The Growing Role of Technology in Healthcare: A Systematic Literature Review and Analytical Assessment of Evidence, Equity and Governance
Keywords:
digital health; telemedicine; artificial intelligence; electronic health records; wearable devices; health equity; systematic literature review; health governanceAbstract
Background: Health systems face ageing populations, rising chronic disease, workforce shortages and escalating costs. Meanwhile, electronic health records (EHRs), telemedicine, artificial intelligence (AI), wearables and connected devices are being adopted at unprecedented speed. The evidence on what these technologies deliver, for whom, and at what risk remains fragmented.
Objective: To synthesise and critically appraise evidence on the clinical, operational, economic, equity and governance consequences of technology in healthcare.
Methods: A protocol-driven systematic literature review (SLR), guided by PRISMA 2020 reporting principles, covered peer-reviewed primary studies, systematic reviews, meta-analyses and authoritative policy documents. Findings were extracted into structured tables and synthesised through narrative and thematic analysis. An analytical research stage followed. It applied an ordinal Translational Readiness and Equity Exposure appraisal, a multi-stakeholder analysis and the derivation of analytical propositions.
Results: Evidence is most mature for EHR-enabled safety gains and for telemedicine in selected chronic conditions. Hospital adoption of at least a basic EHR rose from 9.4% to 83.8% in the United States between 2008 and 2015. Telehealth use stabilised at roughly 38 times its pre-pandemic level. Deep-learning diagnostic systems achieved pooled sensitivity of 87.0% against 86.4% for clinicians, but only a small minority of comparative studies were prospective. Documentation burden, algorithmic bias, weak external validation and the digital divide emerged as the principal threats to value.
Conclusions: Technology creates value only when evidence, equity and embedding in workflows advance together. We propose an Evidence–Equity–Embedding (3E) framework and a set of actor-specific recommendations for clinicians, managers, regulators and researchers.
