Below 5 0.18 (0.05‐0.66) 0.01
a Adjusted for arm, HOSPITAL readmission risk score, Elixhauser comorbidity score, ED visits in the previous 6 months, study month, season, SF‐12 score; primary care practice and inpatient unit as random effects.
Patient Survey Results
In unadjusted analyses, the intervention was associated with few markers of patient
experience, at least in intention‐to‐treat analyses. Surprisingly, the intervention was
associated with a lower likelihood of agreeing or strongly agreeing with the statement “After
I left the hospital, I was able to follow the diet they ordered for me.” This result might have
occurred because of a low response rate for that question (49%) or because patients were
now more aware of what that diet was without having been given sufficient resources to help
them adhere to it. On the other hand, we found positive associations with one question and
one statement that were of borderline significance: “How often did [the medical team] ask if
you might have problems actually doing the recommended treatment [after discharge]?” and
“After I left the hospital, I knew how to contact my doctor if I needed to.” In adjusted
analyses, these associations were no longer statistically significant, although the diet
question remained of borderline significance (Table 10).
Qualitative Analysis Eight focus groups were conducted, involving 21 clinicians (Table 2). The 2 coders
reviewed the themes they had documented and resolved discrepancies. During our focus
groups of clinicians, we identified several themes related to barriers to and facilitators of
implementation of the intervention. Box 2 lists the themes, while Box 3 provides illustrative
quotes for selected themes. Not surprisingly, the barriers and facilitators were often opposite
characteristics of the same attributes (eg, lack versus presence of institutional commitment
to improving care transitions).
Table 10. Effect of Intervention on Patient Experience
Positive Responsea Unadjusted OR (95% CI)
P value
Adjusted Odds Ratio (95% CI)b
p Value Intervention Control
When you were getting ready to leave the hospital 1 month ago, how often did your care team use medical terminology that you did not understand?
86% 85% 1.08 (0.74‐
1.58)
0.67
0.85 (0.47‐1.51)
0.57
How often did you feel confused about what was going on with your medical care because they did not explain
things well?
86% 88% 0.89 (0.60‐
1.31)
0.55
0.75 (0.41‐1.36)
0.35
How often did they give you enough time to say what you thought was important regarding your medical
care?
92% 90% 1.20 (0.76‐
1.91)
0.43
0.70 (0.35‐1.41)
0.32
How often did they listen carefully to what you had to say?
94% 92% 1.27 (0.76‐ 2.12) 0.36
0.74 (0.34‐1.63) 0.45
How often did you feel pressured by them to have a treatment you were not sure you wanted?
95% 95% 1.03 (0.55‐
1.92)
0.92
0.83 (0.33‐2.09)
0.7
How often did they ask if you might have problems actually doing the recommended treatment (for
example, taking the medication correctly or attending follow‐up visits)?
41% 34% 1.34 (0.99‐
1.80)
0.06
1.07 (0.68‐
1.68)
0.78
When I left the hospital, I understood what I was supposed to do to take care of myself.
98% 97% 2.06
(0.85‐4.96)
0.1 1.2 (0.31‐
4.71)
0.79
After I left the hospital, I was able to take each of my medications correctly every day.
95% 95% 1.05 (0.56‐ 1.95) 0.88 0.73 (0.25‐
2.07) 0.55
After I left the hospital, I knew what danger signs to watch out for AND what to do if I had them.
96% 94% 1.46 (0.81‐
2.64)
0.21 1.53 (0.61‐
3.86)
0.37
After I left the hospital, I knew how to contact my doctor if I needed to.
98% 96% 2.21 (0.93‐ 5.27) 0.07 0.65 (0.16‐
2.66) 0.55
After I left the hospital, I was able to get to my doctor's appointments or other tests.
95% 95% 0.97 (0.52‐ 1.77) 0.91 0.61 (0.23‐
1.61) 0.32
After I left the hospital, I was able to follow the diet they ordered for me.
41% 52% 0.65 (0.48‐ 0.88) 0.006 0.61 (0.37‐
1.01) 0.05
After I left the hospital, I had enough support from friends, family, or others to recover from my illness.
97% 96% 1.24 (0.59‐
2.60)
0.57 0.82 (0.24‐
2.80)
0.76
a Agree, strongly agree, always, or mostly for positively worded questions; disagree, strongly disagree, rarely, or never for negatively worded questions. b Adjusted for Elixhauser comorbidity score, number of emergency department visits in the past 6 months, study month, season, HOSPITAL readmission risk score, and baseline functional status.
