Big Data, Big Problems

This article explores how integration of data from clinical registries and electronic health records produces a quality impact within orthopedic practices. Data are differentiated from information, and several types of data that are collected and used in orthopedic outcome measurement are defined. Furthermore, the concept of comparative effectiveness and its impact on orthopedic clinical research are assessed. This article places emphasis on how the concept of big data produces health care challenges balanced with benefits that may be faced by patients and orthopedic surgeons. Finally, essential characteristics of an electronic health record that interlinks musculoskeletal care and big data initiatives are reviewed.

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On selection of electronic health record (EHR) systems, orthopedic surgeons must consider the capability of an EHR to ensure data quality, interoperability, and assistance at point of musculoskeletal care.

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Another challenge faced by orthopedic surgeons is that big data is statistically lagged and may represent findings from time periods that are outdated. Several observational studies assess outcome analysis from select time frames that are restricted by the type of data set used. For example, a study Westermann and colleagues assessed the epidemiology of reverse shoulder arthroplasty in the United States using National Inpatient Sample. The study was inherently limited by coding practices in that before 2011 total shoulder arthroplasty and reverse shoulder arthroplasty were coded identically; however, after 2011 separate codes were formed for each procedure. Therefore, any study assessing shoulder arthroplasty dating before 2011 must be closely assessed by practicing surgeons. Seldom does big data information represent anything in real-time and therefore must be thoroughly investigated before application in clinical settings.

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