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Information, Mental Models, and Provider Choice: Protocol for a Randomized Controlled Trial Among Private Clinic Users in Urban Uganda

Last registered on November 27, 2025

Pre-Trial

Trial Information

General Information

Title
Information, Mental Models, and Provider Choice: Protocol for a Randomized Controlled Trial Among Private Clinic Users in Urban Uganda
RCT ID
AEARCTR-0017156
Initial registration date
October 31, 2025

Initial registration date is when the trial was registered.

It corresponds to when the registration was submitted to the Registry to be reviewed for publication.

First published
November 03, 2025, 10:29 AM EST

First published corresponds to when the trial was first made public on the Registry after being reviewed.

Last updated
November 27, 2025, 12:05 PM EST

Last updated is the most recent time when changes to the trial's registration were published.

Locations

Region

Primary Investigator

Affiliation
London School of Economics and Political Science

Other Primary Investigator(s)

PI Affiliation
London School of Economics and Political Science

Additional Trial Information

Status
On going
Start date
2025-11-27
End date
2026-04-30
Secondary IDs
Prior work
This trial does not extend or rely on any prior RCTs.
Abstract
Healthcare quality in low- and middle-income countries (LMICs) remains low and highly variable across providers, with little oversight from regulators. As healthcare is a credence good market, there are information asymmetries between patients and providers: many (good) bad quality providers are (un)able to signal high quality. In Uganda, where 40% of facilities are private for-profit and out-of-pocket spending reaches 35% of health expenditure, private providers have a large incentive to exploit this asymmetry and to respond to patient beliefs about signals of quality. The result is market failure in healthcare, where patients cannot identify quality providers and overpay for inadequate care.

There are two possible causes of the persistence of this information gap between patients and providers. First, patients have limited access to information, absent of objective signals of quality such as provider ratings. Unable to directly evaluate medical expertise or qualifications of a provider, patients infer from a subset of possible characteristics which they observe during their interactions. Second, patients' mental models about healthcare (or how observable signals are weighted in assessments of provider quality) are often incorrect. For example, patients believe overtreatment and extensive testing signal quality, even in self-limiting cases.

In this study, we seek to cross-evaluate two interventions that target these sources of information asymmetry: 1) providing objective quality signals to individuals and 2) addressing errors in conceptual frameworks of healthcare. Earlier this year, we conducted an independent audit of government, private, and non-profit health centres using standard patients, generating objective evidence on healthcare quality, including provider effort, medical appropriateness, patient experience and cost. In Gulu, the audit revealed private providers performing substantially worse on case management, with only 40% SPs managed correctly (allowing for overtreatment). By contrast, non-profit and government providers managed patients correctly 60% of the time – a 50% improvement on private clinics. This evidence will form the basis of both information treatments. Intervention 1 will be delivered to participants through a short video – a voiced over animated presentation comparing provider quality – and report cards with comparative ratings of types of providers (government, public, non-profit). Intervention 2 will include all the information components, but the video will include an additional section addressing misconceptions about healthcare providers, focusing on private clinics’ tendency to overtreatment, lower medical accuracy, and higher costs.

To evaluate the relative effectiveness of information with / without addressing the misconceptions about provider quality signals, we will conduct a randomised controlled trial in Gulu, the largest city in the Northern region of Uganda. Between November 2025 and January 2026, we will recruit 1,500 current users of private providers in central locations; administer a baseline survey and elicit beliefs about the relative quality of government, private, and non-profit providers; then randomise the participants to either receive intervention 1, intervention 2, or the control condition (no information given). Our primary outcomes of interest are: 1) beliefs about providers’ relative quality, captured in a post-intervention module at the time of the survey; and 2) choice of provider when the participant next seeks care. The choice will be captured for 3 months following the survey using WhatsApp – pictures of patient report cards will be used to verify the provider visited. Jointly, these findings will inform if simple information or more extensive public education campaigns can redirect patients away from low quality providers.
External Link(s)

Registration Citation

Citation
Grabowska, Marta and Mylene Lagarde. 2025. "Information, Mental Models, and Provider Choice: Protocol for a Randomized Controlled Trial Among Private Clinic Users in Urban Uganda ." AEA RCT Registry. November 27. https://doi.org/10.1257/rct.17156-2.0
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Experimental Details

Interventions

Intervention(s)
Using data from an independent audit of government, private, and non-profit health centres using standard patients, we designed three information provision interventions:

Intervention 1) providing objective quality signals to individuals. Delivered to participants through a short video – a voiced over animated presentation comparing provider quality – and report cards with comparative ratings of types of providers (government, public, non-profit).

Intervention 2) objective quality signals + addressing errors in conceptual frameworks of healthcare. Intervention 2 will include all the information components, but the video will include an additional section addressing misconceptions about healthcare providers, focusing on private clinics’ tendency to overtreatment, lower medical accuracy, and higher costs.

