How Much Do Patients’ Preferences Contribute To Resource Use? Anthony D L, Herndon M B, et al....
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Transcript of How Much Do Patients’ Preferences Contribute To Resource Use? Anthony D L, Herndon M B, et al....
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
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
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…”
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.
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%)
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?
Patient Characteristics
Results: Patients’ Preference for Care, By Individual Characteristics
Results: Regression of Outpatient Visits on Patient Characteristics and Stated Preference for Care
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)
Results: Preferences and Regional Variation
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
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.
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”
Questions?