Journal of evaluation in clinical practiceSamuel Atiku, Olufisayo Olakotan
BACKGROUND: The integration of ambient artificial intelligence (AI) scribes into the OpenNotes environment presents a profound governance crisis in healthcare. While patient access to medical records was designed as a transparency reform, the introduction of machine-generated text introduces novel vulnerabilities regarding record integrity, liability, and patients' trust. OBJECTIVE: This study investigates how clinicians discursively negotiate the systemic risks and accountability challenges of patient-facing, AI-assisted documentation. METHODS: Employing a netnographically informed qualitative design, the research conducted a reflexive thematic analysis of 484 relevant comments across 120 threads from eight clinician-oriented subreddits spanning October 2020 to February 2026. RESULTS: The analysis revealed five distinct governance challenges. First, an accountability vacuum exists where the mandatory clinician signature functions merely as a legal shock absorber for institutional AI liability. Second, clinicians frame AI hallucinations as a mathematically inevitable epistemic risk rather than a correctable technical bug. Third, a "dual-audience" problem emerges, as algorithmic optimization compromises both the individual clinical voice needed for peer communication and the empathetic clarity required for patient readers. Fourth, existing privacy frameworks are structurally inadequate to manage commercial data extraction during patient encounters. Finally, institutional productivity demands and AI-driven over-documentation severely threaten the fiscal credibility of the medical record through inadvertent upcoding. CONCLUSIONS: The prevailing regulatory assumption-that a physician's digital signature combined with passive patient visibility guarantees documentation accountability-is a fragile fiction. To protect clinical truth, health systems must transition from models of passive disclosure toward contingent transparency. This requires establishing authoritative, enforceable mechanisms for provenance tracking, error contestation, and vendor accountability.
Breast cancer research and treatmentLejla Kočo, Wendelien B G Sanderink, Mathias Prokop, Ritse M Mann
PURPOSE: Multidisciplinary team meetings (MDTMs) in breast cancer care improve outcomes but are time-consuming and costly. This study investigates using data from the Dutch national cancer registry (NCR) and hospital electronic medical records (EMR) to efficiently calculate MDTM discussion durations, while complying with privacy laws. METHODS: This retrospective study analyzed breast cancer MDTM discussion durations using NCR and EMR data from 2014 to 2018. Discussion times were estimated from EMR timestamps, excluding outliers, and analyzed statistically. Ethical approval was obtained, including only non-opt-out patients. RESULTS: The final dataset included 1,048 tumors, 996 patients, and 1,487 MDTM discussion times after exclusions for missing data, duplicates, and outliers. Discussion durations varied significantly, with pre-operative and no-surgery discussions being longer than post-operative ones (p = 0.000), and factors such as MRI availability, tumor differentiation, malignancy, menopausal status, and cancer stage influencing duration in pre-operative cases. In post-operative discussions, molecular subtype, cancer stage, and tumor size were significant, while age, tumor differentiation, and menopausal status had no impact. CONCLUSION: This study evaluates discussion durations in breast cancer MDTMs using retrospective data from the NCR and EMR, demonstrating a feasible approach to assess MDTM functioning. Differences in discussion duration based on patient and tumor characteristics may help optimize MDTM efficiency. CLINICAL TRIAL NUMBER: Not applicable.
