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Development and evaluation of comparable primary care indicators from administrative health data across three Canadian provinces

Alsabbagh MW, Kueper JK, Wong ST, Burge F, Johnston S, Peterson S, Lawson B, Chung H, Bennett M, Blackman S, McGrail K, Glazier R, Campbell J, Hogg W. Int J Popul Data Sci. 2020; 5(1). 2020; Aug 11 [Epub ahead of print]. DOI: https://doi.org/10.23889/ijpds.v5i1.1340


Introduction — Across the world, data sources to support learning in primary care (PC) lag far behind that of acute care. Having comparable data sources across jurisdictions is essential for scaling and spreading healthcare learnings.

Objectives — The purpose of this work was to 1) identify and develop indicators of PC performance using administrative data and 2) examine the comparability of indicator definitions across three Canadian provinces (Nova Scotia, Ontario, British Columbia). This work is valuable for demonstrating how to arrive at comparable administrative data indicators across jurisdictions with different care patterns.

Methods — The TRANSFORMATION study is a multi-province Canadian study of PC that aims to improve PC performance measurement reporting. We initially compiled a list of PC performance indicators that had been used with existing administrative data. We followed and documented an iterative process to achieve comparable indicator operationalizations across the three provinces in Canada.

Results — Our final list included 21 PC performance indicators pertaining to 1) technical care (n=4), 2) continuity of care (n=6), and 3) health services utilization (n=11). Establishing comparability between these PC performance indicators was possible. The main challenge was major differences in data characteristics and available resources including pre-existing algorithms used in each province to define the indicators. We present examples of these differences including the identification of patients with diabetes and of bone mineral density measurement.

Conclusion — Arriving at comparable definitions of PC performance indicators using administrative data is challenging and time-consuming, but possible — even without data pooling. Through an iterative and well-documented process, research teams and policy-makers can develop and establish comparability of PC performance indicators that can help in supporting continuous improvements in healthcare system performance. More work is necessary to standardize approaches and optimize the comparability of PC performance indicators across jurisdictions.

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