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

Abstract

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.

Machine learning model evaluation on healthcare applications: A Review

“…There are also comments about the need for specialized professionals to participate in model construction and validation to promote better reliability (Wojtusiak, 2021;Risman, Trelles, & Denning, 2021;Harris et al, 2022;Rojas et al, 2022). Specialists can help both in processing and making sense of the data, model performance testing, and defining evaluation methods, thus ensuring that the resulting models are accurate and reliable.…”

“…Another issue pointed out by some works is the need for good model interpretability (Rafiq, Modave, Guha, & Albert, 2020;Harris et al, 2022;Li et al, 2022;Duckworth et al, 2021). ML model interpretability and explainability can help ensure that ML-enabled applications provide coherent and reliable decisions.…”

“…For example, models trained on data derived from a single health institution may not generalize well on multi-institutional scenarios. A variation on this problem is patient selection biases (regional, socioeconomic, and institutional) (Van Helvoort et al, 2020;Carolan et al, 2022;Lam et al, 2022;Birkenbihl et al, 2020;Kamran et al, 2022;Risman, Trelles, & Denning, 2021;Shickel et al, 2020;Bellocchio et al, 2021;Rafiq, Modave, Guha, & Albert, 2020;Harris et al, 2022;The RADAR-CNS Consortium et al, 2021;Li et al, 2022;Lin et al, 2022;Rojas et al, 2022;Yang, Zou, Liu, & Mulligan, 2021;Fries et al, 2019).…”

“…Carolan et al (2022) describes the need for better automation technologies to improve the efficiency of algorithms. There are also opportunities for expert management and monitoring (Algorithmic Stewardship), with projections of the near-future creation of MLOps departments for healthcare services and hospitals (Harris et al, 2022). Other possibilities include integrating equity in the ML lifecycle, removing biases, as well as collecting feedback from experts and other stakeholders to bring human knowledge into the learning process (Human-in-the-Loop Learning), and going beyond statistical metrics in evaluating the model performance, using domain-oriented approaches to measure the usefulness and commercial value of these (Rojas et al, 2022;Yang, Zou, Liu, & Mulligan, 2021).…”

“…Finally, there are opportunities for real-world applications supported by live data where teams can iteratively build and test at the bedside, continuous delivery (CD) MLOps platforms, design and oversight by people with AI security expertise, continuous assessment using randomization to avoid bias, and use of data flows with the HL7-FHIR protocol (Harris et al, 2022).…”