Impact of Predictive Modeling on Time to Palliative Care in an Outpatient Primary Care Population

NCT04604457 | Completed

• 1 other identifier: 20-005977
• Study Type: interventional
• Target: 127,070
• Locations: 1 country
• Sites: 1

Brief Summary

A machine learning algorithm will be used to accurately identify patients in certain primary care units who may benefit from palliative care consults.

Conditions

Palliative Care

Keywords

machine learning; predictive model; palliative care

Outcome Measures

Primary Outcomes (1)

• Timely identification for need of palliative care

  Time to electronic record of consult by the palliative care team in the outpatient setting

  Through study completion, an average of 1 year

Secondary Outcomes (6)

• Number of palliative care consults

  Through study completion, an average of 1 year

• Number of advanced care planning notes documented in the EHR

  Through study completion, an average of 1 year

• Number of billing codes for palliative care

  Through study completion, an average of 1 year

• Positive predictive value of screened patients

  Through study completion, an average of 1 year

• Percent of patients who are eligible for ECH based palliative care

  Through study completion, an average of 1 year

Study Arms (2)

Standard of Care

NO INTERVENTION

Palliative care specialists would not reach out to primary care providers. Palliative care needs would be met via existing mechanisms.

Predictive Model

EXPERIMENTAL

Palliative care specialists review recommendations from the predictive model and contact a patient's primary care provider (PCP) when appropriate to recommend a palliative care consult.

Other: Palliative care contacts primary care

Interventions

Palliative care contacts primary care OTHER

Palliative care specialist reaches out to primary care to recommend a palliative care consult. If the primary care provider agrees, he/she will write an order for a palliative care consult.

Predictive Model

Eligibility Criteria

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

You may qualify if:

• Adult patient assigned to a primary care unit from July 2020 to June 2021.

You may not qualify if:

• Patients that have been seen by Palliative care will be excluded for 75 days
• Patients under the age of 18 years.
• Patients currently enrolled with hospice

Sponsors & Collaborators

• Mayo Clinic: lead

Study Sites (1)

Mayo Clinic in Rochester

Rochester, Minnesota, 55905, United States

Location

Related Publications (1)

• Heinzen EP, Wilson PM, Storlie CB, Demuth GO, Asai SW, Schaeferle GM, Bartley MM, Havyer RD. Impact of a machine learning algorithm on time to palliative care in a primary care population: protocol for a stepped-wedge pragmatic randomized trial. BMC Palliat Care. 2023 Feb 3;22(1):9. doi: 10.1186/s12904-022-01113-0.

  PMID: 36737744 DERIVED

Related Links

• Mayo Clinic Clinical Trials

Study Officials

• Rachel Havyer, MD

  Mayo Clinic

  PRINCIPAL INVESTIGATOR

Study Design

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

Model Details: Step-wedge design with 7 wedges: the first wedge has all primary care teams in the standard of care arm; every six weeks one or two care teams switch to the intervention arm.

Study Record Dates

• First Submitted: October 14, 2020
• First Posted: October 27, 2020
• Study Start: August 31, 2020
• Primary Completion: May 31, 2021
• Study Completion: May 31, 2021
• Last Updated: June 16, 2021

Record last verified: 2021-06

Data Sharing

• IPD Sharing: Will not share

Locations

US (1)