Improve Patient Care Research Articles

Patient Engagement in Mobile Technology-Based Rehabilitation for Arthroplasty: A Scoping Review.

To map the evidence on patient engagement in mobile technology-based rehabilitation for arthroplasty, including outcome indicators, data collection methods, assessment results, facilitators and barriers, and promoting strategies. A scoping review. This study was conducted using a five-stage methodological framework, which included identifying the research questions, identifying relevant studies, selecting the studies, charting the data, and collating, summarising, and reporting the results. Ten computerised databases were searched to identify eligible studies published between January 2015 and March 2024. Forty-seven studies were included in this review. Most studies used data on patient adherence to interventions and programme usage to indicate patient engagement in mobile arthroplasty rehabilitation. Data were primarily collected through mobile device records and online or paper-based surveys. Over half of the studies reported a high level of patient engagement in mobile arthroplasty rehabilitation. Patient engagement was influenced by individual and environmental factors, such as the design of programmes, patients' ability to engage with technology, and the accessibility and functionality of equipment. Strategies to promote patient engagement include applying user-centred design principles, offering support from healthcare professionals, caregivers, and peer patients, and employing behaviour-changing strategies. Existing studies have shown promising results in patient adherence to and use of mobile arthroplasty rehabilitation programmes. Further research can explore engaging patients in programme development, optimising outcome evaluation and data collection, identifying the mechanisms of patient engagement, and testing the effectiveness of promoting strategies. The study findings provide practical implications for nurses and other healthcare professionals to deepen their understanding of patient engagement in mobile arthroplasty rehabilitation. They may consider employing strategies, such as user-centred design, to enhance patient engagement in mobile rehabilitation programmes, thereby improving patient care. This review adhered to the PRISMA-ScR checklist. No patient or public contribution.

The Impact of Delirium on Hospitalized Patients With COPD Exacerbation: Insights From the National In-Patient Sample.

Background: Delirium in hospitalized patients has been consistently associated with worse clinical outcomes, including increased mortality and prolonged hospital stays. COPD exacerbation is a common reason for hospitalization, and understanding the impact of delirium on this population is crucial for improving patient care and outcomes. Methods: We conducted a retrospective cohort study using the National In-Patient Sample dataset to assess the impact of delirium on patients hospitalized with COPD exacerbation. Subjects were identified using International Classification of Diseases, 10th Revision, codes for COPD exacerbation and delirium. Baseline characteristics and outcomes, including in-patient mortality and the use of invasive mechanical ventilation, were compared between subjects with and without delirium using chi-square analysis. Multivariate regression analysis was used to adjust for potential confounders. Results: A total of 8,062,333 subjects hospitalized with COPD exacerbation were included, of whom 574,005 (7.2%) had a diagnosis of delirium. Most subjects in both groups were white, covered by Medicare, and treated in large urban teaching hospitals. Subjects with delirium had higher rates of dementia, malnutrition, and chronic kidney disease. The rates of in-patient mortality (13.85% vs 4.29%, P < .01) and use of invasive mechanical ventilation (26.7% vs 7.79%, P < .01) were significantly higher in the delirium group. Delirium was associated with higher odds of mortality (adjusted odds ratio [aOR] 2.48, P < .01) and use of mechanical ventilation (aOR 3.92, P < .01). Subjects with delirium had longer and more costly hospital stays. Conclusions: Delirium was associated with worse outcomes in subjects hospitalized with COPD exacerbation.

Pragmatic awareness of Artificial Intelligence among medical students: a coss-sectional survey

Background: The integration of Artificial Intelligence (AI) into medicine is revolutionizing both clinical practice and medical education. However, the knowledge, attitudes, and perceptions of medical students towards AI remain underexplored. Objectives: To evaluate the knowledge attitudes and perceptions of medical students towards the integration of AI technologies in medical education, clinical practice, and healthcare decision-making. Materials and Methods: A cross-sectional survey was conducted among medical students at Rehman Medical College (RMC) Peshawar, Pakistan. Data were collected through a structured questionnaire assessing demographics, knowledge of AI, attitudes towards its use in medicine, and perceptions of its role in medical education and practice. Descriptive statistics and inferential analysis were applied to analyze the data. Results: Of 100 medical students, 85% were aged 18-25 years, with 60% females and 40% males. Only 10% had additional AI training, and 20% had attended AI-related talks (p=0.04). About 50% were competent in computer literacy, and 55% frequently used computer technology for learning (p=0.03). Regarding AI’s impact, 40% agreed it reduces medical errors (p=0.02), and 45% felt it facilitates physicians' access to information (p=0.01). However, 15% expressed concerns about AI damaging the physician-patient relationship (p=0.04). In AI in medicine, 92% had used AI applications, and 75% believed AI would positively transform medicine (p=0.02). While 40% feared job reductions in medical staff (p=0.05), 75% thought AI would improve patient care (p=0.04). For AI education, 85% supported its inclusion in the curriculum, with 50% favoring practical content (p=0.02) and case studies (p=0.01). 90% agreed AI education should be available to all medical staff (p=0.01). Conclusion: Medical students at Rehman Medical College showed a positive attitude towards AI’s potential to improve healthcare. However, there is a clear demand for more focused AI education, including practical applications and case studies.

