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Structured Handoff Using Intelligent Framework for Transitions Trial (SHIFT)

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University of Pennsylvania

Status

Enrolling

Conditions

Physician Workflow
Large Language Model
Transitions of Care
Electronic Medical Record
Artificial Intelligence (AI)

Treatments

Other: LLM tool to draft handoff

Study type

Interventional

Funder types

Other

Identifiers

NCT07251907
SHIFT Trial

Details and patient eligibility

About

Inpatient general medicine attendings will be randomized to have an LLM feature turned on to provide a draft of an off-service handoff within Carelign (an EHR-adjacent provider communication tool). Providers who have access to this feature will be clearly instructed that if they use the LLM-generated draft, they must review and edit it as necessary before finalizing. The study will assess measures of documentation burden (as it relates to writing handoff) - including time spent writing handoff - and work exhaustion in both intervention and control groups.

Full description

The Structured Handoff Using Intelligent Framework for Transitions (SHIFT) Trial is a pragmatic, parallel-group randomized controlled trial designed to evaluate whether an artificial intelligence (AI) tool integrated into the Carelign handoff platform can reduce documentation time and burden among inpatient hospitalists. The AI feature uses a large language model (LLM) tuned for clinical summarization to generate an editable draft of the end-of-rotation handoff. Hospitalists randomized to the intervention arm will have access to a "Draft Handoff" button within Carelign, which automatically generates a structured, editable handoff draft using data from recent notes in the electronic health record. Clinicians are required to review and edit all draft content before finalizing and sharing the handoff with colleagues. Providers in the control arm will continue using standard handoff workflows within Carelign without AI assistance.

The study will be conducted on general medicine services at the Hospital of the University of Pennsylvania (HUP) and Penn Presbyterian Medical Center (PPMC). Eligible participants include attending hospitalists scheduled for at least five consecutive days on service. Providers will be randomized 1:1 to the intervention or control arm, stratified by site and team. The study will enroll approximately 90 hospitalists contributing about 120 eligible rotations over a 12-week period. The primary outcome is total time (in minutes) spent drafting the end-of-rotation handoff, measured using automated Carelign audit logs. Secondary outcomes include documentation burden (modified NASA Task Load Index), work exhaustion (Stanford Professional Fulfillment Index), and self-reported drafting time. Exploratory measures include tool usability (Net Promoter Score), receiving provider ratings of handoff quality, and electronic health record (EHR) usage metrics (total and after-hours "pajama time").

The trial is unblinded and will be conducted under real-world conditions to maximize generalizability. Analyses will follow an intention-to-treat approach, using linear mixed-effects models with random intercepts for provider to account for repeated rotations. All data will be stored and analyzed in secure, HIPAA-compliant institutional environments. The study has received approval from the University of Pennsylvania Institutional Review Board and Penn Medicine's AI Governance Council.

Enrollment

90 estimated patients

Sex

All

Ages

18+ years old

Volunteers

No Healthy Volunteers

Inclusion criteria

  • General medicine attending physicians at HUP (Medicine, Solid Oncology) or PPMC (Medicine) services.
  • Scheduled for ≥5 consecutive days on service.

Exclusion criteria

- Jeopardy attendings and moonlighters

Trial design

Primary purpose

Health Services Research

Allocation

Randomized

Interventional model

Parallel Assignment

Masking

None (Open label)

90 participants in 2 patient groups

Standard of care
No Intervention group
Description:
The control arm will continue usual care without access to the AI drafting feature. Attendings will prepare handoffs themselves using current workflows within Carelign.
LLM Tool
Experimental group
Description:
The intervention arm will have access to an LLM-assisted draft generation feature within Carelign. This will generate a draft of the handoff, which the provider will then edit as necessary before finalizing.
Treatment:
Other: LLM tool to draft handoff

Trial contacts and locations

1

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Central trial contact

Eric Bressman, MD; Haideliza Soto Calderon

Data sourced from clinicaltrials.gov

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