Predictors of Avoidable Utilization and Readmission Nicholas K. - - PowerPoint PPT Presentation

Functional Limitations are Key Predictors of Avoidable Utilization and Readmission

Nicholas K. Schiltz, PhD Case Western Reserve University AcademyHealth Annual Research Meeting June 27, 2017

Background

Preventing readmissions and avoidable care are key policy goals to improve quality of care and reduce cost Multimorbidity -> associated with higher rates

Background

However, the specific combinations of MM that have the greatest effect on utilization

• utcomes are not well understood

Multimorbidity

“…[Multimorbidity] population is categorized by tremendous clinical heterogeneity”

– “Developing means for determining homogenous subgroups…is an important step to improve health status of this population.”

2 million unique chronic disease combinations in Medicare alone (Sorace, 2011)

Sorace J, Wong H-H, Worrall C, Kelman J, Saneinejad S, MaCurdy T. The complexity of disease combinations in the Medicare population. Popul. Health Manag. 2011;14(4):161-166

Study Aim

Use a data-driven approach to investigate the specific combinations

• f conditions and disabilities that are

the most pivotal predictors of avoidable utilization and readmission

Data Sources

• Health and Retirement Study

– U.S. representative survey of age 50 and older population – Cohort study – interviewed every two years

• Linked to Medicare claims
• 31,487 person-waves (2002 – 2012)

– Age 65 with 24 months Part A & B.

Independent Variables

• Chronic conditions

– (heart disease, lung disease, cancer, psychiatric disorders, diabetes, stroke, arthritis, hypertension)

• Functional limitations

– (strength, upper & lower body mobility, limitations in Activities of Daily Living, and Instrumental Activities of Daily Living (IADL))

• Geriatric syndromes

– (visual / hearing impairment, incontinence, depressive symptoms, cognitive impairment)

• Demographics, economic, behavioral

Dependent Variable

• Potentially avoidable ED Visits

– NYU avoidable ED visit algorithm

• Preventable Hospitalizations

– AHRQ Prevention Quality Indicator #90

• All-cause 30 day readmission

Analytic Approach

• Classification and Regression Tree

– Tree represents a complete model – GLMM: to account for repeated measures – 10-fold cross-validation

• Bootstrap Aggregation: Random Forest
• R packages: partykit, caret, randomForest

Study Population

Characteristic Total

• No. of person-waves

31,487 Age categories 65–69 26% 70–74 25% 75–79 20% 80+ 29% Female 58% Race/Ethnicity Black non-Hispanic 12% Hispanic 6% Other 1% White non-Hispanic 81%

Multimorbidity

Chronic Conditions % Heart Disease 37% COPD 12% Stroke 21% Cancer 16% Diabetes 23% Arthritis 68% Hypertension 67% Psychiatric Disorders 15% Functional Limitations % Upper mobility 41% Lower mobility 71% Strength 65% Activities of Daily Living (ADL) 6% Instrumental ADLs 19% Geriatric Syndromes Impaired vision 24% Impaired hearing 27% Depressive symptoms 13% Incontinence 26% Severe Pain 6% Poor Cognitive Functioning 8%

Avoidable Utilization / Readmission

• Avoidable ED Use: 29.0%
• Preventable hospitalizations: 8.6%
• 30-day readmissions: 8.9%

Avoidable ED Visits

IADL Limitations Heart Disease ADL Limitations Heart Disease Upper Mobility Lim. Upper Mobility Lim. Lung Disease Lung Disease Age Age HH Income (% of FPL)

Yes Yes Yes No No No None Mild or Severe None Mild or Severe Mild Severe Yes No None Mild or Severe >200% ≤200% >85 ≤85 ≤79 >80

100% 50% 0%

Avoidable ED Visits

IADL Limitations Heart Disease ADL Limitations Heart Disease Upper Mobility Lim. Upper Mobility Lim. Lung Disease Lung Disease Age Age HH Income (% of FPL)

Yes Yes Yes No No No None Mild or Severe None Mild or Severe Mild Severe Yes No None Mild or Severe >200% ≤200% >85 ≤85 ≤79 >80

100% 50% 0%

Avoidable ED Visits

IADL Limitations Heart Disease ADL Limitations Heart Disease Upper Mobility Lim. Upper Mobility Lim. Lung Disease Lung Disease Age Age HH Income (% of FPL)

Yes Yes Yes No No No None Mild or Severe None Mild or Severe Mild Severe Yes No None Mild or Severe >200% ≤200% >85 ≤85 ≤79 >80

100% 50% 0%

Avoidable ED Visits

IADL Limitations Heart Disease ADL Limitations Heart Disease Upper Mobility Lim. Upper Mobility Lim. Lung Disease Lung Disease Age Age HH Income (% of FPL)

Yes Yes Yes No No No None Mild or Severe None Mild or Severe Mild Severe Yes No None Mild or Severe >200% ≤200% >85 ≤85 ≤79 >80