Table 2. Focus Groups Target Group Credentials Number of Participants Discharge advocate, BWH Nurse practitioner 1 Discharge advocate, MGH (attending nurse)
Registered nurse 1
Inpatient nurses Registered nurse 2 Inpatient care coordinators Registered nurse 2 Inpatient attending physicians/hospitalists
Medical doctor 5
Inpatient pharmacists PharmD 5 Primary care physicians Medical doctor 2 Responsible outpatient clinicians Registered nurse, licensed
practical nurse 3
Box 2. Barriers to and Facilitators of Implementation: Themes
a. Barriers i. Task
1. Aspects that are too complex 2. Logistical barriers 3. Poor handoffs between roles or locations
ii. Organization 1. Lack of institutional commitment to improving care transitions 2. Lack of identified leadership 3. Lack of experience in quality improvement 4. Competing priorities 5. Financial restrictions
iii. The clinic or unit where you work 1. Insufficient training provided to staff 2. Lack of time availability (”bandwidth”) 3. Variable staffing 4. Discontinuity of providers in a role or location 5. Problems with policies and procedures regarding transitions 6. Unclear goals and expectations 7. Lack of sufficient authority given to frontline leadership 8. Lack of feedback on providers’ performance 9. Culture that inhibits interdisciplinary communication and teamwork 10. Lack of commitment to patient safety
iv. Staff 1. Required services and help from others not available 2. Transitions of care not valued by other clinicians 3. Feelings of burnout 4. Lack of role clarity 5. Lack of trust in others assigned a task 6. Lack of self‐efficacy 7. Lack of someone else taking responsibility
v. Patients and caregivers 1. Difficult patient population 2. Caregivers not helpful, engaged, or available
vi. Technology and tools (eg, computer systems, templates) 1. Inadequate materials and supplies to accomplish the job 2. Inadequate tools and technology to accomplish the job
3. Inadequate information to accomplish the job b. Facilitators of success: Listen and probe for the following (converse of barriers)
i. Task 1. Easy to do 2. Seen as desirable
ii. Organization 1. Institutional commitment to improving care transitions 2. Effective leadership identified to support the intervention 3. Experience in QI[spell out] 4. Sees transitions as a top priority 5. Financially strong
iii. Environment (clinic/unit) 1. Effective training of staff 2. Adequate time allocated to task 3. Clear policies and procedures 4. Clear goals and expectations 5. Sufficient authority and autonomy given to frontline leadership 6. Useful feedback on performance 7. Culture of safety and interdisciplinary teamwork 8. Commitment to safety
iv. People (staff, patients) 1. Available help from co‐workers 2. Staff engaged, not burned out 3. Staff committed to safety, value transitions of care 4. Study staff helpful 5. Patients receptive to interventions 6. Patients engaged in their own care
v. Technology and tools (eg, computer systems, templates) 1. Useful and easy‐to‐use materials and supplies 2. Useful and easy‐to‐use tools and technology 3. Adequate information to do the job
Box 3. Illustrative Quotes to Support Themes
Barriers: Lack of Time Availability/Bandwidth Interviewer: “When you’re with a patient, do you feel like they get the attention they deserve?” Speaker: “Sometimes. On a couple of occasions I’ve, you know, gotten a page and had to excuse myself and walk out to go to the computer to look at a stat order and the nurse is paging me that they’re out of something or whatever, so I’ve had to walk out and go back in and try to get caught up. So getting the patient back in focus.”‐ PharmD Competing Priorities “I think a lot of it may have to do with so many individual transition of care projects taking place in the hospital where one patient has been approached for similar project delivery and at the end of the day they’ve met so many different research folks so they get confused.”‐Discharge Advocate Lack of Institutional Commitment “I disagree with what X said. I think the organization is dramatically underinvested in what it would mean to create a truly highly functional integrated system. You know, again, I think we are so far from getting out of this volume visit mentality which, you know, is the antithesis of what you need to do in an ACO, that I think the organization, I don’t feel the organization understands this and I think they’re dramatically underinvested in…[in what?]”‐PCP Lack of Communication “Part of the concern with that is that, you know, aside from the initial email I get, whatever is being done by the team is pretty much invisible to me. But I wonder how it is possible for the primary care providing group to know that things are going, that all these things have happened and so we know where to pick up the pieces after 30 days.”‐ PCP Inadequate Materials and Supplies “Also I think that when you find out they’re in the study or you go see them and the discharge is finalized, that is also a problem. It seems to be a barrier to me because they don’t always want to change it, so then you are just saying okay well the vitamins that aren’t on your list, they’re not there but we are going to say those things, and there are 1 or 2 things you can touch base with the team but they don’t want to change the whole paperwork. and then you have to abbreviate so they don’t walk out the door; you have to do limited conversation on the highlights because they’re ready to go and the paperwork is signed.”‐PharmD ‐“Especially, you know, a lot of times if the discharge meds are there and the paperwork is signed, the nurses have already printed off and no one wants to re‐do it.”‐PharmD Logistics “I was mentioning earlier that I feel like sometimes I get the email for it and unfortunately I may not have already met the patient before they have left.”‐Inpatient Attending Nurse Lack of Someone Taking Responsibility
“I think, you know, I think I need to learn more about that transition economics piece. I feel like there has to be a shared responsibility. I am not going to be comfortable as a subspecialist saying, you know, this is not my territory, right? This is everybody’s work. But I feel like in the current thinking, I feel like there needs to be an evolution of thinking at least departmentally in seeing that piece of our work, seeing that as an important piece of our work, because I think that it is. I don’t know how other subspecialists would deal with it.”‐Hospitalist Staff Burnout I think both, yes, that is, if we had more time for the intervention then we would be able to do more with the intervention. But if we had more time to staff we would be better staffed. So I think both are adding to the stress level.”‐PharmD Variable Staffing “I was just wondering, on weekends we lose ARNs [attending nurses] at Mass General. And people still get discharged on weekends. What is that for weekends? I think many of us end up keeping a patient over the weekend to ensure that they have a safer discharge on a weekday as opposed to a weekend.”‐ Inpatient Attending Nurse Facilitators Positive Communication “I’ll say more I just think that, I think the extent that this represents an effort to create much more communication on an ongoing basis when patients are admitted to the hospital between the inpatient and primary care team, I think that is a really positive step.”—PCP Institutional Commitment “And also those within the institution that have also been our strong allies in moving this forward as well and you know they started with us from the beginning and they kept on, allowing us to intervene within their units or their offices.”‐Discharge Advocate Effective Leadership “We have a lot of good support from our nursing director and she is also very protective of the role to a good degree in the sense of how everybody wants a piece of us, but she definitely has to stand up for us and pull us back away from things that aren’t necessarily our responsibility or our purpose.”‐ Inpatient Attending Nurse Environment “I mean I have enjoyed doing the patient education piece and, you know, being able to provide the interventions to the team that otherwise would have been missed because we are not formally involved in the discharge process.” PharmD Available Help from Co‐workers “ Yeah, I feel like social workers are really helpful. Our social worker is great to work with, has all the connections with a lot of the barriers for discharge, and works really hard to coordinate, to make sure they transition home. The care managers also work hard alongside them constantly.” ‐Inpatient Attending Nurse
