How to Teach ‘Evil’ Fee for Service Claims Data New Tricks: Comparing the Medical Productivity of Providers Treating Elderly Patients with and without Mental Illness Carrie D. Parente, M.D., M.S. Stephen T. Parente, Ph.D., M.S. December 2, 2014

Agenda Health care index competitive landscape Medical Productivity Index (MPI) concept MPI Construction Data Results Caveats Conclusions Extensions

Health care index competitive landscape Dartmouth Atlas US – Medical Price Inflation Index Health care stock index

Medical Productivity Index (MPI) Productivity metric Output/Input ratio MPI Components: Input: Medical/physician effort on medical procedures in all settings (e.g., inpatient, outpatient, long term care, office) measures by physician time units by reimbursable procedure codes – heart of Medicare $$ Output: Health status of patient as measured by inverse of illness burden as measured by the number of and severity of medical diagnoses you have as a patient. MPI=Health status/Medical effort

Medical Productivity Index (MPI) How deployed: Use health insurance claims Calculated by insured person and summed up Medical input is 90 day lagged Health is current Currently generated quarterly Could be generated in real time with real data feeds Prototype using faculty licensed Medicare data.

Conceptual Model (1) Hi,t = f(Mi,t-1(li,t-1, ki,t-1),Pi,t(Gi,t,AGi,t,Si,t)) Where H = health level for person i and time period t M=medical care received by person i and time period t-1 l = medical care labor by physicians for M at time period t-1 for patient i k = capital and other attributes part of M at time period t-1 for patient i P = patient i underlying health status at time period t G = genetic predisposition of patient i at time period t AG = age and gender of patient i at time period t S = unexpected health shock to patient i at time period t (2) MPIt = ∑ Hit / Mit-1 over all patients i   For more detail, see: Parente, S. “Development of a Medical Productivity Index for Health Insurance Beneficiaries.” Insurance Markets and Companies: Analyses and Actuarial Computations, Volume, Issue 3, 2011.

Data Medicare claims data 5% standard analytic file. 1.87M traditional (fee-for-service) Medicare beneficiaries Years of service 2007-2011 Physician claims data Use procedure and diagnosis code information Nearly 500 million claims analyzed.

Measuring ‘Health’ Measured per patient per time interval (e.g., quarter) The measure of health from claims data is developed using the John Hopkins Adjusted Clinical Group system (ACG). – reference below. The ACGs use only patient ID, diagnosis code, age and gender to create a vector of 34 binary variables representing different levels of illness burden. To create a metric of health status, a patient level summation of the vector of 34 Adjusted Diagnostic Groups (ADGs) was used to calculate overall illness burden during a contemporary 90 day window. The inverse of this illness burden measure derived from ADGs is used to measure a patient’s health level at time t. To identify patients with mental health conditions, we identify those of the 2 of 34 mental health illness related ADGs. See: Weiner, J.P., B.H. Starfield, D.M. Steinwachs and L.M. Mumford. “Development and Application of a Population-Oriented Measure of Ambulatory Care Case-Mix” Medical Care, 29(5), May, 1991, pp. 452-472.

Measuring Medical Effort Medical effort input metric is identified by the RBRVS physician work effort associated with the CPT/HCPCS procedures performed by medical providers and summing up for 90 days prior to the 90 day health level metric. For example, 2nd quarter of one year’s index represents the average patient ratio of health (calculated in the second quarter) over the summed medical care physician labor effort per patient as calculated in the 1st quarter of the year. Note: Physician labor component is from effort in all settings a physician operates in, including inpatient hospital, outpatient hospital, office and long term care settings.

Interpreting MPI High MPI could reflect Low MPI could reflect Low medical care use, good health status Average medical care use, great health status Low MPI could reflect High medical care use, average health status Average medical care use, low health status

MPI National Snap Shot – 50 States Key: Blue: Productivity High Green: Productivity Average Yellow: Productivity Emerging The Productivity Medical Index is a productivity index measuring the impact of previously received medical care on the health of a person. A high index reflects a person’s health status 90 days following the receipt of medical care. It is based on health insurance claims data commonly used by the Medicare Innovation Center.

General Health MPI National View Key: Blue: Productivity High Green: Productivity Average Yellow: Productivity Emerging The Productivity Medical Index is a productivity index measuring the impact of previously received medical care on the health of a person. A high index reflects a person’s health status 90 days following the receipt of medical care. It is based on health insurance claims data commonly used by the Medicare Innovation Center.

Interpreting MPI – 3rd qtr 2009 General Health High MPI could reflect: Low medical care use, good health status Average medical care use, great health status Low MPI could reflect: High medical care use, average health status Average medical care use, low health status

Interpreting MPI – 3rd qtr 2009 Mental Health High MPI could reflect: Low medical care use, good health status Average medical care use, great health status Low MPI could reflect: High medical care use, average health status Average medical care use, low health status

General Health vs. Mental Health MPI Trend General Health vs. Mental Health

General Health MPI National View Key: Blue: Productivity High Green: Productivity Average Yellow: Productivity Emerging The Productivity Medical Index is a productivity index measuring the impact of previously received medical care on the health of a person. A high index reflects a person’s health status 90 days following the receipt of medical care. It is based on health insurance claims data commonly used by the Medicare Innovation Center.

Mental Health vs. General Health MPI 3rd Quarter 2009, MH - High Key: Blue: Productivity High Green: Productivity Average Yellow: Productivity Emerging The Productivity Medical Index is a productivity index measuring the impact of previously received medical care on the health of a person. A high index reflects a person’s health status 90 days following the receipt of medical care. It is based on health insurance claims data commonly used by the Medicare Innovation Center.

Mental Health vs. General Health MPI 3rd Quarter 2009, MH - Average Key: Blue: Productivity High Green: Productivity Average Yellow: Productivity Emerging The Productivity Medical Index is a productivity index measuring the impact of previously received medical care on the health of a person. A high index reflects a person’s health status 90 days following the receipt of medical care. It is based on health insurance claims data commonly used by the Medicare Innovation Center.

Mental Health vs. General Health MPI 3rd Quarter 2009, MH - Emerging Key: Blue: Productivity High Green: Productivity Average Yellow: Productivity Emerging The Productivity Medical Index is a productivity index measuring the impact of previously received medical care on the health of a person. A high index reflects a person’s health status 90 days following the receipt of medical care. It is based on health insurance claims data commonly used by the Medicare Innovation Center.

Under 65 - 2011

Under 65 - 2011

Caveats and Acknowledged Concerns It is a crude tool But it is consistent The ‘health’ metric does not reflect true health There is almost no physiologic secondary data that does. Too low physician effort could mean high MPI True, but do we have a claims-based LR best practice. High health from systematic under-reporting of insurance care could mean high MPI True, but we don’t know what root problem is too (i.e., shortage, (patient & MD) culture, travel time,

Conclusions Can apply a mental health diagnosis to distinguish a general and mental health productivity metric. The metric produces different rank order for geographic variation in medical productivity Both general and mental health productivity are affected by timing of Great Recession. Mental health productivity bounces back faster than general health.

Extensions Sub populations Anxiety Depression Bipolar Mental health diagnosis and major co-morbidities Compare fee-for-service Medicare to managed care Medicare Advantage Commercial / private insurance comparison Examine impact of mental health parity. Long term use on real time data (e.g., CBO has access to Treasury Medicare data feed) to creating moving averages of medical productivity.