At present, the unbalanced and inadequate development of primary healthcare services does not meet people's growing demand for them in the new era. Identifying the influencing factors and their action paths on the performance of primary healthcare services has always been an issue of common concern in the academic circles.

To clarify the combination of multiple factors and their action paths that affect the performance of primary healthcare services in China, and to provide decision-making basis for further improvement of primary healthcare services.

The study was conducted from November 2022 to August 2023, and the data were obtained from the 2021 China Health Statistics Yearbook and the 2021 China Statistical Yearbook. A fuzzy set qualitative comparative analysis (fsQCA) was conducted with the performance of primary healthcare services in 31 provinces (autonomous regions and municipalities) as the outcome variables, and application of big data, medical technology, government attention, system integration, medical resources and health demand as the conditional variables.

The quality of performance of primary healthcare services is the result of a combination of various factors. The results of configuration analysis show that there are 4 configurations to improve the performance of primary healthcare services, which can be classified into 3 patterns: "technology-environment" "organization-environment" and "technology-organization-environment". Configuration 1: Application of Big Data * - Medical Technology * - Government Attention * - System Integration * Medical Resources. Configuration 2: -Application of Big Data * - Medical Technology * System Integration * Medical Resources * - Health Demand. Configuration 3: Application of Big Data * Medical Technology * System Integration * Medical Resources * Health Demand. Configuration 4: Application of Big data * - Government Attention * System Integration * Medical Resources * Health Demand. (* means "and", - means "non"). The consistency of the four configuration solutions was 0.926, the coverage of the solution was 0.612, the original coverage ranged from 0.314 to 0.396, and the unique coverage was from 0.017 to 0.083. The configurations that improve the performance of primary healthcare services are different in eastern, central and western regions of China.

At present, increasing the investment of medical resources at the grass-roots level is still a universal measure to improve the performance of primary healthcare services in China, but attention should also be paid to effectively combine technological, organizational and environmental (T-O-E) conditions. The action paths of the influencing factors are notably different in the eastern, central and western regions of China. Therefore, regions should choose different action paths based on their own development endowments and conditions, reasonably allocate limited medical resources, improve TOE conditions in a targeted manner, so as to improve the performance of healthcare services and realize the high-quality development of primary healthcare services.

当前发展不平衡、不充分的基层医疗卫生服务难以满足新时期人民群众日益增长的卫生健康服务需求。明晰基层医疗卫生服务绩效的影响因素与作用路径，一直是学术界共同关注的热点问题。

明确影响我国基层医疗卫生服务绩效的多重因素组合与发展路径，为进一步提升基层医疗卫生服务提供决策依据。

于2022年11月—2023年8月开展研究，数据来源于《2021中国卫生健康统计年鉴》《2021中国统计年鉴》等。以31个省（自治区、直辖市）基层医疗卫生服务绩效为结果变量，以大数据应用、医疗技术、政府关注、体系整合、医疗资源、健康需求6个变量为条件变量，开展模糊集定性比较分析（fsQCA）研究。

高水平基层医疗卫生服务绩效是多种因素共同作用的结果，组态分析结果显示，共有4种组态提升基层医疗卫生服务绩效，可归纳为"技术-环境型""组织-环境型""技术-组织-环境型"3种模式。组态1：大数据应用*~医疗技术*~政府关注*~体系整合*医疗资源。组态2：~大数据应用*~医疗技术*体系整合*医疗资源*~健康需求。组态3：大数据应用*医疗技术*体系整合*医疗资源*健康需求。组态4：大数据应用*~政府关注*体系整合*医疗资源*健康需求。（*表示"且"，~表示"非"）。4种组态解的一致性为0.926，解的覆盖度为0.612，原始覆盖度为0.314~0.396，唯一覆盖度为0.017~0.083。我国东、中、西部地区提升基层医疗卫生服务绩效的组态有所不同。

加大对基层的医疗资源投入，目前仍是提高我国基层医疗卫生服务绩效的普适措施，但也要注意技术条件、组织条件、环境条件各种因素的有效结合。东、中、西部地区间的驱动路径存在着明显差异，各地区应结合自身发展禀赋及条件因素，选择不同的驱动路径，合理配置有限的医疗资源，有针对性地改善条件，提升医疗卫生服务绩效，实现基层医疗卫生服务高质量发展。