Work & case studies · Healthcare
Clinical data analysis for a hospital network
Machine-learning analysis of patient records that surfaces patterns and risk indicators for clinicians.
30%
improvement in early risk-flag accuracy vs. manual triage
Minutes
not days, for cohort analysis
Challenge
Clinicians and administrators were spending days pulling cohorts and spotting patterns across fragmented records. Risk flags depended on individual experience.
What we built
- A de-identified analytics dataset assembled from the hospital information system with a documented data-flow and access model.
- Classification models for readmission and deterioration risk, with feature importance shown alongside every score.
- A clinician-facing dashboard for cohort exploration and a daily flagged-patient list.
Results
Risk flags were around 30% more accurate than the manual process in retrospective evaluation, and cohort analyses that took days now take minutes. All models are advisory; clinicians make the call.
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