Real-World Integration of a Sepsis Deep Learning Technology Into Routine Clinical Care: Implementation Study (Preprint)

Sendak, Mark; Ratliff, William; Sarro, Dina; Alderton, Elizabeth; Futoma, Joseph; Gao, Michael; Nichols, Marshall; Revoir, Mike; Yashar, Faraz; Miller, Corinne; Kester, Kelly; Sandhu, Sahil; Corey, Kristin; Brajer, Nathan; Tan, Christelle; Lin, Anthony; Brown, Tres; Engelbosch, Susan; Anstrom, Kevin J.; Elish, Madeleine Clare; Heller, Katherine; Donohoe, Rebecca; Theiling, Jason; Poon, Eric G.; Balu, Suresh; Bedoya, Armando; O’Brien, Cara

doi:10.2196/preprints.15182

Cited by 3 publications

(2 citation statements)

References 42 publications

(28 reference statements)

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“…To date, few of these models have been validated prospectively and adopted in routine practice [35,36]; however implementation studies are beginning to emerge [37]. As ML systems start to infiltrate clinical practice, it is critical that the upstream data-science pipelines for developing and evaluating deep learning models with EHR data are robust and well specified.…”

Section: Introductionmentioning

confidence: 99%

Use of deep learning to develop continuous-risk models for adverse event prediction from electronic health records

et al. 2021

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Early prediction of patient outcomes is key to unlocking the potential for targeted preventive care. This protocol describes a practical workflow for developing deep learning risk models for early prediction of various clinical and operational outcomes using structured electronic health record (EHR) data, discussing the prediction of acute kidney injury (AKI) as an exemplar. The protocol consists of 34 steps grouped into the following stages: formal problem definition, data pre-processing, architecture selection, calibration and uncertainty estimation, generalisability evaluation. Additionally, we demonstrate the application of this protocol to three other endpoints -mortality, length of stay and 30-day hospital readmission -for both continuous predictions (e.g. triggered every 6h) and static predictions (e.g. triggered at 24h post admission). The performance on these additional endpoints exceeded most comparable literature benchmarks. This protocol is accompanied by an open-source codebase that illustrates key considerations for EHR modeling and may be customised to alternate data formats and prediction tasks.

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Section: Introductionmentioning

confidence: 99%

Use of deep learning to develop continuous-risk models for adverse event prediction from electronic health records

et al. 2021

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“…The case study draws on practitioner, clinician, and researcher experiences over the 2.5-year duration of the Sepsis Watch project. A detailed description of clinical implementation is provided elsewhere [53]. The Sepsis Watch team included nurses, physicians across multiple specialties, informaticians, statisticians, data engineers, solution architects and user interface designers.…”

Section: Case Study: Sepsis Watchmentioning

confidence: 99%

"The human body is a black box"

Sendak

Elish

Gao

et al. 2020

Proceedings of the 2020 Conference on Fairness, Accountability, and Transparency

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116

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Machine learning technologies are increasingly developed for use in healthcare. While research communities have focused on creating state-of-the-art models, there has been less focus on real world implementation and the associated challenges to fairness, transparency, and accountability that come from actual, situated use. Serious questions remain underexamined regarding how to ethically build models, interpret and explain model output, recognize and account for biases, and minimize disruptions to professional expertise and work cultures. We address this gap in the literature and provide a detailed case study covering the development, implementation, and evaluation of Sepsis Watch, a machine learning-driven tool that assists hospital clinicians in the early diagnosis and treatment of sepsis. Sepsis is a severe infection that can lead to organ failure or death if not treated in time and is the leading cause of inpatient deaths in US hospitals. We, the team that developed and evaluated the tool, discuss our conceptualization of the tool not as a model deployed in the world but instead as a socio-technical system requiring integration into existing social and professional contexts. Rather than focusing solely on model interpretability to ensure fair and accountable machine learning, we point toward four key values and practices that should be considered when developing machine learning to support clinical decision-making: rigorously define the problem in context, build relationships with stakeholders, respect professional discretion, and create ongoing feedback loops with stakeholders. Our work has significant implications for future research regarding mechanisms of institutional accountability and considerations for responsibly designing machine learning systems. Our work underscores the limits of model interpretability as a solution to ensure transparency, accuracy, and accountability in practice. Instead, our work demonstrates other means and goals to achieve FATML values in design and in practice.

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A Path for Translation of Machine Learning Products into Healthcare Delivery

Sendak¹,

D’Arcy²,

Kashyap³

et al. 2020

EMJ Innov

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Despite enormous enthusiasm, machine learning models are rarely translated into clinical care and there is minimal evidence of clinical or economic impact. New conference venues and academic journals have emerged to promote the proliferating research; however, the translational path remains unclear. This review undertakes the first in-depth study to identify how machine learning models that ingest structured electronic health record data can be applied to clinical decision support tasks and translated into clinical practice. The authors complement their own work with the experience of 21 machine learning products that address problems across clinical domains and across geographic populations. Four phases of translation emerge: design and develop, evaluate and validate, diffuse and scale, and continuing monitoring and maintenance. The review highlights the varying approaches taken across each phase by teams building machine learning products and presents a discussion of challenges and opportunities. The translational path and associated findings are instructive to researchers and developers building machine learning products, policy makers regulating machine learning products, and health system leaders who are considering adopting a machine learning product.

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Real-World Integration of a Sepsis Deep Learning Technology Into Routine Clinical Care: Implementation Study (Preprint)

Cited by 3 publications

References 42 publications

Use of deep learning to develop continuous-risk models for adverse event prediction from electronic health records

Use of deep learning to develop continuous-risk models for adverse event prediction from electronic health records

"The human body is a black box"

A Path for Translation of Machine Learning Products into Healthcare Delivery

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