identifying characteristics of high hospital utilization · for example, just using cluster...

Using JMP® for Group

Segmentation and Predictive

Modeling for Indicators of

High Utilization in a Rural

North Carolina Hospital

Jason Brinkley, PhD - Senior Researcher and Biostatistician

Elizabeth Horner, PhD – Senior Researcher

September 2016

Copyright © 2016 American Institutes for Research. All rights reserved.

2016 - Sept

AMERICAN INSTITUTES FOR RESEARCH

The Current Problem

• Potentially avoidable hospitalizations for acute and

chronic conditions substantially contribute to excess

hospital expenditures.

• Unnecessary and excessive treatment inflate health care

costs leading to waste and inefficiencies that critically

affect the health care system as a whole.

• Continued efforts are needed to advance understanding of

the root causes of persistent high hospital utilization and

to develop innovative, responsive strategies to improve

care delivery processes, particularly, clinically-directed,

patient-centered policies that can have significant

hospital-level impact.

2


The Next Problem

• New payment models such as bundled payments

and upside shared savings programs that

incentivize the provision of efficient, high-quality

care, prioritizing value over volume.

• This is a major shift from traditional fee-for-

service reimbursement.

• In this new reality, controlling utilization is gaining

increasing attention.

• Same problem, increased importance for hospital

administrators and decision makers.

3


Using Data

• Currently, the logic has been that identifying and

predicting high utilization patients is the first step in

tackling the problem.

• Hospitals are having to become more aware of the data

they are collecting and are thinking through how to use

administrative data to tackle this problem.

• A traditional approach to explore the problem is to use

regression models on outcomes such as number of

visits or length of stay with a set of predictors that

include patient characteristics and administrative

records.

4


Problem 1 - Outcomes

• Examining traditional outcomes such as number of

visits or length of stay is problematic.

• Patterns of hospital utilization are multifaceted with

causal mechanisms as diverse as patient populations.

• That is to say that what predicts a high number of visits

for mental health patients is very different than what

we might see for cancer care or for issues in aging.

• So while these metrics are good for deciding on

payment reforms or evaluating quality, they are not a

good choice for understanding utilization patterns.

5


Problem 2 – The Data

• Hospitals are collecting an increasingly large set of

administrative, diagnostic, and patient data.

• Some of that data is being under-utilized while other

factors tend to ‘drive’ statistical modeling.

• The current strategy identifies largely immutable risk

factors (e.g., age, gender, and race)—an approach that

contributes to general public health and health disparities

research, but provides little guidance on policy changes for

rural or community hospitals.

• Hospital decision makers are being routinely given a set of

factors that ‘predict’ higher than expected utilization but are

not factors that they can directly influence.

6


Problem 3 - Regression

• Traditional regression is a time tested method for

exploring the associations between an outcome and

multiple potential predictors simultaneously.

• We start to see issues in traditional regression when we

have a high number of predictors and we believe those

predictors may interact with one another.

• Hospital diagnostic data (i.e. ICD-9 or ICD-10 codes)

provide potentially thousands of unique predictors that

can seemingly overwhelm traditional methods and may

lead to models with poor predictive capacity or fit.

7


Utility

• “All models are wrong, but some are useful”

– George Box

• The problem here, simply put, is that the

models being generated in this framework

are not useful to some.

• Not for those who set local hospital policy

and have a need to understand or explore

potential predictors for which they can

move the dial and have direct impact.

8


Reframing the Question

• Instead of “What people put the

most utilization and cost burden

on the health system?”

• Ask“What conditions put the

most utilization and cost burden

on the health system?”

9


Market Segmentation

• Our alternative approach draws upon the market-

segmentation literature.

• Marketing experts have long supported a technique of

first identifying one or more subgroups of specific

individuals that represent an area of interest in terms of

product utilization.

• Market segmentation provides a way for firms to more

effectively concentrate their resources and respond to

consumers’ needs. These firms are able to divide and

conquer by matching their strengths to specific groups

of customers.

