Orueta Juan F, Mateos Del Pino Maider, Barrio Beraza Irantzu, Nuño Solinis Roberto, Cuadrado Zubizarreta Maite, Sola Sarabia Carlos
O+Berri, Instituto Vasco de Innovación Sanitaria, Sondika, Bizkaia, España.
Aten Primaria. 2013 Jan;45(1):54-60. doi: 10.1016/j.aprim.2012.01.001. Epub 2012 Mar 8.
Predictive models allow populations to be stratified according to their health requirements for the following year. They offer health care organizations the opportunity to act proactively, designing specific interventions adapted to the level of need of different groups of people. The "Strategy for tackling the challenge of chronic illness in the Basque Country" proposes the use of such models, integrating them with other policies. The prospective categorization of all the population assigned to Osakidetza was performed for the first time in 2010 using the Johns Hopkins Adjusted Clinical Groups predictive model (ACG-PM). For this purpose, already recorded information extracted from electronic health records of primary care and hospital discharge reports was used. This article discusses the advantages of the combined use of various sources of information, and describes the application of the stratification in three programs, targeted at chronic patients who suffer different burdens of comorbidity.
预测模型可根据人群来年的健康需求进行分层。它们为医疗保健机构提供了积极行动的机会,使其能够设计出适应不同人群需求水平的特定干预措施。《巴斯克地区应对慢性病挑战战略》提议使用此类模型,并将其与其他政策相结合。2010年,首次使用约翰·霍普金斯调整临床分组预测模型(ACG-PM)对分配给奥萨基德扎的所有人群进行前瞻性分类。为此,使用了从初级保健电子健康记录和医院出院报告中提取的已记录信息。本文讨论了综合使用各种信息来源的优势,并描述了分层在针对患有不同合并症负担的慢性病患者的三个项目中的应用。