Recruiting

AI Tool

Sponsor:

Takeoff41, Inc.

Code:

NCT07414576

Conditions

Intestinal Failure

Eligibility Criteria

Sex: All

Age: 0

Healthy Volunteers: Not accepted

Interventions

AI-driven total parenteral nutrition (TPN)

Study Details

Brief summary:

This study tests whether an artificial intelligence (AI) tool can help doctors order total parenteral nutrition (TPN) for babies in the neonatal intensive care unit (NICU). Premature babies often cannot eat by mouth and need nutrition delivered through an IV. Ordering TPN is complex, time-consuming, and mistakes can happen. This study will test an AI tool that suggests TPN formulas to doctors based on each baby's lab values and health information. Doctors can accept, change, or reject the suggestions at any time. The main goal is to measure how often doctors accept the AI suggestions. The study will also track time to complete TPN orders, weight changes, days on TPN, whether lab values stay in normal ranges, provider satisfaction, and baby health outcomes including complications such as lung disease, brain bleeding, infections, and other conditions common in premature babies. Babies admitted to the NICU who need TPN may participate if their doctors agree to use the tool. Each baby will be in the study while they need TPN, typically about 14 days. The AI tool only makes suggestions and does not replace doctor decision-making. All other care remains the same as standard practice.

Conditions

Intestinal Failure

Study ID

NCT07414576

Start date

Jan 21, 2026

Status verified date

Jan, 2026

Completion date

Feb 21, 2027

Anticipated

Primary completion date

Feb 21, 2027

Anticipated

Eligibility Criteria

Eligibility Criteria

Sex: All

Age: 0

Healthy Volunteers: Not accepted

Inclusion Criteria:

  • Any newborns or infants requiring total parenteral nutrition in a neonatal ICU that performs daily laboratory tests
  • 6-month old at the time of admission
  • Any gestational age or birthweight
  • Any race or sex

Exclusion Criteria:

\- Infants deem unfit for the suggested TPN due to safety concerns by physicians

Study Design

Enrollment

260 participants

Anticipated

Allocation

Non randomized

Intervention Model

Sequential

Primary purpose

Health Services Research

Interventions and Outcome Measures

Arms

no intervention: Standard TPN Ordering (Control)

Patients admitted during Period 1. Providers use current standard TPN ordering practice without AI assistance. Serves as baseline comparison.

experimental: AI-driven total parenteral nutrition (TPN)

Patients admitted during Period 2. Providers use the AI-assisted TPN decision support tool integrated with Epic to order TPN. Providers may opt out and use traditional ordering if needed.

Interventions

AI-driven total parenteral nutrition (TPN)

An AI-driven clinical decision support (CDS) software integrated with EHR system that provides TPN composition recommendations to NICU providers. The tool uses patient lab values, basic profile (days since birth, weight, gestational age), and physician inputs to suggest TPN components. Providers can accept, modify, or decline if needed. The final prescribing authority remains with the providers. The intervention targets provider workflow efficiency while maintaining precision and equivalent patient outcomes (including labs and long-term adverse outcomes).

Primary outcome measure

  • System Acceptance Rate [ Time Frame: 10 months ]

Central Contacts and Locations

Central contacts

Thanaphong Phongpreecha, PhD

551-482-4827joe@takeoff41.com

Chandra Vikram, Bachelor

317-724-2159chandra101@takeoff41.com

Locations

Stanford University

Recruiting

Stanford, California, United States, 94305

Contacts

Thanaphong Phongpreecha, PhD

551-482-4827joe@takeoff41.com

Barbara Chargin

650-723-7222bchargin@stanford.edu

More Information

Sponsor

Takeoff41, Inc.

Last update posted

Feb 17, 2026

Last verified

Jan, 2026

Keywords

  • parenteral nutrition

Trial information was received from ClinicalTrials.gov and was last updated on 2026-09-10. This information was provided to ClinicalTrials.gov by Takeoff41, Inc. on 2026-02-17.