What Do Patients Say About Doctors Online? A Systematic Review of Studies on Patient Online Reviews (Preprint) Institutional Repository Document uri icon



    The number of patient online reviews (PORs) has grown significantly, and PORs have played an increasingly important role in patients choice of health care providers.


    The objective of our study was to systematically review studies on PORs, summarize the major findings and study characteristics, identify literature gaps, and make recommendations for future research.


    A major database search was completed in January 2019. 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.


    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 United States, 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% of the 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. Although most studies were descriptive, some compared PORs with traditional surveys of patient experience and found a high degree of correlation and some compared PORs with clinical outcomes but found a low level of correlation.


    PORs contain valuable information that can generate insights into quality of care and patient-provider relationship, but it has not been systematically used for studies of health care 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.


    International Prospective Register of Systematic Reviews (PROSPERO) CRD42018085057; https://www.crd.york.ac.uk/PROSPERO/display_record.php?RecordID=85057 (Archived by WebCite at http://www.webcitation.org/76ddvTZ1C)

author list (cited authors)

  • Hong, Y. A., Liang, C., Radcliff, T. A., Wigfall, L. T., & Street, R. L.

citation count

  • 3

complete list of authors

  • Hong, Y Alicia||Liang, Chen||Radcliff, Tiffany A||Wigfall, Lisa T||Street, Richard L

Book Title

  • JMIR Preprints

publication date

  • October 2018