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Meredith Schreier

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    User-centred design for machine learning in health care: a case study from care management
    Birju Patel
    Daniel Lopez-martinez
    Doris Wong
    Eric Loreaux
    Janjri Desai
    Jonathan Chen
    Lance Downing
    Lutz Thomas Finger
    Martin Gamunu Seneviratne
    Ming-Jun Chen
    Nigam Shah
    Ron Li
    BMJ Health & Care Informatics (2022)
    Preview abstract Objectives: Few machine learning (ML) models are successfully deployed in clinical practice. One of the common pitfalls across the field is inappropriate problem formulation: designing ML to fit the data rather than to address a real-world clinical pain point. Methods: We introduce a practical toolkit for user-centred design consisting of four questions covering: (1) solvable pain points, (2) the unique value of ML (eg, automation and augmentation), (3) the actionability pathway and (4) the model’s reward function. This toolkit was implemented in a series of six participatory design workshops with care managers in an academic medical centre. Results: Pain points amenable to ML solutions included outpatient risk stratification and risk factor identification. The endpoint definitions, triggering frequency and evaluation metrics of the proposed risk scoring model were directly influenced by care manager workflows and real-world constraints. Conclusions: Integrating user-centred design early in the ML life cycle is key for configuring models in a clinically actionable way. This toolkit can guide problem selection and influence choices about the technical setup of the ML problem. View details
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