ArticleBMJ health & care informatics2022
User-centred design for machine learning in health care: a case study from care management.
Article in BMJ health & care informatics, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.
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Who cites it
11 citing papers in PubMed, 21 citations in OpenAlex.
- Development and validation of an interpretable predictive machine learning model for successful weaning of continuous renal replacement therapy.Scientific reports · 2025Observational
- User-Centered Delivery of AI-Powered Health Care Technologies in Clinical Settings: Mixed Methods Case Study.JMIR human factors · 2025Article
- Partners in Practice: Primary Care Physicians Define the Role of Artificial Intelligence.Healthcare (Basel, Switzerland) · 2025Article
- Utilizing Artificial Intelligence for the Diagnosis, Assessment, and Management of Chronic Pain.Journal of biomedical physics & engineering · 2025Review
- Expectations of Intensive Care Physicians Regarding an AI-Based Decision Support System for Weaning From Continuous Renal Replacement Therapy: Predevelopment Survey Study.JMIR medical informatics · 2025Article
- The promises and limitations of artificial intelligence for quality improvement, patient safety, and research in hospital medicine.Journal of hospital medicine · 2025Article
- Preliminary Evidence of the Use of Generative AI in Health Care Clinical Services: Systematic Narrative Review.JMIR medical informatics · 2024Review
- [Use of AI-based applications by hospital staff: task profiles and qualification requirements].Bundesgesundheitsblatt, Gesundheitsforschung, Gesundheitsschutz · 2024Review
- Applying a user-centred design machine learning toolkit to an autism spectrum disorder use case.BMJ health & care informatics · 2023Article
- Artificial Intelligence and Cancer Control: Toward Prioritizing Justice, Equity, Diversity, and Inclusion (JEDI) in Emerging Decision Support Technologies.Current oncology reports · 2023Review
- Designing AI tools to advance health equity in resource-constrained low- and middle-income countries.Digital healthArticle
Corrections and comments
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Authors and funding
19 authors at 3 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
objectivesFew 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.
methodsWe 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.
resultsPain 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.
conclusionsIntegrating 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.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.