Unknown

Dataset Information

0

Impact of a machine learning algorithm on time to palliative care in a primary care population: protocol for a stepped-wedge pragmatic randomized trial.


ABSTRACT:

Background

As primary care populations age, timely identification of palliative care need is becoming increasingly relevant. Previous studies have targeted particular patient populations with life-limiting disease, but few have focused on patients in a primary care setting. Toward this end, we propose a stepped-wedge pragmatic randomized trial whereby a machine learning algorithm identifies patients empaneled to primary care units at Mayo Clinic (Rochester, Minnesota, United States) with high likelihood of palliative care need.

Methods

42 care team units in 9 clusters were randomized to 7 wedges, each lasting 42 days. For care teams in treatment wedges, palliative care specialists review identified patients, making recommendations to primary care providers when appropriate. Care teams in control wedges receive palliative care under the standard of care.

Discussion

This pragmatic trial therefore integrates machine learning into clinical decision making, instead of simply reporting theoretical predictive performance. Such integration has the possibility to decrease time to palliative care, improving patient quality of life and symptom burden.

Trial registration

Clinicaltrials.gov NCT04604457 , restrospectively registered 10/26/2020.

Protocol

v0.5, dated 9/23/2020.

SUBMITTER: Heinzen EP 

PROVIDER: S-EPMC9896817 | biostudies-literature | 2023 Feb

REPOSITORIES: biostudies-literature

altmetric image

Publications

Impact of a machine learning algorithm on time to palliative care in a primary care population: protocol for a stepped-wedge pragmatic randomized trial.

Heinzen Ethan P EP   Wilson Patrick M PM   Storlie Curtis B CB   Demuth Gabriel O GO   Asai Shusaku W SW   Schaeferle Gavin M GM   Bartley Mairead M MM   Havyer Rachel D RD  

BMC palliative care 20230203 1


<h4>Background</h4>As primary care populations age, timely identification of palliative care need is becoming increasingly relevant. Previous studies have targeted particular patient populations with life-limiting disease, but few have focused on patients in a primary care setting. Toward this end, we propose a stepped-wedge pragmatic randomized trial whereby a machine learning algorithm identifies patients empaneled to primary care units at Mayo Clinic (Rochester, Minnesota, United States) with  ...[more]

Similar Datasets

| S-EPMC8444160 | biostudies-literature
| S-EPMC6661655 | biostudies-literature
| S-EPMC8744030 | biostudies-literature
| S-EPMC7449309 | biostudies-literature
| S-EPMC9292802 | biostudies-literature
| S-EPMC7735899 | biostudies-literature
| S-EPMC9873107 | biostudies-literature
| S-EPMC8340123 | biostudies-literature
| S-EPMC9328081 | biostudies-literature
| S-EPMC7912968 | biostudies-literature