Clinical trial NCT06463977: Using Surveys to Examine the Association of Exposure to ML Mortality Risk Predictions With Medical Oncologists' Prognostic Accuracy and Decision-making
Official title: Using Surveys to Examine the Association of Exposure to ML Mortality Risk Predictions With Medical Oncologists' Prognostic Accuracy and Decision-making
Study type: OBSERVATIONAL
Overall status: COMPLETED
Conditions: Oncology
Lead sponsor: Abramson Cancer Center at Penn Medicine
Sponsor class: OTHER
First posted: 2024-06-18
Enrollment: 52
Sex: ALL
Standard ages: CHILD
ADULT
OLDER_ADULT
Accepts healthy volunteers: no
Observational model: OTHER
Time perspective: CROSS_SECTIONAL
Sampling method: NON_PROBABILITY_SAMPLE
Study population: The study population consisted of a convenience sample of practicing medical oncologists who treated lung cancer in the US. We recruited medical oncologists through direct emails to the Principal Investigator's personal contacts (n=29); direct messages via Doximity, an online networking service for medical professionals (n=17), using a "thoracic oncology" ; and direct messages via X (formerly Twitter) (n=4). Efforts were taken to sample equally from 4 US geographic regions (Northeast, South, Midwest, West).

Brief 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.


Eligibility criteria: Inclusion Criteria:

* Medical oncologists who treat lung cancer

Exclusion Criteria:

* Medical oncologists who do not see lung cancer patients

Primary outcomes:
measure: Prognostic accuracy as assessed via survey
timeFrame: Up to 3 months
Secondary outcomes:
measure: Advance care planning decisions as assessed via survey
timeFrame: Up to 3 months
measure: Palliative care referral as assessed via survey
timeFrame: Up to 3 months
Other outcomes:
Locations:
city: Philadelphia
country: United States
facility: Abramson Cancer Center of the University of Pennsylvania
state: Pennsylvania
Study officials:
References: