Spezia Nicola, Soncin Mara, Masella Cristina, Agasisti Tommaso
Department of Management, Economics, and Industrial Engineering, Politecnico di Milano, Milan, Italy.
J Patient Exp. 2022 Jul 3;9:23743735221107231. doi: 10.1177/23743735221107231. eCollection 2022.
Though many data on the experience of care of patients and caregivers are collected, they are rarely used to improve the quality of health care delivery. One of the main causes is the widespread struggle in interpreting and enhancing these data, requiring the introduction of new techniques to extract intelligible, meaningful, and actionable information. This research explores the potentiality of the latent class analysis (LCA) statistical model in studying experience data. A cross-sectional survey was administered to 482 parents of infants hospitalized in several Italian neonatal intensive care units. Through a 3-step LCA, four subgroups of parents with specific experience profiles, sociodemographic characteristics, and levels of satisfaction were identified. These were composed of parents who reported (1) a positive experience (36%), (2) problematic communication with unit staff (30%), (3) limited access to the unit and poor participation in their baby's care (26%), and (4) a negative experience (8%). Through its explorative segmentation, LCA can provide valuable information to design quality improvement interventions tailored to the specific needs and concerns of each subgroup.
尽管收集了许多关于患者及其护理人员护理体验的数据,但这些数据很少被用于改善医疗服务质量。主要原因之一是在解读和强化这些数据方面存在普遍困难,这需要引入新技术来提取清晰、有意义且可操作的信息。本研究探讨了潜在类别分析(LCA)统计模型在研究体验数据方面的潜力。对意大利几家新生儿重症监护病房住院婴儿的482名家长进行了横断面调查。通过三步LCA,确定了具有特定体验特征、社会人口统计学特征和满意度水平的四个家长亚组。这些亚组包括报告(1)积极体验的家长(36%)、(2)与科室工作人员沟通存在问题的家长(30%)、(3)进入科室受限且参与婴儿护理程度低的家长(26%)以及(4)消极体验的家长(8%)。通过其探索性细分,LCA可以提供有价值的信息,以设计针对每个亚组的特定需求和关注点的质量改进干预措施。