Machine-Generated Mortality Estimates and Nudges to Promote Advance Care Planning Discussion Among Cancer Patients

Not Applicable

Completed

Brief Summary: This study will use a stepped-wedge cluster randomized trial to evaluate the effect of a health system initiative using machine learning algorithms and behavioral nudges to prompt oncologists to have serious illness conversations with patients at high-risk of short-term mortality.

Detailed Description: Patients with cancer often undergo costly therapy and acute care utilization that is discordant with their wishes, particularly at the end of life. Early serious illness conversations (SIC) improve goal-concordant care, and accurate prognostication is critical to inform the timing and content of these discussions. This study will use a stepped-wedge, cluster randomized trial to evaluate the effect of a health system initiative using machine learning algorithms and behavioral nudges to prompt oncologists to have serious illness conversations with patients at high-risk of short-term mortality. Oncology practices will be randomly assigned in sequential four-week blocks to receive the intervention.

Recruitment & Eligibility

Inclusion Criteria

Exclusion Criteria

Providers who care for only patients with benign hematologic disorders
Providers who see only genetic consults
Providers who see less than 12 high-risk patients in either the pre- or post- intervention periods
Visits for patients with lung cancer who are enrolled in an ongoing palliative care clinical trial that may lead to more SICs
Patient visits that are for oncology genetics consults (such patients may still be included if they see their primary oncologist during the trial)
Providers who have not undergone serious illness conversation program training (SIC)

Arm && Interventions

Group	Intervention	Description
Mortality Estimates and Nudges	Nudge	Clinicians will receive a weekly email with upcoming patients that have high mortality estimates to consider for a serious illness conversation. Clinicians will have the opportunity to review the list and pre-commit (using an opt-out design) to patients appropriate for a conversation. They will receive a nudge on the day of the patient visit through a text message reminding them of their pre-commitment to conduct a serious illness conversation

Primary Outcome Measures

Name	Time	Method
Change in the proportion of patients with a documented serious illness conversation (SIC)	16 weeks	The change in the proportion of patients that have an outpatient oncology visit with documentation of a serious illness conversation (SIC)

Secondary Outcome Measures

Name	Time	Method
Change in the proportion of patients with a documented advanced care planning including follow-up	40 weeks	The change in the proportion of patients with documentation of advanced care planning including follow-up
Change in the proportion of patients with a documented SIC among those identified as high-risk by the algorithm including follow-up	40 weeks	The change in the proportion of patients who have an outpatient oncology visit and are identified as high-risk by the machine learning algorithm with documentation of a SIC including follow-up
Change in the proportion of patients with a documented SIC among those identified as high-risk by the algorithm	16 weeks	The change in the proportion of patients who have an outpatient oncology visit and are identified as high-risk by the machine learning algorithm with documentation of a SIC
Change in the proportion of patients with a documented advanced care planning	16 weeks	The change in the proportion of patients with documentation of advanced care planning.
Change in the proportion of patients with a documented serious illness conversation (SIC) including follow-up	40 weeks	The change in the proportion of patients that have an outpatient oncology visit with documentation of a serious illness conversation (SIC) including follow-up