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

Study Overview
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Brief Summary
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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
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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.

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

Conditions
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Palliative Care

Intervention / Treatment
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* Other: Palliative care contacts primary care


Participation Criteria
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Eligibility Criteria
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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. 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

Ages Eligible for Study
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Minimum Age: 18 Years

Sexes Eligible for Study
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All

Accepts Healthy Volunteers
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No

Study Plan
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How is the study designed?
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Design Details

Primary Purpose: Screening
Allocation: Randomized
Intervention Model: Crossover Assignment
Interventional Model Description: 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.
Masking: None (Open Label)


Arms and Interventions

| Participant Group/Arm | Intervention/Treatment |
| --- | --- |
| No Intervention: Standard of Care<br>Palliative care specialists would not reach out to primary care providers. Palliative care needs would be met via existing mechanisms. | |
| Experimental: Predictive Model<br>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<br>* 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.<br>|

What is the study measuring?
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Primary Outcome Measures

| Outcome Measure | Measure Description | Time Frame |
| --- | --- | --- |
| 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 Outcome Measures

| Outcome Measure | Measure Description | Time Frame |
| --- | --- | --- |
| Number of palliative care consults | Number of palliative care consults that occurred on intervention and standard of care arms | Through study completion, an average of 1 year | 
| Number of advanced care planning notes documented in the EHR | Number of advanced care planning notes documented in the EHR on both arms | Through study completion, an average of 1 year | 
| Number of billing codes for palliative care | Number of ICD-10 billing codes for palliative care on both arms | Through study completion, an average of 1 year | 
| Positive predictive value of screened patients | Percentage of screened patients that received palliative care consults | Through study completion, an average of 1 year | 
| Percent of patients who are eligible for ECH based palliative care | Percent of patients who are eligible for employee/community health (ECH) based palliative care compared to the Palliative Care Clinic. | Through study completion, an average of 1 year | 
| Percent agreement between Palliative Care and Primary Care and average time between Primary Care Contact and Response | 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. | Through study completion, an average of 1 year | 

 Terms related to the study
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Keywords Provided by Centre Hospitalier Valida
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machine learning, predictive model, palliative care