Identifying persistent high-cost patients in the hospital for care management: development and validation of prediction models

BackgroundHealthcare use by High-Need High-Cost (HNHC) patients is believed to be modifiable through better coordination of care. To identify patients for care management, a hybrid approach is recommended that combines clinical assessment of need with model-based prediction of cost. Models that predict high healthcare costs persisting over time are relevant but scarce. We aimed to develop and validate two models predicting Persistent High-Cost (PHC) status upon hospital outpatient visit and hospital admission, respectively.MethodsWe performed a retrospective cohort study using claims data from a national health insurer in the Netherlands-a regulated competitive health care system with universal coverage. We created two populations of adults based on their index event in 2016: a first hospital outpatient visit (i.e., outpatient population) or hospital admission (i.e., hospital admission population). Both were divided in a development (January-June) and validation (July-December) cohort. Our outcome of interest, PHC status, was defined as belonging to the top 10% of total annual healthcare costs for three consecutive years after the index event. Predictors were predefined based on an earlier systematic review and collected in the year prior to the index event. Predictor effects were quantified through logistic multivariable regression analysis. To increase usability, we also developed smaller models containing the lowest number of predictors while maintaining comparable performance. This was based on relative predictor importance (Wald chi 2). Model performance was evaluated by means of discrimination (C-statistic) and calibration (plots).ResultsIn the outpatient development cohort (n = 135,558), 2.2% of patients (n = 3,016) was PHC. In the hospital admission development cohort (n = 24,805), this was 5.8% (n = 1,451). Both full models included 27 predictors, while their smaller counterparts had 10 (outpatient model) and 11 predictors (hospital admission model). In the outpatient validation cohort (n = 84,009) and hospital admission validation cohort (n = 20,768), discrimination was good for full models (C-statistics 0.75; 0.74) and smaller models (C-statistics 0.70; 0.73), while calibration plots indicated that models were well-calibrated.ConclusionsWe developed and validated two models predicting PHC status that demonstrate good discrimination and calibration. Both models are suitable for integration into electronic health records to aid a hybrid case-finding strategy for HNHC care management.