AI Algorithm for Identifying Palliative Care Needs in Ovarian Cancer

Age: 18+

Sex: Any

Trial Phase: Academic

Sponsor: Mayo Clinic

Disqualifiers: Seen by palliative care, Hospice, Under 18

No Placebo Group All trial participants will receive the active study treatment (no placebo)

Compensation: Varies

Number of Visits: Unknown

What You Need to Know Before You Apply

What is the purpose of this trial?

This trial tests an AI algorithm, the Palliative Care Identification Algorithm, to determine its ability to identify ovarian cancer patients who might benefit from palliative care. Palliative care aims to improve quality of life and manage symptoms rather than treat the illness itself. The AI analyzes medical records to flag patients with severe symptoms who have not yet been referred for this type of care. Patients with advanced gynecologic cancer who have not recently seen a palliative care team might find this trial relevant. As an unphased trial, this study offers patients the opportunity to contribute to innovative research that could enhance care for others in similar situations.

Will I have to stop taking my current medications?

The trial information does not specify whether you need to stop taking your current medications.

What prior data suggests that this AI algorithm is safe for identifying palliative care needs?

Research shows that AI tools in healthcare can predict medical outcomes and identify patient needs. These tools analyze data from medical records to find patterns. Studies have found these tools to be quite accurate. For example, one study found that an AI tool used for ovarian cancer predicted certain outcomes with 79% accuracy over 180 days.

Regarding safety, using an AI tool does not involve taking a new drug or undergoing a procedure, so there are no physical side effects or risks from the tool itself. The main goal is to assess how well the AI can identify patients who might need extra help, such as palliative care.

In summary, AI tools are safe in this context because they analyze data without directly affecting the body, eliminating the risk of side effects from the tool itself. ^{1 2 3 4 5}

Why are researchers excited about this trial?

Researchers are excited about using an AI algorithm to identify palliative care needs in ovarian cancer because it offers a novel approach to improving patient care. Unlike traditional methods that rely heavily on manual assessments by healthcare professionals, this AI algorithm can rapidly analyze medical records to flag patients who might benefit from palliative care consultations. This efficiency not only saves time but also ensures that patients receive timely, personalized care tailored to their specific needs. Moreover, by potentially improving early identification of palliative care needs, the algorithm might help enhance the quality of life for patients with ovarian cancer.

What evidence suggests that this AI algorithm is effective for identifying palliative care needs in ovarian cancer?

Research has shown that AI technology can greatly assist in healthcare, particularly for cancer patients. In this trial, an AI algorithm will screen participants to identify palliative care needs. For instance, one AI model used for ovarian cancer was 79% accurate in predicting which patients would require additional care within six months. This accuracy indicates the AI's effectiveness in identifying patients who might benefit from more symptom management and support. Furthermore, AI systems have effectively predicted overall outcomes and survival rates for ovarian cancer patients. Another AI model, designed to determine when patients need extra care, achieved a high accuracy score of 0.932. These findings suggest AI could help ensure patients receive the right care at the right time. ^{4 5 6 7 8}

Who Is on the Research Team?

Rachel D. Havyer, MD

Principal Investigator

Mayo Clinic in Rochester

Are You a Good Fit for This Trial?

This trial is for patients with advanced gynecologic cancer, such as genital warts or ovarian cancer. It's designed to see if an AI algorithm can identify those who would benefit from outpatient palliative care to manage symptoms and improve quality of life.

Inclusion Criteria

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

I have an advanced gynecologic cancer.

Exclusion Criteria

I am under 18 years old.

Patients currently enrolled with hospice

I have not seen a palliative care specialist in the last 75 days.

Timeline for a Trial Participant

Screening

Participants are screened for eligibility to participate in the trial

2-4 weeks

AI Algorithm Application

Patients' medical records are reviewed for consideration of palliative care consult using AI algorithm

6 months

Weekly reviews

Follow-up

Participants are monitored for outcomes such as palliative care consultations and advanced care planning notes

6 months

What Are the Treatments Tested in This Trial?

Interventions

• AI Algorithm

Trial Overview The study tests an AI algorithm that reviews electronic health records to pinpoint patients needing palliative care consultations. The goal is to reduce delays in referrals and ensure care aligns with patient goals.

How Is the Trial Designed?

Group I: Screening (AI algorithm) Experimental Treatment 2 Interventions

Patients' medical records are reviewed for consideration of palliative care consult using AI algorithm QW for 6 months.

Who Is Running the Clinical Trial?

Mayo Clinic

Lead Sponsor

Trials

3,427

Recruited

3,221,000+

Published Research Related to This Trial

The Ovarian Cancer Survivorship Survey, conducted from 2009 to 2013, aimed to identify factors contributing to long-term survival in ovarian cancer patients, revealing important prediagnostic symptoms that could aid in early detection.

