{
  "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.",
  "abstract_shown": true,
  "affiliations": [
    "Escuela de Ingeniería en Ciberseguridad, FICA, Universidad de Las Américas, Quito, Ecuador",
    "Carrera de Enfermería, Facultad de Ciencias de la Salud, Universidad de Las Américas, Quito, Ecuador",
    "Grupo de Bio-Quimioinformática, Universidad de Las Américas, Quito, Ecuador",
    "Universidad UTE, Facultad de Ciencias de la Salud Eugenio Espejo, Centro de Investigación Genética y Genómica, Quito, Ecuador"
  ],
  "article_type": "Research Article",
  "arxiv": null,
  "author_total": 4,
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        0
      ],
      "name": "William Villegas-Ch"
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    {
      "affs": [
        1
      ],
      "name": "Alejandro Cabrera-Andrade"
    },
    {
      "affs": [
        2
      ],
      "name": "Diego Buenaño-Fernández"
    },
    {
      "affs": [
        3
      ],
      "name": "Ana Karina Zambrano"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/e88dgwhvs1mfs.bib",
  "cited_by": 0,
  "doi": "10.1371/journal.pdig.0001778",
  "fulltext_url": "https://journals.plos.org/digitalhealth/article?id=10.1371/journal.pdig.0001778",
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  "licence": "CC-BY-4.0",
  "licence_label": "CC BY 4.0",
  "licence_source": "journal",
  "licence_url": "https://creativecommons.org/licenses/by/4.0/",
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  "native_subjects": [
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      "plos",
      "/Biology and life sciences/Neuroscience/Cognitive science/Cognitive psychology/Language"
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      "/Medicine and health sciences/Health care/Health services administration and management"
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      "plos",
      "/Medicine and health sciences/Health care/Medical services/Telemedicine"
    ],
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      "/Medicine and health sciences/Health care/Primary care"
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      "/Research and analysis methods/Database and informatics methods/Database searching"
    ],
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      "/Research and analysis methods/Database and informatics methods/Health informatics"
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  "pdf_url": "https://journals.plos.org/digitalhealth/article/file?id=10.1371/journal.pdig.0001778&type=printable",
  "pmcid": null,
  "pmid": null,
  "published": "2026-11-04",
  "publisher": "Public Library of Science",
  "ris": "https://research.draconitic.com/paper/e88dgwhvs1mfs.ris",
  "sources": [
    {
      "first_seen": "2026-11-05T12:20:11Z",
      "native_id": "10.1371/journal.pdig.0001778",
      "source": "plos",
      "url": "https://journals.plos.org/digitalhealth/article?id=10.1371/journal.pdig.0001778"
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  "subjects": [
    {
      "field": "biology",
      "origin": "plos:neuroscience",
      "subfield": "neuroscience"
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    {
      "field": "medicine",
      "origin": "plos:epidemiology",
      "subfield": "epidemiology-public-health"
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  ],
  "title": "Digital health technologies and measurable management outcomes in primary care: A systematic review, descriptive quantitative synthesis, and temporal sensitivity analysis",
  "title_text": "Digital health technologies and measurable management outcomes in primary care: A systematic review, descriptive quantitative synthesis, and temporal sensitivity analysis",
  "type": "peer-reviewed",
  "updated_at": "2026-11-06T12:20:43Z",
  "url": "https://research.draconitic.com/paper/e88dgwhvs1mfs",
  "venue": "PLOS Digital Health",
  "versions": []
}