title: The learning curve and the cost of heart transplantation.
abstract: The effect of learning on hospital outcomes such as mortality or adverse events (the so-called "practice makes perfect" hypothesis) has been studied by numerous investigators. The effect of learning on hospital cost, however, has received much less attention. This article reports the results of a multiple regression model demonstrating a nonlinear, decreasing trend in operative and postoperative hospital costs over time in a consecutive series of 71 heart transplant patients, all treated in the same institution. The cost trend is shown to persist even after controlling for various preoperative demographic and clinical risk factors and the specific experience of individual surgeons. Using a reference case, the model predicts a cost of $81,297 for the first heart transplant procedure performed at the hospital. If this same patient had been the tenth case rather than the first, with the hospital having benefited from the experience gained in nine previous cases, the model predicts the cost would now be only $48,431, or approximately 60 percent of the cost of the first case. Had this patient been the twenty-fifth case, the predicted cost would be $35,352 (43 percent of the original cost), and had this been the fiftieth case, the cost would be $25,458 (31 percent of the original cost). The longitudinal study design used in this analysis greatly reduces the likelihood that the observed cost reduction is due to economies of scale rather than learning. The results have implications for a policy of regionalization as a tactic for containing hospital cost. Whereas others have pointed to a volume-cost relationship as an argument for the regionalization of expensive and complex hospital procedures, the present data isolate a learning-cost relationship as a separate argument for regionalization.
authors.affiliation: Health Services Research Division of Academic and Medical Affairs, Methodist Hospital of Indiana, Indianapolis 46206.
authors.forename: J R
authors.initials: JR
authors.lastname: Woods
authors.forename: R M
authors.initials: RM
authors.lastname: Saywell
authors.forename: A W
authors.initials: AW
authors.lastname: Nyhuis
authors.forename: S J
authors.initials: SJ
authors.lastname: Jay
authors.forename: R G
authors.initials: RG
authors.lastname: Lohrman
authors.forename: H G
authors.initials: HG
authors.lastname: Halbrook
mesh_terms: D000328:Adult; D002983:Clinical Competence; D004526:Efficiency; D005260:Female; D005544:Forecasting; D017048:Health Care Costs / Q000706:statistics & numerical data / Q000639:trends*; D006302:Health Services Research; D016027:Heart Transplantation / Q000191:economics* / Q000592:standards / Q000706:statistics & numerical data; D006746:Hospital Bed Capacity, 500 and over; D006760:Hospitalization / Q000191:economics* / Q000706:statistics & numerical data; D006801:Humans; D007196:Indiana; D007858:Learning; D007902:Length of Stay / Q000191:economics / Q000706:statistics & numerical data; D008137:Longitudinal Studies; D008297:Male; D017059:Models, Econometric; D017063:Outcome Assessment, Health Care; D011787:Quality of Health Care / Q000639:trends*; D012044:Regression Analysis; D012307:Risk Factors; D013997:Time Factors