Ignite Creation Date:
2024-07-17 @ 11:33 AM
Last Modification Date:
2024-10-26 @ 3:32 PM
Study NCT ID:
NCT06463977
Status:
COMPLETED
Last Update Posted:
2024-06-18
First Post:
2024-04-30
Brief Title:
Using Surveys to Examine the Association of Exposure to ML Mortality Risk Predictions With Medical Oncologists Prognostic Accuracy and Decision-making
Sponsor:
Abramson Cancer Center at Penn Medicine
Organization:
Abramson Cancer Center at Penn Medicine
Study Overview
Official Title:
Using Surveys to Examine the Association of Exposure to ML Mortality Risk Predictions With Medical Oncologists Prognostic Accuracy and Decision-making
Status:
COMPLETED
Status Verified Date:
2024-09
Last Known Status:
None
Delayed Posting:
No
If Stopped, Why?:
Not Stopped
Has Expanded Access:
False
If Expanded Access, NCT#:
N/A
Has Expanded Access, NCT# Status:
N/A
Acronym:
None
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
Detailed Description:
None
Study Oversight
Has Oversight DMC:
None
Is a FDA Regulated Drug?:
False
Is a FDA Regulated Device?:
False
Is an Unapproved Device?:
None
Is a PPSD?:
None
Is a US Export?:
None
Is an FDA AA801 Violation?:
None
Secondary IDs
| Secondary ID | Type | Domain | Link |
|---|---|---|---|
| 850382 | OTHER | None | None |