february, 2007
DESCRIPTION
SOME THOUGHTS ABOUT MONITORING THE PERFORMANCE OF THE “SAFETY NET”. February, 2007. John Billings NYU Center for Health and Public Service Research Robert F. Wagner Graduate School of Public Service. WHAT I’M GOING TO TALK ABOUT. Why the focus on “performance” of the safety net? - PowerPoint PPT PresentationTRANSCRIPT
February, 2007
John BillingsNYU Center for Health and Public Service ResearchRobert F. Wagner Graduate School of Public Service
SOME THOUGHTSABOUT MONITORING THE
PERFORMANCE OF THE “SAFETY NET”
• 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
• 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
• 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
COMPUTERIZEDHOSPITAL DISCHARGE AND
ED VISIT DATA
Preventable/Avoidable HospitalizationsAmbulatory 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
ACS Admissions/1,000By Zip Code Area Income
New York City - Age 18-64 - 2004
0
5
10
15
20
25
30
35
40
45
50
0% 10% 20% 30% 40% 50% 60%
Adms/1,000
R2 = .622LowInc/HiInc = 3.65
Coef Vari = .536Mean Rate = 16.08
Each represents a zip code
Percent of Households with Income <$15,000
Source: NYU Center for Health and Public Service Research
Source: NYU Center for Health and Public Service Research
ACS Admissions/1,000Age 18-64 - 2004
25 to 47 (29)18 to 25 (27)12 to 18 (53)8 to 12 (39)4 to 8 (26)
Unpopulated Areas (3)
New York CityACS Admissions/1,000
Age 18-64 - 2004
NYU EMERGENCY DEPARTMENTCLASSIFICATION ALGORITHM 1.0
Emergent
Primary Care Treatable
ED Care Needed
Not preventable/avoidable
Preventable/avoidable
Non-Emergent
New York CityED Utilization Profile
Adults Age 18-64 - 1998
ED NeededPreventable -
Avoidable7.1%
ED Needed - NotPreventable-
Avoidable18.8%
Non-Emergent39.7%
Emergent - Primary Care Treatable
34.4%
Source: NYU Center for Health and Public Service Research - UHFNYC
Staten Island
Manhattan
Brooklyn
Queens
Bronx
% ED Visits Non-EmergentMedicaid - 1998
45% to 54% (28)42% to 45% (67)40% to 42% (64)20% to 39% (15)Unpopulated Areas (3)
Percent ofNon-Admitted
Emergency Department VisitsThat Are "Non-Emergent"
Medicaid - 1998All Ages
UNDERSTANDING THE CAUSES OFVARIATION IN ACS RATES
AND ED USE
• Theory 1: It’s just New York City
– [Who cares]
– [You’re more or less a different country]
ACS Admissions/1,000By Zip Code Area Income
Baltimore - Age 18-64 - 1999
0
10
20
30
40
50
60
0% 10% 20% 30% 40% 50% 60%
% Housholds Income < $15,000
Ad
ms/
1,0
00
Each represents a zip code
R2 = .899LowInc/HiInc = 3.90Mean Rate = 16.93
Source: NYU Center for Health and Public Service Research
ACS Admissions/1,000By Zip Code Area Income
St. Louis - Age 18-64 - 1999
0
10
20
30
40
50
60
0% 10% 20% 30% 40% 50% 60%
% Housholds Income < $15,000
Ad
ms/
1,0
00
Each represents a zip code
R2 = .870LowInc/HiInc = 3.50Mean Rate = 12.53
Source: NYU Center for Health and Public Service Research
ACS Admissions/1,000By Zip Code Area Income
Memphis - Age 18-64 - 1999
0
10
20
30
40
50
0% 10% 20% 30% 40% 50% 60% 70%
% Housholds Income < $15,000
Ad
ms/
1,0
00
Each represents a zip code
R2 = .887LowInc/HiInc = 2.95Mean Rate = 14.45
Source: NYU Center for Health and Public Service Research
ACS Admissions/1,000By Zip Code Area Income
San Diego - Age 18-64 - 1999
0
5
10
15
20
25
30
0% 10% 20% 30% 40% 50%
% Housholds Income < $15,000
Ad
ms/
1,0
00
Each represents a zip code
R2 = .650LowInc/HiInc = 3.09Mean Rate = 7.16
Source: NYU Center for Health and Public Service Research
ACS Admissions/1,000By Zip Code Area Income
HOUSTON MSA - Age 18-64 - 2002
0
10
20
30
40
50
60
70
80
0% 10% 20% 30% 40% 50% 60% 70%
% Housholds Income < $15,000
Ad
ms/
1,0
00
Each represents a zip code
R2 = .561LowInc/HiInc = 2.71Mean Rate = 14.57
Source: NYU Center for Health and Public Service Research
ACS Admissions/1,000By Zip Code Area IncomeDenver - Age 18-64 - 2002
0
10
20
30
0% 5% 10% 15% 20% 25% 30% 35%
% Housholds Income < $15,000
Ad
ms/
1,0
00
Each represents a zip code
R2 = .709LowInc/HiInc = 2.61Mean Rate = 9.10
Source: NYU Center for Health and Public Service Research
ACS Admissions/1,000By Zip Code Area Income
Portland, OR - Age 18-64 - 1999
0
10
20
30
40
0% 10% 20% 30% 40% 50%
% Housholds Income < $15,000
Ad
ms/
1,0
00
Each represents a zip code
R2 = .739LowInc/HiInc = 4.26Mean Rate = 7.69
Source: NYU Center for Health and Public Service Research
SOUTH CAROLINAED Utilization Profile
Adults Age 18-64 - 1997
ED NeededPreventable -
Avoidable7.1%
ED Needed - NotPreventable-
Avoidable18.8%
Non-Emergent31.9%Emergent - Primary
Care Treatable42.3%
