Modeling help chains in health services as social networks: moving from linearity to complexity

Help Chain (HC) is a problem-solving practice underpinned by lean manufacturing principles and also adopted for health services, which have different complexity characteristics in relation to manufacturing. This study is based on the premise that Social Network Analysis (SNA) might be an effective analytical approach to account for the complexity of HCs in healthcare. Hence, two research questions are addressed: <i>how to model and interpret HCs in health services as a social network</i> and <i>how SNA can either challenge or add to the lean assumptions of HC design.</i> The use of SNA to model HCs was tested in a maternity hospital, where HCs related to five problems were analyzed, concerning the supply of instruments from the centre of sterilised materials to the surgical and obstetrics units. We analyzed the HCs of single problems and then combined the five problem-solving networks to produce a multilayered network that accounted for their interactions. The patterns of social interactions varied according to the problem and three dimensions of actors’ performance (i.e. availability, reliability, and agility). SNA unveiled the complexity of HCs and provided guidance for revising the lean assumptions in their design, matching the realities of health services.

助力链（Help Chain，HC）是一种以精益生产原则为支撑的问题解决实践，同时也被应用于医疗服务领域——医疗服务相较于制造业具有迥异的复杂度特征。本研究的前提假设为：社会网络分析（Social Network Analysis，SNA）或可成为有效分析方法，用以解析医疗领域中助力链的复杂度。据此，本研究旨在解答两个研究问题：<i>如何将医疗服务中的助力链建模并解读为社会网络</i>，以及<i>社会网络分析能否对助力链设计的精益假设提出质疑，或是对其进行补充完善</i>。研究团队选取一家妇产医院，针对该医院中"消毒物料中心向外科与产科病区供应手术器械"相关的五类问题所涉及的助力链展开测试，验证社会网络分析用于助力链建模的有效性。首先分别分析单个问题对应的助力链，随后将五类问题的问题解决网络进行整合，构建出能够反映各类交互关系的多层网络。社交互动模式会随问题类型以及行动者绩效的三个维度（即可用性、可靠性与敏捷性）发生变化。社会网络分析揭示了助力链的复杂度，并为修正助力链设计中的精益假设提供了指导，使其更契合医疗服务的实际运行场景。