How Online Quality Ratings Influence Patients’ Choice of Medical Providers: Controlled Experimental Survey Study

100

Background 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. Objective 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. Methods 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. 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 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. Conclusions 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. Trial Registration 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)

Section: Discussionmentioning

confidence: 99%

What Do Patients Say About Doctors Online? A Systematic Review of Studies on Patient Online Reviews

Hong¹,

Liang²,

Radcliff³

et al. 2019

100

“…Further, the large numbers of reviews we found may address some of the concerns about lack of an adequate sample (because health systems can also wait to post reviews until a sufficient number are collected or can use older surveys to increase the numbers of reviews and narratives) [12,25]. However, it has been reported that patients use reviews from different sources differently, and this makes it difficult to extrapolate how patients will view star ratings and comments posted by health systems [26]. At least one recent study suggests that patients trust commercial rating sites more than health systems’ sites [27].…”

Section: Discussionmentioning

confidence: 99%

Reporting of Patient Experience Data on Health Systems’ Websites and Commercial Physician-Rating Websites: Mixed-Methods Analysis

Lagu¹,

Norton²,

Russo³

et al. 2019

Background Some hospitals’ and health systems’ websites report physician-level ratings and comments drawn from the Consumer Assessment of Healthcare Providers and Systems surveys. Objective The aim was to examine the prevalence and content of health system websites reporting these data and compare narratives from these sites to narratives from commercial physician-rating sites. Methods We identified health system websites active between June 1 and 30, 2016, that posted clinician reviews. For 140 randomly selected clinicians, we extracted the number of star ratings and narrative comments. We conducted a qualitative analysis of a random sample of these physicians’ narrative reviews and compared these to a random sample of reviews from commercial physician-rating websites. We described composite quantitative scores for sampled physicians and compared the frequency of themes between reviews drawn from health systems’ and commercial physician-rating websites. Results We identified 42 health systems that published composite star ratings (42/42, 100%) or narratives (33/42, 79%). Most (27/42, 64%) stated that they excluded narratives deemed offensive. Of 140 clinicians, the majority had composite scores listed (star ratings: 122/140, 87.1%; narrative reviews: 114/140, 81.4%), with medians of 110 star ratings (IQR 42-175) and 25.5 (IQR 13-48) narratives. The rating median was 4.8 (IQR 4.7-4.9) out of five stars, and no clinician had a score less than 4.2. Compared to commercial physician-rating websites, we found significantly fewer negative comments on health system websites (35.5%, 76/214 vs 12.8%, 72/561, respectively; P <.001). Conclusions The lack of variation in star ratings on health system sites may make it difficult to differentiate between clinicians. Most health systems report that they remove offensive comments, and we notably found fewer negative comments on health system websites compared to commercial physician-rating sites.

“…On the one hand, choosing the right physician is critical for patients because health care services are related to health and life [11,12]. However, because of information asymmetry and lack of professional health care knowledge, it is difficult for patients to ascertain a physician’s competency and service quality based on the limited information and knowledge they obtain [13]. Telemedicine markets should provide efficient and adequate information to help patients make decisions about using such services.…”

Section: Introductionmentioning

confidence: 99%

Investigating the Effect of Paid and Free Feedback About Physicians' Telemedicine Services on Patients’ and Physicians’ Behaviors: Panel Data Analysis

Yang¹,

Zhang²

2019

Background In recent years, paid online patient-physician interaction has been incorporated into the telemedicine markets. With the development of telemedicine and telemedicine services, online feedback has been widely applied, helping other patients to identify quality services. Recently, in China, a new type of service feedback has been applied to the telemedicine markets, namely, paid feedback. Patients who are satisfied with a physician’s online service can buy a virtual gift or give a tip to the physicians. This paid feedback can improve the reliability of service feedback and reduce the proportion of false information because it increases the cost for feedback providers. Paid online feedback can benefit the physicians, such as by providing them with monetary incentives; however, research on the impacts and value of such paid feedback from the physician perspective in the telemedicine markets is scant. To fill this research gap, this study was designed to understand the role of paid feedback by developing a research model based on the theories of signaling and self-determination. Objective This study aimed to explore the effects of free and paid feedback on patients’ choice and physicians’ behaviors as well as to investigate the substitute relationship between these 2 types of feedback in the telemedicine markets. Methods A JAVA software program was used to collect online patient-doctor interaction data over a 6-month period from a popular telemedicine market in China (Good Physician Online). This study drew on a 2-equation panel model to test the hypotheses. Both fixed and random effect models were used to estimate the combined effects of paid feedback and free feedback on patients’ choice and physicians’ contribution. Finally, the Hausman test was adopted to investigate which model is better to explain our empirical results. Results The results of this study show that paid feedback has a stronger effect on patients’ choice (a 5 =0.566; t 2192 =9.160; P <.001) and physicians’ contribution (β 4 =1.332; t 2193 =11.067; P <.001) in telemedicine markets than free feedback. Moreover, our research also proves that paid feedback and free feedback have a substitute relationship in determining patients’ and physicians’ behaviors (a 6 =−0.304; t 2191 =−5.805; P <.001 and β 5 =−0.823; t 2192 =−8.136; P <.001). Conclusions Our findings contribute to the extant literature on service feedback in the telemedicine markets and provide insight for relevant stakeholders into how to design an effective f...