Staff Engaged, Not Burned Out “Being able to see the outcomes of these patients and, you know, monitoring our impact I think is really great to see what happens.”‐PharmD Staff Committed to Safety, Value Transitions of Care “I think that it is just nice to know that is another kind of system catching them on the other end so when you do get the email you know that. You know sometimes we feel a little stressed doing the discharge phone calls and did we ask the right questions, and did we connect in the right way. Now you know there is going to be another person or group of people that are going to be doing the same thing or very similar thing, good follow‐up.”‐Inpatient Attending Nurse Patients Receptive to Intervention “But I think overall the patients are really happy to have more detailed conversation about their meds and discharge, and a number of questions come up at the end of the visit and sometimes I am seeing patients after they have already spoken to their nurse about their discharge plans and they wouldn’t otherwise have been answered, so I think overall, whether it shows in numbers or not, patients are pretty happy with it.”‐PharmD Patients Engaged in Their Care “And then at the end of the conversation she said ‘Guess what? I can give this a try cause I have reasons to live and you just made me realize that without properly managing my diabetes I will not be able to live a long life.’ And interestingly at the end of her going home so we were able to get her a meter, she was taking her insulin, and she actually started doing her own injections. The last 2 days before she left she was doing her own injections so she could again learn how to do it. And it’s been, I’ve heard from her within a week after she left and she said ‘I’ve been religiously taking my insulin every day and again thank you.’” – Discharge Advocate
Barriers to Implementation
Some barriers to successful implementation were related to the task itself. For
example, in many cases, especially at BWH, LPNs were given the responsibility of making
postdischarge phone calls, but their level of training and scope of work restrictions meant
that they could do little more than record the answers to the questions and relay them to
the PCP to take action, with uneven results. The task required personnel who could make
judgment calls, modify the questions as needed, and take action independently, often with
the patient still on the phone. More broadly, the intervention involved many different
clinicians. While this setup distributed the work and played to each clinician’s strengths, it
also led to increased requirements for communication and coordination of care. Also,
because the study population of 1668 patients was only a small fraction of all discharged
medical/surgical patients from BWH and MGH, it was difficult for clinicians to identify
patients who were to receive the intervention, remember to deliver it to those patients,
develop proficiency at it, develop systems around it, and build it into workflow.
Clinicians cited numerous logistical barriers. One barrier (described earlier in the
Evolution of the Intervention section) was the different communication styles of inpatient
and outpatient nurses. Another logistical issue was ensuring that centralized intervention
pharmacists received enough notice of patient discharges to deliver the day‐of‐discharge
medication safety intervention. Weekend discharges were also a problem owing to fewer
intervention staff members on duty, lack of availability of other clinicians with whom to
communicate, and discontinuity of care.
Handoffs between roles and locations also created problems. Several clinician types
noted difficulty keeping everyone on the same page, even though they all documented their
activities in the EMR. At times clinicians did not know what services a patient had received,
what issues had been discovered, and what tasks still needed to be done. For example, the
identity of the visiting nurse is usually not known on the day of discharge, and this made it
hard for inpatient nurses and discharge advocates to communicate concerns, issues, and
tasks for visiting nurses to complete.
Several clinicians linked the lack of staffing for this intervention to a lack of
institutional commitment to improving transitions of care. For many clinicians, the
intervention was an addition to their usual workload. Inpatient pharmacists were often pulled
to do other tasks whenever the hospital was short‐staffed. Inpatient nurses cited lack of
sufficient time to involve caregivers in discharge education or to contact ROCs. Outpatient
nurses did not have the time to call nonmedical patients or to reach all patients within 2 days
of discharge. Several clinicians linked the lack of institutional support to the way health care is
currently organized and financed; ie, mostly fee‐for‐service, with limited ways to pay
clinicians and support staff to incentivize this type of work. Presumably, if incentives to
prevent readmissions and improve postdischarge care were sufficiently aligned, additional
resources could be spent on personnel to achieve these goals—an investment that might pay
for itself.
Another barrier was competing priorities, including competing transition programs
(especially the iCMP program); a focus on early discharges (which might compete with
discharge planning and education); and other strains on time, effort, attention, and resources
(eg, planning for a new EMR at BWH).
Within individual units and clinics, clinicians mentioned discontinuity of personnel in a
role. For example, inpatient attending nurses changed every day, so they might not know that
a certain patient needed to communicate with the responsible outpatient clinician or what
issues to convey, while the ROC might not know whom to contact. Inadequate policies and
procedures and unclear expectations led to role confusion; for example, who should initiate
communication between the ROC and the discharge advocate; when PCPs should be
contacted and what information should be communicated to them; and whether attending
nurses or ROCs, or both, should make postdischarge phone calls. A few clinicians mentioned
cultural issues, such as clinicians not valuing interprofessional communication or not taking
responsibility for certain tasks. Different workflows on different units made it difficult to
standardize the intervention and build it into usual care.
Some clinicians noted challenges with the patient population: working with patients
who were sick, not activated, who lacked a clear understanding of their conditions or
medications, and who were focused on going home but not on the tasks that would be
required of them at home. Clinicians noted that patients often do not remember much of
what they are taught on the day of discharge (a point corroborated by patients) because they
are oversedated, sleep‐deprived, malnourished, still not feeling well, and in an artificial
environment. Sometimes the “active learner” is a caregiver and not the patient, but
caregivers were not always available at the time of discharge or did not expect to be involved
in prolonged discharge education.
