PT  - JOURNAL ARTICLE
AU  - Sharada Weir
AU  - Mitch Steffler
AU  - Yin Li
AU  - Shaun Shaikh
AU  - James G. Wright
AU  - Jasmin Kantarevic
TI  - Use of the Population Grouping Methodology of the Canadian Institute for Health Information to predict high-cost health system users in Ontario
AID  - 10.1503/cmaj.191297
DP  - 2020 Aug 10
TA  - Canadian Medical Association Journal
PG  - E907--E912
VI  - 192
IP  - 32
4099  - http://www.cmaj.ca/content/192/32/E907.short
4100  - http://www.cmaj.ca/content/192/32/E907.full
SO  - CMAJ2020 Aug 10; 192
AB  - 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.