1. February, 2007 John Billings NYU Center for Health and Public Service Research Robert F. Wagner Graduate School of Public Service SOME THOUGHTS ABOUT MONITORING THE PERFORMANCE OF THE “SAFETY NET”

2. Why the focus on “performance” of the safety net? –Some caveats and definitions –Some assumptions Some examples of using administrative data to monitor performance The limitations of using administrative data A few suggestions (unsolicited advice) WHAT I’M GOING TO TALK ABOUT

3. The focus of policy should be on: Assuring optimal health for vulnerable populations We need to worry about the resources required to assure optimal health of vulnerable populations These resources are the “safety net” Because resources are limited, it makes sense to examine the performance of this “safety net” But it is important to remind ourselves that this isn’t really a “safety net” –We are flying without a net –No one is particularly safe SOME CAVEATS AND DEFINITIONS

4. Texas is unlikely to enact a universal coverage initiative this year, or next year, or the year after that… There are lots of opportunities to improve health of vulnerable populations in addition to buying coverage or subsidizing care Therefore, it is critical to have a monitoring capacity There is probably not a lot of money around for monitoring things But it is critical to recognize the inherent limits of administrative data SOME ASSUMPTIONS

5. COMPUTERIZED HOSPITAL DISCHARGE AND ED VISIT DATA

6. Preventable/Avoidable Hospitalizations Ambulatory Care Sensitive (ACS) Conditions Conditions where timely and effective ambulatory care help prevent the need for hospitalization Chronic conditions – Effective care can prevent flare-ups (asthma, diabetes, congestive heart disease, etc.) Acute conditions – Early intervention can prevent more serious progression (ENT infections, cellulitis, pneumonia, etc.) Preventable conditions – Immunization preventable illness

7. ACS Admissions/1,000 By Zip Code Area Income New York City - Age 18-64 - 2004 Adms/1,000 R 2 =.622 LowInc/HiInc = 3.65 Coef Vari =.536 Mean Rate = 16.08 Each represents a zip code Percent of Households with Income <$15,000 Source: NYU Center for Health and Public Service Research

9. NYU EMERGENCY DEPARTMENT CLASSIFICATION ALGORITHM 1.0 Emergent Primary Care Treatable ED Care Needed Not preventable/avoidable Preventable/avoidable Non-Emergent

10. New York City ED Utilization Profile Adults Age 18-64 - 1998 Source: NYU Center for Health and Public Service Research - UHFNYC

12. UNDERSTANDING THE CAUSES OF VARIATION IN ACS RATES AND ED USE Theory 1: It’s just New York City –[Who cares] –[You’re more or less a different country]

13. ACS Admissions/1,000 By Zip Code Area Income Baltimore - Age 18-64 - 1999 R 2 =.899 LowInc/HiInc = 3.90 Mean Rate = 16.93 Source: NYU Center for Health and Public Service Research

14. ACS Admissions/1,000 By Zip Code Area Income St. Louis - Age 18-64 - 1999 R 2 =.870 LowInc/HiInc = 3.50 Mean Rate = 12.53 Source: NYU Center for Health and Public Service Research

15. ACS Admissions/1,000 By Zip Code Area Income Memphis - Age 18-64 - 1999 R 2 =.887 LowInc/HiInc = 2.95 Mean Rate = 14.45 Source: NYU Center for Health and Public Service Research

16. ACS Admissions/1,000 By Zip Code Area Income San Diego - Age 18-64 - 1999 R 2 =.650 LowInc/HiInc = 3.09 Mean Rate = 7.16 Source: NYU Center for Health and Public Service Research

17. ACS Admissions/1,000 By Zip Code Area Income HOUSTON MSA - Age 18-64 - 2002 R 2 =.561 LowInc/HiInc = 2.71 Mean Rate = 14.57 Source: NYU Center for Health and Public Service Research

18. ACS Admissions/1,000 By Zip Code Area Income Denver - Age 18-64 - 2002 R 2 =.709 LowInc/HiInc = 2.61 Mean Rate = 9.10 Source: NYU Center for Health and Public Service Research

19. ACS Admissions/1,000 By Zip Code Area Income Portland, OR - Age 18-64 - 1999 R 2 =.739 LowInc/HiInc = 4.26 Mean Rate = 7.69 Source: NYU Center for Health and Public Service Research

20. SOUTH CAROLINA ED Utilization Profile Adults Age 18-64 - 1997 Source: NYU Center for Health and Public Service Research

21. Preventable/Avoidable ED Use/1,000 By Zip Code Area Income Austin Metro Area - Age 0-17 - 2000 Austin Metro Area

22. UNDERSTANDING THE CAUSES OF VARIATION IN ACS RATES AND ED USE Theory 1: Who cares? It’s just New York Theory 2: It’s really pretty complicated –Coverage barriers –Resource supply/capacity –Economic barriers –Provider performance –Quasi-economic barriers Transportation Child care Lost wages –Barriers to social care –Limitations in community social capital –Limitations in personal social capital –Education, motivation, confidence, health beliefs –Physician practice style (Wennberg et al), etc, etc

23. ACS Admissions/1,000 Zip 10016 and Citywide Rates New York City - Age 0-17 – 1982-2001 Adms/1,000 New York City Zip 10016 Source: SPARCS - NYU Center for Health and Public Service Research - UHFNYC

