Trial Outcomes & Findings for A Trial of AI-Powered Text Message Outreach on Well-Child Visit Completion (NCT NCT06698640)
NCT ID: NCT06698640
Last Updated: 2026-06-22
Results Overview
Binary indicator of whether each participant completed at least one well-child visit by December 31, 2025, ascertained through administrative claims using HEDIS technical specifications
COMPLETED
NA
2821 participants
Up to 7 months post-randomization (June 1 - December 31, 2025)
2026-06-22
Participant Flow
Of 3,908 Medicaid beneficiaries meeting eligibility, 3,071 were overdue for a well-child visit per HEDIS. Of these, 2,847 were attributed to the trial's Virginia health system (224 out-of-network not randomized). After excluding 26 (do-not-contact n=19; inactive phone n=7), 2,821 participants in 2,039 households were randomized 1:1:1 on June 1, 2025 (Arm 1: 618 households/927 children; Arm 2: 633/949; Arm 3: 788/945).
Only Medicaid-enrolled children aged 0-21 years were enrolled. Parents/caregivers who received texts or interacted with the AI scheduler on behalf of children were not enrolled; no parent/caregiver data were collected and they are not represented in Baseline Characteristics, Outcome Measures, or Adverse Events. Randomization was conducted at the household level on June 1, 2025, in a 1:1:1 ratio.
Unit of analysis: Household
Participant milestones
| Measure |
Control
Participants received standard health plan outreach consisting of periodic mailed reminders. Families retained access to all standard appointment scheduling methods including telephone calls to provider offices.
|
Automated SMS
Participants received standardized automated text message reminders at predetermined intervals (initial contact, 2-week follow-up, 4-week follow-up). Messages provided general information about visit importance and instructions to call providers to schedule. Messages did not include interactive scheduling or conversational capabilities.
|
Automated SMS + Scheduling Assistance
Participants received automated text message reminders with response options. When families expressed interest, structured follow-up texts collected appointment preferences. An AI-powered telephone scheduling system (GPT-4o, OpenAI) contacted the child's primary care provider directly to book appointments on behalf of families. The system disclosed its automated nature at call initiation and escalated to human staff when clinic policies were incompatible with automated scheduling or technical failures occurred.
|
|---|---|---|---|
|
Overall Study
STARTED
|
927 618
|
949 633
|
945 788
|
|
Overall Study
COMPLETED
|
927 618
|
949 633
|
945 788
|
|
Overall Study
NOT COMPLETED
|
0 0
|
0 0
|
0 0
|
Reasons for withdrawal
Withdrawal data not reported
Baseline Characteristics
A Trial of AI-Powered Text Message Outreach on Well-Child Visit Completion
Baseline characteristics by cohort
| Measure |
Automated SMS
n=949 Participants
Participants received standardized automated text message reminders at predetermined intervals (initial contact, 2-week follow-up, 4-week follow-up). Messages provided general information about visit importance and instructions to call providers to schedule. Messages did not include interactive scheduling or conversational capabilities.
|
Control
n=927 Participants
Participants received standard health plan outreach consisting of periodic mailed reminders. Families retained access to all standard appointment scheduling methods including telephone calls to provider offices.
|
Automated SMS + Scheduling Assistance
n=945 Participants
Participants received automated text message reminders with response options. When families expressed interest, structured follow-up texts collected appointment preferences. An AI-powered telephone scheduling system (GPT-4o, OpenAI) contacted the child's primary care provider directly to book appointments on behalf of families. The system disclosed its automated nature at call initiation and escalated to human staff when clinic policies were incompatible with automated scheduling or technical failures occurred.