Technology and tools were often cited as a barrier. In theory, technology should make
it easier to track interventions that had been delivered and tasks that still needed to be
completed, but such technology did not exist. ROCs noted the lack of a system to track which
patients were in the hospital and which had just been discharged and needed phone calls,
while inpatient pharmacists noted the lack of a system to notify them when a patient was
about to be discharged. Even when technology was available to track all the members of a
patient’s care team, the information was often incomplete. Several clinicians noted a lack of
reminders, reports, and other tools to support workflow. Finally, the unwillingness of some
frontline providers to change discharge documentation in the EMR after it had been signed
led to errors (eg, in medication orders), even if they had been detected by members of the
intervention team, such as inpatient pharmacists.
The barriers most often cited by clinicians were lack of communication (48 mentions)
and lack of time (44 mentions), followed by lack of institutional commitment (16), difficult
patient population (15), competing priorities (14), variable staffing (10), and logistics (10).
Facilitators of Implementation
The focus group participants also cited several facilitators of implementation.
Regarding the tasks, several clinicians noted their inherent value as a facilitator of
implementation. For example, inpatient clinicians noted that having a discharge advocate as a
second set of eyes gave them the confidence to discharge patients safely. A number of PCPs
mentioned the benefits of inpatient pharmacists and better inpatient–outpatient
communication. Attending nurses appreciated the reassurance that came from knowing that
others (ROCs) were also going to make postdischarge phone calls. (In this case, the backup
was a healthy double‐check, although it could also be viewed as an unnecessary redundancy.)
Several clinicians mentioned the importance of institutional commitment at a high
level to improving transitions of care. Regarding the environment, inpatient nurses noted the
24/7 availability of their staff to address concerns if patients called the unit after they were
discharged and their ability to address the issues raised or identify the best person to
manage them. Inpatient nurses also cited their good relationship with inpatient pharmacists
and the ability to divide up the work of discharge counseling. They also appreciated the help
they got from inpatient social workers and care coordinators. ROCs cited several instances of
very productive conversations with inpatient nurses and their dedication to postdischarge
care. Inpatient pharmacists valued the opportunity to get more involved in patient education
and to further the career development of trainees.
Regarding patients, several clinicians noted that some patients were very receptive
to DA coaching or pharmacist medication counseling, understanding the link between their
postdischarge behavior and their ability to meet their recovery goals.
Finally, regarding tools and technology, several clinicians noted the benefits of
counseling scripts provided by the intervention, the use of asynchronous communication
tools (eg, email, notes in the EMR), and the PEPL tool to identify care team members. Group
emails to all clinicians were appreciated as one way to get everyone on the same page.
On‐treatment Analysis
Using the intervention fidelity data from Table 5, we were able to correlate the receipt
of each intervention component with outcomes, producing an on‐treatment analysis. By
definition, control patients were considered not to have received these interventions and so
were grouped with intervention patients who did not receive each component, compared
with those intervention patients who did. The components were put into the model one at a
time, along with covariates, to avoid collinearity. The results are summarized in Table 11.
Table 11. On-treatment Analyses
Intervention Component
Adjusted Effect on Readmissions
OR (95% CI), p value
Adjusted Effect on Preventable Readmissions
OR (95% CI), p value
Adjusted Effect on New or Worsening Symptoms
IRR (95% CI), p value
Adjusted Effect on Adverse Events
IRR (95% CI), p value
Adjusted Effect on Preventable Adverse Events
IRR (95% CI), p value
Discharge advocate BWH: 0.63 (0.32‐ BWH: 0.70 (0.16‐ BWH: 0.87 (0.71‐ BWH: 0.84 (0.41‐ BWH: 0.70 (0.16‐
1.25), p = 0.19 3.05), p = 0.64 1.06), p = 0.18 1.71), p = 0.63 3.05), p = 0.64
MGH: 1.71 (0.76‐ 3.88), p = 0.19
MGH: 3.20 (0.57‐ 18.2), p = 0.19
MGH: 1.18 (0.90‐ 1,53), p = 0.23
MGH: 1.21 (0.41‐ 1.71), p = 0.63
MGH: 3.21 (0.57‐ 18.21), p = 0.39
Inpatient pharmacist 0.79 (0.48‐1,31), 0.63 (0.21‐1.84), 0.96 (0.82‐1.13), 0.68 (0.39‐1.17), 0.63 (0.21‐1.84), p = 0.36 p = 0.39 p = 0.65 p = 0.16 p = 0.39
Partners VNA 0.93 (0.49‐1,79), 0.95 (0.25‐3.53), 0.85 (0.68‐1.05), 1.11 (0.60‐2.05), 0.95 (0.25‐3.53), p = 0.83 p = 0.93 p = 0.13 p = 0.79
P= 0.93
Postdischarge call 1.24 (0.79‐1.94), 1.25 (0.79‐1.94), 1.09 (0.94‐1.26), 1.36 (0.86‐2.15), 1.88 (0.76‐4.61), within 14 days p = 0.36 p = 0.36 p = 0.24 p = 0.19 p = 0.17
Postdischarge PCP 1.16 (0.71‐1,88), 2.58 (1.02-6.48), 1.24 (1.06-1.45), 1.26 (0.79‐2.05), 2.58 (1.02-6.48), visit within 14 days p = 0.56 p = 0.04 p = .006 p = 0.36 p = 0.04
Outpatient 1.08 (0.52‐2,22), (Multivariable model 0.87 (0.68‐1.12), 0.93 (0.42‐2.10), (Multivariable model pharmacist visit p = 0.84 did not converge) p = 0.29 p = 0.87 did not converge)
Total Intervention 0.81 (0.35‐1,88), 1.08 (0.20‐5.98), 0.97 (0.74‐1.27), 0.83 (0.34‐2.00), 1.08 (0.20‐5.98), Fidelity Scorea p = 0.62 p = 0.93 p = 0.84 p = 0.68 p = 0.93
a Fraction of received components over recommended components.
