RESUMEN
We discuss the potential for machine learning (ML) and artificial intelligence (AI) to improve health care, while detailing caveats and important considerations to ensure unbiased and equitable implementation. If disparities exist in the data used to train ML algorithms, they must be recognized and accounted for, so they do not bias performance accuracy or are not interpreted by the algorithm as simply a lack of need. We pay particular attention to an area in which bias in data composition is particularly striking, that is in large-scale genetics databases, as people of European descent are vastly overrepresented in the existing resources.
Asunto(s)
Inteligencia Artificial , Aprendizaje Automático , Humanos , Algoritmos , Atención a la SaludAsunto(s)
Patient Protection and Affordable Care Act/organización & administración , Organizaciones Responsables por la Atención/organización & administración , Control de Costos , Accesibilidad a los Servicios de Salud/organización & administración , Humanos , Reembolso de Seguro de Salud , Patient Protection and Affordable Care Act/economía , Estados UnidosRESUMEN
The use of quality measures to adjust health care payments and to rank providers is growing rapidly, but there are many problems with the quality measures that are currently being used. This article discusses some of these problems and then lays out some principles and procedures that should be used in the development and combination of quality measures. Many of the problems with existing quality measures would have been avoided had these principles been applied as they were developed.
Asunto(s)
Reembolso de Seguro de Salud , Indicadores de Calidad de la Atención de Salud/economía , Hospitales , Mejoramiento de la Calidad , Ajuste de Riesgo/métodosRESUMEN
Long Island Health Network developed a provider-initiated pay-for-performance (PI-PFP) program beginning in 2004 and operated by 10 clinically integrated hospitals. The PI-PFP administrative processes, length of stay, patient satisfaction, and Hospital Quality Alliance measures are elaborated. PI-PFP has evolved over time and supports best practice sharing. We document how the risk amount is determined and then allocated to the Network and the individual hospitals based on performance, and we quantify the success of the program in achieving the goal of improved performance. A PI-PFP can prepare physicians and management for pay-for-performance or value-based purchasing programs operated by payers. Being self-administered, such a program can go beyond payer-run programs to focus attention on issues that are considered important by the hospitals and medical staffs, and that may not be feasible to measure or to include in a mandatory payer-run program.