clinical stakeholder engagement and domain translation

Current medical AI deployment faces a fundamental gap among explainability theory, clinical requirements, and regulatory expectations, compounded by the absence of practical guidance for preclinical evaluation readiness. This study bridges these domains by systematically integrating eXplainable AI (XAI) theory, clinical practice needs, and regulatory frameworks—introducing two foundational preclinical development principles: “Transparency-by-Design” and “Actionability-by-Design” to establish a shared interdisciplinary language. Methodologically, we unify model calibration, uncertainty quantification, and robustness engineering to enable case-level interpretability and full system-behavior traceability. Our contribution is a rigorously defined, empirically verifiable technical boundary and actionable implementation guidelines that significantly reduce the preparation time for clinical evaluation. This work establishes a methodological foundation for compliant, trustworthy, and clinically integrated AI deployment in healthcare. (149 words)

Critical clinical information—such as risk factors and treatment responses—in mental health electronic health records (EHRs) is predominantly unstructured, hindering automated risk identification and precision interventions. Method: We developed VIEWER, a clinical informatics platform that achieves end-to-end integration of EHR semantic parsing, SNOMED CT/ICD-10 terminology standardization, knowledge graph modeling, and real-time decision support—deployed for the first time across a large UK National Health Service (NHS) mental health trust. Its implementation follows a novel cross-institutional, multidisciplinary co-design and deployment paradigm. Contribution/Results: A proof-of-concept evaluation demonstrated significant improvements in timeliness of high-risk patient identification and intervention coverage. VIEWER enables three-tiered decision optimization: individualized care delivery, interdisciplinary team coordination, and organizational-level population health management—thereby advancing scalable, evidence-informed mental healthcare.

In value-based mental health care transformation, misalignment persists among outcomes definition, data collection, and clinical application. Method: We conducted in-depth interviews with 30 U.S. psychiatrists and applied thematic analysis integrated with human-computer interaction design principles and value-based care policy frameworks to systematically identify clinician-informed health IT design opportunities. Contribution/Results: We propose the “dual-goal alignment” framework—simultaneously supporting payment decisions and individualized care delivery. We reconceptualize health IT’s role in enabling patient-reported outcome collection, integrating multi-source data (clinical, insurance, social services), and establishing cross-stakeholder accountability. The study yields 12 context-sensitive health IT design principles, specifying a clinically acceptable minimal data set, incentive-aligned collection mechanisms, and coordinated tripartite (clinician–payer–patient) responsibility pathways—constituting the first empirically grounded, frontline-clinician-derived design guide for value-based health systems.

In healthcare design, practitioners often lack access to real clinical systems, authentic patient data, and collaborative channels with clinicians—hindering deep domain understanding. Method: This paper proposes a “learning-by-making” methodology for data-driven healthcare systems, such as remote patient monitoring (RPM). Grounded in ethnographic field observations, it models clinical workflows, manually constructs high-fidelity synthetic datasets, and iteratively develops lightweight prototypes—integrating data schema design and contextual abstraction directly into the design process. Contribution/Results: The approach enables designers to systematically grasp RPM data flows, clinical logic, and system constraints—even without access to real-world data—thereby bridging critical domain knowledge gaps. Its core contribution is establishing manually crafted synthetic data as a novel cognitive medium for design, offering a reusable methodological framework for interdisciplinary design in closed, sensitive domains.

This study addresses the frequent oversight of sociotechnical risks—such as transparency, privacy, reliability, and patient autonomy—in the rapid deployment of clinical speech-to-text systems. Through an interdisciplinary review integrating AI evaluation, clinical workflows, ethical compliance, and organizational governance, the work proposes the first comprehensive sociotechnical governance framework tailored to this technology. The framework offers actionable implementation pathways, encompassing system readiness assessment, vendor vetting, pilot deployment, staff training, and ongoing monitoring. By aligning these components, it jointly safeguards patient autonomy, ensures documentation integrity, and reinforces institutional trust, thereby enabling healthcare organizations to responsibly adopt speech-to-text technologies.

This study addresses the inefficiency and error-proneness of information extraction in medical form filling by proposing CLAIRE, a hybrid workflow. The method adopts a "schema-grounded, verification-first" architecture that automates form completion through field state discovery, source-to-field mapping, deterministic validation, and audit trails. Large language models from the Qwen series are restricted to assisting with mapping tasks without authorization for critical operations, while a bounded error-correction mechanism ensures data rigor. Benchmark evaluations demonstrate that CLAIRE achieves both a success rate and an accuracy of 1.000, substantially reducing the burden of manual review and enhancing processing throughput.

This study addresses the lack of standardized and automated case planning processes in medical social work, which currently relies heavily on individual practitioner experience and suffers from inefficiency. The authors propose a model-agnostic, open-source large language model (LLM) workflow that systematically integrates established social work practice frameworks into LLM prompt design for the first time. The approach decomposes case planning into six sequential stages—assessment, problem analysis, goal setting, intervention planning, risk anticipation, and outcome evaluation—and combines structured client profiling with staged prompt engineering to generate professional, reviewable draft assessment forms and service plans. Designed to be compatible across multiple LLM platforms, the framework ensures cross-model reproducibility, and its code has been publicly released to provide a standardized tool for advancing intelligent support in medical social work.

This study addresses the challenge that existing healthcare IT systems face in extracting and structuring patient-specific clinical intent from natural language. The authors propose a three-layer framework—comprising documentation, clinical state, and clinical intent—and introduce the “Actionable Clinical Record” (ACR) as the fundamental computable unit at the clinical intent layer. They formalize, for the first time, the concept of “computable clinical intent” and develop a readiness maturity ladder alongside a framework for evaluating executable correctness. By integrating clinical information modeling, natural language processing, and standards such as FHIR, the work enables computable representation and validation of clinical intent. Feasibility of ACRs is demonstrated in specific subtasks, providing reusable foundational components for future research in this direction.

Medical visualization lacks a systematic design process that simultaneously addresses stakeholder differentiation, logical coherence across design stages, and task-type adaptability. Method: This study proposes a cognition-driven, systematic design research model grounded in literature review and cross-disciplinary practice. It innovatively introduces a binary task subclassification—hypothesis-driven versus non-hypothesis-driven—first applied in medical visualization, and refines each design phase according to the complexity of underlying medical problems. The model explicitly emphasizes stakeholder identification, cognitive progression between stages, and fine-grained classification of inferential versus descriptive tasks. Contribution/Results: The model was applied to guide the development of a medical visual analytics method and retrospectively analyzed three canonical works. Evaluation confirms its theoretical rigor, practical feasibility, and cross-case generalizability—demonstrating effectiveness in enhancing systematicity, operationality, and transferability in medical visualization design.

This study addresses the challenge of unreliable AI models and diminished clinical trust stemming from opaque data quality reporting in the secondary use of electronic health records (EHRs). To this end, the authors propose the first comprehensive framework for transparent data quality reporting across the entire EHR lifecycle. The framework innovatively distinguishes between data producers and consumers, explicitly defines five critical phases, and maps established data quality dimensions to specific workflow stages. Through iterative stakeholder and process analysis, a structured reporting mechanism is developed and validated on real-world datasets, demonstrating its ability to effectively trace the origins of data quality issues. The approach significantly enhances data interpretability, fitness-for-use, and governance efficacy, thereby providing a robust foundation for trustworthy AI development and clinical research.