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

Oncology Clinical Trial

Official title:

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

Clinical Trial Details — Status: Completed

Administrative data

• NCT number: NCT06463977
• Other study ID #: UPCC 10524
• Secondary ID: 850382
• Status: Completed
• Start date: March 14, 2023
• Est. completion date: June 14, 2023

Study information

• Verified date: June 2024
• Source: Abramson Cancer Center at Penn Medicine
• Contact: n/a
• Is FDA regulated: No
• Study type: Observational

Clinical Trial 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.

Recruitment information / eligibility

• Status: Completed
• Enrollment: 51
• Est. completion date: June 14, 2023
• Est. primary completion date: June 14, 2023
• Accepts healthy volunteers: No
• Gender: All
• Age group: N/A and older

Eligibility

Inclusion Criteria:

• Medical oncologists who treat lung cancer

Exclusion Criteria:

• Medical oncologists who do not see lung cancer patients

Related Conditions & MeSH terms

• Neoplasms
• Oncology

Intervention

Other:
• Survey
The study consisted of a 3 × 3 online factorial experiment employing a survey instrument hosted via Qualtrics presenting describing three patient vignettes. The three patient vignettes varied by various clinical characteristics including age, gender, performance status, smoking history, extent of disease, symptoms and molecular status. Each patient had advanced non-small cell lung cancer (aNSCLC). Each vignette had two parts: Part 1 described the case history for one of the three patients, after which prognostic estimates and medical decision-making was assessed (i.e. 1, 2, 3). Part 2 immediately followed and described the same vignette from the same patient with added information from a hypothetical ML predictive algorithm (i.e. A, B, C). The order of the vignettes in each survey was randomized with regard to presentation strategies for the ML risk predictions, so that there were 6 versions of the survey to which each participant was randomized.

Locations

Country	Name	City	State
United States	Abramson Cancer Center of the University of Pennsylvania	Philadelphia	Pennsylvania

Sponsors (1)

Lead Sponsor	Collaborator
Abramson Cancer Center at Penn Medicine

Country where clinical trial is conducted

United States, 

Outcome

Type	Measure	Description	Time frame	Safety issue
Primary	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: What is your anticipated life expectancy for this patient, in months?; 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	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
Secondary	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