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.
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.
Study Type
INTERVENTIONAL
Allocation
NA
Purpose
SCREENING
Masking
NONE
Enrollment
221
Undergo medical record review
Use AI algorithm
Mayo Clinic in Rochester
Rochester, Minnesota, United States
Timely identification for need of palliative care
Will be measured as time to the electronic record of consult by the palliative care team in the outpatient setting.
Time frame: Up to 6 months
Number of palliative care consultations
Number of palliative care consultations will be assessed as the number of participants who receive palliative care consultations.
Time frame: Up to 6 months
Number of advanced care planning notes documented in the electronic health record
Participant electronic health records will be reviewed for the number of advanced care planning notes listed.
Time frame: Up to 6 months
Number of billing codes International Classification of Diseases, 10th Revision for palliative care
Participant electronic health records will be reviewed for the number of International Classification of Diseases, 10th Revision (ICD-10) billing codes for palliative care.
Time frame: Up to 6 months
Positive predictive value of screened patients
Will be assessed as the number of patients identified by Artificial Intelligence algorithm who actually received palliative care consultation.
Time frame: Up to 6 months
Performance metrics on reviewer/oncologist handoff
Will be assessed by agreement statistics and descriptive statistics on time between oncology contact and oncology response.
Time frame: Up to 6 months
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