Revisiting the molecular landscape of Rosai-Dorfman disease: insights from whole exome sequencing of Saudi patients

Rosai-Dorfman disease (RDD) has traditionally been viewed as a reactive histiocytic disorder, defined by its unique clinical features and immunophenotype. However, recent genetic studies suggest a more complex molecular landscape, challenging the notion of RDD as solely reactive and hinting at a possible neoplastic component. Mutations in MAPK/ERK pathway genes, such as KRAS and MAP2K1, have been observed in up to 33% of cases. Additional genetic alterations in cell cycle regulation, DNA repair, and other processes, along with low-frequency BRAF mutations, further emphasize this complexity. To better understand the molecular basis of RDD and enhance diagnostic precision, we conducted whole exome sequencing (WES) on seven Saudi patients with RDD, comparing their genetic profiles with existing literature. While no kinase driver mutations were detected, our analysis revealed thirteen distinct mutations. Recurrent mutations were observed in CD207 and TDG, each found in six patients. CD207 is linked to antigen processing, while TDG is associated with DNA repair. MUC4 and PDS5A mutations, related to cell cycle regulation, were each identified in three patients. DNMT3A mutation, affecting DNA methylation, was found in two patients. Single mutations were observed in BRCA1, LATS2, ATM, USP35, and CIC, associated with DNA repair, the ubiquitin proteasome pathway, and transcriptional regulation respectively. These findings offer insights into the genetic makeup of RDD, revealing candidate genes and expanding our understanding of the disease’s molecular complexities. By uncovering these genetic markers, this study contributes to the ongoing efforts to develop more accurate diagnostic tools and refine the classification of RDD, paving the way for improved patient care and disease management.

Beyond diversity recruitment: Next steps to ensure that underrepresented emergency medicine residents thrive

AbstractA diverse workforce in emergency medicine (EM) aims to improve patient care by addressing racism in health care, increasing representation in medicine, and improving the quality of training for all residents. Many EM residency programs have launched recruitment efforts to attract residents from diverse backgrounds. However, recruitment efforts only represent the first step in building a culturally responsible workforce. Trainees who are underrepresented in medicine must be welcomed into an inclusive training environment that has been thoughtfully constructed before they arrive. This type of supportive environment can be achieved by shifting away from majority‐serving ideals and building an informed infrastructure that functions to help all trainees succeed. We expand upon challenges and areas of opportunity at the individual, departmental, and institutional levels and describe common pitfalls when trying to create inclusive spaces for residents including lack of vision alignment, inadequate financial investment, and performative allyship. We also propose strategies that focus specifically on actionable changes that residency program, departmental, and institutional leadership can implement to mitigate these challenges.

Navigating Boundaries: How Pharmacists Develop Their Clinical Identity in a Complex Multidisciplinary Healthcare Setting

Introduction: Clinical pharmacists are crucial for optimizing medication therapy and improving patient outcomes, yet their potential is underutilized in many low- to middle-income countries. Shifting from traditional dispensing to clinical roles requires professional development and identity transformation. In South Africa’s public healthcare system, this shift faces additional challenges, such as a lack of formal positions, limited resources, and role ambiguity. Understanding how clinical pharmacists navigate this transition and develop their clinical identity is essential for their integration into healthcare teams and for improving patient care. Methods: Using a constructivist approach, this qualitative study employed semi-structured interviews with clinical pharmacists (n = 12) across South Africa’s public healthcare sector. We analyzed data through the lens of boundary crossing. Through thematic analysis we explored how pharmacists navigate the complexities of transitioning from dispensing to broader clinical roles, and how these experiences shape their professional identity. Results: Clinical pharmacists navigated both intrapersonal and interpersonal boundaries in their evolving roles. Three key themes were identified: (a) Bridging the gap within: Pharmacists navigate intrapersonal boundaries for clinical identity formation, (b) Bridging the gap between: Pharmacists navigate interprofessional boundaries for collaboration and identity formation, and (c) Building bridges: Pharmacists employ strategies to promote collaboration and recognition. Discussion: This study uncovered the complex interplay between intrapersonal boundaries – such as role ambiguity, self-doubt, reconciling traditional dispensing roles with expanded clinical responsibilities, and the need for mentorship – and interpersonal boundaries, including hierarchical structures, unclear role expectations, limited recognition, and challenges in interprofessional collaboration, in shaping clinical pharmacists’ identities. Fostering boundary crossing competence and interprofessional collaboration can help overcome systemic barriers, enabling pharmacists to navigate their roles, advocate for their expertise, and gain recognition within healthcare teams, ultimately enhancing their integration and improving patient care in resource-constrained settings like South Africa.

Engineering clinical translation of OGSE diffusion MRI.

Oscillating gradient spin echo (OGSE) diffusion MRI (dMRI) can probe the diffusive dynamics on short time scales ≲10 ms, which translates into the sensitivity to tissue microstructure at the short length scales m. OGSE-based tissue microstructure imaging techniques able to characterize the cell diameter and cellular density have been established in pre-clinical studies. The unique image contrast of OGSE dMRI has been shown to differentiate tumor types and malignancies, enable early diagnosis of treatment effectiveness, and reveal different pathophysiology of lesions in stroke and neurological diseases. Recent innovations in high-performance gradient human MRI systems provide an opportunity to translate OGSE research findings in pre-clinical studies to human research and the clinic. The implementation of OGSE dMRI in human studies has the promise to advance our understanding of human brain microstructure and improve patient care. Compared to the clinical standard (pulsed gradient spin echo), engineering OGSE diffusion encoding for human imaging is more challenging. This review summarizes the impact of hardware and human biophysical safety considerations on the waveform design, imaging parameter space, and image quality of OGSE dMRI. Here we discuss the effects of the gradient amplitude, slew rate, peripheral nerve stimulation, cardiac stimulation, gradient driver, acoustic noise and mechanical vibration, eddy currents, gradient nonlinearity, concomitant gradient, motion and flow, and signal-to-noise ratio. We believe that targeted engineering for safe, high-quality, and reproducible imaging will enable the translation of OGSE dMRI techniques into the clinic.

MedManage: Leveraging Technology for Effective Hospital Resource and Patient Management

Abstract - Effective management of hospital resources and patient care is crucial in modern healthcare. MedManage is an integrated digital solution designed to optimize hospital operations through advanced technology. It streamlines patient data handling, automates inventory tracking of medical supplies, enhances communication among healthcare professionals, and ensures real-time monitoring of critical resources. The system utilizes a centralized platform to reduce human error, improve decision-making, and promote efficient allocation of both human and material resources. By leveraging data analytics and cloud-based access, MedManage supports scalable and secure healthcare operations, ultimately improving patient outcomes and administrative productivity. Hospital processes are becoming more and more complicated and require robust management systems that would assist in streamlining the processes, improving patient care, and making hospital processes more efficient. MedManage is a comprehensive HMS, having the ability to provide various functionalities for a hospital under one roof. The following paper discusses the architecture, characteristics, and advantages of MedManage with a specific focus on placing it for the improvement of hospital administration and patient care. The increasing sophistication of operating a hospital necessitates the creation of an effective management system to organize processes and optimize the care of patients, with the appropriate resources. MedManage is an integrated system designed for the needs discussed in this paper. MedManage integrates other operations within a hospital; it captures patient management, appointments, billing, inventory, and reporting into one platform. Through the adoption of new technologies, including cloud computing to machine learning, and data analytics, MedManage brings real-time insight to enhance healthcare decision-making. This paper will introduce the architecture, key features, and advantages of MedManage, as well as a case study that has been conducted in a real-world setting of a hospital. There are numerous issues the healthcare sector has regarding managing patient information, scheduling, billing, and other administrative tasks. MedManage is an integrated HMS that offers means to streamline procedures, enhance patient care, and enhance efficiency in its operations. The paper discusses the architecture, characteristics, and advantages of MedManage and demonstrates its potential to transform hospital administration and patient care. Hospital administration systems must be designed robustly in a manner that their administrative functions can be performed easily, efficiently, and smoothly. The discussion above is also intended towards further enhancement of patient care with complete data integrity. MedManage is a complete HMS, particularly developed to address the above needs. This document describes the architecture, functionality, and advantages of MedManage, taking into account its ability to enhance hospitals' and patients' outcomes' efficiency. Key Words: Hospital Management System, Patient Care, Electronic Health Records, Resource Optimization, Healthcare Technology, Predictive Analytics, Inventory Management.

