Stephen T. Parente, Ph.D. Carlson School of Management, Department of Finance
Fixed and Random Effects Econometric Modeling for Provider Profiling Stephen T. Parente, Ph.D....
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Transcript of Fixed and Random Effects Econometric Modeling for Provider Profiling Stephen T. Parente, Ph.D....
Fixed and Random Effects Econometric
Modeling for Provider Profiling
Stephen T. Parente, Ph.D.University of Minnesota,
Carlson School of Management, Department of Healthcare Management
August 12, 2002
Overview• Provider Profiling Objectives, Language
& Examples• Rationale for Multivariate Models for
Profiling• Identifying Persistent Treatment
Patterns– Exploratory analysis (using random effects)– Confirmatory analysis (using fixed effects)
• Managed Care Results• Modeling Strategies & Summary
Provider Profiling Objectives
• Feedback (to consumer, employers & providers)
• Surveillance & performance assessment
• Focused provider education• Convey financial incentives to
providers• Justification for punitive action
Common Profiling Applications
• HEDIS (Health Employer Data Information Set) ‘Report Cards’ for providers & plans
• Track variation in health care use and cost • Quality of care profiles• Identifying providers for risk-adjusted
prospective capitation payments• Financial incentive report card• Variation in access to care
Language of Profiling
• Unit of Analysis• Unit of Observation• Case-mix adjustment• Risk-adjustment• Resource Use• Practice Variation• Physician vs. Provider vs. Practice
vs. Specialty
Provider Profile Fuel - Claims Data
A Primer• Strengths of Claims Data– Inexpensive to use (RE $$/patient record)– Increasingly standardized across many
populations– Good breadth of information
• Weaknesses of Claims Data– ‘Shallow’ on clinical detail– Can’t identify why a procedure was done,
only what was done.
Profile Examples in Use Today
State of Current Provider Profiles
• Produce simple descriptive statistics– Counts– Means– Variance
• Some case-mix/risk-adjustment• Some statistical significance testing• Used in stealth to detect cost & use outliers• Bluntly used to ‘educate’ and
‘communicate’.
Using Multivariate Regression
to Profile Providers• Approach– Use linear/logistic regression to control for
case-mix and provider attributes when patient is the unit of analysis.
• Uses– Plan specific analysis of patient and provider
factors affecting quality of care.
• Special Data Requirements– Need extremely good provider data along with
claims and membership data.
What’s the Point of Profiling?
• ‘Chance’ of detecting an inappropriate cost outlier at the tail of a distribution.
• ‘Chance’ of detecting poor quality care– Overuse of services– Underuse of services
• ‘Chance’ of detecting physicians gaming the system to induce demand in a fee-for-service reimbursement environment or minimize/moderate demand in a capitated environment.
• It would be good to have a method to improve your chances.
Reduced Form Econometric Model of Provider Treatment
Choices• Yij = B0 + C*cij + P*pj + uj + eij
Where:Yij =patient/provider medical outcome or treatment
i=ith patientJ=jth providerij=patient/provider combined setCij=vector of consumer (patient) factors
Pj=vector of provider attributes
Uj =physician-specific error structure
eij=physcian/patient error structure
Identifying an Outcome/Treatment
• Using Claims:– Medical treatment
• Physician procedure codes (CPT4/HCPCS)• Inpatient surgical codes (ICD-9)• Inpatient DRGs (diagnosis related groups)• Institutional revenue center codes
– Inpatient discharge status• Went home/transferred/died
– Treatment costs• Allowed charges (provider payment + patient
copayment)• Actual costs to treat (much harder to get)
Intervention / Control Variables• Using Provider Data:
– Financial incentive (e.g., capitation, fee-schedule)– Provider characteristics (e.g., specialty, med school)
• Using Membership Data:– Patient demographics– Health plan options
• Using Claims Data:– Case-mix/severity/comorbidity– Episode events & timing
• Using ‘Custom’ Plan-specific Data:– Disease management– Patient outreach
Why Use Random or Fixed Effects Models for Provider
Profiling?• RE/FE explicitly identifies the systematic
persistence of an independent variable on a dependent variable.
• Provides a convenient approach to:– Detect the extent of provider-level
persistence in treatment decisions– Control for idiosyncratic provider
characteristics driving the individual medical treatment decisions that sum to the health economy.
In Manager Speak…
• Use Two approaches to improve profiling:
1. 10,000 foot level: • ID a list of ‘target’ medical cost/utilization/outcome
performance measures. (e.g., 80 HEDIS measures) • Generate a PRIORITY SCORE reflecting targets
where physicians are the greatest contributing factor to variation in meeting a target.