|
Total
n=2821 Participants
Total of all reporting groups
|
|---|---|---|---|---|
|
Age, Continuous
|
13.0 Years
STANDARD_DEVIATION 5.4 • n=20 Participants
|
12.8 Years
STANDARD_DEVIATION 5.4 • n=20 Participants
|
12.7 Years
STANDARD_DEVIATION 5.3 • n=40 Participants
|
12.8 Years
STANDARD_DEVIATION 5.4 • n=6 Participants
|
|
Sex: Female, Male
Female
|
474 Participants
n=20 Participants
|
449 Participants
n=20 Participants
|
455 Participants
n=40 Participants
|
1378 Participants
n=6 Participants
|
|
Sex: Female, Male
Male
|
475 Participants
n=20 Participants
|
478 Participants
n=20 Participants
|
490 Participants
n=40 Participants
|
1443 Participants
n=6 Participants
|
|
Race (NIH/OMB)
American Indian or Alaska Native
|
0 Participants
n=20 Participants
|
0 Participants
n=20 Participants
|
0 Participants
n=40 Participants
|
0 Participants
n=6 Participants
|
|
Race (NIH/OMB)
Asian
|
41 Participants
n=20 Participants
|
70 Participants
n=20 Participants
|
56 Participants
n=40 Participants
|
167 Participants
n=6 Participants
|
|
Race (NIH/OMB)
Native Hawaiian or Other Pacific Islander
|
0 Participants
n=20 Participants
|
0 Participants
n=20 Participants
|
0 Participants
n=40 Participants
|
0 Participants
n=6 Participants
|
|
Race (NIH/OMB)
Black or African American
|
379 Participants
n=20 Participants
|
386 Participants
n=20 Participants
|
378 Participants
n=40 Participants
|
1143 Participants
n=6 Participants
|
|
Race (NIH/OMB)
White
|
331 Participants
n=20 Participants
|
303 Participants
n=20 Participants
|
330 Participants
n=40 Participants
|
964 Participants
n=6 Participants
|
|
Race (NIH/OMB)
More than one race
|
11 Participants
n=20 Participants
|
2 Participants
n=20 Participants
|
6 Participants
n=40 Participants
|
19 Participants
n=6 Participants
|
|
Race (NIH/OMB)
Unknown or Not Reported
|
187 Participants
n=20 Participants
|
166 Participants
n=20 Participants
|
175 Participants
n=40 Participants
|
528 Participants
n=6 Participants
|
|
Ethnicity (NIH/OMB)
Hispanic or Latino
|
1 Participants
n=20 Participants
|
3 Participants
n=20 Participants
|
6 Participants
n=40 Participants
|
10 Participants
n=6 Participants
|
|
Ethnicity (NIH/OMB)
Not Hispanic or Latino
|
762 Participants
n=20 Participants
|
761 Participants
n=20 Participants
|
770 Participants
n=40 Participants
|
2293 Participants
n=6 Participants
|
|
Ethnicity (NIH/OMB)
Unknown or Not Reported
|
186 Participants
n=20 Participants
|
163 Participants
n=20 Participants
|
169 Participants
n=40 Participants
|
518 Participants
n=6 Participants
|
|
Number of Participants with Open Well-Child Visit Gap at Baseline
|
949 Participants
n=20 Participants
|
927 Participants
n=20 Participants
|
945 Participants
n=40 Participants
|
2821 Participants
n=6 Participants
|
PRIMARY outcome
Timeframe: Up to 7 months post-randomization (June 1 - December 31, 2025)Population: All randomized participants with available claims data through December 31, 2025 (intent-to-treat population). Participants with no claims data were assumed to have not completed a well-child visit.
Binary indicator of whether each participant completed at least one well-child visit by December 31, 2025, ascertained through administrative claims using HEDIS technical specifications
Outcome measures
| Measure |
Control
n=927 Participants
Participants received standard health plan outreach consisting of periodic mailed reminders. Families retained access to all standard appointment scheduling methods including telephone calls to provider offices.
|
Automated SMS
n=949 Participants
Participants received standardized automated text message reminders at predetermined intervals (initial contact, 2-week follow-up, 4-week follow-up). Messages provided general information about visit importance and instructions to call providers to schedule. Messages did not include interactive scheduling or conversational capabilities.
|
Automated SMS + Scheduling Assistance
n=945 Participants
Participants received automated text message reminders with response options. When families expressed interest, structured follow-up texts collected appointment preferences. An AI-powered telephone scheduling system (GPT-4o, OpenAI) contacted the child's primary care provider directly to book appointments on behalf of families. The system disclosed its automated nature at call initiation and escalated to human staff when clinic policies were incompatible with automated scheduling or technical failures occurred.