Neurosurgical reviewSerhat Yıldızhan, Usame Rakip, İhsan Canbek, Mehmet Gazi Boyacı, Serhat Korkmaz, Adem Aslan
Chronic subdural hematoma (CSDH) is increasingly common in older adults and in patients receiving antithrombotic therapy. Although burr-hole drainage is generally safe and effective, perioperative mortality remains a concern, and reliable preoperative predictors are incompletely defined. We aimed to identify independent preoperative predictors of in-hospital mortality after burr-hole drainage for CSDH, with explicit characterization of causes of death, comorbidity burden, and the discriminative performance of candidate predictors. This single-center retrospective cohort study included 121 consecutive adult patients surgically treated for CSDH between January 2015 and December 2024. Preoperative variables included demographics, Glasgow Coma Scale (GCS) score, hematoma thickness, midline shift (MLS), cerebral edema on CT, antithrombotic use, and a comprehensive set of comorbidities (chronic kidney disease, chronic obstructive pulmonary disease, congestive heart failure, coronary artery disease, atrial fibrillation, diabetes mellitus, dementia, malignancy). Intensive care unit admission and postoperative complications were also recorded. The primary outcome was in-hospital mortality. Causes of death were systematically categorized. Associations were evaluated using Firth penalized logistic regression. Discriminative performance was assessed using receiver operating characteristic (ROC) analysis with bootstrap confidence intervals. A prespecified exploratory interaction analysis between cerebral edema and significant midline shift was performed. In-hospital mortality was 12.4% (15/121). The principal cause of death was cerebral herniation (9/15, 60.0%); extracranial complications (respiratory, septic, cardiac) accounted for 6/15 (40.0%). In the original multivariable Firth model, GCS ≤ 13 (adjusted OR 11.71, 95% CI 2.82-48.61, p < 0.001) and cerebral edema (adjusted OR 8.90, 95% CI 1.93-40.98, p = 0.005) were independently associated with mortality. In an extended model incorporating comorbidities, chronic kidney disease (OR 23.78, p = 0.025) and congestive heart failure (OR 42.39, p = 0.043) emerged as additional independent predictors, while cerebral edema (OR 61.89, p = 0.008) and GCS ≤ 13 (OR 5.80, p = 0.032) retained their associations. Combined model discrimination was excellent (Model 1: AUC 0.920; Model 2 with comorbidities: AUC 0.958). Subgroup analysis demonstrated a marked mortality gradient: 0% in patients with neither cerebral edema nor significant midline shift (n = 59), versus 72.2% in patients with both findings (n = 18). The exploratory interaction term (cerebral edema × midline shift ≥ 5 mm) was directionally consistent with synergy but did not reach statistical significance (OR 39.19, 95% CI 0.26-∞, p = 0.152), reflecting limited statistical power. Preoperative neurological impairment and cerebral edema are independently associated with in-hospital mortality after burr-hole drainage for CSDH. The coexistence of cerebral edema and significant midline shift identifies a clinically recognizable high-risk phenotype that may warrant heightened perioperative attention. Renal and cardiac comorbidities further contribute to mortality risk. These findings are hypothesis-generating and require validation in larger, prospective cohorts before incorporation into clinical risk stratification.Clinical trial number: not applicable.
Child's nervous system : ChNS : official journal of the International Society for Pediatric NeurosurgeryBeyza Nur Mert Bakal, Selman Kesici, Izzet Turkalp Akbasli, Merve Kasikci Cavdar, Benan Bayrakci
PURPOSE: To identify early neuroprognostic factors associated with functional neurological outcome in pediatric trauma patients requiring PICU admission. The primary outcome was Glasgow Outcome Scale (GOS) at discharge and 6 months; secondary outcomes were in-hospital mortality and brain death. METHODS: We conducted a retrospective cohort study of 425 consecutive pediatric trauma admissions to a tertiary PICU from June 2014 to December 2021. Demographics, mechanisms of injury, admission severity scores (GCS, PRISM III, PTS), cranial CT findings, early laboratory markers, and key interventions were analyzed. Factors independently associated with mortality were assessed using multivariable logistic regression, and discrimination was evaluated with receiver operating characteristic analyses. RESULTS: Median age was 78 months and 64.2% were male. Falls and traffic accidents were the most common mechanisms. Overall mortality was 7.3% and was predominantly attributable to severe traumatic brain injury (29 of 31 deaths). Brain death occurred in 4.5%. At discharge, 349 of 425 patients (82.1%) had a good neurological outcome (GOS 4-5), whereas 76 (17.9%) had a poor outcome (GOS 1-3), including 31 in-hospital deaths (GOS 1). Among 394 hospital survivors, 6-month follow-up was available for 377 (95.7%); of these, 364 (96.6%) achieved good recovery and 13 (3.4%) had persistent poor outcomes. Among survivors with a poor outcome at discharge and available follow-up, 25 of 38 (65.8%) improved to a good outcome by six months. Seventeen survivors were lost to follow-up. Poor outcome (GOS 1 to 3) was associated with low admission GCS, cerebral edema or midline shift, and early physiologic stress including hyperglycemia and elevated lactate. Factors independently associated with mortality were inotropic support (OR 6.98), blood product administration (OR 6.52), albumin administration (OR 3.69), cerebral edema (OR 3.59), and admission GCS 8 or less (OR 6.82). PRISM III and PTS showed strong discrimination. CONCLUSION: In critically injured children, early neurological compromise and systemic instability that are identifiable during initial stabilization were the strongest factors associated with mortality and functional recovery, supporting neurofocused risk stratification and timely escalation of care.
