From 3M Health Information Systems
Q&A with Rich Averill on socioeconomic status and health care delivery system performance
Referred to by many as the “Father of the DRG,” Rich Averill has had a storied health care career going all the way back to the advent of diagnosis-related groups (DRGs) in the early 1980s. DRGs were applied to Medicare payment, and as a result, added decades to the program’s solvency and survival. I recently sat down with Rich to talk about an analysis he conducted with colleague Ron Mills on health care delivery system performance using the Centers for Disease Control and Prevention’s (CDC) Social Value Index (SVI) measures.
You and Ron Mills recently co-authored a report entitled “Socioeconomic status and health care delivery system performance.” What was the catalyst for this research and did you have a hypothesis going in?
The objective of the report was to evaluate the impact of socioeconomic status (SES) on the performance of health care delivery system. There is extensive research that demonstrates that an individual’s SES impacts the health care services provided, health outcomes, patient satisfaction and physician perception. So there was the expectation that SES would have an impact on quality of care and delivery system effectiveness.
Your research looked at the CDC’s Social Vulnerability Index (SVI). Can you describe that index and how would you rate it as a method of measuring socioeconomic status?
The Centers for Disease Control and Prevention (CDC) developed the SVI to assist public health officials and local planners to better prepare communities to respond to emergency events. SVI includes a subsection that measures the SES of a geographic area based on the proportion of the population that is below poverty, unemployed and without a high school diploma as well as average income. The SVI is available at the state, county and census tract level. The SVI is a measure of the SES of the people living in a geographic area and not a measure of the SES of an individual. As a population measure of SES, the SVI is an excellent tool that can also be used to determine the the SES of the patient population being served by individual health care providers.
One of your findings was, “Beneficiaries in low SES counties have more per capita admissions and emergency department visits, more readmission and post-discharge returns to the emergency department, more inpatient complications, and higher surgical mortality than beneficiaries in high SES counties.” Was that surprising or expected?
The finding was not surprising. These measures of performance are negative events which a well functioning delivery system should seek to minimize. Poor performance on these measures is indicative of a health care delivery system that is not functioning as intended for low SES populations, thereby creating heath care equity concerns. What was surprising is that beneficiaries in low SES geographic areas are less likely to be admitted from the emergency department for low severity medical care, less likely to be admitted to a skilled nursing facility or to a rehabilitation facility following hospital discharge and have fewer physician or care management visits than beneficiaries in high SES counties. Performance on these measures can be due to under use (inadequate use) or overuse (discretionary use) of these services with multiple possible root causes including implicit bias, health insurance limitations and maldistribution of health care services.
The SVI looks at socioeconomic status at the county level. Some U.S. counties are very homogenous but others are very diverse from a socioeconomic perspective. How did your study account for this dichotomy?
Unfortunately, the data we had access to did not allow us to identify the zip code of the individual beneficiaries, so we were limited to the county level. Because there can be substantial SES diversity within a county and the analysis was performed at the county level and not the zip code level, the results likely represent an underestimate of the magnitude of the performance difference associated with SES.
Will you describe how you used the various 3M™ Population-focused Preventables (PFP) Classification Methodology for your analysis and what doing so revealed?
Using the 3M PFP measures limited the determination of SES based performance differences to beneficiaries at risk for the performance measure being potentially preventable. Further limiting SES performance difference to the difference between actual performance and expected performance based on comparison to a national risk adjusted norm created a two-tier filtering that identified differences in performance that should be amenable to change and are real opportunities for delivery system improvement. Merely observing that a difference exists does not provide useful information. Identifying performance differences that are potentially preventable and above expected performance levels provides actionable information the can lead to real quality and delivery system improvement.
Regarding your research, are there takeaways for various industry participants like hospitals, policymakers or other types of payers?
The general observation that the health care delivery system is not functioning as intended for low SES populations identifies a problem but provides little direction on how to solve the problem. This research evaluated the impact of SES on nine different aspects of performance which can provide the basis for hospitals, policymakers and payers to focus improvement efforts on areas of performance that have the greatest need for improvement for the low SES populations in specific geographic areas.
Clark Cameron is manager of payer market strategy and development for 3M Health Information Systems.
Richard Averill, MS is a principal at The Hesperium Group. Rich is an international authority on the development of classification, case mix, quality, utilization review and reimbursement methodologies. As one of the original developers of the diagnosis related group (DRGs), he has been a leader in targeting hospital performance and cost variation for more than three decades. Previously, Rich led the clinical and economic research team for 3M HIS, including the work the team does for the Centers for Medicare & Medicaid Services (CMS) on DRGs, outpatient prospective payment, ICD-10 procedure codes and more. He has also worked with the World Health Organization (WHO), the state of New Jersey, the New York State Department of Health, the National Association of Children’s Hospitals and many other organizations.