The study highlights a potential link between endometriosis and early-stage ovarian cancer diagnosis, suggesting that understanding these associations may improve future research and patient outcomes.

NCBI (Pubmed)

pubmed.ncbi.nlm.nih.gov

Design and Implementation of a Comprehensive Web-based Survey for Ovarian Cancer Survivorship with an Analysis of Prediagnosis Symptoms via Text Mining. Sun, J., Bogie, KM., Teagno, J., et al.[2020]

Palliative care (PC) is crucial for improving quality of life and potentially prolonging survival in women with advanced ovarian cancer, yet referrals for PC remain low due to various factors.

A review of 13 articles identified multiple socioecological factors influencing PC referrals, including tumor characteristics, age, marital status, and support systems, indicating that the decision for referral is complex and not solely based on medical conditions.

NCBI (Pubmed)

pubmed.ncbi.nlm.nih.gov

Multilevel Determinants of Palliative Care Referral in Women With Advanced Ovarian Cancer: A Scoping Review. Cho, S., Goff, BA., Berry, DL.[2023]

The use of an AI tool to predict 30-day mortality risk in patients with advanced cancer significantly increased palliative care (PC) consults from 17.3 to 29.1 per 1,000 patients per month after its deployment, indicating improved identification of patients needing supportive care.

Hospice referrals also saw a substantial rise, increasing from 0.2 to 1.6 per 1,000 patients per month, demonstrating that the AI tool effectively facilitated timely end-of-life care aligned with patient preferences.

NCBI (Pubmed)

pubmed.ncbi.nlm.nih.gov

Impact of Augmented Intelligence on Utilization of Palliative Care Services in a Real-World Oncology Setting. Gajra, A., Zettler, ME., Miller, KA., et al.[2023]

A multi-method approach was used to develop a Patient-Reported Outcomes tool tailored for women with endometrial or ovarian cancer undergoing chemotherapy, identifying 21 relevant symptoms from the PRO-CTCAE library.

The final tool includes 44 items that assess the frequency, severity, and impact of these symptoms on daily activities, which can help in monitoring and managing adverse events during treatment.

NCBI (Pubmed)

pubmed.ncbi.nlm.nih.gov

A multi-method approach to selecting PRO-CTCAE symptoms for patient-reported outcome in women with endometrial or ovarian cancer undergoing chemotherapy. Christiansen, MG., Pappot, H., Jensen, PT., et al.[2023]

Most women with ovarian cancer are diagnosed at an advanced stage, leading to a five-year survival rate of less than 50%, highlighting the need for improved care strategies.

There is a growing emphasis on integrating palliative care early in the treatment of ovarian cancer, with recommendations for targeted education for palliative care clinicians to enhance patient management and decision-making.

NCBI (Pubmed)

pubmed.ncbi.nlm.nih.gov

Top 10 Tips Palliative Care Clinicians Should Know When Caring for Patients with Ovarian Cancer. Davidson, BA., Moss, HA., Kamal, AH.[2019]

Machine learning and network analysis can effectively identify different phases of palliative care based on patient-reported symptoms, using data from 1507 patient records in a New Zealand palliative care service.

Key symptoms like Poor Appetite and Loss of Energy were found to be central in predicting transitions between illness phases, suggesting that these insights could enhance continuous monitoring and digital therapeutic approaches in palliative care.

NCBI (Pubmed)

pubmed.ncbi.nlm.nih.gov

Intelligent Palliative Care Based on Patient-Reported Outcome Measures. Sandham, MH., Hedgecock, EA., Siegert, RJ., et al.[2022]

A machine learning model trained on patient-reported outcomes (PRO) data from 245 women with ovarian cancer achieved an accuracy of 79% in predicting 180-day mortality, indicating its potential to improve prognosis estimation.

The model demonstrated good sensitivity (71%) and specificity (80%), suggesting it could effectively guide end-of-life care decisions for ovarian cancer patients, addressing current challenges in treatment delivery.

NCBI (Pubmed)

pubmed.ncbi.nlm.nih.gov

Predicting 180-day mortality for women with ovarian cancer using machine learning and patient-reported outcome data. Sidey-Gibbons, CJ., Sun, C., Schneider, A., et al.[2023]

In a study involving 12,350 cancer patients, incorporating patient-reported outcomes (PROs) with electronic health record (EHR) data significantly improved the prediction of short-term mortality, with the combined model achieving an area under the curve (AUC) of 0.86 in tertiary oncology and 0.89 in community oncology settings.