10


Our Approach

• We propose using a similar framework to identify a high

utilization subgroup of patients based on like utilization

behavior and then applying predictive modeling with

diagnosis codes or admin data to determine how well we

can predict whether patients would end up in that

subgroup.– Defining a Subgroup: utilizing a combination of clinical wit and data driven analytics

to explore electronic health records, claims or other administrative data to identify key

high utilization and/or high-cost patient subgroups that exhibit similar utilization

behavior;

– Predictive Modeling: creating models with diagnosis codes (e.g., The International

Classification of Diseases, 9th Revision, Clinical Modification (ICD-9-CM)) or other

data to identify clinical predictors of belonging to the high utilizer subgroups

– Data Translation: using the identified clinical predictors to develop targeted policy

interventions for patients presenting to the hospital with those diagnoses to help

prevent or reduce unnecessary hospital utilization.

11


Rural Hospitals

• Rural hospitals are slowly coming around to a data

driven point of view. But the lack of resources makes

utilizing data to it’s full potential challenging.

• Therefore the rural settings are increasingly relying on

the study and knowledge of larger systems to help

direct their efforts.

• This has seen mixed results as some issues are more

‘rural’ or ‘urban’ specific while some solutions do not

scale in the rural setting well.

12


Nash County, NC

• Nash County has a population of approximately 93,919 as of

2015; its estimated population density is 177.3 people per

square mile (2010).

• The Robert Wood Johnson Foundation ranks the health of

nearly all counties in the US from 1 (best) to 100 (worst),

including a 2015 rating of Nash County. Nash County has

several poor results indicating a generally unhealthy

population, including length of life (72), health behaviors (76),

and socioeconomic factors (76).

• This suggests, as Nash County representatives noted in a

posting on their website, that “not only do our citizens not

practice healthy behaviors, but that our overall environment

and infrastructure in Nash County may not be as conducive to

living healthy as it could be.”

13

AMERICAN INSTITUTES FOR RESEARCH 14

Characteristic Nash County

North

Carolina

USA

Median age (2010) 38.4 37.4 37.2

Percent female (2010) 52% 51% 51%

Percent White (2010) 56% 70% 72%

Percent Black (2010) 37% 23% 13%

Percent Hispanic, any race (2010) 6% 8% 16%

Percent with high school degree or higher (2014) 84% 85% 86%

Percent with college degree or higher (2014) 18% 27% 29%

Median household income (2014) $43,341 $46,693 $53,482

Percent households below poverty line (2014) 13% 13% 12%

Percent households w/ children and below poverty line (2014)

21% 21% 18%


Nash County Hospital In-Patient High Utilization 2010-2013

(2 or more visits per year)

15

Variables Utilization

Count Percent Mean St Dev Sex

Female 2,198 56% 2.96 1.47

Male 1,731 44% 2.98 1.46 Race

Black 2,094 53% 3.09 1.63

White 1,725 44% 2.70 1.02

Other 110 3% 2.83 1.24

Age

0 to 24 194 5% 2.69 1.58

25 - 44 498 12% 2.91 1.61

45 - 64 408 10% 3.08 1.67

65 - 84 2,415 60% 2.94 1.25

85 and up 491 12% 2.91 1.21

Overall Utilization Characteristics 2.97 1.5


Creating a Segment – Transition in Care

16


Wrangling ICD Codes

• Clinicians from our source hospital entered International

Classification of Disease version 9 (ICD-9) codes for

these inpatient visits to indicate the health problems

experienced by patients. ICD-9 has over 14,000 codes.

• Clinical Classification Software (CCS) developed by the

Healthcare Cost and Utilization Project (HCUP) groups

ICD-9 codes into groups.

• Specifically, this sample of patients had 3,352 distinct

ICD-9 codes that were regrouped into 240 distinct

diagnosis groupings.

17


Results

18

• Notice that the patients who transition have a higher

number of visits compared to those either continuously

released to home care or continuously released to

LTCF.

• We also see more variability in that segment of

patients, note the minimum is 2 for all.

• Also note the median age differential.


