Predictive analytics for health-system decision support using population health data: a global scoping review of...

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.

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.

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.

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.