Clinical Evaluation of AI Decision Support for Early Rehabilitation After Surgery

NCT07406269 | Not Yet Recruiting

• 1 other identifier: DESIRE implementation
• Study Type: interventional
• Target: 103
• Locations: 1 country
• Sites: 1

Brief Summary

After gastrointestinal or oncology surgery, it can be difficult to determine when a patient is ready to safely begin early rehabilitation or move toward discharge. Delays may prolong hospital stay, while premature decisions may increase risks. This study evaluates an artificial intelligence (AI)-based decision support tool that analyzes routinely collected hospital data to identify patients who are likely ready for early rehabilitation and discharge planning after surgery. The tool provides a simple yes/no output to support clinicians in their decision-making. The AI tool does not replace clinical judgment. Treating physicians remain fully responsible for all care decisions. The purpose of this study is to examine how well this tool performs in clinical practice and how it can be safely and effectively implemented to support postoperative care.

Conditions

Oncologic Surgery; Gastrointestinal Cancers

Keywords

Artificial Intelligence; Clinical Decision Support System; Postoperative Care; Early Rehabilitation; Gastrointestinal Surgery; Oncological Surgery; Predictive Modeling

Outcome Measures

Primary Outcomes (1)

• Proportion of patients requiring unplanned escalation of hospital-specific care within 30 days after early transfer to rehabilitation area.

  This is a composite outcome, consisting of any of the following events: ICU admission Re-operation Radiological intervention Administration of intravenous antibiotics Respiratory failure (new need for supplemental oxygen) 30-day mortality 30-day emergency readmission

  From postoperative day 2 (time of AI prediction and potential transfer to rehabilitation area) through 30 days after surgery

Study Arms (1)

Cohort of 103 patients undergoing GE/oncological surgery and admitted >2 days after surgery

EXPERIMENTAL

Device: DESIRE: AI-Based Clinical Decision Support for Postoperative Rehabilitation Planning

Interventions

DESIRE: AI-Based Clinical Decision Support for Postoperative Rehabilitation Planning DEVICE

The intervention consists of the clinical use of a locked, non-adaptive artificial intelligence (AI)-based clinical decision support system (DESIRE) that analyzes routinely collected electronic health record data to predict, on postoperative day 2, the risk that a patient will require hospital-specific interventions after gastrointestinal or oncological surgery. The system automatically extracts demographic, perioperative, vital sign, laboratory, and medication-related variables and generates a binary (yes/no) output indicating whether the patient is likely to be at low risk for requiring additional hospital care. A predefined conservative threshold is used to identify patients eligible for early rehabilitation.

Cohort of 103 patients undergoing GE/oncological surgery and admitted >2 days after surgery

Eligibility Criteria

• Age: 18 Years+
• Sex: all
• Healthy Volunteers: No
• Age Groups: Adult (18-64), Older Adult (65+)

You may qualify if:

• Adults aged 18 years or older
• Undergoing gastrointestinal or oncological surgery
• Postoperatively admitted to the surgical ward
• Expected to remain admitted for at least 2 days after surgery

You may not qualify if:

• Admitted to the intensive care unit (ICU) at the time of prediction on postoperative day 2
• Inability to provide informed consent in Dutch or English

Sponsors & Collaborators

• Erasmus Medical Center: lead

Study Sites (1)

Erasmus MC, University Medical Center Rotterdam

Rotterdam, Netherlands

Location

MeSH Terms

Conditions

Gastrointestinal Neoplasms

Condition Hierarchy (Ancestors)

Digestive System Neoplasms; Neoplasms by Site; Neoplasms; Digestive System Diseases; Gastrointestinal Diseases

Central Study Contacts

Denise Hilling, MD, PhD

CONTACT

+31 10 704 19 02; d.hilling@erasmusmc.nl

Study Design

• Study Type: interventional
• Phase: not applicable
• Allocation: NA
• Masking: NONE
• Purpose: DIAGNOSTIC
• Intervention Model: SINGLE GROUP
• Sponsor Type: OTHER
• Responsible Party: PRINCIPAL INVESTIGATOR
• PI Title: Surgeon and Data Scientist

Study Record Dates

• First Submitted: February 6, 2026
• First Posted: February 12, 2026
• Study Start: June 1, 2026
• Primary Completion (Estimated): June 1, 2027
• Study Completion (Estimated): July 1, 2027
• Last Updated: February 12, 2026

Record last verified: 2026-02

Locations

NL (1)