Centre de recherche du centre hospitalier de l'Université de Montréal (CRCHUM), Université de Montréal, Montréal, Québec, Canada.
Department of Statistics, University of Oxford, Oxford, United Kingdom.
PLoS One. 2018 Aug 13;13(8):e0201355. doi: 10.1371/journal.pone.0201355. eCollection 2018.
Patient engagement helps to improve health outcomes and health care quality. However, the overall relationships among patient engagement measures and health outcomes remain unclear. This study aims to integrate expert knowledge and survey data for the identification of measures that have extensive associations with other variables and can be prioritized to engage patients.
We used the 2014 International Health Policy Survey (IHPS), which provided information on elder adults in 11 countries with details in patient characteristics, healthcare experiences, and patient-physician communication. Patient engagement or support was measured with eight variables including patients' treatment choices, involvement, and treatment priority setting. Three types of care were identified: primary, specialist and chronic illness care. Specialists were doctors specializing in one area of health care. Chronic illness included eight chronic conditions surveyed. Expert knowledge was used to assist variable selection. We used Bayesian network models consisting of nodes that represented variables of interest and arcs that represented their relationships.
Among 25,530 participants, the mean age was 68.51 years and 57.40% were females. The distributions of age, sex, education, and patient engagement were significantly different across countries. For chronic illness care, written plans provided by professionals were linked to treatment feasibility and helpfulness. Whether professionals contacted patients was associated with the availability of professionals they could reach for chronic illness care. For specialist care, if specialists provided treatment choices, patients were more likely to be involved and discuss about what mattered to them.
The strategies to engage patients may depend on the types of care, specialist or chronic illness care. For the study on the observational IHPS data, network modeling is useful to integrate expert knowledge. We suggest considering other theory-based patient engagement in major surveys, as well as engaging patients in their healthcare by providing written plans and actively communicating with patients for chronic illnesses, and encouraging specialists to discuss and provide treatment options.
患者参与有助于改善健康结果和医疗质量。然而，患者参与措施与健康结果之间的总体关系仍不清楚。本研究旨在整合专家知识和调查数据，以确定与其他变量广泛相关且可优先用于使患者参与的措施。
我们使用了 2014 年国际卫生政策调查（IHPS）的数据，该调查提供了 11 个国家的老年人信息，包括患者特征、医疗保健经验和医患沟通的详细信息。患者参与或支持用包括患者治疗选择、参与和治疗优先级设定在内的八个变量进行衡量。三种类型的护理被确定：初级护理、专科护理和慢性病护理。专科医生是指专门从事医疗保健某一领域的医生。慢性病包括调查的八种慢性病。专家知识用于协助变量选择。我们使用了由代表感兴趣变量的节点和代表它们关系的弧线组成的贝叶斯网络模型。
在 25530 名参与者中，平均年龄为 68.51 岁，57.40%为女性。年龄、性别、教育和患者参与的分布在各国之间存在显著差异。对于慢性病护理，专业人员提供的书面计划与治疗可行性和有益性相关。专业人员是否联系患者与慢性病护理中他们可以联系到的专业人员的可用性相关。对于专科护理，如果专科医生提供治疗选择，患者更有可能参与并讨论对他们重要的事情。
使患者参与的策略可能取决于护理类型，专科护理或慢性病护理。对于 IHPS 观察数据的研究，网络建模可用于整合专家知识。我们建议在主要调查中考虑其他基于理论的患者参与，并通过为慢性病提供书面计划和积极与患者沟通，以及鼓励专科医生讨论并提供治疗选择来使患者参与他们的医疗保健。
