A Pilot Ptudy of an LLM Tool to Support Frontline Health Workers in Low-Resource Settings
Trial Snapshot
- Phase
- Not Applicable
- Status
- Completed
- Sponsor
- Yale University
- Enrollment
- 491
- Locations
- 2
- Primary Endpoint
- Indicator for an Error in the Treatment plan (with the Potential for Harm)
Study Overview
Brief Summary
The goal of this study is to learn if computer-assisted advice can help improve patient care in Nigerian health clinics. The main question it aims to answer is: does giving healthcare workers instant computer feedback help them make better decisions about patient care?
Researchers will compare patient care notes written by healthcare workers before and after they receive computer feedback to see if the feedback improves care quality. A doctor who doesn't know if feedback was given will review these notes.
Participants will:
- Be seen by a community healthcare worker who uses the computer feedback system
- Be treated by a fully trained medical doctor
- Get tested for malaria, anemia, or urinary tract infections if they have certain symptoms
Detailed Description
This project tests whether Large Language Models (LLMs) can improve patient care in Nigerian primary care clinics by giving customized and instant feedback to the provider in natural language. An LLM-based tool integrated into an electronic patient record management system provides "second opinions" to community health extension workers (CHEWs) at two clinics in Nigeria. These second opinions are intended to mirror what a reviewing physician might advise the CHEWs after seeing or hearing their initial report on a patient.
For the main analysis, this study employs a within-patient comparison of two patient notes created by the CHEW; one during the initial patient consultation, and one after the LLM feedback was received. The patient is also seen by a fully trained medical officer who is in charge of patient care. The MO conducts a blinded review of the CHEW's patient notes to measures changes in the CHEW's care as a result of the LLM feedback. The data comes from the information captured in the electronic medical record (EMR) of the patient and from survey data collected from CHEWs, reviewing MOs, and a panel of reviewing Medical Doctors.
Study Design
- Study Type
- Interventional
- Allocation
- Na
- Intervention Model
- Single Group
- Primary Purpose
- Health Services Research
- Masking
- None
Masking Description
The Medical Officers and the panel of Medical Doctors are both blinded to which note has been generated with LLM assistance.
Eligibility Criteria
- Sex
- All
- Accepts Healthy Volunteers
- No
Inclusion Criteria
- •Patient is at the clinic for outpatient consultation
- •Parent/guardian consent is required for individuals under 18
Exclusion Criteria
- •Patient does not require emergency care
- •Patient is not at the clinic for a checkup (e.g. weight, blood pressure, follow up after recovery)
- •Patient is not a trauma patient (visit is not for an accident, wound or injury)
- •Patient is not at the clinic for a scheduled procedure or a birth
Arms & Interventions
Clinical Assessment with and without LLMs
The investigators employ a within-patient design. Patients receive two sequential assessments from a Community Health Extension Worker: first without and then with Large Language Model assistance.
Intervention: Large Language Model Clinical Decision Support (Other)
Outcomes
Primary Outcomes
Indicator for an Error in the Treatment plan (with the Potential for Harm)
Time Frame: Through study completion, an average of six months
During SOAP note evaluation, the MO is asked to indicate whether the treatment plan for the patient contains any errors, conditional on the MO's own diagnosis. This is coded as 1 if the MO indicates there is an error and 0 otherwise. The introductory text (here for SOAP Note A) is: Please evaluate whether the treatment in SOAP Note A is appropriate for this patient's condition. Please base this on your own diagnosis, not the CHEW's diagnosis in SOAP Note A. This is followed by the question: Is the treatment plan for the patient in SOAP Note A completely appropriate given your own diagnosis (accounting for conditional treatments based on medical tests)? Answer "No" if the patient should receive different medical care given your diagnosis. This can include both minor differences (for example, the patient should be advised to rest) and major errors (for example, the patient should receive a completely different set of medications). (Answer options: yes/no/unsure)
Indicator for an Error in the Treatment Plan that Causes a Loss of at least X Quality-Adjusted Life Days
Time Frame: Through study completion, an average of six months
This variable is coded as 1 if the MO indicates there is such an error and 0 otherwise. X is defined to be the highest benchmark on the appropriate DALY scale so that at least 5% of patients have an error that large in the unassisted SOAP note. In other words, severe errors are any errors that generate a harm rating at or above the 95th percentile of harm on the unassisted scale (pooling child and adult scales).
Indicator for the Better Treatment Plan (as Determined by the MOs)
Time Frame: Through study completion, an average of six months
Based on the DALY rating of SOAP Note A vs. B (counting instances with no errors as 0 DALY loss), the indicator is coded as 1 if the SOAP note has the better treatment plan (lower DALY loss) and 0 if MOs judge both notes to be the same in response to the following question: Are there any meaningful differences in the treatment plans of SOAP Note A and B?
Indicator for whether Treatment is Consistent with a Predetermined "Standard of Care"
Time Frame: Through study completion, an average of six months
At-risk patients receive malaria, anemia and UTI screening in accordance with certain demographic criteria. A dataset is then constructed with one observation for each (patient, screening test, note), up to six per patient. The indicator of treatment misallocation records whether a patient was incorrectly treated for a condition based on the test result or lack of symptoms. The variable is coded as 1 if the patient tested positive and either received inappropriate or no treatment. It is also coded as 1 if the patient tested negative or was not tested based on the symptom screen but received treatment for the condition. The variable is only coded as 0 if the patient tested negative and was correctly not treated for the corresponding condition, or if they tested positive and received the correct treatment.
Secondary Outcomes
- Indicators Denoting Diagnosis and Treatment Alignment Between CHEWs and MOs(Through study completion, an average of six months)
- Alternative Indicators for Treatment Misallocation(Through study completion, an average of six months)
- Relationship of QALY Loss to Severity of Patient Condition(Through study completion, an average of six months)
- Indicators for the Appropriateness of Medical Testing Decisions(Through study completion, an average of six months)
- Average and Distribution of DALY Lost(Through study completion, an average of six months)
- MO Evaluation of SOAP Notes: Deviations from the MO's SOAP(Through study completion, an average of six months)
- MO Evaluation of SOAP Notes: Types of Harm Incurred(Through study completion, an average of six months)
- MO Evaluation of SOAP Notes: Measuring Healthy Time Lost in DALY(Through study completion, an average of six months)
- MD Evaluation of CHEW and MO Notes: Flagging MO Error(Through study completion, an average of six months)
- MD Evaluation of CHEW and MO Notes: SOAP Note Rating(Through study completion, an average of six months)
- MD Evaluation of CHEW and MO Notes: LLM Review(Through study completion, an average of six months)
- Indicator for the Appropriateness of Triage Decisions(Through study completion, an average of six months)
Investigators
Jason Abaluck
Professor of Economics, Yale School of Management
Yale University