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Related Experiment Videos

Defining the practice population in fee-for-service practice

B G Hutchison1, J Hurley, S Birch

  • 1Department of Clinical Epidemiology and Biostatistics, McMaster University, Hamilton, Ontario, Canada.

Health Services Research
|April 1, 1997
PubMed
Summary
This summary is machine-generated.

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Defining a family practice population using multiyear fee-for-service billings data is effective. This method accurately identifies patients, improving practice population estimation for capitation models.

Area of Science:

  • Health Services Research
  • Family Medicine
  • Health Informatics

Background:

  • Accurate definition of practice populations is crucial for healthcare management and resource allocation.
  • Transitioning from fee-for-service to capitation payment models necessitates reliable patient roster data.
  • Existing methods for defining practice populations may not fully capture patient continuity or accurately reflect physician workload.

Purpose of the Study:

  • To develop and validate a method for defining a discrete practice population using multiyear family practice fee-for-service billings data.
  • To assess the accuracy and reliability of a billings-based definition of the practice population (EPP) compared to patient rosters.
  • To evaluate the utility of fee-for-service data in estimating the denominator for capitation-based payment systems.

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Main Methods:

  • Utilized fee-for-service billings data from 19 Ontario family physicians for three years prior to their transition to capitation.
  • Defined the practice population (EPP) based on billing frequency and recency, and compared it with patient rosters (RPP) from Year 1 and Year 3.
  • Calculated sensitivity, positive predictive value, agreement, and the ratio of EPP to RPP, adjusting for roster false-negatives.

Main Results:

  • The billings-based definition (EPP) demonstrated a mean sensitivity of 95.3% and a positive predictive value of 87.4%.
  • Agreement between EPP and RPP averaged 84.4%, with an initial EPP to RPP ratio of 1.21.
  • Adjusting for roster false-negatives improved EPP accuracy, increasing sensitivity and predictive value, and reducing the EPP/RPP ratio to 1.068.

Conclusions:

  • Multiyear fee-for-service billings data provide a useful and validated method for defining family practice populations.
  • This approach aids in accurately estimating patient denominators for capitation models.
  • Further research into alternative encounter-based definitions for practice populations is recommended.