Comparing healthcare utilization patterns via global differences in the endorsement of current procedural terminology codes. (English) Zbl 1380.62257

Shi, Xu; Pashova, Hristina; Heagerty, Patrick J.

Ann. Appl. Stat. 11, No. 3, 1349-1374 (2017).

Summary: The linkage of electronic medical records (EMR) across clinics, hospitals, and healthcare systems is opening new opportunities to evaluate factors associated with both individual treatment benefit and potential harm. For example, the FDA Sentinel initiative seeks to create a surveillance network with over 100 million patient lives [R. E. Behrman et al., “Developing the sentinel system – a national resource for evidence development”, New Engl. J. Med. 364, 498–499 (2011; doi:10.1056/NEJMp1014427)], while PCORnet has created multiple networks that include linked electronic medical records from geographic regions such as entire cities or states, with the ultimate goal of facilitating comparative effectiveness research [F. S. Collins et al., “PCORnet: turning a dream into reality”, J. Am. Med. Inf. Assoc. 21, No. 4, 576–577 (2014; doi:10.1136/amiajnl-2014-002864)]. However, one key challenge to the use of electronically assembled cohorts is the potential for variation in both the choice of specific healthcare procedures and coding practices due to differences in patient populations and/or financial incentives within care delivery networks. In order to explore variation in patient care or procedure coding, we review and develop statistical methods that can permit testing and estimation of subgroup differences in code assignments. We focus on Current Procedural Terminology (CPT) codes which are used in a standardized fashion to capture patient treatment details and to record medical histories, but the methods we develop can be used for any structured EMR data. We specifically study testing procedures that can be valid for comparing both rare and common counts as routinely encountered with medical procedure codes, and we transfer methods from studies of genetic association. Hierarchical structure in terms of both thematically grouped medical codes and provider-level clustering adds unique complexity to the analysis of EMR data. We detail penalized regression methods unifying estimation and inference to leverage the hierarchical structure and stabilize rate ratio estimates for rare procedures. We also expand inference methods to account for potential within provider correlation of patient utilization. We illustrate methods comparing the endorsement of CPT codes for subjects enrolled in a back pain cohort study where interest is in the differences across recruitment centers in the use of CPT codes [J. G. Jarvik, “Study protocol: the back pain outcomes using longitudinal data (BOLD) registry”, BMC Musculoskelet. Disord. 13, No. 1, Paper No. 64, 12 p. (2012; doi:10.1186/1471-2474-13-64)].

MSC:

62P10	Applications of statistics to biology and medical sciences; meta analysis
92B15	General biostatistics
92C50	Medical applications (general)

Keywords:

electronic medical records; hierarchical structure; dynamic graphics