Using Diagnoses to Estimate Health Care Cost Risk in Canada

Until recently, the options for summarizing Canadian patient complexity were limited to health risk predictive modeling tools developed outside of Canada. This study aims to validate a new model created by the Canadian Institute for Health Information (CIHI) for Canada's health care environment.

This was a cohort study.

The rolling population eligible for coverage under Ontario's Universal Provincial Health Insurance Program in the fiscal years (FYs) 2006/2007-2016/2017 (12-13 million annually) comprised the subjects.

To evaluate model performance, we compared predicted cost risk at the individual level, on the basis of diagnosis history, with estimates of actual patient-level cost using "out-of-the-box" cost weights created by running the CIHI software "as is." We next considered whether performance could be improved by recalibrating the model weights, censoring outliers, or adding prior cost.

We were able to closely match model performance reported by CIHI for their 2010-2012 development sample (concurrent R=48.0%; prospective R=8.9%) and show that performance improved over time (concurrent R=51.9%; prospective R=9.7% in 2014-2016). Recalibrating the model did not substantively affect prospective period performance, even with the addition of prior cost and censoring of cost outliers. However, censoring substantively improved concurrent period explanatory power (from R=53.6% to 66.7%).

We validated the CIHI model for 2 periods, FYs 2010/2011-2012/2013 and FYs 2014/2015-2016/2017. Out-of-the-box model performance for Ontario was as good as that reported by CIHI for the development sample based on 3-province data (British Columbia, Alberta, and Ontario). We found that performance was robust to variations in model specification, data sources, and time.