Research Appraisals
Evidence-based critical appraisals of the latest medical research, systematically evaluated using Oxford CEBM methodology.
Showing 6 appraisals
Journal of medical Internet research
Digital Transformation in Health Care: Are We on the Right Track?
Health care digital transformation is gaining increasing attention, despite the observed challenges in its implementation. The envisioned benefits, together with the growing need for better health care, are motivating academia, organizations, regulatory agencies, and governments to develop more effective digital health care solutions. Through extensive debates among the authors, this paper discusses how digital transformation is being conducted in the health care sector. Our discussion relies on concepts from sociotechnical systems theory, categorizing it into 3 social (people, culture, and goals) and 3 technical (processes/procedures, infrastructure, and technology) dimensions. Drawing on both the literature discussed in this paper and our combined academic and practical experience-each author contributing over a decade of work in health care digital transformation-we examined current digitalization efforts from a sociotechnical standpoint. Overall, we argue that both social and technical dimensions present elements that have either encouraged or discouraged the progress of health care digital transformation. The identification of current trends on such (on- and off-track) elements allowed the formulation of 12 propositions across each sociotechnical dimension for future testing and validation. This approach can help establish better government policies, foster private initiatives, and shift regulatory guidelines to support a successful digital transformation in health systems. Finally, from a research perspective, we outline some opportunities for further interdisciplinary investigation in the field, promoting advances in the understanding of health care digital transformation.
22 July 2026
Read appraisal →Studies in health technology and informatics
Digital Twins for Decision Making: Combining Ambulance and ED Facility Resources
Emergency department (ED) walk-ins and ambulance arrivals are often modelled independently, overlooking their interdependence and the impact of fixed treatment space (TS) capacity. An established discrete event simulation algorithm has been tailored as the basis of a digital twin (DT). Driven by regular streams of incoming ED presentations, patient off-stretcher time (POST), ambulance bay and TS occupation across a 24-hour period contingent on patient flow and ED TS capacity was simulated. The simulation was calibrated using 19,570 ambulance records and data from a 250-bed hospital (2021-2022). Scenarios of five ambulance bays with 43, 38, and 34 TS capacities were investigated, revealing patterns of TS depletion and recovery, including cases where recovery failed within 24 hours, underscoring the sensitivity of patient flow to resource constraints and the value of DT-based simulation for capacity planning.
17 July 2026
Read appraisal →Health care management science
An online algorithm for integrated scheduling of pre-treatment and treatment appointments in radiotherapy using deep reinforcement learning
Scheduling patients for radiotherapy within the oncology department is vital for ensuring timely and effective treatment. The patient's therapy consists of a sequence of pre-treatment consultations, i.e. preliminary investigations to define the required treatment, followed by the radiotherapy treatment sessions on linear accelerators. Timely scheduling of pre-treatment consultations is crucial to prevent unnecessary delays in radiotherapy treatment, thereby impacting patient health. Therefore, we suggest integrating both scheduling problems. We employ a Reinforcement Learning-based scheduling technique to solve the multi-appointment scheduling problem in an online fashion. We design a reward function comprising three components to guide the agent towards learning an effective scheduling policy involving multiple appointments. Additionally, we compare our proposed model with benchmark online heuristics. We further provide insight into the reward function design, which steers the agent to learn a feasible policy that leads to high-quality solutions. Experiments show that our algorithm outperforms the benchmark heuristics.
12 July 2026
Read appraisal →Journal of medical Internet research
A Futures Framework for Clinical AI Governance: Anticipating Emerging Risks, Shifting Roles, and Regulatory Challenges
This viewpoint develops the futures framework for clinical artificial intelligence governance (FF-CAIG), a conceptual and anticipatory framework for organizing emerging governance challenges in clinical artificial intelligence (AI). Although life cycle-oriented oversight is increasingly reflected in clinical AI regulation and institutional governance, existing approaches remain more developed for near-term validation, current-state assurance, and retrospective risk detection than for longer-horizon sociotechnical change. This gap is increasingly relevant, as AI systems become more complex, adaptive, and autonomous, and as they become more deeply embedded in care relationships and accountability structures. FF-CAIG is grounded in 3 futures methodologies: the 3 horizons model, scenario planning, and causal layered analysis. It is operationalized through an emerging clinical AI risk taxonomy that links these methods to governance domains. Its practical outputs include horizon classification, risk-domain mapping, scenario stress-testing findings, accountability-chain mapping, and horizon-scaled minimum governance actions for deployment or continued use. Applied across near-term, transitional, and longer-term horizons, the framework proposes cross-horizon priorities, including stronger predeployment equity evaluation, clearer life cycle accountability, clinician AI oversight competencies, and safeguards for increasingly autonomous or AI-mediated care systems. We illustrate FF-CAIG through 3 representative clinical AI deployment patterns and discuss its limitations, including differential compliance burdens, risks of overdocumentation, variable feasibility across jurisdictions, and the need for empirical validation. FF-CAIG is intended not as a prescriptive policy instrument or validated assessment tool, but as a structured analytic approach for regulators, health system leaders, developers, and researchers seeking prospective and systems-oriented approaches to clinical AI governance.
30 June 2026
Read appraisal →Journal of health organization and management
Cloud-based healthcare architecture for securing and monitoring healthcare data
PURPOSE: A new method known as Lionized Remora optimization based Recurrent Neural Network (LRObRNN) is recommended to enhance the safety of medical information stored on cloud servers to tackle these issues. DESIGN/METHODOLOGY/APPROACH: To safeguard patient data, healthcare organizations must thoughtfully choose reliable and compliant cloud service providers while implementing robust security measures. Storing patient information in cloud systems raises issues with illegal access and data breaches. FINDINGS: The LRObRNN generates a secret key using Lionized Remora optimization and employs cryptography to encrypt sensitive healthcare data. Continuous monitoring ensures the security of data transmission by identifying irregularities. ORIGINALITY/VALUE: Leveraging Recurrent Neural Networks the system analyzes sequential data, enabling the detection of patterns and potential security breaches during data transmission. The performance evaluation includes metrics such as encryption and decryption time, confidentiality rate, processing time, resource usage and efficiency, which are compared with other existing models.
27 June 2026
Read appraisal →Philosophical transactions. Series A, Mathematical, physical, and engineering sciences
Cognitive glues are shared models of relative scarcities: the economics of collective intelligence
Collective intelligence has emergent problem-solving capacities that are different than those of its subunits. The plethora of multi-scale systems within nature and society makes it imperative to understand the interaction policies necessary and sufficient for emergence of collective intelligences. The economy is a complex system consisting of autonomous elements at multiple scales and which exhibits adaptive problem-solving capabilities, suggesting that the economy offers an interesting, important example of collective intelligence. We identify the price system as the cognitive glue of the economy by acting as a coordinating affordance that enables members to form plans that are mutually compatible. Using the collective intelligence framework of Technological Approach to Mind Everywhere, we elaborate on various aspects of the economy that make it useful to model the economy as a collective intelligence. We argue that any cognitive glue must solve the same kind of problem that the price system solves in broadly the same way that the price system solves it, and thus the price system serves as a generic template or abstract model for all cognitive glues. Finally, we describe some research ideas that combine concepts from biology and economics in the hopes of inspiring interdisciplinary collaboration. This article is part of the theme issue 'World models in natural and artificial intelligence'.
15 May 2026
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