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

Results pending

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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Recruitment Status

COMPLETED

Clinical Phase

NA

Total Enrollment

78 participants

Study Classification

INTERVENTIONAL

Study Start Date

2019-07-15

Study Completion Date

2020-04-19

Brief Summary

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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

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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.

Conditions

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Oncology

Study Design

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Allocation Method

RANDOMIZED

Intervention Model

PARALLEL

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.
Primary Study Purpose

HEALTH_SERVICES_RESEARCH

Blinding Strategy

DOUBLE

Investigators Outcome Assessors
The principal study investigator and data analyst will not have knowledge of when the practices are randomized to the intervention.

Study Groups

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Control

Clinicians will receive current standard communications regarding serious illness performance.

Group Type NO_INTERVENTION

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

Group Type EXPERIMENTAL

Nudge

Intervention Type BEHAVIORAL

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.

Intervention Type BEHAVIORAL

Eligibility Criteria

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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)
Minimum Eligible Age

18 Years

Eligible Sex

ALL

Accepts Healthy Volunteers

No

Sponsors

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University of Pennsylvania

OTHER

Sponsor Role lead

Responsible Party

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Responsibility Role SPONSOR

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

Site Status

Countries

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United States

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.

Reference Type DERIVED
PMID: 39036423 (View on PubMed)

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.

Reference Type DERIVED
PMID: 36633868 (View on PubMed)

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.

Reference Type DERIVED
PMID: 35984992 (View on PubMed)

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.

Reference Type DERIVED
PMID: 34767481 (View on PubMed)

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.

Reference Type DERIVED
PMID: 33057696 (View on PubMed)

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.

Reference Type DERIVED
PMID: 31982648 (View on PubMed)

Other Identifiers

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833178

Identifier Type: -

Identifier Source: org_study_id

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