How Much Do Patients’ Preferences Contribute To Resource Use?

1. How Much Do Patients’ Preferences Contribute To Resource Use? Anthony D L, Herndon M B, et al. Health Affairs, 28, no. 3 (2009): 864-873

2. Health Care Cost Variation in the News… • New Yorker article “The Cost Conundrum” by Atul Gawande, June 1, 2009 • Two similar Texas towns with drastically different Medicare costs • McAllen, TX: $14,946 per beneficiary • El Paso, TX: $7,504 per beneficiary • Dartmouth Atlas of Health Care 2008: “Tracking the Care of Patients with Severe Chronic Illness” • Costs driven by volume of care, not by price of individual treatments • High utilization of services does not result in better outcomes or higher patient satisfaction

3. Health Care Cost Variation Locally… • On 3/20, VT House and Senate unanimously passed Legislation S129: Variation in Health Care Utilization, which tasks the state government to: • “…analyze variations in the use of health care provided by hospitals and by physicians treating Vermont residents…” • “…identify treatments and procedures for which the utilization rate varies significantly among geographic regions in Vermont…” • “…determine the reasons for variations…and recommend solutions to contain health care costs by appropriately reducing variation…”

4. Chicken and Egg Does the high volume of health care services in some regions result in higher utilization of these services? (If you build it, they will come) OR Do patients in some regions demand more intensive care, resulting in a migration of health care services to these areas? This paper explores the contribution of patient preference to regional variation in utilization.

5. Data and Methodology • Data • National random survey of preferences for care in elderly Medicare beneficiaries (n=2,515, 65% response rate) • Funded by National Institute on Aging (NIA) • Dual-mode (telephone followed by mailed questionnaire) • Individual utilization of services • Actual outpatient visits from Medicare claims data • 481 respondents excluded for incomplete data (~19%)

6. Data and Methodology • Care-Seeking Preferences • Two clinical vignettes: chest pain, residual cough • Preference to see a doctor right away • Preference to have tests (even if doctor did not recommend) • Preference to see a specialist • Preference for primary care from a general physician or from several specialists • Unmet desire for care (past 12 months) • Did the respondent desire tests or treatments that they did not get? • Did the respondent desire to see a specialist but was not able to?

7. Patient Characteristics

8. Results: Patients’ Preference for Care, By Individual Characteristics

9. Results: Regression of Outpatient Visits on Patient Characteristics and Stated Preference for Care

10. Results: Preferences and Regional Variation • Survey respondents assigned to Hospital Referral Regions (HRRs) based on zip code • Outpatient visit rate for each HRR calculated from Medicare claims data (adjusting for age, race, and gender) • Varied threefold from 3.6 to 10.5 visits per beneficiary across HRRs • Authors developed quintiles based on outpatient visit rate (low, low moderate, moderate, high moderate, high)

11. Results: Preferences and Regional Variation

12. Discussion: Variations in Preference • At an individual level, there is considerable variation in patient preference • Some preferences are predictive of the actual number of physician visits • Variations in individual preference are linked to variations in individual use • Adjusted for health status and demographic factors

13. Discussion: Regional Variations • Variation in preferences across regions is minimal • Patient preference has only minor influence on regional patterns of variation • Since areas with a high volume of services typically experience higher utilization, this suggests that patients are being influenced by the health care system as opposed to their own preferences.

14. Limitations • Use of hypothetical scenarios to understand patient preferences • Survey items were oversimplified to gain insight into broad preferences • Potential for bias in survey response • Individuals with unmet needs may “have a bone to pick”

15. Questions?