Outcome Prediction Models in Healthcare: Using RWD to Build Predictive Analytics
Key Takeaways Claims dataset suitability for predictive model training requires explicit evaluation of feature completeness, outcome labelling quality, temporal structure, population representativeness, and bias patterns before model development begins. Missing values in clinical datasets are rarely random — distinguishing between clinically significant missingness and administrative artefacts requires structured completeness protocols, not standard imputation defaults. AI-ready clinical datasets require deep provenance tracking, statistical distribution validation, and semantic consistency across time periods — characteristics that go beyond conventional FAIR data quality standards. Internal validation is necessary but insufficient — temporal validation, external validation, and where relevant clinical validation are the standards required for regulatory and market access purposes. Data leakage in longitudinal claims data inflates internal validation metrics and goes undetected until ...