24. ACS Admissions/1,000 By Zip Code Area Income New York City - Age 18-64 - 2002 Adms/1,000 R 2 =.622 LowInc/HiInc = 3.65 Coef Vari =.536 Mean Rate = 16.08 Each represents a zip code Percent of Households with Income <$15,000 Source: NYU Center for Health and Public Service Research

27. ACS Admissions/1,000 By Zip Code Area Income Miami - Age 18-64 - 1999 R 2 =.330 LowInc/HiInc = 1.89 Mean Rate = 14.82 Source: NYU Center for Health and Public Service Research Each represents a predominantly Cuban-American zip code

28. ACS Admissions/100,000 By Ward Code and Deprivation Index London, UK - Age 15-64 - 2001/2-2002/3 Each “♦” represents a ward Note: All data are for 2001/2 and 2002/3 R 2 =.387 HighDI/LowDI = 2.10 Mean Rate = 881.0

29. ACS Admissions/1,000 Low and High Income Areas Admissions Per 1,000 New York City MSA – Age 40-64 Adms/1,000 Low Income Areas High Income Areas Source: NYU Center for Health and Public Service Research

30. ACS Admissions/1,000 Low and High Income Areas Admissions Per 1,000 New York City – Age 0-17 Adms/1,000 Low Income Areas High Income Areas Source: NYU Center for Health and Public Service Research $50,000,000

31. WHAT’S GOING ON HERE? It’s an improvement in clinical medicine (e.g., asthma) Changes in composition of NYC’s low income population It’s related to changes in the factors that contribute to health disparities –Coverage expansion (???) –Supply expansion (???) –Service improvement: greater “competition for patients” –Changes in social context –Etc, etc, etc… 1. It isn’t anything 2. It is something:

32. Change in ED Visits/1,000 New York City Medicaid FFS – ADC/HR Girls Age 6mos-14yrs 1994-1999 Asthma Injuries % Change (Log Scale) -33% - -20% - -50% - +25% - +50% - +100% - ACS - No Asthma Source: NYU Center for Health and Public Service Research

33. Change in Percent of ED Visits Resulting In Admission New York City Medicaid FFS – ADC/HR Girls Age 6mos-14yrs 1994-1999 Asthma Injuries % Change (Log Scale) -33% - -20% - -50% - +25% - +50% - +100% - ACS - No Asthma Source: NYU Center for Health and Public Service Research

34. ACS Admissions/1,000 Low Income Areas New York MSAs - Age 0-17 Adms/1,000 Source: NYU Center for Health and Public Service Research New York City Buffalo Rochester Syracuse

35. ACS (W/o Asthma) Admissions/1,000 Low Income Areas California MSAs and New York City - Age 0-17 Adms/1,000 Source: NYU Center for Health and Public Service Research Los Angeles San Diego San Francisco New York City Oakland

36. USING MEDICAID CLAIMS DATA TO MONITOR PROVIDER PERFORMANCE

37. OUR APPROACH We examined fee-for-service paid Medicaid claims Patients are linked to their primary care provider –Linking based on primary care visits (not ED or specialty care) –Patients with 3+ primary care visits linked to provider having the majority of primary care visits [“predominant provider’] –Patients with fewer than 3 visits examined separately Performance of providers for their patients is then examined

38. GETTING BEYOND ADMINISTRATIVE DATA IN MONITORING THE SAFETY NET

39. So If “Provider Performance” Matters… What Factors Influence “Provider Performance? Hours of operation (?) “Cycle time” (?) Wait time for appointment (?) Language barriers (?) Doctor-patient interaction [respect, courtesy, communication] (?) Staff-patient interaction [respect, courtesy, communication] (?) Content of care: doctor skill (?) Content of care: patient education on self-management (?) Staffing mix (MD type, nurse practitioner, etc.) Staffing mix (use of medical residents) Patient “outreach” (?) Easy telephone access (?) MIS systems [notification that patient is in ED] (?) Etc, etc, etc.

40. Factors That Matter to Patients “I Would Recommend This Place to My Friends” Source: NYU Center for Health and Public Service Research Things that matter most –The facility is pleasant and clean –I saw the doctor I wanted to see –The office staff were respectful and courteous –The doctor was respectful and courteous Things that matter somewhat –The office staff explained things in a way I could understand –The location is convenient for me –I waited a short time to see the doctor –It is easy to get an appointment when I need it Things that don’t seem to matter as much –The doctor spent enough time with me –The doctor/nurse/office staff listened to me carefully –It is easy to get advice by telephone –The hours are convenient

41. Most patients wait a considerable amount of time before heading to the ED But they are unlikely to have contacted the health care delivery system before the visit Convenience is the leading reason for ED use Many are not well-connected to the health system Source: NYU/UHF survey of ED patients in 4 Bronx hospitals - 1999 FINDINGS FROM INTERVIEWS OF ED PATIENTS

42. It is critical to know… –Are things getting better or worse? –What are the biggest problems? –Where are the biggest problems? Support evidence-based policy making - Use data to: –Identify the areas and populations in greatest need –Understand the nature and characteristics of that need –Assess impact of interventions –Learn from natural experiments –Get answers for some of things we don’t know Oh, and talk to patients once in a while –They know what they want better than you do –It is important to understand what’s driving their use patterns FINAL THOUGHTS ABOUT MONITORING THE SAFETY NET