title: Assessing changes over time in healthcare provider performance: addressing regression to the mean over multiple time points.
abstract: In recent years, the evaluation of healthcare provider performance has become standard for governments, insurance companies, and other stakeholders. Often, performance is compared across providers using indicators in one time period, for example a year. However it is often important to assess changes in the performance of individual providers over time. Such analyses can be used to determine if any providers have significant improvements, deteriorations, unusual patterns or systematic changes in performance. Studies which monitor healthcare provider performance in this way have to date typically been limited to comparing performance in the most recent period with performance in a previous period. It is also important to consider a longer-term view of performance and assess changes over more than two periods. In this paper, we develop test statistics that account for variable numbers of prior performance indicators, and show that these are particularly useful for assessing consecutive improvements or deteriorations in performance. We apply the tests to coronary artery bypass graft mortality rates in New York State hospitals, and mortality data from Australian and New Zealand intensive care units. Although our applications are to medical data, the new tests have broad application in other areas.
authors.affiliation: Department of Epidemiology and Preventive Medicine, Monash University, Melbourne, VIC 3004, Australia.
authors.forename: Jessica
authors.initials: J
authors.lastname: Kasza
authors.forename: John L
authors.initials: JL
authors.lastname: Moran
authors.forename: Patricia J
authors.initials: PJ
authors.lastname: Solomon
mesh_terms: D001699:Biometry / Q000379:methods*; D001026:Coronary Artery Bypass / Q000401:mortality; D006282:Health Personnel; D006761:Hospitals / Q000706:statistics & numerical data; D006801:Humans; D007362:Intensive Care Units / Q000706:statistics & numerical data; D015233:Models, Statistical; D011785:Quality Assurance, Health Care; D012044:Regression Analysis; D013997:Time Factors
keywords: Hierarchical models; Institutional comparisons; Mortality rates; Performance changes; Regression-to-the-mean