JMIR medical informaticsChristian Gulden, Marvin Kampf, Detlef Kraska, John Grimes, Thomas Ganslandt, Hans-Ulrich Prokosch, Susanne A Seuchter, Jonathan M Mang, Peter Pallaoro, Paul-C…
BACKGROUND: Electronic health records offer vast clinical data for health care research, but interoperability challenges often hinder comprehensive analysis. The Health Level Seven Fast Healthcare Interoperability Resources (FHIR) standard addresses these challenges, although its nested and interconnected resource format can be complex for analytics. Several tools have emerged to facilitate analytical access, either by querying FHIR servers via representational state transfer (REST) APIs or encoding resources in relational formats. However, the performance implications of these methods remain largely unexplored. OBJECTIVE: This study aimed to benchmark the performance characteristics of different FHIR-based analytical approaches comparing REST API queries against SQL- and Spark-based big data frameworks operating on FHIR-encoded data. METHODS: We benchmarked the FHIR-PYrate library, which interfaces with a FHIR server's REST API, against Pathling, a library built for analytics based on Apache Spark, and Trino, a general-purpose SQL query engine. We defined and implemented multiple queries in each engine using 3 common analytics scenarios-data aggregation, counting, and extraction. Execution times were measured across Synthea-generated datasets of increasing size. RESULTS: On the largest dataset, containing 71,285,064 FHIR resources, Trino completed the aggregate query more than 12,000 times faster, and Pathling did so approximately 500 times faster than FHIR-PYrate. On average across all queries, Trino outperformed FHIR-PYrate, executing extraction queries 33 times faster and count queries 1.8 times faster. Pathling achieved a 2.6-time speedup for extraction queries, but FHIR-PYrate was approximately 13 times faster for count queries. CONCLUSIONS: While the REST-based FHIR search API is useful for standard queries and retrieving specific patient records and can outperform alternatives for some count queries, it generally lacks the performance and expressiveness needed for complex analytics. In contrast, alternative engines such as Trino and Pathling demonstrated substantial performance advantages for these scenarios.
American journal of physical medicine & rehabilitationMarlon L Addison, Hayden T Nevills, Jacob W Brubacher, Mitchell Birt, Matt Luetke, Sarah M Eickmeyer, Jordan A Borrell
PURPOSE: This quality improvement study evaluated a novel, electronic medical record (EMR)-based order set designed to standardize interdisciplinary care coordination for patients undergoing lower extremity amputation at a large academic medical center. When activated, the order set automatically placed consultations for an interdisciplinary team consisting of physical therapy, occupational therapy, physical medicine and rehabilitation (PM&R), chaplaincy, and orthotics/prosthetics. METHODS: Patients (n=637) who underwent lower extremity amputation were divided into 2 cohorts: those with and without use of the amputation order set. Interdisciplinary consultation patterns, length of stay (LOS), and discharge disposition were analyzed. RESULTS: The order set significantly increased consultations with PM&R, chaplaincy, and orthotics/prosthetics services. While overall hospital LOS was longer in the order set group, patients who received preoperative order set placement had significantly shorter time from surgery to discharge. In addition, patients in the order set group were more likely to be discharged to inpatient rehabilitation facilities, as opposed to skilled nursing facilities or directly home. CONCLUSIONS: Findings suggest that structured interdisciplinary coordination, particularly when initiated preoperatively, can enhance discharge planning and facilitate higher-quality rehabilitation pathways. The implementation of a standardized amputation consultation panel within the EMR supports provider adoption and may reduce disparities in care access.
