Digital health technologies and measurable management outcomes in primary care: A systematic review, descriptive quantitative synthesis, and temporal sensitivity analysis
4 affiliations
- 1Escuela de Ingeniería en Ciberseguridad, FICA, Universidad de Las Américas, Quito, Ecuador
- 2Carrera de Enfermería, Facultad de Ciencias de la Salud, Universidad de Las Américas, Quito, Ecuador
- 3Grupo de Bio-Quimioinformática, Universidad de Las Américas, Quito, Ecuador
- 4Universidad UTE, Facultad de Ciencias de la Salud Eugenio Espejo, Centro de Investigación Genética y Genómica, Quito, Ecuador
Abstract
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. Author summary: Digital technologies are increasingly used in primary care, but their management impact is difficult to compare across studies because they use different indicators and reporting formats. We reviewed evidence from five databases and analyzed a final cleaned corpus of 172 studies. Telemedicine/mHealth was the most common category, followed by EHR/interoperability and AI/ML/predictive analytics. Patient or professional satisfaction and operating costs were the most frequently reported indicators, whereas waiting times, administrative efficiency, and resource utilization were measured less consistently. Because the evidence was heterogeneous, we used a descriptive quantitative synthesis rather than a meta-analysis, complemented by a temporal sensitivity analysis and a Joanna Briggs Institute (JBI) subsample risk-of-bias check. Stronger evidence will require standardized indicators, transparent reporting, bias-aware evaluation, and prospective real-world studies.
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BibTeX
@article{villegasch2026digital,
title = {{Digital health technologies and measurable management outcomes in primary care: A systematic review, descriptive quantitative synthesis, and temporal sensitivity analysis}},
author = {William Villegas-Ch and Alejandro Cabrera-Andrade and Diego Buenaño-Fernández and Ana Karina Zambrano},
journal = {PLOS Digital Health},
year = {2026},
month = nov,
doi = {10.1371/journal.pdig.0001778},
url = {https://journals.plos.org/digitalhealth/article?id=10.1371/journal.pdig.0001778},
note = {CC BY 4.0},
}
RIS
TY - JOUR
TI - Digital health technologies and measurable management outcomes in primary care: A systematic review, descriptive quantitative synthesis, and temporal sensitivity analysis
AU - William Villegas-Ch
AU - Alejandro Cabrera-Andrade
AU - Diego Buenaño-Fernández
AU - Ana Karina Zambrano
PY - 2026
DA - 2026/11/04
JO - PLOS Digital Health
DO - 10.1371/journal.pdig.0001778
UR - https://journals.plos.org/digitalhealth/article?id=10.1371/journal.pdig.0001778
AB - 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. Author summary: Digital technologies are increasingly used in primary care, but their management impact is difficult to compare across studies because they use different indicators and reporting formats. We reviewed evidence from five databases and analyzed a final cleaned corpus of 172 studies. Telemedicine/mHealth was the most common category, followed by EHR/interoperability and AI/ML/predictive analytics. Patient or professional satisfaction and operating costs were the most frequently reported indicators, whereas waiting times, administrative efficiency, and resource utilization were measured less consistently. Because the evidence was heterogeneous, we used a descriptive quantitative synthesis rather than a meta-analysis, complemented by a temporal sensitivity analysis and a Joanna Briggs Institute (JBI) subsample risk-of-bias check. Stronger evidence will require standardized indicators, transparent reporting, bias-aware evaluation, and prospective real-world studies.
SN - 2767-3170
ER -