Machine-Generated Mortality Estimates and Nudges to Promote Advance Care Planning Discussion Among Cancer Patients
NCT ID: NCT03984773
Last Updated: 2020-04-24
Study Results
The study team has not published outcome measurements, participant flow, or safety data for this trial yet. Check back later for updates.
Basic Information
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COMPLETED
NA
78 participants
INTERVENTIONAL
2019-07-15
2020-04-19
Brief Summary
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Detailed Description
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Conditions
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Study Design
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RANDOMIZED
PARALLEL
HEALTH_SERVICES_RESEARCH
DOUBLE
Study Groups
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Control
Clinicians will receive current standard communications regarding serious illness performance.
No interventions assigned to this group
Mortality Estimates and Nudges
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
Nudge
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.
Interventions
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Nudge
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.
Eligibility Criteria
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Inclusion Criteria
* 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 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)
18 Years
ALL
No
Sponsors
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University of Pennsylvania
OTHER
Responsible Party
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Principal Investigators
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Mitesh S Patel, MD
Role: PRINCIPAL_INVESTIGATOR
University of Pennsylvania
Locations
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Penn Medicine
Philadelphia, Pennsylvania, United States
Countries
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References
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Patel TA, Heintz J, Chen J, LaPergola M, Bilker WB, Patel MS, Arya LA, Patel MI, Bekelman JE, Manz CR, Parikh RB. Spending Analysis of Machine Learning-Based Communication Nudges in Oncology. NEJM AI. 2024 Jun;1(6):10.1056/aioa2300228. doi: 10.1056/aioa2300228. Epub 2024 May 15.
Manz CR, Zhang Y, Chen K, Long Q, Small DS, Evans CN, Chivers C, Regli SH, Hanson CW, Bekelman JE, Braun J, Rareshide CAL, O'Connor N, Kumar P, Schuchter LM, Shulman LN, Patel MS, Parikh RB. Long-term Effect of Machine Learning-Triggered Behavioral Nudges on Serious Illness Conversations and End-of-Life Outcomes Among Patients With Cancer: A Randomized Clinical Trial. JAMA Oncol. 2023 Mar 1;9(3):414-418. doi: 10.1001/jamaoncol.2022.6303.
Parikh RB, Manz CR, Nelson MN, Ferrell W, Belardo Z, Temel JS, Patel MS, Shea JA. Oncologist Perceptions of Algorithm-Based Nudges to Prompt Early Serious Illness Communication: A Qualitative Study. J Palliat Med. 2022 Nov;25(11):1702-1707. doi: 10.1089/jpm.2022.0095. Epub 2022 Aug 18.
Li EH, Ferrell W, Klaiman T, Kumar P, O'Connor N, Schuchter LM, Chen J, Patel MS, Manz CR, Parikh RB. Impact of Behavioral Nudges on the Quality of Serious Illness Conversations Among Patients With Cancer: Secondary Analysis of a Randomized Controlled Trial. JCO Oncol Pract. 2022 Apr;18(4):e495-e503. doi: 10.1200/OP.21.00024. Epub 2021 Nov 12.
Manz CR, Parikh RB, Small DS, Evans CN, Chivers C, Regli SH, Hanson CW, Bekelman JE, Rareshide CAL, O'Connor N, Schuchter LM, Shulman LN, Patel MS. Effect of Integrating Machine Learning Mortality Estimates With Behavioral Nudges to Clinicians on Serious Illness Conversations Among Patients With Cancer: A Stepped-Wedge Cluster Randomized Clinical Trial. JAMA Oncol. 2020 Dec 1;6(12):e204759. doi: 10.1001/jamaoncol.2020.4759. Epub 2020 Dec 10.
Manz CR, Parikh RB, Evans CN, Chivers C, Regli SH, Bekelman JE, Small D, Rareshide CAL, O'Connor N, Schuchter LM, Shulman LN, Patel MS. Integrating machine-generated mortality estimates and behavioral nudges to promote serious illness conversations for cancer patients: Design and methods for a stepped-wedge cluster randomized controlled trial. Contemp Clin Trials. 2020 Mar;90:105951. doi: 10.1016/j.cct.2020.105951. Epub 2020 Jan 23.
Other Identifiers
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833178
Identifier Type: -
Identifier Source: org_study_id
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