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

Not Applicable

Completed

• Conditions: Palliative Care
• Interventions: Other: Palliative care contacts primary care

• Registration Number: NCT04604457
• Lead Sponsor: Mayo Clinic

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.

Detailed Description

A machine learning algorithm will be used to accurately identify patients in certain primary care units who may benefit from palliative care consults. These patients will be presented weekly to a palliative care specialist in a custom user interface. The palliative care specialist will reach out to primary care teams if she determines that the patient would benefit from palliative care. If the primary care provider agrees, he/she would write a palliative care consult order for the patient. The goal is to reduce the time to palliative care for these patients, who may not have been identified as quickly without the algorithm.

Study Timeline

• Study Start: 2020-08-31
• Study First Submitted: 2020-10-14
• Study First Submitted QC: 2020-10-21
• Study First Posted: 2020-10-27
• Primary Completion: 2021-05-31
• Study Completion: 2021-05-31
• Status Verified: 2021-06
• Last Update Submitted: 2021-06-14
• Last Update Posted: 2021-06-16

Recruitment & Eligibility

• Status: COMPLETED
• Sex: All
• Target Recruitment: 127070

Inclusion Criteria

• Adult patient assigned to a primary care unit from July 2020 to June 2021.
• Weekly the palliative care specialists will select patients by looking at patients in sorted order starting with the highest score and proceeding down the list and evaluating each patient for exclusion criteria.

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Exclusion Criteria

• 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

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Study & Design

• Study Type: INTERVENTIONAL
• Study Design: CROSSOVER

Arm && Interventions

Group	Intervention	Description
Predictive Model	Palliative care contacts primary care	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.

Primary Outcome Measures

Name	Time	Method
Timely identification for need of palliative care	Through study completion, an average of 1 year	Time to electronic record of consult by the palliative care team in the outpatient setting

Secondary Outcome Measures

Name	Time	Method
Number of palliative care consults	Through study completion, an average of 1 year	Number of palliative care consults that occurred on intervention and standard of care arms
Positive predictive value of screened patients	Through study completion, an average of 1 year	Percentage of screened patients that received palliative care consults
Number of advanced care planning notes documented in the EHR	Through study completion, an average of 1 year	Number of advanced care planning notes documented in the EHR on both arms
Number of billing codes for palliative care	Through study completion, an average of 1 year	Number of ICD-10 billing codes for palliative care on both arms
Percent of patients who are eligible for ECH based palliative care	Through study completion, an average of 1 year	Percent of patients who are eligible for employee/community health (ECH) based palliative care compared to the Palliative Care Clinic.
Percent agreement between Palliative Care and Primary Care and average time between Primary Care Contact and Response	Through study completion, an average of 1 year	Agreement statistics (percent agreement and Kappa statistics) between Palliative Care and Primary Care and descriptive statistics (mean, etc.) on time between primary care contact and response.

Trial Locations

Locations (1)

Mayo Clinic in Rochester; 🇺🇸 Rochester, Minnesota, United States