Bootstrap Forest in JMP – All Code Groups

19

• So here is a very well fitting

forest model to this data. I

show only a subset of the 240

code groups we input into the

model.

• No patient characteristics,

only ICD-9 code groups.


Only some predictors are really useful

• The major conditions found in the bootstrap forest were

examined more closely by the analytic and clinical group.

• The table below represents the most useful set of

diagnostic codes for early warning purposes.

20


Bootstrap Forest – Key Predictors

21


Logistic Regression - Comparisons

22

Key Factors Key Factors + R/G/A


Key Points

• The goal may be to have the model with the highest

predictive capacity from all ICD-9 records. We tend to

think of that as less useful because while predictive it

does not leave the door open to actionable clinical

thought.

• We also do not want to stress a set of specific techniques.

For example, JUST using cluster analysis for

segmentation or JUST using random forests for

prediction.

• The most useful output will likely come from a

combination of analytics and clinical insight.

• The most useful models may not actually have best fit.

23


Concluding Remarks

• The traditional model for understanding hospital utilization

relies on 3 focal points: Regression modeling on a mix of

characteristics and health indicators to directly predict

utilization or cost outcomes.

• Our approach is to segment the problem into distinct

areas (similar to triaging patients) then find targeted

health indicators that well predict the segment of interest.

• This approach is aided by clinical input and creates more

useful models from which to explore admission data.

• Ideally, one should think of this process as a cycle of

continuous improvement where the patient population is

broken up and dealt with in targeted chunks.

24


References• Berwick, D.M. & Hackbarth, A.D. Eliminating Waste in US Health Care. Journal of the American Medical Association,

307(4), 1513-1526.

• Bottle, A. (2006). Identifying patients at high risk of emergency hospital admissions: A logistic regression analysis.

Journal of the Royal Society of Medicine, 99(8), 406-414. doi:10.1258/jrsm.99.8.406

• Joynt, K. E. (2011). Thirty-Day Readmission Rates for Medicare Beneficiaries by Race and Site of Care. Jama, 305(7),

675. doi:10.1001/jama.2011.123

• Wu, J., Grannis, S. J., Xu, H., & Finnell, J. T. (2016). A practical method for predicting frequent use of emergency

department care using routinely available electronic registration data. BMC Emergency Medicine BMC Emerg Med,

16(1). doi:10.1186/s12873-016-0076-3

• Frank, R. E., Massy, W. F., & Wind, Y. (1972). Market segmentation. Englewood Cliffs, NJ: Prentice-Hall.

• United States Census Bureau. Quick Facts. Nash County, North Carolina.2015. Available at:

http://www.census.gov/quickfacts/table/PST045215/37127

• County Health Rankings & Roadmaps. 2015. Available at: http://www.countyhealthrankings.org/app/north-

carolina/2015/rankings/nash/county/outcomes/overall/snapshot

• Update on County Health Rankings Nash County.2014. Available at:

http://www.co.nash.nc.us/DocumentCenter/View/808

• Healthcare Cost and Utilization Project.2015. Clinical Classifications Software (CCS) for ICD-9-CM. Available at:

https://www.hcup-us.ahrq.gov/toolssoftware/ccs/ccs.jsp

25

http://www.census.gov/quickfacts/table/PST045215/37127

http://www.countyhealthrankings.org/app/north-carolina/2015/rankings/nash/county/outcomes/overall/snapshot

http://www.co.nash.nc.us/DocumentCenter/View/808

https://www.hcup-us.ahrq.gov/toolssoftware/ccs/ccs.jsp

Jason Brinkley

919-918-2318

[email protected]

100 Europa Drive, Suite 315

Chapel Hill, NC 27517-2310

General Information: 919-918-2324

www.air.org

26


Appendix – Example Raw Data

(Not Real Patient)

27

ReferenceNumber of Visits Visit Date Dischg Date

Visit Number

Diagnosis Number Admit Origin Admit from Visit Type Discharge Status Age Race Ethnicity Sex Language