BMC medical informatics and decision makingJoseph Owusu-Marfo, Mark Anthony Azongo, Jonathan Kissi
BACKGROUND: The use of technology in healthcare to manage patient records, guide diagnosis, and make referrals is termed electronic healthcare. An electronic health record system called Lightwave Health Information Management System (LHIMS) was implemented in 2021 at Bolgatanga Regional Hospital (BRH). This study evaluated the opinion of users on the use of LHIMS among healthcare workers, focusing on the extent to which its use has enhanced the main dimensions of clinical work. METHOD: A qualitative research design was employed to explore healthcare providers' experiences with the LHIMS. Purposive sampling was employed to recruit eleven (11) participants comprising nurses and doctors who had at least two years of experience using the LHIMS. An interview guide was used to facilitate in-depth, face-to-face interviews with all participants. RESULTS: Healthcare providers expressed overall satisfaction with LHIMS, citing its time-saving features, efficiency, data security, cost-effectiveness, and ease of use. Users reported receiving support from IT personnel, experienced colleagues, and stable network systems; however, challenges included inadequate staff training, documentation difficulties among nurse midwives, limited computer availability, insufficient user manuals, and frequent power interruptions. CONCLUSION: The study found that most LHIMS users were satisfied with the system, particularly its user-friendly interface and efficiency in managing and synchronizing patient data. To enhance system performance and sustain user satisfaction, the hospital should ensure uninterrupted power supply, provide mandatory training for new staff, integrate a comprehensive user manual into the LHIMS platform, and supply adequate computer resources to support effective use.
Journal of medical systemsAbdul Rehman Khalid, Haider Ali, Kounen Fathima, Kouayep Sonia Carole, Hee-Cheol Kim
International Classification of Diseases (ICD) codes enable correct billing, insurance reimbursement, and healthcare analytics. However, manual coding is time-consuming, expensive, and error-prone, creating bottlenecks in clinical workflow and limiting scalability. Artificial intelligence (AI) has emerged as a promising solution for automated ICD code assignment from unstructured clinical text. This systematic review explores the current state of automated ICD coding research, examining models applied to diverse clinical documents including discharge summaries, electronic health records, nursing notes, and pathology reports. Following PRISMA guidelines, we searched six databases for studies published between 2019 and 2024, selecting 54 relevant studies from 4,280 initial citations. Our analysis reveals the use of diverse datasets, preprocessing techniques, and feature extraction methods, alongside a clear evolution from traditional machine learning to deep learning approaches, with substantial architectural diversity across convolutional, recurrent, transformer, and hybrid models. Performance varies considerably across dataset configurations, with models achieving higher accuracy on frequent code subsets compared to full label spaces. However, critical gaps persist: overreliance on single-language, single-institution datasets limits generalizability; difficulties in predicting rare codes remain unresolved; lack of model interpretability undermines clinical trust; and inconsistent evaluation protocols hinder meaningful comparison. To address these challenges, we propose a 5P evidence-grounded research agenda: Population Diversity, Performance Robustness, Prediction of Rare Codes, Provenance Transparency, and Practical Integration. These findings underscore AI's potential to transform ICD coding while highlighting the need for standardized benchmarks, rigorous external validation, multilingual datasets, and explainable architectures to enable equitable and effective deployment in real-world healthcare systems.
Studies in health technology and informaticsMelinda Wassell, Kerryn Butler-Henderson, Karin Verspoor
Data quality (DQ) and transparency of secondary data are critical factors that delay the adoption of clinical AI models and affect clinician trust in them. Many DQ studies fail to clarify where, along the lifecycle, quality checks occur, leading to uncertainty about provenance and fitness for reuse. This study develops a framework for transparent reporting of DQ assessments across the clinical electronic health record (EHR) data lifecycle. The reporting framework was developed through iterative analysis to identify actors and phases of the clinical data lifecycle. The framework distinguishes between data-generating organisations and data-receiving organisations to allow users to map DQ parameters to stages across the data lifecycle. The framework defines five key lifecycle phases and multiple actors. When applied to the real-world dataset, the framework demonstrated applicability in revealing where DQ issues may originate. The framework provides a structured approach for reporting DQ assessments, which can enhance transparency regarding data fitness for reuse, supporting reliable clinical research, AI model development, and internal organisational governance. This work provides practical guidance for researchers to understand data provenance and for organisations to target DQ improvement efforts across the data lifecycle.
