Accepted for/Published in: Journal of Medical Internet Research
Date Submitted: Oct 17, 2018
Open Peer Review Period: Oct 25, 2018 - Dec 6, 2018
Date Accepted: Jan 31, 2019
(closed for review but you can still tweet)
What do patients say about doctors online? A systematic review of studies on patient online reviews
ABSTRACT
Background:
The number of patient online reviews (PORs) have grown significantly and PORs have played an increasingly important role in patients’ choice of healthcare providers.
Objective:
To systematically review studies on PORs, summarize the major findings and study characteristics, identify literature gaps, and make recommendations for future research.
Methods:
Major database were searched from November 2017 to January 2018. Studies were included if they 1) focused on PORs of physicians and hospitals, 2) reported qualitative or quantitative results from analysis of PORs, and 3) peer-reviewed empirical studies. Study characteristics and major findings were synthesized using predesigned tables.
Results:
A total of 63 studies (69 articles) that met the above criteria were included in the review. Most studies (n = 48) were conducted in the U.S., including Puerto Rico, and the remaining were from Europe, Australia, and China. Earlier studies (published before 2010) used content analysis with small sample sizes; more recent studies retrieved and analyzed larger datasets using machine learning technologies. The number of PORs ranged from fewer than 200 to over 700,000. About 90% studies were focused on clinicians, typically specialists such as surgeons; 27% covered health care organizations, typically hospitals; and some studied both. A majority of PORs were positive and patients’ comments on their providers were favorable. While most studies were descriptive, some compared PORs with traditional surveys of patient experience and found a high degree of correlation; some compared PORs with clinical outcomes but found a low level of correlation.
Conclusions:
PORs contain valuable information that can generate insights on quality of care and patient-provider relationship, but have not been systematically used for studies of healthcare quality. With the advancement of machine learning and data analysis tools, we anticipate more research on PORs based on testable hypotheses and rigorous analytic methods. Clinical Trial: Systematic review registration: PROSPERO CRD42018085057
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