Source: NYU Center for Health and Public Service Research
Preventable/Avoidable ED Use/1,000By Zip Code Area Income
Austin Metro Area - Age 0-17 - 2000
Preventable/Avoidable ED Visits Per CapitaChildren - Age 0-17 - 2001
300 to 546 (17)200 to 300 (13)120 to 200 (16)60 to 120 (14)12 to 60 (15)
Low Population Area* (4)
Austin Metro Area
UNDERSTANDING THE CAUSES OFVARIATION 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
ACS Admissions/1,000Zip 10016 and Citywide Rates
New York City - Age 0-17 – 1982-2001
0
5
10
15
20
25
30
35
40
45
50
55
60
65
82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 00 01
Adms/1,000
New YorkCity
Zip 10016
Source: SPARCS - NYU Center for Health and Public Service Research - UHFNYC
ACS Admissions/1,000By Zip Code Area Income
New York City - Age 18-64 - 2002
0
5
10
15
20
25
30
35
40
45
50
0% 10% 20% 30% 40% 50% 60%
Adms/1,000
R2 = .622LowInc/HiInc = 3.65
Coef Vari = .536Mean Rate = 16.08
Each represents a zip code
Percent of Households with Income <$15,000
Source: NYU Center for Health and Public Service Research
Staten Island
Manhattan
Brooklyn
Queens
Bronx
% ED Visits Non-EmergentMedicaid - 1998
45% to 54% (28)42% to 45% (67)40% to 42% (64)20% to 39% (15)Unpopulated Areas (3)
Percent ofNon-Admitted
Emergency Department VisitsThat Are "Non-Emergent"
Medicaid - 1998All Ages
Staten Island
Manhattan
Brooklyn
Queens
Bronx
% ED Visits Non-EmergentSelfpay/Uninsured - 1998
45% to 54% (19)42% to 45% (51)40% to 42% (75)22% to 39% (29)Unpopulated Areas (3)
Percent ofNon-Admitted
Emergency Department VisitsThat Are "Non-Emergent"Selfpay/Uninsured - 1998
All Ages
ACS Admissions/1,000By Zip Code Area IncomeMiami - Age 18-64 - 1999
0
10
20
30
40
50
0% 10% 20% 30% 40% 50% 60% 70%
% Housholds Income < $15,000
Ad
ms/
1,0
00
R2 = .330LowInc/HiInc = 1.89Mean Rate = 14.82
Source: NYU Center for Health and Public Service Research
Each represents a predominantly Cuban-American zip code
ACS Admissions/100,000By Ward Code and Deprivation Index
London, UK - Age 15-64 - 2001/2-2002/3
0
500
1000
1500
2000
2500
0 10 20 30 40 50 60 70 80
Deprivation Index
Ad
ms/
10
0,0
00
Each “♦” represents a ward
Note: All data are for 2001/2 and 2002/3
R2 = .387HighDI/LowDI = 2.10Mean Rate = 881.0
ACS Admissions/1,000Low and High Income Areas
Admissions Per 1,000New York City MSA – Age 40-64
0.0
10.0
20.0
30.0
40.0
50.0
60.0
70.0
80.0
82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 00 01 02 03 04
Adms/1,000
Low IncomeAreas
High IncomeAreas
Source: NYU Center for Health and Public Service Research
ACS Admissions/1,000Low and High Income Areas
Admissions Per 1,000New York City – Age 0-17
0.0
10.0
20.0
30.0
40.0
82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 00 01 02 03 04
Adms/1,000
Low IncomeAreas
High IncomeAreas
Source: NYU Center for Health and Public Service Research
$50,000,000
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:
Change in ED Visits/1,000New York City
Medicaid FFS – ADC/HR Girls Age 6mos-14yrs1994-1999
94 95 96 97 98 99
Asthma
Injuries
% Change (Log Scale)
-33% -
-20% -
-50% -
+25% -
+50% -
+100% -
ACS - NoAsthma
Source: NYU Center for Health and Public Service Research
Change in Percent of ED Visits Resulting In Admission New York City
Medicaid FFS – ADC/HR Girls Age 6mos-14yrs1994-1999
94 95 96 97 98 99
Asthma
Injuries
% Change (Log Scale)
-33% -
-20% -
-50% -
+25% -
+50% -
+100% -
ACS - NoAsthma
Source: NYU Center for Health and Public Service Research
ACS Admissions/1,000Low Income Areas
New York MSAs - Age 0-17
0.0
5.0
10.0
15.0
20.0
25.0
30.0
35.0
40.0
82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 00 01 02
Adms/1,000
Source: NYU Center for Health and Public Service Research
New York City
Buffalo
Rochester
Syracuse
ACS (W/o Asthma) Admissions/1,000Low Income Areas
California MSAs and New York City - Age 0-17
0.0
5.0
10.0
15.0
20.0
25.0
30.0
35.0
40.0
83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 00 01 02
Adms/1,000
Source: NYU Center for Health and Public Service Research
Los AngelesSan DiegoSan Francisco
New York City
Oakland
USING MEDICAID CLAIMS DATATO MONITOR PROVIDER PERFORMANCE
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
GETTING BEYOND ADMINISTRATIVE DATAIN MONITORING THE SAFETY NET
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.
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
• 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 OFED PATIENTS
• 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 ABOUTMONITORING THE SAFETY NET