Studies in health technology and informaticsRebecca Jedwab, Janette Gogler, Rebecca Brook, Joanne Foster, James-Norbert Garduce, Anthony Pham, Naomi Dobroff
The Initial Patient Assessment (IPA) documentation within the selected healthcare organisation's electronic medical record (EMR) is a key component of nursing documentation and planning for patients' hospitalisations. Since its implementation in 2019, the EMR IPA documentation has not been re-assessed for usefulness or completeness. A project was developed to assess, re-design and implement an updated IPA using the Exploration, Preparation, Implementation, Sustainment (EPIS) framework. New IPA forms were co-designed with consumers and nurses to be more conversational and implemented across the organisation for neonatal admissions, paediatric admissions, adult admissions, and adult and paediatric day ward admissions. Improvement in completeness and usefulness was sustained, and this project filled a gap in the literature by providing a co-designed approach to nursing documentation within the EMR with end-users and consumers.
Studies in health technology and informaticsJörn Guy Süss, Michael Osborne, John Carter
HL7® FHIR® is increasingly used for health information exchange, yet delivery outcomes vary and teams frequently re-solve similar problems. This paper presents a "towards" contribution that captures proven practice as two complementary pattern languages: one for authoring FHIR artefacts (profiles, terminology, implementation guides) and one for implementing distributed systems that exchange FHIR at scale. The pattern languages make forces and trade-offs explicit, enabling reuse across teams and contexts. A publication pipeline produces multiple output formats-static website, EPUB, and DocBook-from a single machine-readable source, including a machine-consumable llms.txt index to support AI-assisted navigation. The approach is described, the generated outputs presented, and the plans for operational validation and AI-assisted quick-start pathways are outlined.
Renal failureSiyu Tang, Chaoqun Niu, Huan Jiang, Yunchao Ling, Zihao Zheng, Jingquan Liu, Run Zhang, Jun Hong, Bai Xu, Guoqing Zhang, Xianghong Yang
This study aimed to develop and externally validate a real-time, continuous prediction model for 48-h acute kidney injury (AKI) risk in critically ill patients using a dual-channel deep learning model (DC-AKI). The model was developed using electronic health records from 28,099 patients at Beth Israel Deaconess Medical Center and externally validated on two independent cohorts: 3,108 patients from the eICU Database and 2,808 patients from Zhejiang Provincial People's Hospital. Thirty-one time-varying features were updated every 6 h. The DC-AKI model's dual-channel architecture integrated BiGRU networks, convolutional layers, and attention mechanisms to capture multiscale temporal dependencies. The model achieved areas under the receiver operating characteristic curve (AUC) of 0.720 (95% CI, 0.714-0.728) in internal validation, and 0.577 (95% CI, 0.570-0.583) and 0.798 (95% CI, 0.795-0.799) in the two external cohorts. Interpretability analysis via SHAP identified key clinical predictors and individual risk trajectories. In conclusion, DC-AKI demonstrated strong predictive performance in the development cohort and one external validation site, although performance varied substantially across institutions. Further validation and local calibration are warranted to support its clinical deployment.