Interestingly, a PCP visit within 14 days was associated with higher rates of readmission, new
or worsening symptoms, and preventable adverse events. We saw a similar but nonsignificant
trend for postdischarge phone calls and nonsignificant trends toward improved outcomes
among those who received the inpatient pharmacist intervention. Finally, we saw a trend for
the discharge advocate to be associated with better outcomes at BWH, while the DA was
associated with worse outcomes at MGH.
Mixed Methods
We performed several analyses to correlate structure, process, and outcome,
especially as they pertained to the ability to carry out interventions and the success of those
interventions. Table 12 shows an example—a correlation between the inpatient and
outpatient inventories (ie, capacity to carry out transitional care interventions in each
inpatient unit and outpatient practice) and patient outcomes. We saw no statistically
significant findings but a few of borderline significance; for example, a correlation between
the number of transitional care interactions typically performed at postdischarge PCP visits
with fewer new or worsening symptoms.
Discussion
Context for Study Results
In this study as implemented, we found that a multifaceted, multidisciplinary
intervention had no effect on adjusted 30‐day readmission rates. However, the intervention
was associated with an approximately 22% lower rate of new or worsening signs or symptoms
in the 30 days after discharge (an absolute difference of about 20 events per 100 patients), a
48% reduction in postdischarge adverse events (our primary outcome, an absolute difference
of about 10 events per 100 patients), and a 63% reduction in preventable postdischarge
adverse events (an absolute difference of approximately 6 events per 100 patients). The
increasing relative reduction for these 3 outcomes in this order would be expected, as each
one is successively more sensitive to interventions.
Table 12. Effects of Inpatient and Outpatient Inventories on Readmission and Adverse Event Rates
Item Effect on New
or Worsening
Symptoms: OR
(95% CI), p
value
Effect on
Readmissions:
OR (95% CI), p value
Effect on Adverse
Events:
IRR (95% CI), p value
Effect on
Preventable
Adverse Events:
IRR (95% CI), p
value
Inpatient Inventory Discharge and follow‐up planning (percentage out of 6 0.9948 (0.9860‐ 0.9990 (0.9820‐ 1.0076 (0.9813‐ 0.9914 (0.9621‐ domains mostly or always addressed) 1.0036), p = 0.25 1.0162), p = 0.91 1.0110), p = 0.60 1.0215), p = 0.57
Discharge documentation (percentage out of 4
1.0018 (0.9938‐ 1.0009 (0.9857‐ 1.0066 (0.9859‐ 0.9967 (0.9691‐ mostly or always addressed) 1.0099), p = 0.66 1.0164), p = 0.90 1.0117), p = 0.84 1.0251), p = 0.82
Goals of care and recovery discussion (percentage out
0.9999 (0.9947‐ 1.0051 (0.9932‐ 1.0046 (0.9926‐ 1.0057 (0.9857‐ 4 domains mostly or always addressed) 1.0051), p = 0.96 1.0171), p = 0.40 1.0107), p = 0.72 1.0261), p = 0.58
Medication reconciliation and patient education 1.0009 (0.9947‐ 0.9989 (0.9868‐ 1.0059 (0.9903‐ 1.0038 (0.9794‐ (percentage out of 4 domains mostly or always 1.0071), p = 0.78 1.0111), p = 0.86 1.0132), p = 0.78 1.0289), p = 0.76 addressed)
Discharge instructions, coaching, preparation for 1.0037 (0.9986‐ 0.9981 (0.9874‐ 1.0045 (0.9896‐ 1.0064 (0.9885‐ follow‐up (percentage out of 9 domains mostly or 1.0089), p = 0.16 1.0090), p = 0.74 1.0071), p = 0.71 1.0246), p = 0.49 always addressed) Outpatient Inventory Contact with patient and provider while patient is 1.0027 (0.9921‐ 0.9759 (0.9391‐ 1.0147 (0.9584‐ 0.9813 (0.9220‐ still hospitalized 1.0134), p = 0.62 1.0142), p = 0.21 1.0147), p = 0.34 1.0443), p = 0.55
Content of postdischarge call (percentage out of 11 1.0093 (0.9991‐ 0.9859 (0.9509‐ 1.0138 (0.9628‐ 0.9921 (0.9306‐ domains usually or always addressed) 1.0197), p = 0.07 1.0222), p = 0.44 1.0161), p = 0.42 1.0575), p = 0.81
Content of postdischarge visit (percentage out of 14 0.9915 (0.9826‐ 1.0097 (0.9777‐ 1.0123 (0.9903‐ 1.0130 (0.9575‐ domains usually or always addressed) 1.0006), p = 0.07 1.0427), p = 0.56 1.0389), p = 0.25 1.0717), p =.65
Inpatient contact provider: percentage PCP, NP, PA,
1.0001 (0.9950‐ 0.9987 (0.9823‐ 1.0064 (0.9905‐ 1.0084 (0.9790‐ pharmacist out of all providers making contact while patient still hospitalized 1.0052), p = 0.98 1.0153), p = 0.88 1.0157), p = 0.64 1.0386), p = 0.58
Call provider: percentage PCP, NP, PA, RN, pharmacist 0.9990 (0.9957‐ 0.9972 (0.9870‐ 1.0039 (0.9857‐ 0.9939 (0.9772‐ out of all providers making postdischarge call 1.0024), p = 0.57 1.0075), p = 0.60 1.0009), p = 0.09 1.0109), p = 0.48
Follow‐up visit provider: percentage PCP, NP, PA, RN, 1.0021 (0.9973‐ 1.0146 (0.9969‐ 1.0071 (0.9909‐ 1.0238 (0.9921‐ pharmacist out of all providers 1.0069), p = 0.39 1.0327), p = 0.11 1.0190), p = 0.50 1.0565), p = 0.14
Page 50 of 53
The lack of effect of the intervention on hospital readmissions was likely due to several
factors, including lower than expected intervention fidelity and a low proportion of
readmissions that were truly preventable. Regarding the latter, on the basis of a recently
published multicenter study of 1000 patients (of which Dr. Schnipper was senior author) and
using a 360‐degree approach and a rather utopian view of how transitions could be delivered,
we estimated that only 27% of readmissions are likely to be preventable.37 It might be easier
to reduce postdischarge signs and symptoms, postdischarge adverse events, and preventable
postdischarge adverse events, which are very important outcomes to patients and caregivers.
