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An Artificial Intelligence Algorithm for Identifying Gynecologic Cancer Patients in Need of Outpatient Palliative Care

NCT06182332 · Status: COMPLETED · Phase: NA · Type: INTERVENTIONAL · Enrollment: 221

Last updated 2025-04-04

No results posted yet for this study

Summary

This clinical trial tests an artificial intelligence (AI) algorithm for its ability to identify patients who may benefit from a palliative care consult for gynecologic cancer that has spread from where it first started to nearby tissue, lymph nodes, or distant parts of the body (advanced). A significant delay in referral to palliative care often occurs among patients with cancer. This delay can lead to poorer symptom management, decreased quality of life, and care that does not align with patient goals or values. AI algorithms are computer programs that use step-by-step procedures to solve a problem. In this trial, an AI algorithm is applied to patients' medical records in order to identify patients with a high burden of disease. Information gathered from this study may help researchers learn whether this AI algorithm is useful for identifying patients who could benefit from outpatient palliative care consultation.

Conditions

  • Advanced Malignant Female Reproductive System Neoplasm

Interventions

OTHER

Electronic Health Record Review

Undergo medical record review

OTHER

Internet-Based Intervention

Use AI algorithm

Sponsors & Collaborators

Principal Investigators

  • Rachel D. Havyer, MD · Mayo Clinic in Rochester

Study Design

Allocation
NA
Purpose
SCREENING
Masking
NONE
Model
SINGLE_GROUP

Eligibility

Min Age
18 Years
Sex
ALL
Healthy Volunteers
No

Timeline & Regulatory

Start
2023-12-11
Primary Completion
2024-06-26
Completion
2024-07-26

Countries

  • United States

Study Locations

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Entities

Companies

Read the full study record

This page highlights key information. For complete eligibility criteria, study locations, investigator contacts, and the full protocol, visit the original record on ClinicalTrials.gov.

View NCT06182332 on ClinicalTrials.gov