JMIR Publications

ABSTRACT

Background:

Successful integrations of machine learning into routine clinical care are exceedingly rare and barriers to adoption are poorly characterized in the literature.

Objective:

To report a quality improvement effort to integrate a deep learning sepsis detection and management platform, Sepsis Watch, into routine clinical care.

Methods:

In 2016, a multidisciplinary team consisting of statisticians, data scientists, data engineers, and clinicians was assembled by the leadership of an academic health system to radically improve the detection and treatment of sepsis. This report of the quality improvement effort follows the learning health system framework to describe the problem assessment, design, development, implementation, and evaluation plan of Sepsis Watch.

Results:

Sepsis Watch was successfully integrated into routine clinical care and reshaped how local machine learning projects are executed. Front-line clinical staff were highly engaged in the design and development of the workflow, machine learning model, and application. Novel machine learning methods were developed to detect sepsis early and implementation of the model required robust infrastructure to minimize maintenance effort. Development of the technology was necessary, but not sufficient to impact clinical care. Significant investment was required to align stakeholders, develop trusting relationships, define roles and responsibilities, and train front-line staff. Three partnerships were established with internal and external research groups to evaluate Sepsis Watch.

Conclusions:

Machine learning models are commonly developed to enhance clinical decision making, but successful integrations of machine learning into routine clinical care are rare. While there is no playbook for integrating deep learning into clinical care, learnings from the Sepsis Watch integration can inform efforts to develop machine learning technologies at other health care delivery systems.