Mental health in primary care: What's new in the scientific literature in 2025?

The management of patients with psychological symptoms is common practice for family physicians. Regarding screening, numerous validated tools exist. The most useful are derived from the Patient Health Questionnaire (for anxiety-depressive syndromes) and the Patient Stress Questionnaire (Alcohol Use Disorders Identification Test-Concise for alcohol-related problems and General Anxiety Disorder Questionnaire for anxiety disorders). With the aim of improving patient care, better integration of somatic and psychological approaches is essential. Several options are possible, and their respective evaluations are encouraging: integration of mental health professionals into primary care settings and, conversely, integration of primary care professionals into community mental health structures.

Integrin signaling in tumor biology: mechanisms of intercellular crosstalk and emerging targeted therapies

Integrins, a family of transmembrane cell adhesion receptors, mediate intercellular and cell–extracellular matrix crosstalk via outside-in and inside-out signaling pathways. Integrins, categorized into 24 distinct combinations of α and β subunits, exhibit tissue-specific expression and perform unique or overlapping roles in physiological and pathophysiological processes. These roles encompass embryonic angiogenesis, tissue repair, and the modulation of tumor cell angiogenesis, progression, invasion, and metastasis. Notably, integrins are significant contributors to tumor development, offering valuable insights into the potential of integrin-targeted diagnostics and therapeutics. Currently, there are various preclinical and clinical trials aiming to harness integrin antagonists that are safe, efficacious, and exhibit low toxicity. Owing to the functional redundancy across integrin types and the complexity of the mechanisms of integrin-mediated multiple key processes associated with tumor biology, challenges exist that impede advancements in integrin-targeted therapy. Nevertheless, innovative strategies focused on integrin modulation represent significant breakthroughs for improving patient care and promoting comprehensive insights into the underlying mechanisms of tumor biology. This review elucidates the impact of integrins on three distinct cell types in multiple key processes associated with tumor biology and explores the emerging integrin-targeted therapeutic approaches for the treatment of tumors, which will provide ideas for optimal therapeutic approaches in the future.

Elearning Strategies to Enhance Eye Care Training

Myopia prevalence in Asia remains among the highest globally, accompanied by widespread misconceptions about its management. For eye care practitioners, staying updated on emerging research and debunking myths is critical to delivering effective patient care. However, traditional approaches to professional development, such as in-person workshops or online modules, often fail to account for the time constraints and demanding schedules of practitioners. Effective learning strategies must go beyond mere knowledge delivery to address the behavioral and motivational challenges that impact the application of knowledge in real-world settings. This paper explores an award-winning eLearning initiative by the Singapore National Eye Centre designed to advance professional development in basic myopia management. By leveraging innovative online learning strategies, this project equips practitioners with evidence-based insights while fostering meaningful behavioral change. The program’s design aligns with the evolving demands of healthcare education, emphasizing practical application, adaptability, and outcomes-driven learning. In the context of a rapidly changing healthcare landscape, this initiative demonstrates how well-structured eLearning can empower practitioners to navigate complex research, dispel prevalent myths, and ultimately improve patient care.

SoK: Federated Learning and Unlearning for Medical Image Analysis

ABSTRACTMedical image analysis is a critical component of modern healthcare, enabling accurate disease diagnosis and effective patient treatment. However, the process is fraught with challenges, including inter‐ and intra‐observer variability, time constraints, and data‐related issues such as privacy, heterogeneity and accessibility. Within this framework, Federated Learning (FL) has emerged as a promising solution, allowing collaborative model training across distributed healthcare entities without sharing sensitive patient data. This study provides a comprehensive Systematization of Knowledge (SoK) review of FL and its extension, Federated Unlearning (FU), within the context of medical image analysis. FL enables privacy‐preserving, decentralised model training, while FU addresses the ‘Right To Be Forgotten’, ensuring compliance with data protection regulations like GDPR and HIPAA. We explore the opportunities and challenges of FL and FU, detailing their methodologies, frameworks, datasets, and evaluation metrics. The review highlights the potential of FL and FU to enhance diagnostic accuracy, improve patient care, and foster trust in AI‐driven healthcare systems. We also identify research gaps and propose future directions for advancing FL and FU in medical imaging, emphasising the need for interdisciplinary collaboration and the development of dedicated frameworks. Thus, this study aims to bridge the gap between theoretical advancements and practical applications, paving the way for more robust and privacy‐compliant AI models in healthcare.