Translator Indicator

Mode of Arrival

Primary Insurance

Secondary Insurance

Employent Status Admitting DX Diagnosis Description

111111 3 5/17/2017 5/20/2017 1 1 Emergency Room EMERGENCY DISCH TO HOME (ROUTINE) 56 Black NON-HISPANIC F English NO Stretcher BCBSNC SELF PAY On Disabil CHEST PAIN NOS PRIM CARDIOMYOPATHY NEC

111111 3 5/17/2017 5/20/2017 1 2 Emergency Room EMERGENCY DISCH TO HOME (ROUTINE) 56 Black NON-HISPANIC F English NO Stretcher BCBSNC SELF PAY On Disabil CHEST PAIN NOS MAL HYPERT HRT WO FAIL

111111 3 5/17/2017 5/20/2017 1 3 Emergency Room EMERGENCY DISCH TO HOME (ROUTINE) 56 Black NON-HISPANIC F English NO Stretcher BCBSNC SELF PAY On Disabil CHEST PAIN NOS INTERMED CORONARY SYND

111111 3 5/17/2017 5/20/2017 1 4 Emergency Room EMERGENCY DISCH TO HOME (ROUTINE) 56 Black NON-HISPANIC F English NO Stretcher BCBSNC SELF PAY On Disabil CHEST PAIN NOS CORONARY ATHEROSCLEROSIS

111111 3 5/17/2017 5/20/2017 1 5 Emergency Room EMERGENCY DISCH TO HOME (ROUTINE) 56 Black NON-HISPANIC F English NO Stretcher BCBSNC SELF PAY On Disabil CHEST PAIN NOS DYSTHYMIC DISORDER

111111 3 5/17/2017 5/20/2017 1 6 Emergency Room EMERGENCY DISCH TO HOME (ROUTINE) 56 Black NON-HISPANIC F English NO Stretcher BCBSNC SELF PAY On Disabil CHEST PAIN NOS HYPERLIPIDEMIA NEC/NOS

111111 3 5/17/2017 5/20/2017 1 7 Emergency Room EMERGENCY DISCH TO HOME (ROUTINE) 56 Black NON-HISPANIC F English NO Stretcher BCBSNC SELF PAY On Disabil CHEST PAIN NOS DM UNCOMP TYP II UNCNTRD

111111 3 5/17/2017 5/20/2017 1 8 Emergency Room EMERGENCY DISCH TO HOME (ROUTINE) 56 Black NON-HISPANIC F English NO Stretcher BCBSNC SELF PAY On Disabil CHEST PAIN NOS LNG-TERM CURR USE OT MED

111111 3 5/17/2017 5/20/2017 1 9 Emergency Room EMERGENCY DISCH TO HOME (ROUTINE) 56 Black NON-HISPANIC F English NO Stretcher BCBSNC SELF PAY On Disabil CHEST PAIN NOS LONG-TERM USE INSULIN

111111 3 5/22/2017 5/24/2017 2 1 Emergency Room EMERGENCY DISCH TO HOME (ROUTINE) 56 Black NON-HISPANIC F English NO Stretcher BCBSNC SELF PAY On Disabil MUSCLE WEAKNESS GENERALZ IATROGEN CV INFARC/HMRHG

111111 3 5/22/2017 5/24/2017 2 2 Emergency Room EMERGENCY DISCH TO HOME (ROUTINE) 56 Black NON-HISPANIC F English NO Stretcher BCBSNC SELF PAY On Disabil MUSCLE WEAKNESS GENERALZ CEREBRAL EMBOLISM W CI

111111 3 5/22/2017 5/24/2017 2 3 Emergency Room EMERGENCY DISCH TO HOME (ROUTINE) 56 Black NON-HISPANIC F English NO Stretcher BCBSNC SELF PAY On Disabil MUSCLE WEAKNESS GENERALZ APHASIA

111111 3 5/22/2017 5/24/2017 2 4 Emergency Room EMERGENCY DISCH TO HOME (ROUTINE) 56 Black NON-HISPANIC F English NO Stretcher BCBSNC SELF PAY On Disabil MUSCLE WEAKNESS GENERALZ UNSPEC HEMIPLEG/HEMIPRES

