基于系统动力学的社区高血压单病种管理路径的复杂动态分析

背景　目前社区慢性病管理效果的研究多采用单一指标法、多元线性回归、灰色模型、ARIMA模型等，常依赖数据指标的完善、全面和时间序列长度，本研究利用仿真结果更接近真实情况且数据要求更宽松的系统动力学模型，为社区单病种管理路径的决策优化提供新方法。目的　以社区高血压健康管理路径为例，探索社区高血压健康管理模式的动态复杂模型，为下一步定量研究社区高血压管理的运行效果和优化策略奠定理论基础。方法　于2017年1—5月，采集上海市徐汇区枫林街道社区卫生服务中心高血压健康管理的运行流程与2015—2017年社区高血压健康管理的历史数据，基于半结构化访谈法、系统动力学的系统思考方法和文献回顾法，将系统中存在因果关系的关键变量进行梳理，构建社区高血压健康管理系统思考模型和因果回路图。结果　社区高血压健康管理服务提供系统包括社区人群分类子系统、高血压高危人群管理子系统、高血压患病人群管理子系统和社区投入子系统。社区高血压管理模式的因果关系可通过“社区特定人群的高血压管理数量控制因果回路图”和“社区特定人群的高血压管理质量控制因果回路图”来体现。结论　社区高血压健康管理服务提供系统思考模型可作为社区高血压健康管理标准体系中运行效果评价与流程优化的重要工具，为标准体系的可持续性改善提供了可能。同时，本研究的系统思考模型和因果回路图，可为社区开展其他单病种管理路径的分析提供借鉴。

Complex Dynamic Analysis of Community-based Hypertension Management Pattern Based on System Dynamics

Background　At present，single index method，multiple linear regression，grey model and ARIMA model make mostly use of studying the management effect of chronic diseases in communities.These methods often depend on the completeness of data indicators and the length of time series.We present the application of the system dynamics model，which the simulation results are closer to the real situation and the data requirements are more relaxed.This study provides a new method for decision optimization of single disease management path in community.Objective　To explore a dynamic complex model for community-based management pattern of hypertension，providing a reference for quantitative research on the effect and improvement of community-hypertension management pattern.Methods　From January to May 2017，by use of semi-structured interviews with the front-line key health workers of Xuhui District Fenglin Community Health Center，Shanghai，we obtained the data concerning the process of hypertension management used in this center，and we collected the data regarding community-based hypertension management between 2015 and 2017 by web searching.Using systems thinking approach and system dynamics modeling software，we constructed a systems thinking models of community-based hypertension management on the basis of the interview results，literature review of the collected study data and policy documents，and drew causal loop diagrams using the identified key factors in the management system among which causal relationships exist.Results　The system of hypertension community-based hypertension management pattern includes community-dwelling population classification subsystem，high-risk population for hypertension management subsystem，hypertension population management subsystem and community input subsystem.The causal relationships between the factors included in the community-based hypertension management pattern are explained well by the causal loop diagrams of quantity control of targeted populations under community-based hypertension management and quality control in community-based hypertension management for targeted populations.Conclusion　The systems thinking models of community-based hypertension management can be used as an important assistant tool for assessing the performance of the standardized community-based hypertension management system and for optimizing the management procedure.Moreover，the systems thinking models and causal loop diagrams provide references for the development of community-based management patterns of other single diseases.