JMIR Publications

ABSTRACT

Background:

Increasing numbers of patients consult online rating platforms before making healthcare decisions. These platforms often provide ratings from other patients, reflecting their subjective experience. However, patients often lack the knowledge to be able to judge the objective quality of health services. To account for this potential bias, many rating platforms complement patient ratings with more objective expert ratings, which can lead to conflicting signals as these different types of evaluations are not always aligned.

Objective:

This study aims to fill the gap on how consumers combine information from two different sources—patients or experts—to form opinions and purchase decisions in a healthcare context. More specifically, we assess prospective patients’ trade-offs when considering both types of ratings simultaneously on an online rating platform. Additionally, we examine how the influence of patient and expert ratings is conditional upon rating volume (i.e., the number of patient opinions).

Methods:

In a field study we analyze a dataset from an online physician rating platform containing clickstream data for more than 5,000 US doctors. We complement this with an experimental lab study consisting of a sample of 112 students from a Dutch university. The average age was 23.1 years and 60.7% (68/112) of the respondents were female.

Results:

The field data indeed illustrates the moderating effect of rating volume. In case the patient advice is based on small numbers, prospective patients tend to base their selection of a physician on expert rather than patient advice (profile clicks β=.14, P<.001; call clicks β=.28, P=.03). However, when the group of patients substantially grows in size, prospective patients start to rely on patients rather than the expert (profile clicks β=.23, SE=.07, P=.004; call clicks β=.43, SE=.32, P=.10). The experimental study replicates and validates these findings for conflicting patient versus expert advice in a controlled setting. When patient ratings are aggregated from a high number of opinions, prospective patients’ evaluations are affected more strongly by patient than expert advice (Mpatient positive/expert negative=3.06, SD=.94; Mexpert positive/patient negative=2.55, SD=.89; F1, 108=4.93, P=.03). Conversely, when patient ratings are aggregated from a low volume, participants are affected more strongly by expert compared to patient advice (Mpatient positive/expert negative=2.36, SD=.76; Mexpert positive/patient negative=3.01, SD=.81; F1, 108=8.42, P=.004). This effect occurs despite the fact that they indeed believe patients to be less knowledgeable than experts.

Conclusions:

When confronted with information from both sources simultaneously, prospective patients are influenced more strongly by other patients. This effect reverses when the patient rating has been aggregated from a (very) small number of individual opinions. This has important implications for how to present healthcare provider ratings to prospective patients in order to aid their decision making process.