111111 3 5/22/2017 5/24/2017 2 5 Emergency Room EMERGENCY DISCH TO HOME (ROUTINE) 56 Black NON-HISPANIC F English NO Stretcher BCBSNC SELF PAY On Disabil MUSCLE WEAKNESS GENERALZ PRIM CARDIOMYOPATHY NEC

111111 3 5/22/2017 5/24/2017 2 6 Emergency Room EMERGENCY DISCH TO HOME (ROUTINE) 56 Black NON-HISPANIC F English NO Stretcher BCBSNC SELF PAY On Disabil MUSCLE WEAKNESS GENERALZ CORONARY ATHEROSCLEROSIS

111111 3 5/22/2017 5/24/2017 2 7 Emergency Room EMERGENCY DISCH TO HOME (ROUTINE) 56 Black NON-HISPANIC F English NO Stretcher BCBSNC SELF PAY On Disabil MUSCLE WEAKNESS GENERALZ HYPERTENSION NOS

111111 3 5/22/2017 5/24/2017 2 8 Emergency Room EMERGENCY DISCH TO HOME (ROUTINE) 56 Black NON-HISPANIC F English NO Stretcher BCBSNC SELF PAY On Disabil MUSCLE WEAKNESS GENERALZ DM UNCOMP TYP II UNCNTRD

111111 3 5/22/2017 5/24/2017 2 9 Emergency Room EMERGENCY DISCH TO HOME (ROUTINE) 56 Black NON-HISPANIC F English NO Stretcher BCBSNC SELF PAY On Disabil MUSCLE WEAKNESS GENERALZ HYPERLIPIDEMIA NEC/NOS

111111 3 5/22/2017 5/24/2017 2 10 Emergency Room EMERGENCY DISCH TO HOME (ROUTINE) 56 Black NON-HISPANIC F English NO Stretcher BCBSNC SELF PAY On Disabil MUSCLE WEAKNESS GENERALZ DYSTHYMIC DISORDER

111111 3 5/22/2017 5/24/2017 2 11 Emergency Room EMERGENCY DISCH TO HOME (ROUTINE) 56 Black NON-HISPANIC F English NO Stretcher BCBSNC SELF PAY On Disabil MUSCLE WEAKNESS GENERALZ ANEMIA NOS

111111 3 5/22/2017 5/24/2017 2 12 Emergency Room EMERGENCY DISCH TO HOME (ROUTINE) 56 Black NON-HISPANIC F English NO Stretcher BCBSNC SELF PAY On Disabil MUSCLE WEAKNESS GENERALZ OBESITY NOS

111111 3 5/22/2017 5/24/2017 2 13 Emergency Room EMERGENCY DISCH TO HOME (ROUTINE) 56 Black NON-HISPANIC F English NO Stretcher BCBSNC SELF PAY On Disabil MUSCLE WEAKNESS GENERALZ BD MS INDX 38.0-38.9 ADL

111111 3 5/22/2017 5/24/2017 2 14 Emergency Room EMERGENCY DISCH TO HOME (ROUTINE) 56 Black NON-HISPANIC F English NO Stretcher BCBSNC SELF PAY On Disabil MUSCLE WEAKNESS GENERALZ LNG-TERM CURR USE OT MED

111111 3 5/22/2017 5/24/2017 2 15 Emergency Room EMERGENCY DISCH TO HOME (ROUTINE) 56 Black NON-HISPANIC F English NO Stretcher BCBSNC SELF PAY On Disabil MUSCLE WEAKNESS GENERALZ ABN REACT-CARDIAC CATH

111111 3 11/7/2017 11/12/2017 3 1 Phy Referrral EMERGENCY DISCH TO HOME (ROUTINE) 57 Black NON-HISPANIC F English NO Stretcher BCBSNC SELF PAY On Disabil HYPOTENSION NOS LA EFF CEREB,HEMIPLEGIA

