Data, Methods & Measurement: Commentary

1. Data, Methods & Measurement:Commentary Vincent Mor, Ph.D. Public Health Program

2. Common Issues • All authors call for more and more detailed data • What health care workers DO; not just a count • What is an error and how is it avoidable • Understanding insurance affordability to understand health insurance choices and preferences • All call for more detailed data on physicians, hospitals and health care consumers. • But, labor market variation, differences in medical culture and practice as well as local insurance market variation mean universal data is needed • Conflict between detailed data on a sample and limited data on the population

3. Health Care Workforce • Authors make a good point that “head counts” are not enough and that counting specialists is not enough; we need to know what providers do • Volatility of workforce supply estimates seen most clearly in nursing; after years, no longer a shortage in this economic downturn. Now that over 50% of physicians are women and employed, will labor supply fluctuate? Implications of workforce planning efforts?

4. Health Care Workforce (cont) • While counting may be insufficient, using claims does offer a picture of new developments • Recent NEJM paper documented rise of hospitalists and regional variation • Recent JAMA paper examined “continuity” of inpatient and outpatient physician care • Advantage of aggregating all provider behavior from UPINs via claims, given large labor market variation; BUT, requires universal linked claims • Need to explore ways to cross-walk universal claims data with more detailed data collected on a sample of providers.

5. The Science of Health Care Delivery • Important to identify “preventable” harm; one reason its been so difficult to develop rigorous measures to evaluate and compare providers’ safety profiles • To differentiate type of harm, to adjust for preventability or to link processes to outcomes requires detailed data on treatment events, the people treated and the outcomes experienced • All complicated by heterogeneity of providers and “patient sharing” that complicates attribution

6. Health Care Delivery (cont) • Regional variation in practices also alters the denominator and context • Re-hospitalization of post-acute SNF Medicare pts varies from 10% in 30 days to over 30% by state; correlates with overall Medicare spending ; errors during hospitalizations will be more prevalent • Hospital Adverse drug reactions rise with hours post admission, even controlling for admit time • Very meaning of “avoidable errors” can change as a function of Managed Care interventions to discharge patients post-surgery • Finally, process to outcome relations not simple

7. The Uninsured • Identify 3 key issues we need to understand better to develop better policy • Why are folks not insured • The implications of being uninsured • Defining underinsurance • All need better and more complete survey data with panels able to address changing insurance patterns with changing personal employment and health situations

8. The Uninsured (cont) • Propose a longitudinal panel frequent enough to capture the many subtle changes in coverage, income and competing household choices enriched with data on available choices • States’ policy differences, local market conditions, provider competition and volatility of the insurance market means planning such a comprehensive effort on a sample will miss a lot • Might consider a transparent insurance choice market like Part D BUT this implies universal insurance mandate to get information on all people

9. Summary • Papers on very different policy issues clearly identify the data needs and challenges • In each case, the tension between information depth and universal information can be seen BUT labor, insurance and provider markets are so different • Not possible to have all data on all events of interest to all researchers need models of integrating claims and “research” data • Use Claims as a sampling frame (MCBS, NHATS, etc.) • Predict behavior of population based upon research data • Research data may only be a hueristic • Big Challenge to integrate these kinds of research