---
title: An Artificial Intelligence Algorithm for Identifying Gynecologic Cancer Patients in Need of Outpatient Palliative Care
nct_id: NCT06182332
overall_status: COMPLETED
phase: NA
sponsor: Mayo Clinic
study_type: INTERVENTIONAL
primary_condition: Advanced Malignant Female Reproductive System Neoplasm
countries: United States
canonical_url: "https://parkinsonspathways.com/agent/trials/NCT06182332.md"
clinicaltrials_gov: "https://clinicaltrials.gov/study/NCT06182332"
ct_last_update_post_date: 2025-04-04
last_seen_at: "2026-05-12T07:24:59.485Z"
source: ClinicalTrials.gov (mirrored, no enrichment)
---
# An Artificial Intelligence Algorithm for Identifying Gynecologic Cancer Patients in Need of Outpatient Palliative Care

**Official Title:** Piloting an Artificial Intelligence Algorithm Used to Identify Patients in Need of Outpatient (or Ambulatory) Palliative Care in an Oncology Population

**NCT ID:** [NCT06182332](https://clinicaltrials.gov/study/NCT06182332)

## Key Facts

- **Status:** COMPLETED
- **Phase:** NA
- **Study Type:** INTERVENTIONAL
- **Target Enrollment:** 221
- **Lead Sponsor:** Mayo Clinic
- **Conditions:** Advanced Malignant Female Reproductive System Neoplasm
- **Start Date:** 2023-12-11
- **Completion Date:** 2024-07-26
- **CT.gov Last Update:** 2025-04-04

## Brief 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.

## Detailed Description

PRIMARY OBJECTIVE:

I. To pilot an oncology risk prediction model to identify patients who may benefit from outpatient palliative care consultation to improve symptom management and goal-concordant care in this population.

OUTLINE:

Patients' medical records are reviewed for consideration of palliative care consult using AI algorithm once a week (QW) for 6 months.

## Eligibility

- **Minimum age:** 18 Years
- **Sex:** ALL
- **Healthy Volunteers:** No

```
Inclusion Criteria:

* Adult patient in Enhanced, Electronic health record (EHR)-facilitated Cancer Symptom Control (E2C2) with a diagnosis of advanced gynecologic malignancy (International Classification of Diseases \[ICD\] codes C51 through C58)
* Weekly the reviewers will select patients by looking at patients in sorted order starting with the highest score and proceeding down the list and evaluating each patient for exclusion criteria

Exclusion Criteria:

* Patients that have been seen by palliative care will be excluded for 75 days
* Patients under the age of 18 years
* Patients currently enrolled with hospice
```

## Arms

- **Screening (AI algorithm)** (EXPERIMENTAL) — Patients' medical records are reviewed for consideration of palliative care consult using AI algorithm QW for 6 months.

## Interventions

- **Electronic Health Record Review** (OTHER) — Undergo medical record review
- **Internet-Based Intervention** (OTHER) — Use AI algorithm

## Primary Outcomes

- **Timely identification for need of palliative care** _(time frame: Up to 6 months)_ — Will be measured as time to the electronic record of consult by the palliative care team in the outpatient setting.

## Secondary Outcomes

- **Number of palliative care consultations** _(time frame: Up to 6 months)_
- **Number of advanced care planning notes documented in the electronic health record** _(time frame: Up to 6 months)_
- **Number of billing codes International Classification of Diseases, 10th Revision for palliative care** _(time frame: Up to 6 months)_
- **Positive predictive value of screened patients** _(time frame: Up to 6 months)_
- **Performance metrics on reviewer/oncologist handoff** _(time frame: Up to 6 months)_

## Locations (1)

- Mayo Clinic in Rochester, Rochester, Minnesota, United States

## Recent Field Changes (last 30 days)

- `status.overallStatus` — added _(2026-05-12)_
- `status.primaryCompletionDate` — added _(2026-05-12)_
- `status.completionDate` — added _(2026-05-12)_
- `status.lastUpdatePostDate` — added _(2026-05-12)_
- `design.phases` — added _(2026-05-12)_
- `design.enrollmentCount` — added _(2026-05-12)_
- `eligibility.criteria` — added _(2026-05-12)_
- `eligibility.minAge` — added _(2026-05-12)_
- `eligibility.sex` — added _(2026-05-12)_
- `outcomes.primary` — added _(2026-05-12)_
- `outcomes.secondary` — added _(2026-05-12)_
- `armsInterventions.arms` — added _(2026-05-12)_
- `armsInterventions.interventions` — added _(2026-05-12)_
- `sponsor.lead` — added _(2026-05-12)_
- `results.hasResults` — added _(2026-05-12)_
- `locations.mayo clinic in rochester|rochester|minnesota|united states` — added _(2026-05-12)_

---

*Canonical: https://parkinsonspathways.com/agent/trials/NCT06182332.md*  
*Source data (authoritative): https://clinicaltrials.gov/study/NCT06182332*  
*This page is a raw mirror with no AI summary, no editorial enrichment, and no Parkinson's-specific filtering.*