Use of the Population Grouping Methodology of the Canadian Institute for Health Information to predict high-cost health system users in Ontario

BACKGROUND:Prior research has consistently shown that the heaviest users account for a disproportionate share of health care costs. As such, predicting high-cost users may be a precondition for cost containment. We evaluated the ability of a new health risk predictive modelling tool, which was developed by the Canadian Institute for Health Information (CIHI), to identify future high-cost cases.METHODS:We ran the CIHI model using administrative health care data for Ontario (fiscal years 2014/15 and 2015/16) to predict the risk, for each individual in the study population, of being a high-cost user 1 year in the future. We also estimated actual costs for the prediction period. We evaluated model performance for selected percentiles of cost based on the discrimination and calibration of the model.RESULTS:A total of 11 684 427 individuals were included in the analysis. Overall, 10% of this population had annual costs exceeding $3050 per person in fiscal year 2016/17, accounting for 71.6% of total expenditures; 5% had costs above $6374 (58.2% of total expenditures); and 1% exceeded $22 995 (30.5% of total expenditures). Model performance increased with higher cost thresholds. The c-statistic was 0.78 (reasonable), 0.81 (strong) and 0.86 (very strong) at the 10%, 5% and 1% cost thresholds, respectively.INTERPRETATION:The CIHI Population Grouping Methodology was designed to predict the average user of health care services, yet performed adequately for predicting high-cost users. Although we recommend the development of a purpose-designed tool to improve model performance, the existing CIHI Population Grouping Methodology may be used — as is or in concert with additional information — for many applications requiring prediction of future high-cost users.

A substantial literature across health systems shows that the highest users of services account for disproportionate shares of the public costs of health care. It has recently been reported that more than three-quarters of individual health care costs in Ontario were incurred by just 10% of the population.¹ Similarly, an Ontario Ministry of Health and Long-Term Care (MOHLTC) analysis of inpatient and home care costs found that the top 5% of patients were responsible for 61% of spending in those domains.² Consistent findings have been reported for Manitoba, Alberta and British Columbia.^3–7Some of the highest-cost cases may be explained by rare, unpredictable events, but others arise in the presence of multiple chronic conditions. Research from the United States has suggested that spending on chronic conditions accounts for the majority of health care expenditures.⁸ Predicting high-cost users may help us to understand and better manage public spending on health care.Cognizant of this need, the Ontario MOHLTC developed a predictive model for high-cost users based on sociodemographic, utilization and clinical diagnostic characteristics.⁹ Although the model performed well, it relied on a coarse categorization of 20 diagnostic variables consisting of broadly defined chapters of the International Statistical Classification of Diseases and Related Health Problems, 10th Revision (ICD-10) and a small number of chronic conditions, which limited its utility for explaining predictions. Moreover, this model is not available for use outside the MOHLTC. As such, there is a need for a predictive model that can be applied more widely by researchers and other stakeholders with an interest in health policy and spending in Canada.The Canadian Institute for Health Information (CIHI) has recently released a new population-based case mix product, the Population Grouping Methodology, which uses diagnoses obtained from patient health care encounters in multiple settings to summarize the universe of diagnosis codes into a clinically meaningful set of 226 health conditions. The grouping and modelling methodologies are described in more detail in Appendix 1 (available at www.cmaj.ca/lookup/suppl/doi:10.1503/cmaj.191297/-/DC1) and in previous reports.^10,11 The CIHI grouping methodology was not designed to predict high-cost cases, and previous work has already shown that the model performs better for low- and moderate-cost users than for highest-cost users (i.e., those with annual costs exceeding $25 000).¹¹ We evaluated the suitability of CIHI’s model for predicting future high-cost users in Ontario by examining the predicted costs for individuals who exceeded the top 10%, 5%, and 1% thresholds of actual cost.