The findings suggest that PROs provide valuable prognostic information that EHR data alone cannot capture, enabling earlier and more targeted supportive care for cancer patients.

NCBI (Pubmed)

pubmed.ncbi.nlm.nih.gov

Development of Machine Learning Algorithms Incorporating Electronic Health Record Data, Patient-Reported Outcomes, or Both to Predict Mortality for Outpatients With Cancer. Parikh, RB., Hasler, JS., Zhang, Y., et al.[2023]

Artificial intelligence tools, like machine learning and natural language processing, can enhance decision-making in palliative care by identifying patients at high risk of mortality and improving prognostic abilities, which can lead to better treatment discussions earlier in the disease process.

While these tools can support essential aspects of palliative care, such as advance care planning, it is crucial to ensure they do not disrupt the vital doctor-patient relationship, especially when communicating sensitive information about prognosis.

NCBI (Pubmed)

pubmed.ncbi.nlm.nih.gov

[Artificial intelligence and palliative care: opportunities and limitations.] Peruselli, C., De Panfilis, L., Gobber, G., et al.[2022]

The rate of hospice discharge for metastatic ovarian cancer patients increased from 9.2% in 2004 to 11.1% in 2011, indicating a growing recognition of hospice care as a viable option for end-of-life care.

Documented palliative care services also rose significantly from 2.7% to 10.4% during the same period, while inpatient mortality decreased from 9.6% to 7.4%, suggesting improved care and support for patients with metastatic ovarian cancer.

NCBI (Pubmed)

pubmed.ncbi.nlm.nih.gov

Trends in hospice discharge, documented inpatient palliative care services and inpatient mortality in ovarian carcinoma. Uppal, S., Rice, LW., Beniwal, A., et al.[2017]

An augmented intelligence tool was developed to predict 30-day mortality risk in cancer patients using socioeconomic and clinical data, demonstrating its feasibility in a large community hematology/oncology practice.

Patients identified as high risk for short-term mortality had a significantly higher event rate of 4.9%, compared to just 0.7% for low-risk patients, indicating the tool's potential to effectively identify those needing urgent interventions or palliative care.

NCBI (Pubmed)

pubmed.ncbi.nlm.nih.gov

Augmented intelligence to predict 30-day mortality in patients with cancer. Gajra, A., Zettler, ME., Miller, KA., et al.[2021]

Citations

1. withpower.com withpower.com/trial/phase-genital-neoplasms-female-2024-2765c

AI Algorithm for Identifying Palliative Care Needs in ... A machine learning model trained on patient-reported outcomes (PRO) data from 245 women with ovarian cancer achieved an accuracy of 79% in predicting 180-day ...

2. pmc.ncbi.nlm.nih.gov pmc.ncbi.nlm.nih.gov/articles/PMC11920737/

AI Models Predict Ovarian Cancer Survival: Systematic Review This systematic review investigates the use of machine learning (ML) algorithms in predicting survival outcomes for ovarian cancer (OC) patients.

3. pmc.ncbi.nlm.nih.gov pmc.ncbi.nlm.nih.gov/articles/PMC12268085/

Enhancing ovarian cancer prognosis with an artificial ... According to numerous studies and reports, AI systems have demonstrated superior accuracy in predicting the prognosis of ovarian cancer patients ...

4. nature.com nature.com/articles/s41598-025-98434-w

AI Tool Predicts Ovarian Cancer Treatment Outcomes We developed a novel deep-learning framework using pre-treatment laparoscopic images to assess clinical outcomes following upfront standard treatment.

5. ascopubs.org ascopubs.org/doi/10.1200/JCO.2024.42.16_suppl.1559

AI/ML Model for Timely Palliative Care for Cancer Patients The model had an overall AUC of 0.932 (0.929, 0.935 – 95% CI). We saw a decrease in performance of the AI/ML model in the Oncology population ...

6. ajmc.com ajmc.com/view/ai-outperforms-traditional-methods-in-predicting-ovarian-cancer-surgery-outcomes

AI Outperforms Traditional Methods in Predicting Ovarian ... The study's outcomes included AI's diagnostic accuracy in predicting patients' overall survival (OS), no macroscopically residual disease (R0), ...

7. pmc.ncbi.nlm.nih.gov pmc.ncbi.nlm.nih.gov/articles/PMC12421415/

A Review on Biomarker‐Enhanced Machine Learning for ... Although future advancements in multi‐omics AI models may further enhance ovarian cancer prediction, biomarker‐based ML approaches currently ...

8. cancer.gov cancer.gov/research/participate/clinical-trials-search/v?id=NCI-2023-09943

An Artificial Intelligence Algorithm for Identifying ... 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 ...