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出境医 / 临床实验 / Machine-Generated Mortality Estimates and Nudges to Promote Advance Care Planning Discussion Among Cancer Patients

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

Study Description
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.

Condition or disease Intervention/treatment Phase
Oncology Behavioral: Nudge Not Applicable

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.
Study Design
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Study Type : Interventional  (Clinical Trial)
Actual Enrollment : 78 participants
Allocation: Randomized
Intervention Model: Parallel Assignment
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.
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.
Primary Purpose: Health Services Research
Official Title: A Stepped-Wedge Cluster Randomized Trial Using Machine-Generated Mortality Estimates and Behavioral Nudges to Promote Advance Care Planning Discussion Among Cancer Patients
Actual Study Start Date : July 15, 2019
Actual Primary Completion Date : November 1, 2019
Actual Study Completion Date : April 19, 2020
Arms and Interventions
Arm Intervention/treatment
No Intervention: Control
Clinicians will receive current standard communications regarding serious illness performance.
Experimental: 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
 Behavioral: 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.
Outcome Measures
Primary Outcome Measures :
  1. Change in the proportion of patients with a documented serious illness conversation (SIC) [ Time Frame: 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 :
  1. Change in the proportion of patients with a documented SIC among those identified as high-risk by the algorithm [ Time Frame: 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

  2. Change in the proportion of patients with a documented advanced care planning [ Time Frame: 16 weeks ]
    The change in the proportion of patients with documentation of advanced care planning.

  3. Change in the proportion of patients with a documented serious illness conversation (SIC) including follow-up [ Time Frame: 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

  4. Change in the proportion of patients with a documented SIC among those identified as high-risk by the algorithm including follow-up [ Time Frame: 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

  5. Change in the proportion of patients with a documented advanced care planning including follow-up [ Time Frame: 40 weeks ]
    The change in the proportion of patients with documentation of advanced care planning including follow-up


Other Outcome Measures:
  1. Oncology Evaluation Center admissions [ Time Frame: 40 weeks ]
    The number of Oncology Evaluation Center admissions

  2. Healthcare utilization and receipt of chemotherapy in the last 30 days of life [ Time Frame: 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

  3. Number of Emergency department admissions [ Time Frame: 40 weeks ]
    The number of emergency department admissions

  4. Inpatient admissions [ Time Frame: 40 weeks ]
    The number of inpatient hospital admissions

  5. Intensive care unit admissions [ Time Frame: 40 weeks ]
    The number of intensive care unit admissions


Eligibility Criteria
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Ages Eligible for Study:   18 Years and older   (Adult, Older Adult)
Sexes Eligible for Study:   All
Accepts Healthy Volunteers:   No
Criteria

