广西医学

目的　探讨广西11个地级市医疗服务能力及其优化路径，为健康广西建设及医疗服务事业高质量发展提供精准决策参考。方法　以广西统计数据库中相关医疗服务信息指标数据为依据，基于技术-组织-环境（TOE）框架选取技术（医疗基础设施、医疗机构负担）、组织（医疗服务人员）、环境（医疗服务需求）维度变量，采用fsQCA 4.0软件和Microsoft Excel软件进行数据校准、必要条件检验及充分性组态分析。结果　单一条件（医疗基础设施、医疗机构负担、医疗服务人员规模或医疗服务需求强度）均无法构成高/低医疗服务能力的必要条件。高医疗服务能力形成两类组态：（1）“人员-需求驱动型”（核心条件为医疗服务人员充足与医疗服务需求旺盛，医疗服务设施与机构负担无强制要求），代表城市为南宁市、柳州市和桂林市；（2）“设施-机构负担-需求驱动型”（核心条件为医疗基础设施完善、医疗机构负担合理且医疗服务需求旺盛，医疗服务人员为边缘条件），代表城市为北海市。低医疗服务能力亦分为两类：（1）“高负担-设施缺失-需求不足型”（核心条件为医疗机构负担过重、医疗基础设施和医疗服务需求缺失），代表城市为梧州市和河池市；（2）“设施-人员双缺失型”（核心条件为医疗基础设施缺失、医疗服务人员短缺，医疗机构负担较轻和医疗服务需求不足为边缘条件），代表城市为崇左市。结论　广西医疗服务能力提升并非单一因素作用的结果，而是技术、组织、环境多维度因素组态耦合的产物。应重视各关键因素的联动协同效应，针对不同城市的组态特征实施差异化策略——高医疗服务能力城市强化资源辐射，低医疗服务能力城市靶向补齐设施与人才短板，通过强化人员队伍建设与需求侧管理、优化设施配置与负担控制、规避“双缺失”风险等路径，推动广西医疗服务水平均衡提升。

Objective To explore the medical service capacity and its optimization pathways in 11 prefecture‑level cities of Guangxi, providing precise decision‑making references for the construction of Healthy Guangxi and the high‑quality development of medical services. Methods Based on relevant medical service indicator data from the Guangxi Statistical Database, variables were selected within the technology‑organization‑environment (TOE) framework, including technological dimensions (medical infrastructure, medical institution burden), organizational dimension (medical service personnel), and environmental dimension (medical service demand). Data calibration, necessary condition testing, and sufficiency configuration analysis were conducted using fsQCA 4.0 software and Microsoft Excel software. Results No single condition (medical infrastructure, medical institution burden, medical service personnel size, or medical service demand intensity) constituted a necessary condition for high or low medical service capacity. Two configurations were identified for high medical service capacity: (1) “personnel‑demand‑driven” type (core conditions containing sufficient medical service personnel and strong medical service demand, and no mandatory requiements for medical infrastructure and institution burden), represented by Nanning, Liuzhou, and Guilin. (2) “Facility‑institution burden‑demand‑driven” type (core conditions concerning well‑established medical infrastructure, reasonable medical institution burden, strong medical service demand, and medical service personnel as a peripheral condition), represented by Beihai. Two configurations were also identified for low medical service capacity: (1) “high burden‑facility deficit‑demand deficit” type (core conditions including excessive medical institution burden, and deficits in medical infrastructure and medical service demand), represented by Wuzhou and Hechi. (2) “Facility‑personnel dual deficit” type (core conditions with respect to deficits in both medical infrastructure and medical service personnel, and light medical institution burden and insufficient medical service demand as peripheral conditions), represented by Chongzuo. Conclusion The improvement of medical service capacity in Guangxi is not the result of a single factor, but rather the product of configurational coupling across technological, organizational, and environmental dimensions. Attention should be paid to the synergistic and interactive effects among key factors. Differentiated strategies should be implemented according to the configurational characteristics of each city: cities with high medical service capacity should strengthen resource radiation, while cities with low capacity should target infrastructure and talent shortages. By enhancing workforce development and demand‑side management, optimizing facility allocation and burden control, and avoiding the risk of “dual deficits”, the overall medical service capacity across Guangxi can be improved in a balanced manner.

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