Competetive Rating Systems in Healthcare Credence Goods Markets

We experimentally investigate the effect of 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. To explore whether strategic considerations influence physician behaviour, we employ one condition, where physicians only see their own public rating, but not that of other pyhsicians, and a second condition where they see both, their own, and the public rating of others.
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 whether strategic considerations regarding the relative rank in the rating system influence physician behaviour.

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) 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 choose one physician from a list of four.
3) 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).
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.
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.


[Treatment Variation]
As explained above, we plan to run three treatments:
[Experimental Condition 1] — No Feedback-Mechanism
[Experimental Condition 2] — Public Feedback-Mechanism without Feedback-Competition
[Experimental Condition 3] — Public Feedback-Mechanism with Feedback-Competition

2022-03-15

2022-05-31

Overtreatment-Rates

Overtreatment-Rates are defined as situations where patients with a minor disease receive the intensive treatment.

Market Efficiency.

For each interaction, efficiency is defined to be one if the patient was treated appropriately and zero otherwise (if overtreated).

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 answer several questions concerning their strategy in the game and beliefs about others.
Stage 6: Participants participate in additional games: an individual risk preference task, a dictator game, a lying task, and a trust game.
Stage 7: Participants fill out a questionnaire.

Randomization is carried out in the experiment by a computer.

at the session level

Yes

6 clusters á 8 individuals per experimental condition.

48 (6 x 8) individuals per experimental condition.

144 (3 x 48) individuals (students at the University of Innsbruck).

Based on previous findings, we performed a power calculation, indicating that we need six clusters á 8 subjects per experimental condition when aiming for a power of 80%.