Predictive analytics for health-system decision support using population health data: a global scoping review of implementation, governance and decision integration.
TL;DR
A global scoping review of peer-reviewed studies published from 2014 to 2025 across five databases found peer-reviewed evidence remains dominated by model development, while integration into decision pathways and routine workflows is infrequently documented.
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.
Conclusions
Peer-reviewed evidence remains dominated by model development, while integration into decision pathways and routine workflows is infrequently documented. The three supplementary implementations from grey literature illustrate a practical role for predictive informatics in identifying system-level risks and directing planning, prevention or investigation. Advancing predictive analytics to operational decision support requires evaluation of the complete decision-integration chain, comprising decision actors, predictive outputs and associated uncertainty, delivery mechanisms, decision rules, actions, governance, workflow integration and lifecycle monitoring.