Seid Michael, Bridgeland David, Schuler Christine L, Hartley David M
Department of Pediatrics, University of Cincinnati College of Medicine, Cincinnati, Ohio, United States of America.
Division of Pulmonary Medicine, Cincinnati Children's Hospital Medical Center, Cincinnati, Ohio, United States of America.
PLoS One. 2025 Sep 9;20(9):e0332054. doi: 10.1371/journal.pone.0332054. eCollection 2025.
Improving the healthcare system is a persistent and pressing challenge. Collaborative Learning Health Systems, or Learning Health Networks (LHNs), are a novel, replicable organizational form in healthcare delivery that show substantial promise for improving health outcomes. To realize that promise requires a scientific understanding that can serve LHNs' improvement and scaling. We translated social and organizational theories of collaboration to a computational (agent-based) model to develop a computer simulation of an LHN and demonstrate the potential of this new tool for advancing the science of LHNs. Model sensitivity analysis showed a small number of parameters with outsized effect on outcomes. Contour plots of these influential parameters allow exploration of alternative strategies for maximizing model outcomes of interest. A simulated trial of two common health system interventions - pre-visit planning and use of a registry - suggested that the efficacy of these could depend on LHN current state. By translating heuristic theories of LHNs to a specifiable, reproducible, and explicit model, this research advances the scientific study of LHNs using tools available from complex systems science.
改善医疗保健系统是一项长期而紧迫的挑战。协作学习卫生系统，即学习健康网络（LHNs），是医疗服务中一种新颖且可复制的组织形式，在改善健康结果方面显示出巨大潜力。要实现这一潜力，需要一种能够为LHNs的改进和扩展提供支持的科学理解。我们将协作的社会和组织理论转化为一个计算（基于主体）模型，以开发LHNs的计算机模拟，并展示这一新工具在推进LHNs科学研究方面的潜力。模型敏感性分析表明，少数参数对结果有巨大影响。这些有影响力参数的等高线图允许探索最大化感兴趣模型结果的替代策略。对两种常见医疗系统干预措施——就诊前规划和登记册使用——的模拟试验表明，这些措施的效果可能取决于LHNs的当前状态。通过将LHNs的启发式理论转化为一个可指定、可重复且明确的模型，本研究利用复杂系统科学提供的工具推进了对LHNs的科学研究。
