Oncology
Conditions
Keywords
serious illness conversations, advance care planning, mortality estimates, nudge, pre-commitment, opt-out
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.
Interventions
BEHAVIORALNudge
Oncology practices will be randomly assigned to receive an intervention, in which individual clinicians will receive a weekly audit email detailing how many serious illness conversations (SIC) they have had compared to the recommended level, and a link to a list of their patients scheduled in clinic next week at high risk of short-term mortality as identified by a mortality prediction algorithm. Clinicians will have the chance to review the opt-out list and pre-commit to a serious illness conversation with appropriate patients. Clinicians will receive nudge on the day of the patient visit via text message reminding them of their pre-commitment to conduct a serious illness conversation.
Sponsors
University of Pennsylvania
Study design
Allocation
RANDOMIZED
Intervention model
PARALLEL
Primary purpose
HEALTH_SERVICES_RESEARCH
Masking
DOUBLE (Investigator, Outcomes Assessor)
Masking description
The principal study investigator and data analyst will not have knowledge of when the practices are randomized to the intervention.
Intervention model description
Practices will be cluster-randomized in 4-week blocks to the intervention over a 16-week period, after which all practices will receive the email intervention.
Eligibility
Sex/Gender
ALL
Age
18 Years to No maximum
Healthy volunteers
No
Inclusion criteria
* Care for adults with cancer at the following clinics at Perelman Center for Advanced Medicine * Breast Oncology * Gastrointestinal Oncology * Genitourinary Oncology * Lymphoma * Melanoma and Central Nervous System Oncology * Myeloma * Thoracic / Head and Neck Oncology * Care for adults with cancer at the Pennsylvania Hospital Oncology clinic
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)
Design outcomes
Primary
| Measure | Time frame | Description |
|---|---|---|
| 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
| Measure | Time frame | Description |
|---|---|---|
| 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 |
| 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 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 advanced care planning including follow-up | 40 weeks | The change in the proportion of patients with documentation of advanced care planning including follow-up |
Other
| Measure | Time frame | Description |
|---|---|---|
| Healthcare utilization and receipt of chemotherapy in the last 30 days of life | 40 weeks | Healthcare utilization in the last 30 days of life in Penn Medicine facilities including acute care utilization as above and receipt of chemotherapy |
| Number of Emergency department admissions | 40 weeks | The number of emergency department admissions |
| Inpatient admissions | 40 weeks | The number of inpatient hospital admissions |
| Intensive care unit admissions | 40 weeks | The number of intensive care unit admissions |
| Oncology Evaluation Center admissions | 40 weeks | The number of Oncology Evaluation Center admissions |
Countries
United States
Outcome results
None listed