Leveraging quality improvement initiatives to support development of decision support tools in healthcare

Modelling and simulation studies have been used to inform the choices and development of quality improvement (QI) initiatives in health care, for example, by helping refine the intervention to be implemented or support decisions around the management of demand and capacity. We do not know whether a modelling study can itself be informed by a QI project and what are the associated benefits and challenges. In this research, we sought to investigate the opportunities and challenges associated with an ongoing health service-led QI project in informing the development of a stochastic simulation-based decision support tool to inform decisions around the commissioning of anticoagulation services for patients with atrial fibrillation. We found that the positive synergies offered by the QI project included good access to stakeholders and envisaged end users, co-producing relevant and impactful scenarios for experimentation, as well as access to good quality individual patient level data. On the other hand, substantial effort was required to populate input parameters with values that pertain to the natural history of the disease and the effectiveness of the different treatments. Our findings indicate that, if stakeholders require modelling results to inform aspects of a QI project, upfront investment is needed to ensure timely interaction between the two studies.

建模与仿真研究已被用于为医疗保健领域的质量改进（Quality Improvement，QI）举措的选型与开发提供决策依据，例如协助优化待实施的干预方案，或辅助开展需求与产能管理相关决策。目前我们尚不明确，建模研究本身能否依托质量改进项目开展，以及相关的收益与挑战具体为何。本研究旨在探究一项正在进行的、由医疗服务主导的质量改进项目，在为基于随机模拟（stochastic simulation）的决策支持工具开发提供支撑时所蕴含的机遇与挑战——该工具将为心房颤动（atrial fibrillation）患者的抗凝服务（anticoagulation services）委托决策提供参考。研究发现，该质量改进项目带来的积极协同效应包括：可便捷触达利益相关方与预期终端用户、协同构建具备相关性与实践影响力的实验场景，以及可获取高质量的个体患者级数据。另一方面，为输入参数赋值以贴合疾病自然史与不同治疗方案的有效性，则需投入大量工作量。研究结果表明，若利益相关方需依托建模结果为质量改进项目的相关环节提供支撑，则需开展前置投入，以确保两项研究间能够实现及时交互。