However, the efficacy of the intervention on these outcomes was also likely affected by low
intervention fidelity. We suspect that the effect of the intervention on new or worsening
symptoms and on adverse events would have been greater had intervention fidelity been
higher. Efficacy was also likely affected by the lack of postdischarge coaching, which is one of
the more evidence‐based interventions in the literature22 but one that was never adopted by
Partners Healthcare. Another factor that may have affected the success of the intervention is
the fact that some practices started the intervention later than their assigned start date, and
we conducted an intention‐to‐treat analysis based on the assigned date. But these delays
were relatively small and affected only a few practices, so the overall effect was likely small.
To the extent that the intervention was successful, we believe (on the basis of exit
interviews of patients and caregivers conducted by the PFAC, as well as the focus groups of
clinicians) that its effects were attributable to several features, including improved
patient/caregiver engagement in the hospital, improved communication between inpatient
and outpatient clinicians, pharmacist interventions to improve medication safety, and
perhaps better postdischarge follow‐up.
Putting the results of this study in context, we should note that the literature on
interventions to improve the transition of care is confusing.12 Although several studies report
successful interventions, many—often using similar components—report unsuccessful ones.
A number of recent studies have focused on single‐component interventions, looking for a
“silver bullet” that is effective, easy to implement, and relatively inexpensive. Almost all of
them have been unsuccessful. Our bridge diagram of the ideal transition in care is a good
Page 50 of 53
analogy for a number of reasons. Like a bridge, a good transition requires a number of
supports—the more supports, the stronger the structure. A silver bullet solution is unlikely
for this problem.38 Another conclusion to draw from our study as it relates to the literature is
that success is often the result of adequate resources and attention to a thousand details of
implementation.39 These interventions are not just pills to be administered. Research of the
type conducted here allows us to learn not just whether something works, but how, when,
where, and why it works . . . or doesn’t work.17 It is also important to keep in mind that
readmission is not the only important endpoint, although the financial implications of
readmission often drive these efforts. It might be much easier to reduce postdischarge pain
and suffering, the importance of which should not be underestimated.
Generalizability of the Findings This study demonstrates the potential for multifaceted interventions to achieve their
aims within an accountable care organization, the many barriers to successful
implementation, and possible ways to overcome these barriers. Threats to the
generalizability of the study include its implementation at 2 academic medical centers,
although that setting had the required structure given the design of our study (ie, hospitals
and patient‐centered medical homes integrated within an ACO). Some of the barriers we
identified might have been unique to our particular health care setting, but it is likely that
many are fairly universal. In other words, our findings are likely generalizable to other
academic medical centers within a large ACO.
Implementation of Study Results
Why was intervention fidelity low? On the basis of our focus groups and our
inventories of transitional care tasks, we believe a primary factor was the lack of internal
resources to pay for several intervention components, which meant that existing personnel
were too stretched to conduct them reliably or thoroughly. We saw substantial variation in
resource allocation toward these tasks at baseline by inpatient unit and outpatient primary
care practice. The lack of resources was likely driven by the existing fee‐for‐service structure
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of health care, in which incentives are not completely aligned with preventing readmissions,
even with the federal Pioneer ACO program of which Partners is a member. Our mixed
methods analysis was unable to prove these hypotheses, but these results might have been
subject to the “ecological fallacy” (asking about conditions in general and applying the
answers to specific patient interactions). For example, just because a particular primary care
practice is well (or poorly) staffed overall does not mean that a particular patient received
abundant (or inadequate) care from his or her providers.
This lack of aligned incentives to improve transitions of care in the Partners ACO is
notable. In theory, the federal Pioneer ACO program is supposed to reward value of care over
volume of services by allowing integrated health care systems to share in savings accrued
measured against an expenditure benchmark. However, because the rest of the health care
system (at least at Partners, and likely for many others) is still largely fee‐for‐service, the
Pioneer program might not be enough by itself to encourage large health care systems to
completely change the way they do business. For example, with few exceptions (such as
bundled payments for a limited set of procedures) hospitals are still paid when patients are
readmitted. It is likely that a much larger proportion of revenues would need to be at risk (as
high as 60% by some estimates) before large health care systems begin to redesign the way
care is delivered, focusing on areas such as preventive health, primary care, mental and
behavioral health, and transitions of care, where up‐front investments can lead to better
outcomes and lower health care costs in the future. Of all these investments, those in
transitions of care might accrue benefits most quickly.
Patient‐centered medical homes are starting to change their structures to promote
patient self‐management, care coordination, and other goals consistent with this
intervention. For some advanced PCMHs, it could be rather easy to adopt the goals of the
study’s interventions that are under their purview. However, if PCPs are still paid for office
visits (and little else, as is largely the case), it will be difficult for them to spend additional
time communicating with or visiting patients while they are in the hospital, communicating at
length with hospital‐based providers, calling patients or visiting them at home, or doing other
things that improve transitions of care. Furthermore, PCMHs often have little direct control
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over hospital personnel and, therefore, their ability to carry out the inpatient side of the
intervention or to coordinate with outpatient providers. If hospitals and PCMHs are part of
the same ACO, greater potential exists to improve this situation, but only if the ACO is
properly incentivized.
