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Abstract
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Before
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 buy-rating condition (R-Buy), on top of the ratings provided by patients, we allow physicians to buy up to three additional ratings of five stars in at the end of each playing period.
Our design allows us to investigate the robustness of public rating mechanisms to fraud by introducing the possibility to cheat.
References
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.
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After
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. We are planning to run at least four conditions of market interactions with 48 undergraduate students either in the role of physicians or patients. In the baseline condition, no reputation building is possible between physicians and patients. In the rating conditions, 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 (2+) buy-rating conditions, on top of the ratings provided by patients, we allow physicians to buy up to four additional ratings of five stars in at the beginning of each playing period. These buy-rating conditions vary in the costs of the additional ratings.
Our design allows us to investigate the robustness of public rating mechanisms to fraud by introducing the possibility to cheat.
References
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.
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Trial Start Date
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November 15, 2021
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May 14, 2022
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Last Published
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November 18, 2021 12:06 PM
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May 23, 2022 07:34 AM
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Intervention (Public)
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Before
We experimentally investigate the impact of cheating a public rating system in healthcare credence goods markets. Therefore, we plan to employ a laboratory experiment framed in a healthcare context, where experts are called physicians and consumers are called patients, using a student sample from the University of Innsbruck.
To start with, we plan to run three experimental conditions. In the baseline condition, there is no feedback mechanism in place. Next, we introduce a public rating mechanism into the market, where patients can rate the interactions with physicians on a five-star-rating-scale. Given that the feedback mechanism enhances market outcomes, we plan to run a follow-up treatment where we investigate the robustness of the feedback mechanism to cheating, i.e. we allow physicians to buy up to three additional five-star ratings per period to improve their public rating.
Our design allows us to investigate the robustness of public rating mechanisms to fraud by introducing the possibility to cheat.
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After
We experimentally investigate the impact of cheating a public rating system in healthcare credence goods markets. Therefore, we plan to employ a laboratory experiment framed in a healthcare context, where experts are called physicians and consumers are called patients, using a student sample from the University of Innsbruck.
To start with, we plan to run at least four experimental conditions. In the baseline condition, there is no feedback mechanism in place. Next, we introduce a public rating mechanism into the market, where patients can rate the interactions with physicians on a five-star-rating-scale. Given that the feedback mechanism enhances market outcomes, we plan to run at least two follow-up conditions where we investigate the robustness of the feedback mechanism to cheating, i.e. we allow physicians to buy up to four additional five-star ratings per period to improve their public rating. The conditions where physicians can buy additional ratings vary in the costs of additional ratings. We will start with costs of 1 ECU per additional rating and — depending on its’ effect on market outcomes — will increase (decrease) the cost of additional ratings in the following condition(s).
Our design allows us to investigate the robustness of public rating mechanisms to fraud by introducing the possibility to cheat.
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Intervention Start Date
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November 16, 2021
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May 24, 2022
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Intervention End Date
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January 31, 2022
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July 15, 2022
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Experimental Design (Public)
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Before
We plan to use a student sample from the University of Innsbruck and run each experimental condition with 48 subjects (as suggested by our power analysis). Therefore, we plan to run two sessions with 24 subjects each in every experimental condition. All sessions are run computerized using z-Tree and students are recruited using hroot. Participants do not know which experiment they are going to participate in when they register. They only receive information about the expected duration of the experiment (2h).
Our experiment is structured as follows for all our conditions:
Stage 1: The experimenter explains the experiment and participants read the instructions.
Stage 2: Participants answer several control questions to ensure they understood the game.
Stage 3: The computer randomly assigns roles and markets to participants.
Stage 4: Participants play the game for 16 periods.
Stage 5: Participants participate in additional games: an individual risk preference task, a dictator game, a lying task, and a trust game.
Stage 6: Participants fill out a questionnaire.
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After
We plan to use a student sample from the University of Innsbruck and run each experimental condition with 48 subjects (as suggested by our power analysis). Therefore, we plan to run two sessions with 24 subjects each in every experimental condition. All sessions are run computerized using z-Tree and students are recruited using hroot. Participants do not know which experiment they are going to participate in when they register. They only receive information about the expected duration of the experiment (1:45h).
