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

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data methods measurement commentary

Data, Methods & Measurement:Commentary

Vincent Mor, Ph.D.

Public Health Program

common issues
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
health care workforce
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?
health care workforce cont
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.
the science of health care delivery
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
health care delivery cont
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
the uninsured
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
the uninsured cont
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
  • 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