To be fair, some barriers to implementation had little or nothing to do with resources
and bandwidth. For example, because any given clinician had only a few intervention
patients, it was difficult to build the intervention into the workflow or even identify which
patients needed it. We had hoped that the intervention would become the new standard of
care, but this did not happen during the study period, owing in part to staffing limitations but
also because of logistical challenges. In addition, we may have had “too many cooks”
implementing the program, creating additional coordination needs and causing problems
with quality control. Having fewer clinicians delivering the intervention might have been
more effective. In fact, our PFAC told us that patients prefer having one point person to rely
on, but we did not incorporate this advice into our intervention as much as we could have.
We spread the intervention over several clinician types to minimize the additional burden on
any of them, to minimize additional costs, and to play to each clinician’s expertise, but this
may not have been the right approach. While hiring a single additional person to perform the
discharge coach role for a group of patients might seem more expensive than asking existing
personnel to do a little more, in the end all additional tasks come with a cost, whether
hidden or not, and the former approach might ultimately be more effective.
We encountered competing programs, such as the integrated Care Management
Program, and competing priorities, such as early day discharges and short lengths of stay,
both of which save money and increase revenue under the current payment system.
Logistical challenges included enrolling patients in the home pharmacist program, finding a
good way for inpatient and outpatient nurses to communicate with each other (outpatient
nurses use email and are stationed at a computer most of the day, while inpatient nurses
communicate by phone or pager and move from patient room to patient room most of the
day), and identifying patients about to be discharged in time for inpatient pharmacists to
provide counseling. Preparation for a new electronic medical record at BWH diverted
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leadership (not to mention time, resources, and energy) away from other quality
improvement and patient safety initiatives. And the implementation lacked information
technology programs that could have kept everyone on the same page regarding the status
of each patient (eg, recently discharged, in need of a phone call) or the intervention itself (eg,
received inpatient pharmacist counseling).
Finally, while we did try to incorporate PFAC input into the design of the intervention,
we could have involved the council members earlier in the process and incorporated their
input more thoroughly, which could have increased the efficacy of the intervention. For
Partners, much work is needed to improve transitions of care, but this study has provided
many lessons and some needed momentum to continue the process. Possible actions include
the following (including an evaluation of their efficacy, taking into account all the
implementation lessons learned from this study):
1. Inpatient nurses incorporate the discharge advocate role into usual care,including communicating with outpatient nurses, using the discharge preparation checklist, identifying needed services tailored to each patient,coaching patients and caregivers and conducting motivational interviews.
2. More inpatient pharmacists (ideally unit‐based and already familiar with the patients and with the other members of the care team) provide a variety of medication safety interventions at the exact time they are needed.
3. More outpatient nurses (ideally RNs) complete transitional care tasks, including postdischarge phone calls as well as interprofessional office visits and follow‐up coaching interventions.
4. More support for home‐based coaching; for example, community health workers for high‐risk patients (eg, those who have trouble with the discharge readiness checklist).
5. Build enhancements into Partners’ new EMR (Epic) to better support transitions of care, including improved templates for postdischarge calls and visits, a dashboard to track patients throughout the continuum of care and to track interventions that have been performed, reports to track tasks still requiring completion, and risk stratification tools to identify patients in need of additional interventions.
6. Consolidate the discharge coach role across as few people as possible. For example, roles 1, 3, and 4 could all be done by the same person for a given patient.
Many of these actions will require concerted efforts regarding staffing, training, policies,
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procedures, and workflow.
Moving beyond Partners, we need to consider what it will take to improve transitions of
care in the United States. A concerted research effort is required to continuously evaluate what
we already know, the implications of that knowledge, and what studies still need to be done.
Any implications for how health care is organized and financed should be made explicit and
effectively communicated to the public and to policymakers. Lessons learned regarding
implementation and sustainability should also be effectively communicated to the appropriate
stakeholders, including leaders of hospitals, primary care practices, and integrated health care
systems. Requirements in staffing, training, workflow, policies, and technology will have to be
effectively disseminated. And where our metrics or risk adjustment methods fall short, new
metrics and methods will be required. We are proud to be part of PCORI’s Transitional Care
Evidence to Action Network, which is beginning this journey.
Subpopulation Considerations
The results of the subgroup analyses were less helpful than we hoped. However, some
of the findings of borderline significance—such as a possibly greater effect of the intervention
on surgical patients, younger patients, and patients with fewer comorbidities—were
unexpected and deserve further exploration in future studies.
Study Limitations
Our study had several limitations. For example, our on‐treatment analyses were limited
by variable amounts of confounding by indication, not to mention co‐interventions and natural
variation by inpatient unit and outpatient practice. This limited our ability to accurately
determine the efficacy of each component.
Second, our measure of readmission is imperfect, as we do not have statewide or
national data (which take over a year to become available). However, our combination of
administrative data for Partners readmissions plus self‐report for non‐Partners readmissions is
as accurate as we can make it with the data we have, and has been shown to be fairly reliable in
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previous studies conducted at Partners.40
Third, our outcome adjudicators were not blinded. We did not tell them the exact
nature of the stepped wedge (ie, which practices went live with the intervention at which
times), but we cannot exclude the possibility that they noted some of the intervention
components while conducting chart review; we could not blind them to admission dates, and it
is possible that they vaguely understood that later hospitalizations were more likely to involve
patients in the intervention arm. Also, we cannot exclude the possibility that the adjudication
process slowly changed over time, and this could lead to bias because later cases were more
likely to involve intervention patients.