100% 50% 0%

Preventable hospitalization

IADL Limitations Heart Disease Lung Disease Heart Disease Heart Disease Age Age Lower Mobility Lim. ADL Limitations

None Mild or Severe None Mild or Severe None Mild or Severe None or Mild Severe No Yes Yes No Yes No >85 ≤85 ≤78 >79

100% 50% 0%

Preventable hospitalization

IADL Limitations Heart Disease Lung Disease Heart Disease Heart Disease Age Age Lower Mobility Lim. ADL Limitations

None Mild or Severe None Mild or Severe None Mild or Severe None or Mild Severe No Yes Yes No Yes No >85 ≤85 ≤78 >79

100% 50% 0%

Preventable hospitalization

IADL Limitations Heart Disease Lung Disease Heart Disease Heart Disease Age Age Lower Mobility Lim. ADL Limitations

None Mild or Severe None Mild or Severe None Mild or Severe None or Mild Severe No Yes Yes No Yes No >85 ≤85 ≤78 >79

100% 50% 0%

Preventable hospitalization

IADL Limitations Heart Disease Lung Disease Heart Disease Heart Disease Age Age Lower Mobility Lim. ADL Limitations

None Mild or Severe None Mild or Severe None Mild or Severe None or Mild Severe No Yes Yes No Yes No >85 ≤85 ≤78 >79

100% 50% 0%

Preventable hospitalization

IADL Limitations Heart Disease Lung Disease Heart Disease Heart Disease Age Age Lower Mobility Lim. ADL Limitations

None Mild or Severe None Mild or Severe None Mild or Severe None or Mild Severe No Yes Yes No Yes No >85 ≤85 ≤78 >79

100% 50% 0%

30-day readmission

IADL Limitations Heart Disease ADL Limitations Heart Disease Lower Mobility Lim. Upper Mobility Lim. Diabetes Stroke

Yes Yes Yes No No No None Mild or Severe Yes No None Mild or Severe

ADL Limitations

Yes No Yes No

ADL Limitations

None Mild or Severe None or Mild Severe

100% 50% 0%

30-day readmission

IADL Limitations Heart Disease ADL Limitations Heart Disease Lower Mobility Lim. Upper Mobility Lim. Diabetes Stroke

Yes Yes Yes No No No None Mild or Severe Yes No None Mild or Severe

ADL Limitations

Yes No Yes No

ADL Limitations

None Mild or Severe None or Mild Severe

100% 50% 0%

30-day readmission

IADL Limitations Heart Disease ADL Limitations Heart Disease Lower Mobility Lim. Upper Mobility Lim. Diabetes Stroke

Yes Yes Yes No No No None Mild or Severe Yes No None Mild or Severe

ADL Limitations

Yes No Yes No

ADL Limitations

None Mild or Severe None or Mild Severe

100% 50% 0%

Which predictors are most important?

Which predictors are most important?

Strengths

• Non-parametric approach

– non-linear relationships

• Can empirically identify

combinations without:

– a priori knowledge of what the most salient combos are – Constraint of addititive (linear) relationship

• Tree models easy to

interpret

• Useful for large number of

predictors

Limitations

• Produces a single tree

– Ensemble methods out perform for prediction – Prediction not our goal – RF showed consistency

• May not best approach if

hypothesis testing

– Algorithm guides the splitting

• Misspecification of

“avoidable” utilization

Conclusions

• Functional limitations, particular those related

to IADL and ADL, are among the most influential characteristics in predicting readmission and other avoidable utilization measures.

• Major chronic conditions like heart and lung

disease are secondary in importance, while demographic, economic, and behavioral factors have less impact.

Implications for Policy/Practice

• Limitations in functional abilities may hamper

self-management of chronic disease

– increasing likelihood of readmission and other avoidable utilization – Discharge to home health care, assisted living, or SNF could help this vulnerable group

• Routine assessment of functional abilities in well-

care settings would help identify those most at risk, and to permit tailored care management

Acknowledgements

• Co-authors: Warner DF, Bakaki PM, Smyth KA,

Stange KC, Gravenstein S, and Koroukian SM

• Funding:

– AHRQ #1R21HS023113-01 (PI: Koroukian) – CDC #3U48DP005030-01S3 (PI: Koroukian & Schiltz) – NIH #KL2 TR000440 (PI: Konstan)

Thank you!

Nicholas Schiltz: nks8@case.edu

• PQI #1 Diabetes Short-Term Complications
• PQI #3 Diabetes Long-Term Complications
• PQI #5 COPD or Asthma in Older Adults
• PQI #7 Hypertension
• PQI #8 Heart Failure
• PQI #10 Dehydration
• PQI #11 Bacterial Pneumonia
• PQI #12 Urinary Tract Infection
• PQI #13 Angina Without Procedure
• PQI #14 Uncontrolled Diabetes
• PQI #15 Asthma in Younger Adults
• PQI #16 Lower-Extremity Amputation among Patients with Diabetes