Intervention 3) A final intervention is a simple screen where we reveal which providers performed best within each category according to SP study results. We do not compare how they did *across* provider types.
Intervention Start Date
2025-11-27
Intervention End Date
2026-01-30

Primary Outcomes

Primary Outcomes (end points)
1) Beliefs about providers' medical quality - by provider
2) Did not choose private provider when seeking care in 3 months subsequent to intervention
Primary Outcomes (explanation)
1) will be captured during the survey through a numerical scale from 0 to 10 using the question "In facilities in Gulu, out of 10 patients, how many received the right diagnosis and drugs for their condition in government health centre / private clinic / non-proft clinic". This question was piloted and revealed good comprehension and variation in answers. The order of providers in the beliefs elicitation will be randomised to prevent ordering effects. The question will be posed in the pre-intervention module and re-elicited after the intervention to provide a baseline control.

2) A dummy variable = 1 if visited a private provider in 3 months after intervention. Primary measure of provider choice will be constructed through verified patient visit records or receipts submitted by the participants over the course of 3 months following the intervention. Participants will be incentivised to submit records - we will run a lottery amongst those who submit with a chance to win 50,000 Ugandan shillings - roughly USD 15. We will set up a business whatsapp account and operate it through a business phone to ensure no data sensitivity issues. We will also remind the participants to submit their patient records every two weeks.

Secondary Outcomes

Secondary Outcomes (end points)
Marginal preferences for providers characterised by: 1) overtreatment, 2) low cost, 3) higher diagnostic effort, 4) better patient manner, 5) nicer facility conditions, 6) less time at facility
Secondary Outcomes (explanation)
Preference parameters are elicited using a discrete choice experiment (DCE). Each subject will complete 4 choice sets in their DCE, with each choice set containing two alternatives for the six attributes. 8 choice sets (16 pairs) in 2 blocks were selected to ensure d-efficiency of the design and randomly assigned to participants.

A logit model will be estimated to obtain preference estimates for the different provider attributes. In addition, the coefficient estimates on the attributes will be compared between the treatment groups - the DCE will be conducted after the intervention to test if preference parameters shift in response to the information videos. We will use treatment groups T0 and T1 (600 obs) to estimate the untreated population preference model, as they receive no information about the performance of private providers versus others. We will then compare these parameters to T2 and T3 group parameters.

Experimental Design

Experimental Design
Sample and Recruitment:

We will conduct a randomized controlled trial with 1,600 participants in Gulu, Uganda. Participating adults will be eligible if 1) they had sought care within the past 12 months; 2) they had sought care from private healthcare providers within the past 12 months; 3) they are able to give a mobile phone contact number (essential for a part of the primary outcome, and enabling future follow-up data collection if desired).

We will recruit individuals who have recently used private health facilities in Gulu. In a recent pilot, conducted in a busy market location and a well-off neighbourhood of Gulu, 92% of respondents sought care in past 12 months and 2/3 of those had used private providers. We will recruit in central locations in the city. Participants will be screened for eligibility and invited to participate in a 35 minute survey. To avoid interviewing the same individuals, enumerators will work in teams under the guidance of a supervisor and recruitment sites will be split between the teams.

Randomization:
After completing the baseline portion of the survey and the belief elicitation module, participants will be randomly assigned to one of four groups:
- Control group (T0): Receives no additional information; views a placebo video of unrelated results from the SP study. n=300
- Best performer group (T1): Received the placebo video; in addition, learns only information on which providers did best in the SP study, by category. This group tests if preferred provider can be shifted *within* provider, if not between types. n=300
- Objective Quality Information (T2): Receives objective information on provider quality based on standardized patient audits conducted earlier this year. n=500
- Conceptual Framework Correction (T3): Receives treatment T2 and additional information addressing common misconceptions about healthcare quality signals. n=500
Experimental Design Details
Not available
Randomization Method
SurveyCTO calculations, performed per survey
Randomization Unit
Individual
Was the treatment clustered?
No

Experiment Characteristics

Sample size: planned number of clusters
N/A
Sample size: planned number of observations
1,600
Sample size (or number of clusters) by treatment arms
300 T0, 300 T1, 500 T2, 500 T3.
Minimum detectable effect size for main outcomes (accounting for sample design and clustering)
Primary outcome: beliefs about private providers (expecting decrease) and government providers (expecting increase), using one-sided tests and ANCOVA specification with baseline controls, α = 0.025 per primary test, expected MDEs: 0.30-0.35 points (4-5% of scale) Primary outcome: choice of provider. As we select for private provider users, I expect 90% of control group to visit the private provider (the rate observed in the pilot was higher). In comparisons to control group, with 30% attrition and two-sided (one-sided) tests the MDE is 6.9 (5.9) percentage points. With comparisons between treatment 1 and 2, the MDE depends crucially on the effectiveness of treatment 1. If we assume (conservatively) that in treatment 1, 50% (the least powered) choose the private provider, with 30% attrition and two-sided (one-sided) tests we can detect a 10.5 (9.3) percentage points change (reduction).
Supporting Documents and Materials

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IRB

Institutional Review Boards (IRBs)

IRB Name
LSE Research Ethics Committee
IRB Approval Date
2024-09-05
IRB Approval Number
406984
IRB Name
Uganda National Council for Science and Technology (UNCST)
IRB Approval Date
2025-04-11
IRB Approval Number
SS3699ES
IRB Name
Lacor Hospital Institutional REC
IRB Approval Date
2024-12-30
IRB Approval Number
LACOR-2024-360