People, Place, Provider: Solving the Puzzle

1. People, Place, Provider: Solving the Puzzle Jason Mitchell, MS The Colorado Foundation for Medical Care (CFMC) December 1, 2010

2. Outline • Introduction • Relevance and Commitment • Overlap • Feedback

3. The 8th Scope of Work • QIOs hoped to improve the quality of care provided to Medicare beneficiaries, as measured on a provider basis, through interventions on the provider level • The 8th Scope of Work measured the success of interventions through denominators focusing on the provider, e.g., • Hospitals: reduce 30-day heart failure readmissions • Nursing Homes: reduce pressure ulcers • Home Health Agencies: reduce acute care hospitalizations

4. The 9th Scope of Work (Care Transitions) • QIOs hope to improve the quality of care provided to Medicare beneficiaries, as measured on a community* basis, through interventions on the provider level • The 9th Scope of Work (Care Transitions) measures the success of interventions through denominators focusing on the community, e.g., • Service area of hospital(s): reduce 30-day readmissions • Service areas of hospital(s) and nursing home(s): implement the Care Transitions intervention *The Care Transitions Theme defines a community as a defined geographic region, specifically, sets of U.S. Postal Service ZIP Codes

5. Cohorts • In order to calculate metrics, we need to identify the cohort in which we wish to measure improvement • Our options include… • Providers • Regions

6. Definitions • These two competing cohorts were first described by J. R. Griffith in Quantitative Techniques for Hospital Planning and Control in 1972 • Relevance – Given a region, the proportion of beneficiaries tied to a provider • Denominators based on regions • Commitment – Given a provider, the proportion of beneficiaries tied to a region • Denominators based on providers • In tying provider to place, we ask two questions: • Given a region, which providers are relevant? • Given a hospital, which regions are committed?

7. Examples • Houston, TX ZIP Code 77023 • The expected relevance of a local hospital is low. • Houston is a large and sprawling metropolitan area. Beneficiaries have a large degree of choice; so, given 77023, the relevance of a local hospital is low. • The proportion of benes in 77023 who utilize a local hospital is low. • American Samoa LBJ Tropical Medical Center • The expected commitment of a local ZIP Code is high. • The LBJ Tropical Medical Center is the only acute care facility on the island. Beneficiaries have a low degree of choice; so, given LBJ, the commitment of a local ZIP Code is high. • The proportion of benes in LBJ who originate from a local ZIP Code is high.

8. Practicalities • Identifying committed beneficiaries is easy • These are just the set of beneficiaries identified as tied to a certain provider, let’s say a hospital Set of beneficiaries tied to a hospital

9. Adding ZIPs • Identifying the relevant region for a provider is harder • We must add ZIPs, one at a time, to form a geographic region in which the provider of interest operates Set of beneficiaries tied to a hospital Set of beneficiaries resident in a ZIP Code Relevant, yet uncommitted beneficiaries Relevant, and committed beneficiaries Committed, yet irrelevant beneficiaries

10. Which ZIPs work? • Identifying the relevant region for a provider is harder • We could add too many ZIPs, or we could add too few ZIPs Too many ZIPs: Incorporates too many uncommitted beneficiaries – even if relevant Too few ZIPs: Incorporates too many irrelevant beneficiaries – even if committed

11. Goldilocks Solution • Identifying the relevant region for a provider is harder • In practice, we can find a Goldilocks solution so that the set of ZIPs added is just right Goldilocks Solution: minimize irrelevant and uncommitted beneficiaries

12. Reflection • In looking at the various Venn diagram schematics in the previous slides, beneficiaries can either be… • Relevant, yet uncommitted, • Committed, yet irrelevant, • Relevant and committed Set of beneficiaries tied to a hospital Set of beneficiaries resident in a ZIP Code Relevant, yet uncommitted beneficiaries Relevant, and committed beneficiaries Committed, yet irrelevant beneficiaries

13. Revisiting Examples • Beneficiaries meeting both relevance and commitment, for a given ZIP Code and hospital combination, are superlative • Maximizing this count, with respect to the others, is the key to maximize both commitment and relevance in communities • i.e., we want the green to be large compared to the sum of the green, the blue, and the yellow. A community in American Samoa A community In Houston, TX

14. Overlap • In Care Transitions, we call the proportion of the Venn diagram that is in the green, the overlap of the defined community • High overlap helps guarantee success of interventions implemented through providers which, in turn, are expected to show results in a community. • High overlap means many of the beneficiaries in your community visit your providers – so, they are dosed with your interventions • Low overlap means many beneficiaries either • Live in your community, yet visit other providers (relevant, but uncommitted), or • Live outside your community, yet visit your providers (irrelevant, but committed) • High overlap is achieved through appropriate choice of ZIPs

15. Conclusion • Introduction • Relevance and Commitment • Overlap • Feedback

16. Feedback • Questions? • Comments? • Concerns? • Issues? • Debates? • Dilemmas? This material was prepared by CFMC, the Medicare Quality Improvement Organization for Colorado, under contract with the Centers for Medicare & Medicaid Services (CMS), an agency of the U.S. Department of Health and Human Services. The contents presented do not necessarily reflect CMS policy. PM-4010-034 CO 2012