Table 3

Recommendations for good practice population-based health risk assessment

DomainRecommendationsLevel of evidence
Type of risk stratification toolPredictive model using a population health approachHigh15–21
Validation of the modelLongitudinal follow-upHigh41
Predicted/explained outcomesUnplanned hospital-related events; risk of institutionalisation; death; case prognosisHigh15–21
Source sampleWhole regional populationHigh15 16
Statistical modelPredictive modellingHigh15–21
Statistical indicesStandardisation on reporting performance (positive predictive value, PPV)41 and sensitivity across risk bandsModerate41*
Population usefulnessRisk adjustment; planning and commissioning health services
Support to novel reimbursement models
High22–24
Clinical and social usefulnessIdentification of patients at high risk and cost-effective preventive clinical and social interventionsHigh15–22
Periodicity of updatesSemesterLow†
Clinical accessibilityAvailable in the professional workstation through clinical decision support systemsHigh‡
Flexibility and transferabilityOpen algorithms, open source, reduced or no licence binding. Morbidity grouper based on statistical criteria adjusted to the target populationHigh
  • *To report metrics indicating sensitivity/specificity of predictions is recommended for good practice. However, some regions adopt a pragmatic approach classifying individuals into specific of the risk-strata pyramid without informing on sensitivity/specificity because of rather poor robustness of predictions provided by most of the models.

  • †Periodicity of updates depends on the logistics available in each site. A yearly or 6-monthly basis seem reasonable.

  • ‡Development of adequate clinical decision support systems (CDSS) depends on three main factors: (1) robustness of computational modelling feeding the CDSS; (2) refinement of the CDSS generated by the clinical feedback and (3) appropriate dashboard providing a user-friendly interface.