Our experiment is structured as follows for all our conditions:
Stage 1: The experimenter explains the experiment and participants read the instructions.
Stage 2: Participants answer several control questions to ensure they understood the game.
Stage 3: The computer randomly assigns roles and markets to participants.
Stage 4: Participants play the game for 16 periods.
Stage 5: Participants participate in additional games: an individual risk preference task, a dictator game, a lying task, and a trust game.
Stage 6: Participants fill out a questionnaire.
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Sample size (or number of clusters) by treatment arms
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Before
192 (3 x 48) individuals (students at the University of Innsbruck).
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at least 192 (4 x 48) individuals (students at the University of Innsbruck).
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Intervention (Hidden)
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Before
The experiment will be conducted at the Econ Lab at the University of Innsbruck. We build our experimental design on the credence goods framework of Dulleck and Kerschbamer (2006) and slightly adapt it to better resemble healthcare markets. Throughout the experiment, we implement a health care framing in which we refer to consumers of the credence good as patients and sellers as physicians, respectively.
Experiment
The basic set-upput and parameterization:
In our basic set-up, patients and physicians are grouped in a market of 8 subjects (4 patients & 4 physicians). Patients suffer from a major health problem with probability h = 0.5 and a minor one with probability (1-h). The probability h=0.5 is common knowledge. Patients decide whether to consult a physician knowing that they suffer from some health problem in every period. They do not get information about the severity of their health problem. Physicians diagnose their patients’ health problem with certainty and at zero costs. They provide one of two treatments, a major or a minor treatment. The cost for the physician to provide the major treatment (cH) is 4 ECU (Experimental Currency Unit). The cost for the minor treatment (cL) is 2 ECU. Treatment prices, paid by an insurance company (not represented by participants in the lab), are either 8 ECU (pH) or 4 ECU (pL). The major treatment cures both, the major and the minor health problem, while the minor treatment only cures the minor one. Patients obtain 6 ECU (v) if cured, and zero if treated insufficiently. The payoff for patients consulting a physician is the difference between the obtained value v and a disutility, which depends on the type of treatment. If patients have to go through major treatment, they bear a disutility of 4 ECU (dH), while the disutility for a minor treatment is 1 ECU (dH). Hence, the payoff for patients is 2 ECU if undergoing major treatment and 5 ECU for undergoing minor treatment. For physicians, the payoff is the spread between the price charged (pH or pL) and the cost for the chosen treatment (cH or cL). In case a patient decides against consulting a physician, the patient receives an outside option of (-0.5) ECU (oPat). Physicians receive oPhy = 0 if they do not interact with any patient in a given round. Compared to the framework of Dulleck and Kerschbamer (2006), our basic model differs in three dimensions. First, the outside option of patients is negative (oPat = -0.5) illustrating the disutility of an uncured health problem. Second, pH and pL are exogenously fixed, which is common in highly regulated health care markets. Third, patients are insured, i.e. they do not have to pay the price for the treatment. However, unlike in other credence goods markets (e.g. car repair), the patients themselves have to undergo the treatments, and thus, compared to a minor treatment, a major treatment results in a higher disutility, irrespective of the type of health problem. Throughout our experiment, we implement verifiability, that is, physicians can only charge the price for the treatment they perform (i.e. overcharging is ruled out by design) and participants are not identifiable such that reputation building in the treatments without feedback mechanism is excluded.
The structure of the stage-game is as follows:
1) For each patient, nature draws the type of health problem. With probability h patients have a major health problem, and with probability (1-h) patients have a minor health problem.
2) Patients decide whether to consult a physician. If patients decide not to visit a physician, the period ends. Otherwise, they choose one physician from a list of four.
3) Physicians are informed about the health problem and provide a treatment (q_H or q_L).
4) Patients and physicians observe their payoff in the respective period. Note that patients cannot infer whether their physician treated them appropriately, they only learn which treatment was chosen and whether it was sufficient to cure the health problem.
5) In the conditions with a public rating system: After learning the payoff for the respective period, patients decide whether to rate the interaction with their treating physician. If they decide to rate the interaction, they choose the rating on a scale between 0 and 5 stars which is shown to the treating physician afterward.
