DEFINING THE PRACTICE POPULATION IN FEE-FOR-SERVICE PRACTICE

Objective. To develop and validate a technique for defining a practice population of discrete individuals based on multiyear family practice fee-for-service billings data. Data Sources/Study Setting. Nineteen f amily physicians in Ontario, Canada who converted from fee-for-service to capitation payment. Data sources were fee-for-service billings dat a for the three-year period prior to the conversion from fee-for-servi ce to capitation payment and the rosters of enrolled patients for the first and third years after the change to capitation payment. Study De sign. The billings-based definition of the physician's practice popula tion was compared against the Year 1 roster. We also compared the bill ings-based practice population and the Year 1 roster to the physician' s Year 3 roster to identify patients who might have been missed during the roster development process. Our principal analyses were an assess ment of the sensitivity of the billings-based definition of the practi ce population (EPP), the positive predictive value of EPP, and the agr eement between EPP and the rostered patient population (RPP). We also examined the ratio between EPP and RPP to determine EPP's accuracy in estimating the practice denominator. Data Collection/Extraction Method s. The practice population for each physician at the time of conversio n from fee-for-service to capitation payment was defined as (a) all pe rsons for whom the physician billed the provincial health insurance pl an for at least one visit during the year immediately prior to joining the capitation-funded program; and (b) all additional patients for wh om the physician billed the plan for at least one service in each of t he two preceding years. Data extraction was carried out within the Min istry of Health in order to preserve the anonymity of patients and phy sicians. Data were provided to the investigators stripped of patient a nd physician identifiers. Principal Findings. The mean sensitivity and positive predictive value of EPP were 95.3 percent and 87.4 percent, respectively. The level of agreement between EPP and RPP averaged 84.4 percent. The mean ratio of EPP to RPP was 1.21 (95 percent C.I. 1.030 -1.213). Correction for roster false-negatives increased the sensitivi ty, positive predictive value, and agreement between EPP and the pract ice population, and reduced the mean ratio of EPP to the practice popu lation to 1.068 (95 percent C.I. 1.010-1.127). Conclusions. The practi ce population can usefully be defined in fee-for-service family practi ce on the basis of multiyear fee-for-service billings data. Further re search examining alternative encounter-based practice population defin itions would be valuable.