The Effects of Price Reductions on Uptake of Preventive Dental Care

Patients in this trial will be randomized to either one Holdout condition, where patients do not receive any WhatsApp message or communications about dental check-ups, or to one of three treatment conditions where patients receive one WhatsApp message encouraging them to get preventive dental check-ups, offered by their hospital. The three treatment conditions differ in the price of the check-up stated in the message and the inclusion of a discount coupon.

1. Large price discount condition: Patients receive an offer to get a preventive dental check-up for $4,990 CLP.
2. Intermediate price discount condition: Patients receive an offer to get a preventive dental check-up for $9,990 CLP.
3. Intermediate price discount plus coupon condition: Patients receive an offer to get a preventive dental check-up for $9,990 CLP plus a discount coupon for future treatments. The coupon value is CLP 20,000 for treatments costing over CLP 150,000 and is subject to additional terms and conditions by the hospital.

The original price of the preventive dental check-up is $24,990 CLP.

Randomization to study conditions will be stratified by whether patients are from Group 1 or 2 (see participation criteria).

The investigators ask the following research questions:

1. Does sending patients WhatsApp messages encouraging them to schedule preventive dental check-ups increase dental check-up uptake? The investigators hypothesize that sending patients WhatsApp messages will increase uptake of preventive dental checkups relative to not sending them any messages.
2. How do discounts affect preventive dental check-up uptake? The investigators expect higher uptake of preventive dental checkups in the 4,990 CLP condition relative to the 9,990 CLP condition. The investigators also test whether presenting a coupon for future treatment increases uptake of preventive dental check-up.
3. Do discounts influence downstream engagement with dental care? Beyond initial scheduling, the investigators examine how a larger discount and the provision of a coupon for future treatment affect patients' uptake of additional dental treatment.

Analysis:

The investigators will run ordinary least squares regressions (OLS) with robust standard errors to predict outcome variables.

To test whether the WhatsApp messages increase dental check-up uptake, the investigators will compare the holdout condition to the combination of the three treatment arms. The key independent variable in the OLS regressions will be an indicator for whether each patient received (vs. did not receive) a WhatsApp message in the experiment.

To examine the impact of discounts, the investigators will estimate models with indicator variables for (1) the large discount (4,990 CLP) and (2) the coupon condition (9,990 CLP with coupon), using 9,990 CLP without a coupon as the reference category. This specification allows the investigators to separately estimate (a) the effect of large versus intermediate discount (4990 vs. 9,990 CLP) and (b) the effect of presenting a coupon for future service (9,990 with vs. without a coupon).

These regressions will be run with and without control variables. Control variables include:

• Time fixed effects
• Participant age
• Participant gender (male, female, other/unknown)
• Whether patients had had at least one dental visit at this hospital in the past
• Region or clinic indicators, if available
• An indicator for whether patients are in Group 1 or Group 2 (see participation criteria)

For robustness, the investigators will conduct logit models as well.

The investigators will examine whether treatment effects (i.e., differences in uptake between the discount and coupon conditions as well as between the treatment and holdout conditions) vary across patient subgroups. In particular, the following moderators will be tested:

• Whether the patient had completed any dental visit prior to the experiment (based on the data the hospital can provide)
• Whether the patient had completed any preventive medical exam (e.g., mammogram) prior to the experiment (based on the data the hospital can provide)
• Prior visits to the clinic network that were not for dental purposes (based on the data the hospital can provide)
• Insurance type (public vs. private)
• Patient age
• Patient gender (male vs. female)

For exploratory purposes, the investigators will look at longer time windows (e.g., 90 days) for preventive check-up appointment attendance and examine other behaviors related to preventive check-up appointment (e.g., rescheduling, cancellation)