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Dormechele, William, Guven-Uslu, Pinar 
ORCID: https://orcid.org/0000-0003-3935-8280 and De La Iglesia, Beatriz ORCID: https://orcid.org/0000-0003-2675-5826
(2026)
Predictive analytics for health-system decision support using population health data: a global scoping review of implementation, governance and decision integration.
International Journal of Medical Informatics, 222.
ISSN 1386-5056
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Abstract
Background: Predictive analytics is increasingly applied to routine and population health data, but its translation into operational health-system decisions remains uncertain. We examined implementation maturity, data integration, governance, workflow integration, uncertainty and documented decision pathways. Methods: We conducted a global scoping review of peer-reviewed studies published from 2014 to 2025 across five databases. Eligible studies applied predictive or forecasting methods to routine healthcare or population-level data for health-system decision-making. Findings were synthesised descriptively and narratively and reported in accordance with PRISMA-ScR. An additional assessment of Overton, WHO IRIS and PAHO IRIS examined implementation evidence in grey literature. Results: Of 2,623 screened records, 161 articles were included; 128 (79.5%) were from high-income settings. The highest documented stage was development in 139 articles (86.3%), validation in 10 (6.2%), pilot implementation in 3 (1.9%) and operational deployment in 9 (5.6%). Although 118 articles (73.3%) were positioned as relevant to resource allocation or capacity planning, only 9 (5.6%) documented an output-to-decision pathway and 14 (8.7%) reported routine workflow integration, revealing a marked claim-to-action reporting gap between stated relevance and documented action. Implementation barriers most often concerned data quality and interoperability (112; 69.6%) and validation and transportability (104; 64.6%). Only 24 articles (14.9%) described how uncertainty informed decisions. Supplementary grey literature identified three additional implementations, two operational and one pilot, supporting stroke-service planning, neighbourhood risk targeting and claims anomaly investigation.
| Item Type: | Article |
|---|---|
| Additional Information: | Data sharing statement: The extracted review dataset, coding framework, summary tables and reproducible analysis scripts supporting this review are provided as supplementary materials. No individual participant data were used. Further information can be obtained from the corresponding author upon reasonable request. |
| Uncontrolled Keywords: | predictive analytics,health-system decision support,routine health data,machine learning,public health informatics,workflow integration,implementation science,sdg 3 - good health and well-being ,/dk/atira/pure/sustainabledevelopmentgoals/good_health_and_well_being |
| Faculty \ School: | Faculty of Social Sciences > Norwich Business School Faculty of Science > School of Computing Sciences |
| UEA Research Groups: | Faculty of Social Sciences > Research Groups > Accounting & Quantitative Methods Faculty of Science > Research Groups > Norwich Epidemiology Centre Faculty of Medicine and Health Sciences > Research Groups > Norwich Epidemiology Centre Faculty of Medicine and Health Sciences > Research Centres > Norwich Institute for Healthy Aging Faculty of Science > Research Groups > Health Computing Faculty of Science > Research Groups > Data Science and AI |
| Depositing User: | LivePure Connector |
| Date Deposited: | 25 Sep 2026 11:15 |
| Last Modified: | 27 Sep 2026 23:03 |
| URI: | https://ueaeprints.uea.ac.uk/id/eprint/104642 |
| DOI: |
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