Using Surveys to Examine the Association of Exposure to ML Mortality Risk Predictions With Medical Oncologists' Prognostic Accuracy and Decision-making
NCT Number: NCT06463977
Status: COMPLETED
Study Type: OBSERVATIONAL
Conditions: Oncology
Interventions: OTHER: Survey
Sponsor: Abramson Cancer Center at Penn Medicine
Sex: ALL
Age: CHILD, ADULT, OLDER_ADULT
Enrollment: 51
Study Design: Observational Model: |Time Perspective: p
Start Date: 2023-03-14
Completion Date: 2023-06-14
Locations: Abramson Cancer Center of the University of Pennsylvania, Philadelphia, Pennsylvania, 19104, United States

Summary:
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.

Primary Outcome Measures: Prognostic accuracy as assessed via survey, Prognostic estimates were measured using two items administered after Parts 1 and 2 of each of the 3 vignettes:

1. What is your anticipated life expectancy for this patient, in months?
2. What do you think is the likelihood that she will die within 12 months? Please provide a percentage on a scale of 0% to 100%.

Accurate prognoses were defined as whether the reported life expectancy estimate was within 33% of the LCPI estimate, as modified after the focus groups. Participants answered the first question in months and the second question as a percentage between 0-100%., Up to 3 months
Secondary Outcome Measures: Advance care planning decisions as assessed via survey, ACP decision-making was assessed using the following item administered after Parts 1 and 2 of each of the 3 vignettes:

1) Would you have a discussion about advance care planning at this point in her disease course?

Each question was operationalized as a Yes/No answer and was followed by a free response box asking, "Please share your reason for this decision.", Up to 3 months|Palliative care referral as assessed via survey, Palliative care referral was assessed using the following item administered after Parts 1 and 2 of each of the 3 vignettes:

1) Would you refer him/her to a palliative care specialist at this point in her disease course?

Each question was operationalized as a Yes/No answer and was followed by a free response box asking, "Please share your reason for this decision.", Up to 3 months