|
|---|---|---|---|
|
Number of Participants With Well-Child Visit Completion
|
218 Participants
|
209 Participants
|
280 Participants
|
SECONDARY outcome
Timeframe: Up to 7 months post-randomization (June 1 - December 31, 2025)Population: Time-motion analysis at the appointment level. For traditional scheduling (Arm 1), 100 randomly sampled appointments scheduled manually by care team staff were analyzed using EHR timestamps, Twilio call logs, and activity logs. For AI-facilitated scheduling (Arm 3), all 63 successfully booked appointments were analyzed. Arm 2 contributed no appointments as that arm received reminders only without scheduling assistance. In both analyzed arms, each appointment corresponded to a unique participant.
Mean staff time in minutes per successfully scheduled appointment, assessed via time-motion analysis. For traditional care team scheduling, staff time was measured from initial scheduling activity to appointment confirmation using EHR timestamps, Twilio call logs, and activity logs. For AI-facilitated scheduling, staff time was measured from appointment request to completion of quality assurance review.
Outcome measures
| Measure |
Control
n=100 Scheduled appointment
Participants received standard health plan outreach consisting of periodic mailed reminders. Families retained access to all standard appointment scheduling methods including telephone calls to provider offices.
|
Automated SMS
Participants received standardized automated text message reminders at predetermined intervals (initial contact, 2-week follow-up, 4-week follow-up). Messages provided general information about visit importance and instructions to call providers to schedule. Messages did not include interactive scheduling or conversational capabilities.
|
Automated SMS + Scheduling Assistance
n=63 Scheduled appointment
Participants received automated text message reminders with response options. When families expressed interest, structured follow-up texts collected appointment preferences. An AI-powered telephone scheduling system (GPT-4o, OpenAI) contacted the child's primary care provider directly to book appointments on behalf of families. The system disclosed its automated nature at call initiation and escalated to human staff when clinic policies were incompatible with automated scheduling or technical failures occurred.
|
|---|---|---|---|
|
Staff Time Per Successfully Scheduled Appointment
|
14.0 Minutes
Standard Deviation 4.1
|
—
|
2.0 Minutes
Standard Deviation 1.0
|
SECONDARY outcome
Timeframe: Up to 2 weeks from initiation of first automated scheduling attemptPopulation: All Arm 3 participants for whom at least one automated scheduling attempt was initiated during the intervention period (June 9 - July 14, 2025).
Percentage of automated scheduling attempts in Arm 3 requiring human staff intervention, with reasons for escalation documented.
Outcome measures
| Measure |
Control
n=75 Scheduling attempts
Participants received standard health plan outreach consisting of periodic mailed reminders. Families retained access to all standard appointment scheduling methods including telephone calls to provider offices.
|
Automated SMS
Participants received standardized automated text message reminders at predetermined intervals (initial contact, 2-week follow-up, 4-week follow-up). Messages provided general information about visit importance and instructions to call providers to schedule. Messages did not include interactive scheduling or conversational capabilities.
|
Automated SMS + Scheduling Assistance
Participants received automated text message reminders with response options. When families expressed interest, structured follow-up texts collected appointment preferences. An AI-powered telephone scheduling system (GPT-4o, OpenAI) contacted the child's primary care provider directly to book appointments on behalf of families. The system disclosed its automated nature at call initiation and escalated to human staff when clinic policies were incompatible with automated scheduling or technical failures occurred.
|
|---|---|---|---|
|
Human Escalation Rate for Automated Scheduling Attempts
|
16.0 Percentage of scheduling attempts
|
—
|
—
|
Adverse Events
Control
Automated SMS
Automated SMS + Scheduling Assistance
Serious adverse events
Adverse event data not reported
Other adverse events
Adverse event data not reported
Additional Information
Results disclosure agreements
- Principal investigator is a sponsor employee
- Publication restrictions are in place