Fourth, while the relatively short study period (no more than 22 months for any given
site) and adjustment for study month should mitigate most concerns about our results being
confounded by general improvements in transitions over time, we cannot exclude the
possibility of confounding, as most intervention patients were admitted later in the study
period (by design), and our model might not have accounted for some nonlinear improvements
in health care delivery over time.
Fifth, while we adjusted for several social determinants of health at least indirectly (eg,
health literacy, cognitive status, functional status, need for a caregiver, and median income by
zip code), we did not capture information on other social determinants, such as access to
transportation, money for prescriptions, healthy diet, and getting enough sleep. The impact of
these factors on our study outcomes is important but was not the focus of this study. As for
effect modification by these factors, we purposely limited the number of subgroup analyses, as
is commonly recommended, and did not choose social determinants (other than health literacy,
an indirect measure) because they are often harder to collect and we wanted to specify
patients who were most likely to benefit from the interventions and who could be easily
identified by most health systems. Nevertheless, this is a limitation. Social complexity (as
opposed to medical complexity) might identify patients most likely to benefit from certain
transitional care interventions; therefore, this should be explored in future studies.
Sixth, because the intervention was often implemented over time rather than all at once
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in each practice, the intention‐to‐treat analyses biased toward the null. Also, we did not collect
baseline data on the primary outcomes for each unit or clinic to determine margins for
improvement or for adjustment for confounding.
One potentially concerning finding is that the effect of the intervention on new or
worsening signs or symptoms was seen only in the risk‐adjusted model. This raises the
possibility that the findings are simply an artifact of our risk‐adjustment strategy. However, we
knew from the outset that the 2 arms of the study were likely to be different, because each
primary care practice, with its own patient population, spent different amounts of time in the 2
arms of the study because of the stepped‐wedge design. In addition, the effects of the
intervention were robust to the number of predictors in the model. We ran a model with only 6
covariates and then the full model with 14 covariates, and the results were virtually the same,
suggesting that the results are not just an artifact of our particular risk‐adjustment strategy.
Plus, the intervention had an even greater effect on postdischarge adverse events—new or
worsening symptoms judged to be the result of medical care. Thus, we believe these effects are
real.
The on‐treatment analyses were likely affected by variable effects of confounding by
indication (ie, when clinicians are concerned that a patient is at high risk for poor postdischarge
outcomes, they are more likely to implement transitional care interventions, such as an early
follow‐up appointment). For example, we knew in advance that the inpatient pharmacist
intervention was the least likely to be affected by confounding by indication: The pharmacists
saw intervention patients whenever they could, limited by their availability. The same was true
for the discharge advocate at BWH (a single nurse practitioner co‐investigator). Most of the
other interventions—including the discharge advocates at MGH, the postdischarge phone calls,
and the postdischarge visits—were delivered by personnel outside our direct control and were
thus more susceptible to confounding by indication. Indeed, the first group of interventions
(those least likely to be affected by confounding by indication) were associated with better
outcomes, while the latter group were associated with worse outcomes, especially the
postdischarge visits. In a separate study conducted at Florida State University (submitted for
publication) we also found that early postdischarge visits were associated with an increased
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rate of postdischarge adverse events, suggesting confounding by indication.
Nevertheless, it was reassuring to see the first group of interventions associated with
improved outcomes, even if they were of borderline significance. This lack of statistical
significance was likely due to limited statistical power, given that an individual component can
be only so effective and each component was delivered to a limited number of patients. It is
highly plausible that these several components combined, even if not delivered to every
patient, could produce a statistically significant improvement in outcomes when analyzed in a
manner less subject to confounding (ie, the stepped‐wedge methodology, with an intention‐to‐
treat analysis based on when each practice was selected to move from usual care to the
intervention). This theory is substantiated by Table 8, in which we describe several adverse
events that occurred in the control arm; these events were judged to be preventable by the
adjudicators and deemed by us to be potentially addressable by our intervention, thus
providing biological plausibility for our findings.
Future Research
Several future directions are indicated for this research, for Partners, and for the field of
transitions in general. For this research, further exploration is required to better elucidate the
connections among environmental context, intervention fidelity, and intervention efficacy. We
also want to explore in more depth the natural variation by inpatient unit and primary care
practice to understand baseline variation in postdischarge outcomes and the likelihood of
benefiting from the intervention. For example, baseline performance might be a result of
contextual factors (eg, patient safety culture), structural factors (eg, type and quantity of
staffing), or processes of transitional care (eg, how often conducted and by whom).
Likelihood of benefiting from the intervention could be the result of room for
improvement (ie, baseline performance, baseline processes), enough staffing to take on new
roles, and contextual factors, such as management culture and patient safety culture. Each of
these could be correlated with intervention fidelity and then with patient outcomes.
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Conclusions
Reducing readmissions is difficult and requires a serious investment, multiple
interventions, an increased focus on self‐management, and close monitoring. We also need to
have realistic expectations regarding how much readmission rates can actually be reduced. It
might be easier to reduce postdischarge suffering, improve functional status, and help patients
attain their own goals for the recovery period. While the intervention was not completely
successful in achieving its goals, the study provides numerous lessons for how to improve
transitions of care locally and nationally.
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Disclaimer: The [views, statements, opinions] presented in this report are solely the responsibility of the author(s) and do not necessarily represent the views of the Patient‐Centered Outcomes Research Institute® (PCORI®), its Board of Governors or Methodology Committee.
Acknowledgement: Research reported in this report was [partially] funded through a Patient‐Centered Outcomes Research Institute® (PCORI®) Award (#811) Further information available at: https://www.pcori.org/research‐results/2012/using‐transitional‐care‐program‐prepare‐patients‐take‐care‐themselves‐after