King Caroline, Lavergne M Ruth, McGrail Kimberlyn, Strumpf Erin C
Department of Epidemiology, Biostatistics and Occupational Health, McGill University, Montreal, QC, Canada.
Department of Family Medicine, Dalhousie University, Halifax, NS, Canada.
PLoS One. 2024 Dec 2;19(12):e0314381. doi: 10.1371/journal.pone.0314381. eCollection 2024.
Having a regular medical doctor is associated with better process of care and health outcomes. The goal of this study was to harness the richness in health administrative data to create a measure which accurately predicted whether patients self-identified as having a regular medical doctor. The Canadian Community Health Survey (2007-2012) was linked with health administrative data (HAD) (2002-2012) from Quebec, Canada's second largest province. The Canadian Community Health Survey includes respondents' answer to whether they have a regular medical doctor, but health administrative data does not. We therefore used LASSO and Random Forests to build prediction models that predict whether a patient reports having a regular medical doctor using their data only available in the HAD. Our results show that predicting patient responses to 'do you have a regular medical doctor?' using an average of single-year Usual Provider Continuity over 3 years results in an area under the receiver operator characteristic curve of 0.782 (0.778-0.787). This was almost a 14% improvement in predictive accuracy compared to the frequently used single-year Usual Provider Continuity (0.688 (0.683-0.694)). We have called this new measure the Reporting a Regular Medical Doctor (RRMD) index. The RRMD index is easy to implement in HAD, is an elegant solution to the difficulties associated with low-users having unstable UPC scores, and brings a patient-oriented perspective to previous efforts to capture patient-physician affiliations in HAD. We recommend that researchers seeking to measure whether patients have a regular medical doctor using HAD consider using the RRMD index.
拥有一名固定的医生与更好的医疗过程和健康结果相关。本研究的目的是利用健康管理数据的丰富性来创建一种能够准确预测患者是否自我认定有固定医生的指标。加拿大社区健康调查(2007 - 2012年)与来自加拿大第二大省魁北克的健康管理数据(2002 - 2012年)相链接。加拿大社区健康调查包含受访者关于是否有固定医生的回答,但健康管理数据中没有这一信息。因此,我们使用套索回归(LASSO)和随机森林算法构建预测模型,利用健康管理数据中仅有的数据来预测患者是否报告有固定医生。我们的结果表明,使用3年的单年常规医疗服务连续性平均值来预测患者对“你有固定医生吗?”这一问题的回答,在受试者工作特征曲线下的面积为0.782(0.778 - 0.787)。与常用的单年常规医疗服务连续性(0.688(0.683 - 0.694))相比,预测准确性提高了近14%。我们将这一新指标称为报告有固定医生(RRMD)指数。RRMD指数易于在健康管理数据中实施,是解决低使用者常规医疗服务连续性得分不稳定相关难题的巧妙方法,并且为以往在健康管理数据中捕捉患者 - 医生关系的努力带来了以患者为导向的视角。我们建议,寻求利用健康管理数据来衡量患者是否有固定医生的研究人员考虑使用RRMD指数。