title: Episode-based payment: evaluating the impact on chronic conditions.
abstract: BACKGROUND
  Policy makers are interested in aggregating fee-for-service reimbursement into episode-based bundle payments, hoping it will lead to greater efficiency in the provision of care. The focus of bundled payment initiatives has been upon surgical or discrete procedures. Relatively little is known about calculating and implementing episode-based payments for chronic conditions.
  
  
  OBJECTIVE
  Compare the differences in two different episode-creation algorithms for two common chronic conditions: diabetes and coronary artery disease (CAD).
  
  
  METHODS
  We conducted a retrospective evaluation using enrollees with continuous coverage in a self-funded plan from 2003 to 2006, meeting Healthcare Effectiveness Data and Information Set (HEDIS) criteria for diabetes or CAD. For each condition, an annual episode-based payment was assessed using two algorithms: Episode Treatment Groups (ETGs) and the Prometheus model.
  
  
  RESULTS
  We began with 1,580 diabetes patients with a 4-year total payment mean of $67,280. ETGs identified 1,447 (92%) as having diabetes with 4-year episode-based mean payments of $12,731; while the Prometheus model identified 1,512 (96%) as having diabetes, but included only 1,195 of them in the Prometheus model with mean diabetes payments of $23,250. Beginning with 1,644 CAD patients with a 4-year total payment mean of $65,661, ETGs identified 983 patients (60%) with a 4-year episode-based mean of $24,362. The Prometheus model identified 1,135 (69%) as CAD patients with 948 CAD patients having a mean of $26,536.
  
  
  CONCLUSIONS
  The two episode-based methods identify different patients with these two chronic conditions. In addition, there are significant differences in the episode-based payment estimates for diabetes, but similar estimates for CAD. Implementing episode-based payments for chronic conditions is challenging, and thoughtful discussions are needed to determine appropriate payments.
authors.affiliation: Mayo Clinic.
authors.forename: Thomas J
authors.initials: TJ
authors.lastname: O'Byrne
authors.affiliation: Mayo Clinic.
authors.forename: Nilay D
authors.initials: ND
authors.lastname: Shah
authors.affiliation: Mayo Clinic.
authors.forename: Douglas
authors.initials: D
authors.lastname: Wood
authors.affiliation: Mayo Clinic.
authors.forename: Robert E
authors.initials: RE
authors.lastname: Nesse
authors.affiliation: Mayo Clinic.
authors.forename: Patrick J F
authors.initials: PJ
authors.lastname: Killinger
authors.affiliation: Mayo Clinic.
authors.forename: William J
authors.initials: WJ
authors.lastname: Litchy
authors.affiliation: Mayo Clinic.
authors.forename: Robert J
authors.initials: RJ
authors.lastname: Stroebel
authors.affiliation: Mayo Clinic.
authors.forename: Amy E
authors.initials: AE
authors.lastname: Wagie
authors.affiliation: Mayo Clinic.
authors.forename: James M
authors.initials: JM
authors.lastname: Naessens
mesh_terms: D000293:Adolescent; D000328:Adult; D000368:Aged; D000465:Algorithms; D002908:Chronic Disease / Q000191:economics* / Q000453:epidemiology; D003324:Coronary Artery Disease / Q000191:economics / Q000453:epidemiology; D003920:Diabetes Mellitus / Q000191:economics / Q000453:epidemiology; D018588:Fee-for-Service Plans / Q000191:economics* / Q000706:statistics & numerical data; D017048:Health Care Costs / Q000706:statistics & numerical data; D006801:Humans; D008875:Middle Aged; D015233:Models, Statistical; D012189:Retrospective Studies; D014481:United States / Q000453:epidemiology; D055815:Young Adult
keywords: Bundled Payments; Chronic Disease; Episodes; Relevant Services
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