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

NCT ID: NCT04604457

Last Updated: 2021-06-16

Study Results

Results pending

The study team has not published outcome measurements, participant flow, or safety data for this trial yet. Check back later for updates.

Basic Information

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Recruitment Status

COMPLETED

Clinical Phase

NA

Total Enrollment

127070 participants

Study Classification

INTERVENTIONAL

Study Start Date

2020-08-31

Study Completion Date

2021-05-31

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.

Conditions

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Palliative Care

Study Design

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Allocation Method

RANDOMIZED

Intervention Model

CROSSOVER

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.
Primary Study Purpose

SCREENING

Blinding Strategy

NONE

Study Groups

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Standard of Care

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

Group Type NO_INTERVENTION

No interventions assigned to this group

Predictive Model

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.

Group Type EXPERIMENTAL

Palliative care contacts primary care

Intervention Type 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.

Interventions

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Palliative care contacts primary care

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.

Intervention Type OTHER

Eligibility Criteria

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Inclusion Criteria

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

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
Minimum Eligible Age

18 Years

Eligible Sex

ALL

Accepts Healthy Volunteers

No

Sponsors

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Mayo Clinic

OTHER

Sponsor Role lead

Responsible Party

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Rachel D. Havyer

Principal Investigator

Responsibility Role PRINCIPAL_INVESTIGATOR

Principal Investigators

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Rachel Havyer, MD

Role: PRINCIPAL_INVESTIGATOR

Mayo Clinic

Locations

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Mayo Clinic in Rochester

Rochester, Minnesota, United States

Site Status

Countries

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United States

References

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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.

Reference Type DERIVED
PMID: 36737744 (View on PubMed)

Related Links

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Other Identifiers

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20-005977

Identifier Type: -

Identifier Source: org_study_id

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