Digital health technologies and measurable management outcomes in primary care: A systematic review, descriptive quantitative synthesis, and temporal sensitivity analysis

The rapid digital transformation of primary health care has integrated electronic health records (EHRs), telemedicine, mobile health (mHealth), artificial intelligence (AI), machine learning (ML), and natural language processing (NLP) into routine service management. However, evidence on their relationship with operational efficiency and management performance remains fragmented. This systematic review synthesized evidence on digital health technologies and measurable management outcomes in primary care, including waiting times, administrative efficiency, resource utilization, operating costs, and patient or professional satisfaction. Following PRISMA 2020 principles and the PICO framework, searches were conducted in PubMed/MEDLINE, Scopus, Web of Science, CINAHL, and IEEE Xplore for records published from January 1, 2018, to October 7, 2025. A structured eligibility audit reduced the candidate analytical corpus from 256 records to 172 studies included in the final descriptive quantitative synthesis. Telemedicine/mHealth was the largest category (n = 98; 57.0%), followed by EHR/interoperability (n = 35; 20.3%) and AI/ML or predictive analytics (n = 29; 16.9%). The most frequent indicators were patient or professional satisfaction (n = 24) and operating costs (n = 20). Because of methodological heterogeneity, formal meta-analysis was not performed. Temporal sensitivity analysis showed continuity in EHR/interoperability and telemedicine/mHealth, with a recent increase in AI/ML representation. A stratified preliminary JBI check in 40 studies found mostly low or moderate risk classifications, but it was used only to contextualize reporting quality and does not represent a full risk-of-bias assessment of all 172 studies.