Learning from individualised variation for evidence generation within a learning health system

Wilson, Matthew; Asselbergs, Folkert W.; Harris, Steve

doi:10.1016/j.bja.2022.02.008

Cited by 1 publication

(1 citation statement)

References 7 publications

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“…As such, progress will require careful attention to a range of concerns. At our own institution, we have extended this ethical and safety case one step further, and we are piloting a study design where the randomization is non-mandatory: a nudge not an order (76). The clinician is explicitly invited to only comply with the randomization where they have equipoise themselves.…”

Section: Continuous Clinical Evaluationmentioning

confidence: 99%

Clinical deployment environments: Five pillars of translational machine learning for health

Harris

Bonnici

Keen

et al. 2022

Front. Digit. Health

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Machine Learning for Health (ML4H) has demonstrated efficacy in computer imaging and other self-contained digital workflows, but has failed to substantially impact routine clinical care. This is no longer because of poor adoption of Electronic Health Records Systems (EHRS), but because ML4H needs an infrastructure for development, deployment and evaluation within the healthcare institution. In this paper, we propose a design pattern called a Clinical Deployment Environment (CDE). We sketch the five pillars of the CDE: (1) real world development supported by live data where ML4H teams can iteratively build and test at the bedside (2) an ML-Ops platform that brings the rigour and standards of continuous deployment to ML4H (3) design and supervision by those with expertise in AI safety (4) the methods of implementation science that enable the algorithmic insights to influence the behaviour of clinicians and patients and (5) continuous evaluation that uses randomisation to avoid bias but in an agile manner. The CDE is intended to answer the same requirements that bio-medicine articulated in establishing the translational medicine domain. It envisions a transition from “real-world” data to “real-world” development.

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Section: Continuous Clinical Evaluationmentioning

confidence: 99%