International emergency nursingSantiago Morejón Bandrés, José Luis Martin Conty, Begoña Polonio-López, Samantha Diaz Gonzalez, Cristina Rivera Picón, Sergio Rodríguez Cañamero, Juan José Ber…
BACKGROUND: Seizures are one of the most attended neurological emergencies in the prehospital context. The categorization of the risk of these patients is a great challenge for health professionals, due to the limited information, leading to inaccurate diagnoses. The aim of this study was to explore the development and validation of a long-term mortality predictive score that considers vital signs and biomarkers in seizure patients. METHODS: A prospective, multicenter study was conducted by emergency medical services (EMS) in Spain, including five advanced life support units, 27 basic life support units and four emergency services. The sample consisted of adults who suffered prehospital seizures, in which vital signs and blood tests were recorded using point-of-care tests (POCTs) to predict long-term all cause 1 year mortality. RESULTS: The sample consisted of 198 patients, in whom 33 mortality events were recorded. Our predictive model identified age, Glasgow Coma Scale (GCS) score, hemoglobin, serum Anion Gap (SAG), international normalized ratio (INR), and Charlson comorbidity index as risk factors, revealing and AUC of the score of 0.743 (95%CI 0.624-0.862). CONCLUSION: This study has identified hemoglobin, SAG and INR as prehospital biomarkers capable of predicting long-term mortality in patients who have suffered prehospital seizures. The combination of these new biomarkers to age and GCS into a score available for EMS staff could be a practical and effective tool that improves risk stratification and patient management.
International journal of medical informaticsJack Lott, Brett Stubbert, David McShannon, Joseph Bellissimo, Dhruv Patel, Nicholas Dietrich
BACKGROUND: The National Institutes of Health Stroke Scale (NIHSS) is critical to acute stroke care but is often documented in unstructured notes. Large language models (LLMs) can enable automated extraction, though smaller models often underperform relative to frontier systems. Chain-of-Verification (CoVe) prompting introduces a structured self-verification step that may improve performance. METHODS: We evaluated eight LLMs on 312 discharge summaries. Small models included LLaMA 3.2 3B, Ministral 3B, Gemma 3 4B, and Qwen 3 4B. Frontier models included GPT-5.2, Gemini 3 Pro, Claude Opus 4.5, and Grok 4. Each model was tested under a baseline and CoVe prompt. Outcomes were subscore exact-match accuracy, subscore mean absolute error (MAE), total score exact-match accuracy, and total score MAE. RESULTS: At baseline, small models achieved 53.2 ± 10.0% subscore accuracy and subscore MAE 0.84 ± 0.22, compared with 88.5 ± 10.1% and 0.15 ± 0.16 in frontier models (both p < 0.001). Total exact accuracy was low in both groups (7.7 ± 12.9% vs 35.9 ± 32.4%). CoVe significantly improved small-model performance (subscore accuracy 65.0 ± 10.9%; subscore MAE 0.55 ± 0.21; total MAE 4.84 ± 2.30 vs 7.19 ± 3.54 at baseline; all p < 0.001), although total exact accuracy remained modest (9.6 ± 15.7%). Frontier models showed no significant group-level change with CoVe. CONCLUSION: CoVe prompting substantially improves NIHSS extraction in small LLMs while producing negligible effects in frontier models. Although smaller model performance remains insufficient for standalone clinical deployment, CoVe prompting offers a promising avenue for further exploration.
International journal of medical informaticsAlexandra Dahlberg, Olli Tapiola, Rami Luisto, Tuukka Puranen, Enni Sanmark, Ville Vartiainen
BACKGROUND: Embedding models are an integral part of generative AI architectures, transforming text into embedding vectors that represent semantic content in numerical form. Despite their central role, their performance in clinical settings remains underexplored. We evaluated embedding models across two tasks: semantic difference detection in clinical notes, and data retrieval from patient records. METHODS: Eight models were applied to synthetic discharge summaries in English, Swedish, and Finnish. Semantic sensitivity was assessed by introducing controlled perturbations (deletion, modification, and paraphrasing) at three levels of severity; cosine similarity, L1 and Euclidean distances were computed between the vectors of the original and perturbed texts. Partial vectors were compared to explore dimensionality reduction. Two models with the biggest contrast in semantic difference detection were evaluated on retrieval of relevant information from real Finnish vascular surgery records. RESULTS: Embedding vectors captured semantic differences in clinical notes: content deletion and modification produced larger increases in vector distance than paraphrasing. On average, models detected the direction of semantic change correctly, but case-level performance varied considerably. Qwen3-Embedding-8B produced no case-level (directional) errors whereas multilingual-E5-large produced them the most (12.2%). In retrieval this contrast transferred only partially and was task-dependent: sufficiency scores favoured Qwen3-Embedding-8B for the vascular-diagnosis question (2.25 vs 1.15 out of 5) but were comparable for the antithrombotic-medication question (3.25 vs 3.17 out of 5). For some models, as few as 0.6-1.2% of dimensions sufficed to replicate full-vector accuracy; principal component analysis and coordinate-level analysis did not account for this finding. CONCLUSIONS: Our results show that the choice of embedding model is important: performance differences between models can be large enough to determine whether clinically relevant information reaches the end user, and model weaknesses can be both task-specific and context-dependent.
