Artificial Intelligence/Machine Learning Modeling on Time to Palliative Care Review in an Inpatient Hospital Population

NCT03976297 | Completed

• 1 other identifier: 19-002315
• Study Type: interventional
• Target: 2,231
• Locations: 1 country
• Sites: 1

Brief Summary

Investigators are testing whether machine learning prediction models integrated into a health care model will accurately identify participants who may benefit from a comprehensive review by a palliative care specialist, and decrease time to receiving a palliative care consult in an inpatient setting.

Conditions

Palliative Care

Keywords

Machine learning; Artificial Intelligence; Pragmatic Clinical Trial; Healthcare Delivery

Outcome Measures

Primary Outcomes (1)

• Timely identification for need of palliative care

  Measured as time in hours to the electronic record of consult by the palliative care team in the inpatient setting.

  12 months

Secondary Outcomes (10)

• The number of inpatient palliative care consults

  12 months

• Timely identification for need of palliative care per unit

  12 months

• Transition time to hospice-designated bed

  12 months

• Time to hospice designation

  12 months

• Emergency Department visit within 30 days of discharge

  12 months

Study Arms (2)

Control Tower Intervention

EXPERIMENTAL

For participants in the intervention arm, the results of the prediction model will be presented through a GUI interface hereby known as the Control Tower. Participants receive scores from Control Tower (0-100; higher score indicating increased need) for palliative care and are subsequently ranked from highest to lowest. Red (7 or greater) is considered high risk. The intervention will include a Control Tower operator who will interact with the inpatient palliative care consult service. The operator will monitor the Control Tower during weekday normal business hours and select daily a cohort of participants in the intervention units with the highest need of palliative care review. The final list of participants will then be sent to palliative care. The palliative care team who is on service will also assess the need for each participants, and those participants which they agree could benefit they will approach the attending clinical team to suggest a palliative care referral.

Other: Control Tower

Standard of Care

NO INTERVENTION

For participants who are not in an intervention period they will receive the standard of care commensurate with their clinical unit. This is feasible given that this is a pragmatic clinical trial where the investigators can easily control the communication between the control tower operator and palliative care team to prevent any contamination between clusters. In addition to the usual source of care control the investigators intentionally have calibrated the prediction model and the Control Tower review to match the average capacity of the palliative care service, knowing that that the team will still receive palliative care consults through the traditional pathway i.e. the attending care team consulting palliative care directly.

Interventions

Control Tower OTHER

A workstation and software tool that extracts medical data from Mayo's data mart and electronic health record, and processes it through a prediction model that determines whether a patient is suited for a palliative care consult.

Control Tower Intervention

Eligibility Criteria

• Age: 18 Years+
• Sex: all
• Healthy Volunteers: No
• Age Groups: Adult (18-64), Older Adult (65+)

You may qualify if:

• Admitted to Mayo Clinic St. Mary's Hospital and Methodist Hospital during August 19, 2019 - August 19, 2020.
• Once a day Monday through Friday, the CT operator selects 12 patients from all of the nursing units that are participating in the trial (whether or not they are currently in the intervention group) with palliative scores of at least 7 (out of 100), i.e., those that are high risk and displayed as red in the CT GUI (unless they are already being seen by palliative care.)

You may not qualify if:

• Once the CT operator identifies 12 appropriate patients or once they reaches the end of the high-risk patients (score of 7 or higher) they stop.
• We will exclude all patients who do not provide research authorization to review their medical records for general research studies in accordance with Minnesota Statute 144.335.
• We will exclude patients under the age of 18 years of age.
• We will exclude patients previously seen by Palliative care during the index hospital visit (i.e., green icon within CT user interface regardless of score)
• We will exclude patient who no longer have an active encounter (patients who have died or patients who have transferred to another facility are excluded) at the time of the review
• We will exclude patients currently enrolled with the Hospice service at Mayo
• We will exclude patients currently enrolled in the Palliative Homebound program (an alternative healthcare model at Mayo)
• We will exclude patients who are about to be discharged in the next 24 hours through indication of note

Sponsors & Collaborators

• Mayo Clinic: lead

Study Sites (1)

Mayo Clinic

Rochester, Minnesota, 55906, United States

Location

Related Publications (1)

• Wilson PM, Philpot LM, Ramar P, Storlie CB, Strand J, Morgan AA, Asai SW, Ebbert JO, Herasevich VD, Soleimani J, Pickering BW. Improving time to palliative care review with predictive modeling in an inpatient adult population: study protocol for a stepped-wedge, pragmatic randomized controlled trial. Trials. 2021 Sep 16;22(1):635. doi: 10.1186/s13063-021-05546-5.

  PMID: 34530871 DERIVED

Related Links

• Mayo Clinic Clinical Trials

Study Officials

• Jon O Ebbert, MD

  Mayo Clinic

  PRINCIPAL INVESTIGATOR

Study Design

• Study Type: interventional
• Phase: not applicable
• Allocation: RANDOMIZED
• Masking: NONE
• Purpose: HEALTH SERVICES RESEARCH
• Intervention Model: CROSSOVER
• Sponsor Type: OTHER
• Responsible Party: PRINCIPAL INVESTIGATOR
• PI Title: Principal Investigator

Model Details: Pragmatic Stepped Wedge Cluster Randomized Trial

Study Record Dates

• First Submitted: June 3, 2019
• First Posted: June 6, 2019
• Study Start: August 19, 2019
• Primary Completion: November 18, 2020
• Study Completion: December 20, 2020
• Last Updated: December 30, 2020

Record last verified: 2020-12

Data Sharing

• IPD Sharing: Will not share

Locations

US (1)