Liver transplantation : official publication of the American Association for the Study of Liver Diseases and the International Liver Transplantation Society · 2010
Review of methods for measuring and comparing center performance after organ transplantation.
СтатьяОбзорНазвание и аннотация: Перевод пока не подготовлен
Обработка статьи · этапы, ошибки и управление
Загрузка состояния…
Ошибки и ограничения
История · было → стало
Состояние обновляется каждые 5 секунд, пока блок открыт. Процент показывается только там, где известен объём работы. Пауза останавливает продолжение этапа; текущий запрос к источнику может завершиться.
Neuberger James, Madden Sue, Collett David
Аннотация
The assessment of outcomes after transplantation is important for several reasons: it provides patients with data so that they can make informed decisions about the benefits of transplantation and the success of the transplant unit; it informs commissioners that resources are allocated properly; and it provides clinicians reassurance that results are acceptable or, if they are not, provides early warning so that problems can be identified, corrections can be instituted early, and all interested parties can be reassured that scarce resources are used fairly. The need for greater transparency in reporting outcomes after liver transplantation and for comparisons both between and within centers has led to a number of approaches being adopted for monitoring center performance. We review some of the commonly used methods, highlight their strengths and weaknesses, and concentrate on methods that incorporate risk adjustment. Measuring and comparing outcomes after transplantation is complex, and there is no single approach that gives a complete picture. All those using analyses of outcomes must understand the merits and limitations of individual methods. When used properly, such methods are invaluable in ensuring that a scarce resource is used effectively, any adverse trend in outcomes is identified promptly and remedied, and best performers are identified; they thus allow the sharing of best practices. However, when they are used inappropriately, such measurements may lead to inappropriate conclusions, encourage risk-averse behavior, and discourage innovation.