Impact of AI Care Summaries on Caregiver Comprehension
Purpose
The primary objective is to assess if an AI-generated plain language summary of an inpatient progress note can improve caregiver understanding of their child's hospitalization by measuring differences in Hospital Course Comprehension scores, based on their responses to a survey regarding their child's hospital course. Secondary objectives include exploring the caregiver's perspective on the use of AI in clinical care, as well as 7-day and 30-day readmission rates, 7-day and 30-day emergency department (ED) re-utilization rates, length of stay, and environmental impact measures.
Conditions
- Health Literacy
- Comprehension
- Patient Satisfaction
Eligibility
- Eligible Ages
- Over 18 Years
- Eligible Sex
- All
- Accepts Healthy Volunteers
- No
Inclusion Criteria
Caregiver of a patient 10 years and younger admitted to Children's National Hospital, whose preferred language is English
Exclusion Criteria
- Caregivers of patients older than age 10 years - Caregivers of patients not admitted to the Hospital Medicine service - Caregivers of admitted patients whose preferred language is non-English - Patients whose care summaries are deemed inaccurate by their attending physician and not eligible for sharing with their caregiver.
Study Design
- Phase
- N/A
- Study Type
- Interventional
- Allocation
- Randomized
- Intervention Model
- Parallel Assignment
- Primary Purpose
- Health Services Research
- Masking
- Single (Outcomes Assessor)
Arm Groups
| Arm | Description | Assigned Intervention |
|---|---|---|
|
No Intervention Standard Hospital Communication |
Standard hospital communication |
|
|
Experimental Standard Hospital Communication Plus AI Care Summary |
Attending-approved written AI Care Summary provided to caregiver |
|
Recruiting Locations
Washington D.C., District of Columbia 20010
Kristen Johnson, MD, MS
More Details
- Status
- Recruiting
- Sponsor
- Children's National Research Institute
Detailed Description
This randomized controlled trial evaluates whether AI-generated plain-language summaries of inpatient progress notes improve caregiver understanding of a child's hospitalization. The intervention uses a Children's National HIPAA-compliant large language model hosted within the institution's Azure environment to generate caregiver-facing summaries from attending-signed inpatient progress notes. Eligible caregivers are randomized to receive either standard hospital communication alone or standard communication plus an attending-approved AI-generated care summary. Before distribution to caregivers, attending physicians review generated summaries for clinical appropriateness and accuracy. Caregiver understanding of the hospitalization is assessed using a structured survey administered during the hospitalization. Responses are compared with information documented in the medical record to evaluate comprehension. Additional exploratory analyses evaluate implementation outcomes, caregiver perceptions of AI-assisted communication, physician acceptance of generated summaries, healthcare utilization outcomes following hospitalization, and operational measures related to AI summary generation. Statistical analyses will compare outcomes between randomized groups while controlling for relevant covariates when appropriate.