A key characteristic of health care markets is the information asymmetry between patients and physicians. Physicians know more about the disease and the appropriate treatment than patients. This may result in different forms of physician misbehavior: providing more treatments than necessary, i.e. overtreatment; providing less treatment than necessary, i.e. undertreatment or charging more treatments than provided, i.e. overcharging. Patients have to trust in physicians that they receive appropriate treatment. This is why health services are often referred to as credence goods (Darby and Karni 1973, Dulleck and Kerschbamer 2006).
The provision of feedback on rating platforms and the associated reputation building has gained more and more attention in the past two decades in the context of physician-patient interactions. In Germany, for instance, about 70% of physician-rating website users are influenced by the rating in their physician choice (Emmert and Meszmer 2018). However, patients base their ratings often on characteristics unrelated to the quality of care (Emmert et al. 2020), thus introducing noise into the quality ratings. We capture these recent developments and investigate the effectiveness of public rating systems on the quality of care with the use of a laboratory experiment.
Based on the credence goods framework established by Dulleck and Kerschbamer (2006) and Dulleck et al. (2011), we introduce a toy model that enables us to derive hypotheses and test them in a laboratory experiment. In total, three conditions of market interactions are planned with 148 undergraduate students either in the role of physicians or patients. In the baseline condition (B), no reputation building is possible between physicians and patients. In the rating condition (R), we introduce the possibility to rate physicians on a rating scale between zero and five stars. The rating is based on the payoff information of patients resulting from the interaction between physician and patient. In the random rating condition (R-Random), on top of the ratings provided by patients, we add noise to the average rating publicly visible to all market participants by introducing an additional random rating between 0 and 5 stars for each rating provided by patients.
Our design allows us to investigate the effect of a public rating mechanism on outcomes in healthcare credence goods markets. Furthermore, it enables us to explore the robustness of public rating mechanisms to noise by introducing additional random ratings.
Darby, M. R. and E. Karni (1973). "Free Competition and the Optimal Amount of Fraud." Journal of Law & Economics 16(1): 67-88.
Dulleck, U. and R. Kerschbamer (2006). "On Doctors, Mechanics, and Computer Specialists: The Economics of Credence Goods." Journal of Economic Literature 44(1): 5-42. DOI: https://doi.org/10.1257/002205106776162717.
Dulleck, U., R. Kerschbamer and M. Sutter (2011). "The Economics of Credence Goods: An Experiment on the Role of Liability, Verifiability, Reputation, and Competition." American Economic Review 101(2): 526-555. DOI: https://doi.org/10.1257/aer.101.2.526.
Emmert, M., S. Becker, N. Meszmer and U. Sander (2020). "Spiegeln Facebook-Bewertungen Die Versorgungsqualität Und Patientenzufriedenheit Von Krankenhäusern Wider? Eine Querschnittstudie Am Beispiel Der Geburtshilfe in Deutschland." Gesundheitswesen 82(06): 541-547. DOI: https://doi.org/10.1055/a-0774-7874.
Emmert, M. and N. Meszmer (2018). "Eine Dekade Arztbewertungsportale in Deutschland: Eine Zwischenbilanz Zum Aktuellen Entwicklungsstand." Gesundheitswesen 80(10): 851-858. DOI: https://doi.org/10.1055/s-0043-114002.