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). STUDY DESIGN: 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. PRINCIPAL FINDINGS: 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.
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). STUDY DESIGN: 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. PRINCIPAL FINDINGS: We began with 1,580 diabetespatients 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: James M Naessens; Robert J Stroebel; Dawn M Finnie; Nilay D Shah; Amy E Wagie; William J Litchy; Patrick J F Killinger; Thomas J D O'Byrne; Douglas L Wood; Robert E Nesse Journal: Am J Manag Care Date: 2011-02 Impact factor: 2.229
Authors: Peter S Hussey; Melony E Sorbero; Ateev Mehrotra; Hangsheng Liu; Cheryl L Damberg Journal: Health Aff (Millwood) Date: 2009 Sep-Oct Impact factor: 6.301
Authors: Trisha M Parekh; Surya P Bhatt; Andrew O Westfall; James M Wells; Denay Kirkpatrick; Anand S Iyer; Michael Mugavero; James H Willig; Mark T Dransfield Journal: Am J Accountable Care Date: 2017-12-08
Authors: Amity E Quinn; Dominic Hodgkin; Jennifer N Perloff; Maureen T Stewart; Mary Brolin; Nancy Lane; Constance M Horgan Journal: J Subst Abuse Treat Date: 2017-09-21