6) In the conditions where physicians may buy ratings: After learning the payoff for the respective period, physicians decide whether to buy up to three additional 5-star-ratings. For each additional rating, the physician has to pay 1 ECU .
[Treatment Variation]
As explained above, we plan to run (at least) four treatments:
[Experimental Condition 1] — No Feedback-Mechanism
[Experimental Condition 2] — Public Feedback-Mechanism
[Experimental Condition 3] — Public Feedback-Mechanism + Buy Rating 3
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After
The experiment will be conducted at the Econ Lab at the University of Innsbruck. We build our experimental design on the credence goods framework of Dulleck and Kerschbamer (2006) and slightly adapt it to better resemble healthcare markets. Throughout the experiment, we implement a health care framing in which we refer to consumers of the credence good as patients and sellers as physicians, respectively.
Experiment:
The basic set-up and parameterization:
In our basic set-up, patients and physicians are grouped in a market of 8 subjects (4 patients & 4 physicians). Patients suffer from a major health problem with probability h = 0.5 and a minor one with probability (1-h). The probability h=0.5 is common knowledge. Patients choose a physician knowing that they suffer from some health problem in every period. They do not get information about the severity of their health problem. Physicians diagnose their patients’ health problem with certainty and at zero costs. They provide one of two treatments, a simple or an intensive treatment. The cost for the physician to provide the intensive treatment (cI) is 10 ECU (Experimental Currency Unit). The cost for the simple treatment (cS) is 5 ECU. Treatment prices, paid by an insurance company (not represented by participants in the lab), are either 20 ECU (pI) or 10 ECU (pS). Patients pay an insurance premium of 15 ECUs. The intensive treatment cures both, the major and the minor health problem, while the simple treatment only cures the minor one. Patients obtain 25 ECU (v) if cured, and zero if treated insufficiently. The payoff for patients consulting a physician is the difference between the obtained value v, the insurance premium and a disutility, which depends on the type of treatment. If patients have to go through intensive treatment, they bear a disutility of 5 ECU (dI), while the disutility for a simple treatment is zero ECU (dS). For physicians, the payoff is the spread between the price charged (pI or pS) and the cost for the chosen treatment (cI or cS). Patients have to choose exactly one physician in every round. Physicians receive oPhy = 0 if they do not interact with any patient in a given round. Throughout our experiment, we implement verifiability, that is, physicians can only charge the price for the treatment they perform (i.e. overcharging is ruled out by design). Furthermore, physcians have to provide sufficient treatment to patients (i.e. undertreament is ruled out by design). Participants are not identifiable in the experimental setting. Therefore, reputation building is possible only in the conditions with feedback mechanisms.
The structure of the stage-game is as follows:
1) In the conditions where physicians may buy ratings: Physicians decide whether to buy up to four 5-star-ratings. The costs of the additional ratings vary, depending on the experimental condition. We will start with a cost of 1 ECU per additional rating.
2) For each patient, nature draws the type of health problem. With probability h patients have a major health problem, and with probability (1-h) patients have a minor health problem.
3) Patients decide whether to consult a physician. If patients decide not to visit a physician, the period ends. Otherwise, they choose one physician from a list of four.
4) Physicians are informed about the health problem and provide a treatment (q_I or q_S). If a patient has a major health problem, physicans have to provide the intensive treatment (q_I).
5) Patients and physicians observe their payoff in the respective period. Note that patients cannot infer whether their physician treated them appropriately, they only learn which treatment was chosen and whether it was sufficient to cure the health problem.
6) In the conditions with a public rating system: After learning the payoff for the respective period, patients decide whether to rate the interaction with their treating physician. If they decide to rate the interaction, they choose the rating on a scale between 0 and 5 stars which is shown to the treating physician afterward.
[Treatment Variation]
As explained above, we plan to run (at least) four treatments:
[Experimental Condition 1] — No Feedback-Mechanism
[Experimental Condition 2] — Public Feedback-Mechanism
[Experimental Condition 3] — Public Feedback-Mechanism + Buy Rating (1 ECU)
[Experimental Condition 4 …] — Public Feedback-Mechanism + Buy Rating (* ECU)
Depending on the experimental results of condition 3, we will (decrease) increase the costs of additional ratings to test the boundaries of the effectiveness of the feedback mechanism.
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