Using Surveys to Examine the Association of Exposure to ML Mortality Risk Predictions With Medical Oncologists' Prognostic Accuracy and Decision-making

Abramson Cancer Center at Penn Medicine

Status: Completed

Conditions: Oncology

Treatments: Other: Survey

Study type: Observational

Funder types: Other

Identifiers

NCT06463977

UPCC 10524

850382 (Other Identifier)

Details and patient eligibility

About

Nearly half of cancer patients in the US will receive care that is inconsistent with their wishes prior to death. Early advanced care planning (ACP) and palliative care improve goal-concordant care and symptoms and reduce unnecessary utilization. A promising strategy to increase ACP and palliative care is to identify patients at risk of mortality earlier in the disease course in order to target these services. Machine learning (ML) algorithms have been used in various industries, including medicine, to accurately predict risk of adverse outcomes and direct earlier resources. "Human-machine collaborations" - systems that leverage both ML and human intuition - have been shown to improve predictions and decision-making in various situations, but it is not known whether human-machine collaborations can improve prognostic accuracy and lead to greater and earlier ACP and palliative care. In this study, we contacted a national sample of medical oncologists and invited them complete a vignette-based survey. Our goal was to examine the association of exposure to ML mortality risk predictions with clinicians' prognostic accuracy and decision-making. We presented a series of six vignettes describing three clinical scenarios specific to a patient with advanced non-small cell lung cancer (aNSCLC) that differ by age, gender, performance status, smoking history, extent of disease, symptoms and molecular status. We will use these vignette-based surveys to examine the association of exposure to ML mortality risk predictions with medical oncologists' prognostic accuracy and decision-making.

Enrollment: 52 patients

Sex: All

Volunteers: No Healthy Volunteers

Inclusion criteria

• Medical oncologists who treat lung cancer

Exclusion criteria

• Medical oncologists who do not see lung cancer patients

Trial design

52 participants in 6 patient groups

1A 2B 3C

Description:

1. Intermediate; 1.A. Reference dependent; 2. Poor; 2.B. Absolute prognosis; 3. Good; 3.C. Both

Treatment:

Other: Survey

1A 2C 3B

Description:

1. Intermediate; 1.A. Reference dependent; 2. Poor; 2.C. Both; 3. Good; 3.B. Absolute prognosis

Treatment:

Other: Survey

1B 2A 3C

Description:

1. Intermediate; 1.B. Absolute; 2. Poor; 2.A. Reference dependent; 3. Good; 3.C. Both

Treatment:

Other: Survey

1B 2C 3A

Description:

1. Intermediate; 1.B. Absolute; 2. Poor; 2.C. Both; 3. Good; 3.A. Reference dependent

Treatment:

Other: Survey

1C 2A 3B

Description:

1. Intermediate; 1.C. Both; 2. Poor; 2.A. Reference dependent; 3. Good; 3.B. Absolute

Treatment:

Other: Survey

1C 2B 3A

Description:

1. Intermediate; 1.C. Both; 2. Poor; 2.B. Absolute; 3. Good; 3.A. Reference dependent

Treatment:

Other: Survey