Advanced Malignant Female Reproductive System Neoplasm
Conditions
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.
Interventions
OTHERInternet-Based Intervention
Use AI algorithm
OTHERElectronic Health Record Review
Undergo medical record review
Sponsors
Mayo Clinic
Study design
Allocation
NA
Intervention model
SINGLE_GROUP
Primary purpose
SCREENING
Masking
NONE
Eligibility
Sex/Gender
ALL
Age
18 Years to No maximum
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
Design outcomes
Primary
| Measure | Time frame | Description |
|---|---|---|
| Timely identification for need of palliative care | 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
| Measure | Time frame | Description |
|---|---|---|
| Number of palliative care consultations | Up to 6 months | Number of palliative care consultations will be assessed as the number of participants who receive palliative care consultations. |
| Number of advanced care planning notes documented in the electronic health record | Up to 6 months | Participant electronic health records will be reviewed for the number of advanced care planning notes listed. |
| Number of billing codes International Classification of Diseases, 10th Revision for palliative care | Up to 6 months | Participant electronic health records will be reviewed for the number of International Classification of Diseases, 10th Revision (ICD-10) billing codes for palliative care. |
| Positive predictive value of screened patients | Up to 6 months | Will be assessed as the number of patients identified by Artificial Intelligence algorithm who actually received palliative care consultation. |
| Performance metrics on reviewer/oncologist handoff | Up to 6 months | Will be assessed by agreement statistics and descriptive statistics on time between oncology contact and oncology response. |
Countries
United States
Outcome results
None listed