International journal of medical informaticsWenyong Wang, Mahnaz Samadbeik, Gaurav Puri, Donald S A McLeod, Elton Lobo, Tuan Duong, Titus Kirwa, Clair Sullivan
BACKGROUND: Electronic medical records (EMRs) are widely implemented across health settings and function as sociotechnical systems that shape clinical workflows, information use, and patient-clinician interaction. While EMR impacts on clinician experience have been extensively studied, patient experience of EMR-enabled care remains underexplored. This study aims to examine patient experience in an EMR-enabled outpatient clinic and identify actionable recommendations to optimise clinic outcomes. METHODS: A cross-sectional, convergent mixed-methods survey was conducted in a fully digital public diabetes outpatient clinic in Queensland, Australia. Quantitative data, collected using the Patient Experience Monitor (PEM) Adult Outpatient short-form aligned with Picker principles, assessed patient experience across multiple outpatient care domains. Qualitative data, collected through two open-ended items, explored how patients experienced care in the context of clinician-mediated EMR use during consultations and identified opportunities for improvement. Data were collected concurrently and analysed separately. Integration occurred at the reporting stage, where qualitative findings were used to explain and contextualise the quantitative results and to inform practical recommendations. RESULTS: One hundred patients participated. Quantitative findings showed highly favourable but ceiling-affected patient experience ratings across PEM domains. Qualitative analysis identified four themes: perceived facilitation of informational continuity and coordination of care; perceived reduction in personal interaction; limited patient and GP access beyond the public hospital EMR environment; and background trust and neutral perceptions of EMR use. Integration of findings informed a set of actionable recommendations to optimise EMR-supported workflows, preserve interpersonal engagement, strengthen information continuity across care settings, and enable more participatory models of outpatient care. CONCLUSIONS: Patients perceived aspects of EMR-enabled outpatient care as supporting patient-centred care, particularly when clinicians used integrated information effectively during consultations. Findings highlight the importance of implementing EMRs as sociotechnical systems that not only align with consultation workflows but also preserve interpersonal connection and support participatory care. Achieving this requires meaningful information access and sharing across patients, clinicians, and care settings, providing practical guidance for designing digitally enabled outpatient services.
Journal of biomedical informaticsDaniel J Tan, Jiayang Chen, Dilruk Perera, Kay Choong See, Mengling Feng
OBJECTIVE: Enteral nutrition (EN) delivery in the ICU remains suboptimal due to limited personalization and uncertainty regarding appropriate calorie, protein, and fluid targets under dynamic metabolic demands. We introduce DeepEN, a reinforcement learning (RL) framework for personalized EN optimization using electronic health record data. METHODS: DeepEN was trained on over 11,000 ICU patients from MIMIC-IV to generate 4-hourly, patient-specific caloric, protein, and fluid targets. The state representation incorporated demographics, comorbidities, vital signs, laboratory values, and recent interventions. A physiologically aligned reward framework balanced biomarker stability with long-term survival. Policy learning employed a dueling double deep Q-network with Conservative Q-Learning regularization to enable safe offline training. RESULTS: DeepEN achieved the highest estimated policy value (Vπ=9.48) and the lowest calibrated mortality (18.8 ± 1.0%), representing a 4.0 percentage-point absolute reduction compared with clinician practice (22.8%). The policy also demonstrated superior metabolic stability, achieving the highest proportion of glucose, phosphate, and sodium values within target range. Furthermore, deviation from the DeepEN policy was independently associated with increased mortality and biomarker instability, whereas deviation from a random policy showed no such association. Interpretability analyses further indicated that recommendations were conditioned on physiologically relevant markers of organ function and metabolic status rather than static dosing heuristics. CONCLUSION: DeepEN demonstrates the feasibility of conservative offline RL for safe, individualized EN optimization, highlighting the potential of data-driven personalization to complement guideline-based approaches in critical care.