111111 3 11/7/2017 11/12/2017 3 2 Phy Referrral EMERGENCY DISCH TO HOME (ROUTINE) 57 Black NON-HISPANIC F English NO Stretcher BCBSNC SELF PAY On Disabil HYPOTENSION NOS PRIM CARDIOMYOPATHY NEC

111111 3 11/7/2017 11/12/2017 3 3 Phy Referrral EMERGENCY DISCH TO HOME (ROUTINE) 57 Black NON-HISPANIC F English NO Stretcher BCBSNC SELF PAY On Disabil HYPOTENSION NOS OCC/STEN CAR ART W/O CI

111111 3 11/7/2017 11/12/2017 3 4 Phy Referrral EMERGENCY DISCH TO HOME (ROUTINE) 57 Black NON-HISPANIC F English NO Stretcher BCBSNC SELF PAY On Disabil HYPOTENSION NOS HYPERTENSION NOS

111111 3 11/7/2017 11/12/2017 3 5 Phy Referrral EMERGENCY DISCH TO HOME (ROUTINE) 57 Black NON-HISPANIC F English NO Stretcher BCBSNC SELF PAY On Disabil HYPOTENSION NOS DYSTHYMIC DISORDER

111111 3 11/7/2017 11/12/2017 3 6 Phy Referrral EMERGENCY DISCH TO HOME (ROUTINE) 57 Black NON-HISPANIC F English NO Stretcher BCBSNC SELF PAY On Disabil HYPOTENSION NOS HYPERLIPIDEMIA NEC/NOS

111111 3 11/7/2017 11/12/2017 3 7 Phy Referrral EMERGENCY DISCH TO HOME (ROUTINE) 57 Black NON-HISPANIC F English NO Stretcher BCBSNC SELF PAY On Disabil HYPOTENSION NOS ANEMIA NOS

111111 3 11/7/2017 11/12/2017 3 8 Phy Referrral EMERGENCY DISCH TO HOME (ROUTINE) 57 Black NON-HISPANIC F English NO Stretcher BCBSNC SELF PAY On Disabil HYPOTENSION NOS MORBID OBESITY

111111 3 11/7/2017 11/12/2017 3 9 Phy Referrral EMERGENCY DISCH TO HOME (ROUTINE) 57 Black NON-HISPANIC F English NO Stretcher BCBSNC SELF PAY On Disabil HYPOTENSION NOS ADHESIVE CAPSULIT SHLDER

111111 3 11/7/2017 11/12/2017 3 10 Phy Referrral EMERGENCY DISCH TO HOME (ROUTINE) 57 Black NON-HISPANIC F English NO Stretcher BCBSNC SELF PAY On Disabil HYPOTENSION NOS OBSTRUCTIVE SLEEP APNEA

111111 3 11/7/2017 11/12/2017 3 11 Phy Referrral EMERGENCY DISCH TO HOME (ROUTINE) 57 Black NON-HISPANIC F English NO Stretcher BCBSNC SELF PAY On Disabil HYPOTENSION NOS DIAB W MANIF NEC TYPE II

111111 3 11/7/2017 11/12/2017 3 12 Phy Referrral EMERGENCY DISCH TO HOME (ROUTINE) 57 Black NON-HISPANIC F English NO Stretcher BCBSNC SELF PAY On Disabil HYPOTENSION NOS HYPOTENSION NOS

111111 3 11/7/2017 11/12/2017 3 13 Phy Referrral EMERGENCY DISCH TO HOME (ROUTINE) 57 Black NON-HISPANIC F English NO Stretcher BCBSNC SELF PAY On Disabil HYPOTENSION NOS LONG-TERM USE ANTICOAGUL

111111 3 11/7/2017 11/12/2017 3 14 Phy Referrral EMERGENCY DISCH TO HOME (ROUTINE) 57 Black NON-HISPANIC F English NO Stretcher BCBSNC SELF PAY On Disabil HYPOTENSION NOS LNG-TERM CURR USE OT MED

identifying characteristics of high hospital utilization · for example, just using cluster...

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