Background In recent years, online physician-rating websites have become prominent and exert considerable influence on patients’ decisions. However, the quality of these decisions depends on the quality of data that these systems collect. Thus, there is a need to examine the various data quality issues with physician-rating websites. Objective This study’s objective was to identify and categorize the data quality issues afflicting physician-rating websites by reviewing the literature on online patient-reported physician ratings and reviews. Methods We performed a systematic literature search in ACM Digital Library, EBSCO, Springer, PubMed, and Google Scholar. The search was limited to quantitative, qualitative, and mixed-method papers published in the English language from 2001 to 2020. Results A total of 423 articles were screened. From these, 49 papers describing 18 unique data quality issues afflicting physician-rating websites were included. Using a data quality framework, we classified these issues into the following four categories: intrinsic, contextual, representational, and accessible. Among the papers, 53% (26/49) reported intrinsic data quality errors, 61% (30/49) highlighted contextual data quality issues, 8% (4/49) discussed representational data quality issues, and 27% (13/49) emphasized accessibility data quality. More than half the papers discussed multiple categories of data quality issues. Conclusions The results from this review demonstrate the presence of a range of data quality issues. While intrinsic and contextual factors have been well-researched, accessibility and representational issues warrant more attention from researchers, as well as practitioners. In particular, representational factors, such as the impact of inline advertisements and the positioning of positive reviews on the first few pages, are usually deliberate and result from the business model of physician-rating websites. The impact of these factors on data quality has not been addressed adequately and requires further investigation.

BACKGROUND Online physician rating websites have witnessed a steep rise in prominence over the years and exert considerable influence on high-stake patient decisions. However, the quality of these decisions depends on the quality of data collected by these systems. Hence, there is a need to understand the various data quality issues that exist in such websites. OBJECTIVE The purpose of this systemic review is to collect the data quality issues discussed in previous studies and classify them based on the data quality framework put forward by Wang et al. This review summarizes the findings and provides an in-depth discussion of various categories of data quality issues and their implications on the users of physician rating websites. METHODS We performed a systematic literature search in ACM Digital Library, EBSCO, Springer, PubMed, and Google Scholar. We identified any quantitative, qualitative and mixed-method paper that investigated data quality issues in physician rating websites. Over 192 articles were screened, and 33 were analyzed and summarized for this systematic review. RESULTS We identified 33 studies to collect 19 unique data quality issues that afflict physician rating sites. We classify these issues into 4 categories: Intrinsic, Contextual, Accessible and Representational. 58% (19/33) papers reported the presence of Intrinsic data quality errors, 48% (16/33) highlighted Contextual data quality issues. A small yet considerable number of studies (10/33) discussed issues of Representational & Accessibility data quality. More than half of the papers discuss multiple categories of data quality issues. CONCLUSIONS The results of this review demonstrate the presence of a range of data quality issues. While Intrinsic and Contextual factors have been well researched, Accessibility and Representational issues warrant more attention from researchers as well as practitioners. Notably, the Representational factors such as the impact of inline advertisements, positioning of positive reviews, which are usually deliberate and an artifact of the business or revenue model of the PRWs warrant more emphasis.