International journal of medical informaticsVanessa Pereira Corrêa Rampinelli, Ranieri Alves Dos Santos, Ianka Cristina Celuppi, Gabriel Norde Santos, Jades Fernando Hammes, Célio Luiz Cunha, Raul Sidnei…
BACKGROUND: The Fast Healthcare Interoperability Resources (FHIR) standard is a global benchmark for digital health data exchange. Despite its widespread adoption, the scientific literature on the methodological processes for creating FHIR Implementation Guides (IGs) remains fragmented and lacks systematization. OBJECTIVE: This scoping review aims to synthesize the scientific literature on the process of developing FHIR IGs for electronic health records, identifying methodological steps, toolchains, governance patterns, and critical gaps that limit clinical adoption. METHODS: Following JBI and PRISMA-ScR guidelines, a comprehensive search was conducted across nine databases in August 2025. From an initial 5,552 records, eleven studies published between 2021 and 2025 were selected for analysis. Data extraction focused on development stages, team composition, authoring tools, and validation workflows. RESULTS: The study identified a synthesized seven-step implementation lifecycle: requirements, modeling, terminology, narrative documentation, technical validation, clinical validation, and publication. A significant methodological shift toward "Infrastructure as Code" was observed, with frequent use of FHIR Shorthand (FSH) and Continuous Integration/Continuous Deployment (CI/CD) pipelines, particularly in European national initiatives. While technical validation was nearly universal (10 out of 11 studies), clinical validation was inconsistently addressed (7 out of 11 studies), often relegated to future work, resulting in IGs that are syntactically correct but insufficiently aligned with real-world clinical workflows. Narrative documentation, essential for non-technical stakeholders, was reported as comprehensive in only four studies, limiting broader clinical adoption. All studies reported multidisciplinary team involvement, confirming that IG development is fundamentally an exercise in clinical governance and consensus. CONCLUSION: The creation of FHIR IGs has evolved into a complex discipline requiring a convergence of software engineering, clinical semantics, and institutional governance. This review advances beyond existing FHIR literature by providing the first systematic synthesis focused specifically on the IG creation process, presenting a replicable seven-step cycle that can guide implementers, researchers, and policy makers. Closing the gap between technical readiness and clinical applicability remains a critical challenge. Future research should prioritize ongoing clinical validation, the establishment of standardized reporting frameworks for IG development, and the integration of generative AI tools to enhance narrative documentation and terminology binding.
Pediatric clinics of North AmericaNohra Ghaoui, Katelyn Breznak, Kalyani Marathe
Pediatric dermatology remains an underserved subspecialty due to workforce shortages, geographic disparities, and systemic barriers. This 4-year, single-center initiative implemented 15 targeted interventions, including optimization of provider roles, referral redesign, teledermatology expansion, and electronic health record-based scheduling strategies, to improve access and efficiency. The program achieved sustained improvements in fill rates, reduced no-shows, and decreased appointment lag times, enabling same-day appointments. By combining structural workflow redesign with technology-driven solutions, the initiative demonstrates a replicable model for expanding equitable access to pediatric dermatology care and may inform similar efforts in other underserved medical fields.
Rheumatic diseases clinics of North AmericaSuzanne Tamang
Artificial intelligence (AI) is increasingly embedded in clinical tools used in rheumatology, including imaging interpretation, longitudinal disease monitoring, and electronic health record-based decision support. AI has moved from the periphery of biomedical research to an operational component of clinical care, increasingly embedded in electronic health records, imaging platforms, and decision support systems. In rheumatology, where care is longitudinal, AI systems offer substantial promise-but also poses distinct risks. Together, these commitments-advancing humanity, ensuring equity, engaging impacted individuals, improving workforce well-being, monitoring performance, innovating and learning, and promoting sustainability-operationalize trustworthy AI systems across a patient's care trajectory.