2. 500 foot level:• Take top 5 performance level targets with highest
priority score and identify which physicians are contributing to the variance.
• When identifying physicians, control for patient case-mix and other demand factors that should not affect medical treatment choice.
Statistical Application
• 10,000’ Level: Use random effects to identify a population effect of physicians (as a group) persistence of treatment choice/outcomes.
• 500’ Foot Level: Use fixed effects to identify individual physician effects in the persistence of treatment choice/outcomes.
• Control for patient, hospital, group practice and physician characteristics.
Populations Used for AnalysisRochester (New York) Blue Cross Blue Shield
Insurance DataCholecystectomy48.7% laparoscopic 3,262 patients68 physicians16 hospitals6 surgical group
practices1990-1992
First-time deliveries22.4% cesarean7,151 patients112 physicians11 hospitals7 group practices1987-1992
Modeling Specifications Used
• Logistic binomial regression with random effects.– 12 points of quadrature.– Mixed model of physician random effects
combined with hospital and group practice fixed effects.
• GLS with random effects for linear fit estimates.
• Hausman test to identify variables unaffected by random effects specification.
Additional Physician Characteristics Used
• Age • Gender • Years in practice• Medical school & residency• Academic appointment• Peer review participant• Malpractice history
Using RE to Show Persistent Treatment Patterns
Population Level - Cholecystectomies
0 20 40 60
+MDcharacterics
+GroupPractice
+Hospitals
+Case-mix
+Year &Quarter
Age &Gender
Total %varianceexplainedMD %varianceexplained
• Y=1 (lap/chole), 0 open• Graph shows share of
variance explained by different attributes.
• Share of MD variance explained decreases as additional variables are added.
• % variance explained does not improve dramatically as more variables are added.
• Physician variance appears correlated to other factors.
Using RE to Show Persistent Treatment Patterns
Population Level - Deliveries
0 10 20 30
+MDcharacterics
+GroupPractice
+Hospitals
+Case-mix
Age, year &quarter
Total %varianceexplainedMD %varianceexplained
• Y=1(c-section), 0 vaginal• Graph shows share of
variance explained by different attributes.
• Share of MD variance explained decreases as additional variables are added.
• % variance explained does not improve dramatically as more variables are added.
• Physician variance appears correlated to other factors.
Was Random Effects an Appropriate Specification?
• Hausman (1978) specification test for examining the equality of the coefficients estimated by fix and random effects.
• Results: – Deliveries: Prob>chi2 = 0.3889– Cholecystectomies: Prob>chi2 =
Identifying Persistent Treatment Patterns
Provider Level - Cholecystectomies
-0.6
-0.4
-0.2
0
0.2
0.4
0.6
0.8
1 4 7 10 13 16 19 22 25 28 31 34 37 40 43 46 49 52 55
Marginal Propensity of Each Physician to Use a laparoscopic procedure, controlling for case-mix, group practice, hospital, and secular trends
Fixed Effects Results
Identifying Persistent Treatment Patterns
Provider Level - Deliveries
-0.5
-0.4
-0.3
-0.2
-0.1
0
0.1
0.2
0.3
0.4
0.5
1 5 9 13 17 21 25 29 33 37 41 45 49 53 57 61 65 69 73 77 81 85 89 93
Marginal Propensity of Each Physician to Use a C-section,controlling for case-mix, group practice, hospital, and secular trends
Fixed Effects Results
A Checklist of Profiling Issues - I
___ Data• Are all necessary variables present?• Can the data be manipulated easily?• Is the data generalizable to other populations?
___ Methods• What is the denominator for the analysis?• Is this a one-time approach or is the code re-
usable?• Is case-mix adjustment necessary?• What statistical methods will be employed?
A Checklist of Profiling Issues - II
___ ‘Research’ Questions• What is the best possible outcome you hope
to achieve?• What is the worse case scenario for the
effort involved?• Are there unintended consequences to your
approach?• Are you providing information that allows
providers to game the system?
Modeling Strategies
• When profiling multiple performance measures, use random effects to prioritize provider profiling and gain further insight.
• If physician persistence for treatment choices remains high, conduct a physician-fixed effects model to identify variation in order to develop an intervention to change practices or patient demand.
Summary
• Results show how random effects and fixed effects can be used to improve provider profiles.
• Employing strategies will generate improved accuracy for provider identification for intervention programs.
• RE then FE strategy may lead to quick surveillance of many other measures to detect new activities to be profiled.