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)
Contacts and Locations

Locations
Layout table for location information
United States, Pennsylvania
Penn Medicine
Philadelphia, Pennsylvania, United States, 19103
Sponsors and Collaborators
University of Pennsylvania
Investigators
Layout table for investigator information
Principal Investigator: Mitesh S Patel, MD University of Pennsylvania
Tracking Information
First Submitted Date  ICMJE June 10, 2019
First Posted Date  ICMJE June 13, 2019
Last Update Posted Date April 24, 2020
Actual Study Start Date  ICMJE July 15, 2019
Actual Primary Completion Date November 1, 2019   (Final data collection date for primary outcome measure)
Current Primary Outcome Measures  ICMJE
 (submitted: June 10, 2019) 		Change in the proportion of patients with a documented serious illness conversation (SIC) [ Time Frame: 16 weeks ]
The change in the proportion of patients that have an outpatient oncology visit with documentation of a serious illness conversation (SIC)
Original Primary Outcome Measures  ICMJE Same as current
Change History
Current Secondary Outcome Measures  ICMJE
 (submitted: June 10, 2019)
  • Change in the proportion of patients with a documented SIC among those identified as high-risk by the algorithm [ Time Frame: 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 [ Time Frame: 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 [ Time Frame: 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 including follow-up [ Time Frame: 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 [ Time Frame: 40 weeks ]
    The change in the proportion of patients with documentation of advanced care planning including follow-up
Original Secondary Outcome Measures  ICMJE Same as current
Current Other Pre-specified Outcome Measures
 (submitted: June 13, 2019)
  • Oncology Evaluation Center admissions [ Time Frame: 40 weeks ]
    The number of Oncology Evaluation Center admissions
  • Healthcare utilization and receipt of chemotherapy in the last 30 days of life [ Time Frame: 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 [ Time Frame: 40 weeks ]
    The number of emergency department admissions
  • Inpatient admissions [ Time Frame: 40 weeks ]
    The number of inpatient hospital admissions
  • Intensive care unit admissions [ Time Frame: 40 weeks ]
    The number of intensive care unit admissions
Original Other Pre-specified Outcome Measures
 (submitted: June 10, 2019)
  • Oncology Evaluation Center admissions [ Time Frame: 40 weeks ]
    The number of Oncology Evaluation Center admissions
  • Healthcare utilization in the last 30 days of life [ Time Frame: 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
  • Emergency department admissions [ Time Frame: 40 weeks ]
    The number of emergency department admissions
  • Inpatient admissions [ Time Frame: 40 weeks ]
    The number of inpatient hospital admissions
  • Intensive care unit admissions [ Time Frame: 40 weeks ]
    The number of intensive care unit admissions
 
Descriptive Information
Brief Title  ICMJE Machine-Generated Mortality Estimates and Nudges to Promote Advance Care Planning Discussion Among Cancer Patients
Official Title  ICMJE A Stepped-Wedge Cluster Randomized Trial Using Machine-Generated Mortality Estimates and Behavioral Nudges to Promote Advance Care Planning Discussion Among Cancer Patients
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.
Study Type  ICMJE Interventional
Study Phase  ICMJE Not Applicable
Study Design  ICMJE Allocation: Randomized
Intervention Model: Parallel Assignment
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.
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.
Primary Purpose: Health Services Research
Condition  ICMJE Oncology
Intervention  ICMJE Behavioral: 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.
Study Arms  ICMJE
  • No Intervention: Control
    Clinicians will receive current standard communications regarding serious illness performance.
  • Experimental: 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
    Intervention: Behavioral: Nudge
Publications *
  • 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.

*   Includes publications given by the data provider as well as publications identified by ClinicalTrials.gov Identifier (NCT Number) in Medline.
 
Recruitment Information
Recruitment Status  ICMJE Completed
Actual Enrollment  ICMJE
 (submitted: April 23, 2020) 		78
Original Estimated Enrollment  ICMJE
 (submitted: June 10, 2019) 		90
Actual Study Completion Date  ICMJE April 19, 2020
Actual Primary Completion Date November 1, 2019   (Final data collection date for primary outcome measure)
Eligibility Criteria  ICMJE

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)
Sex/Gender  ICMJE
Sexes Eligible for Study: All
Ages  ICMJE 18 Years and older   (Adult, Older Adult)
Accepts Healthy Volunteers  ICMJE No
Contacts  ICMJE Contact information is only displayed when the study is recruiting subjects
Listed Location Countries  ICMJE United States
Removed Location Countries  
 
Administrative Information
NCT Number  ICMJE NCT03984773
Other Study ID Numbers  ICMJE 833178
Has Data Monitoring Committee No
U.S. FDA-regulated Product
Studies a U.S. FDA-regulated Drug Product: No
Studies a U.S. FDA-regulated Device Product: No
IPD Sharing Statement  ICMJE
Plan to Share IPD: No
Responsible Party University of Pennsylvania
Study Sponsor  ICMJE University of Pennsylvania
Collaborators  ICMJE Not Provided
Investigators  ICMJE
Principal Investigator: Mitesh S Patel, MD University of Pennsylvania
PRS Account University of Pennsylvania
Verification Date April 2020

ICMJE     Data element required by the International Committee of Medical Journal Editors and the World Health Organization ICTRP

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