Driving Industry Transformation with Advanced Data Engineering: Scalable Solutions for Healthcare, Retail, and Agriculture

The rapid acceleration of data-driven decision-making across multiple sectors has catalyzed significant demand for sophisticated data engineering solutions. This article examines how data engineering transforms three critical industries—healthcare, retail, and agriculture—through the implementation of advanced technologies, including Apache Spark, Hadoop, and cloud platforms such as AWS. Data engineers are revolutionizing these sectors by developing robust data pipelines, implementing both SQL and NoSQL database architectures, and leveraging containerization strategies to create scalable solutions. The integration of REST and GraphQL APIs facilitates seamless data exchange between disparate systems, while orchestration tools like Airflow and Kubernetes enable complex workflow management. Through the exploration of industry-specific applications, this article demonstrates how strategic data engineering delivers measurable value in improving patient care, enhancing retail customer experiences, and optimizing agricultural productivity in an increasingly data-dependent business landscape.

Enhanced Recovery after Pediatric Cardiac Surgery: A Meta-Analysis

AbstractThe Enhanced Recovery After Surgery (ERAS) protocols are a set of steps taken before, during, and after surgery to improve patient care and outcomes. While ERAS is well known for its benefits in various surgeries, its application in pediatric cardiac surgery is relatively new. With the recent emergence of studies on its implementation in pediatric cardiac surgery, this study is the first to systematically review the current evidence on the efficacy of ERAS in the field.A meta-analysis was performed according to Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. Two reviewers independently searched PubMed, Cochrane, Google Scholar, Web of Science, Embase, and Scopus databases for comparative studies with control groups that described the use of ERAS in all types of pediatric cardiac surgeries from 2000 to 2024. The data collected included study design, patient demographics, elements of the ERAS protocols, and postoperative outcomes. The random-effects model was used to calculate the pooled odds ratios (ORs) and mean differences (MDs) with the corresponding confidence intervals (CIs) for proportional and continuous variables, respectively.Five studies, involving 1,008 patients, were included in the final analysis: three randomized controlled trials (RCTs), one retrospective cohort, and one case-control study. The ERAS protocols were applied in 430 (43%) patients, and standard perioperative care was applied in 578 (57%) patients. The analysis revealed that implementing the ERAS protocol significantly reduced ICU length of stay (I 2 = 98.26%; MD = −1.441; 95% CI: −2.610 to −0.273; p = 0.016). The ERAS group had a comparable rate of postoperative complications to the standard care group (I 2 = 15.3%; OR: 0.889; 95% CI: 0.622–1.269; p = 0.516).The ERAS protocols in pediatric cardiac surgery appear to be safe and effective in improving certain short-term outcomes. However, evidence is limited due to the small number of studies. Further multicenter RCTs that fully incorporate the ERAS protocol elements and assess both immediate and long-term outcomes are needed.

Enhancing Follow-up Care of Sexually Assaulted Children: Outcomes of a Forensic Nurse Care Coordination QI Initiative.

Nurse care coordination for children and adolescents experiencing acute sexual assault is rarely done but could yield positive outcomes. Demonstrating the value of forensic nurse care coordination (RNCC) is challenging: No benchmarks or metrics existed to measure the impact of RNCC on patient care or outcomes. Hours could be tracked, contact attempts could be tallied, but evaluating the value and outcomes of a forensic RNCC was lacking. Using quality improvement (QI) methodology, we identified measures to quantify the impact of care coordination by a forensic nurse for pediatric and adolescent patients who had experienced acute sexual assault. Measures included attending follow-up clinic, obtaining follow-up labs at 6 weeks and 3 months post-assault, and patient concordance with an established clinical care follow-up pathway. We implemented the forensic RNCC between October 2022 and June 2023, with pre- and post-QI intervention chart review to establish baseline and post-QI outcome measures. The forensic RNCC role was successfully implemented. All measures demonstrated improvement from pre- to post-intervention. Clinic follow-up increased from 70% to 98% of patients. Patients obtaining screening labs 6 weeks post-assault increased from 53% pre-intervention to 92% post-intervention. The pathway recommends patients obtain labs 3 months post-assault as well, and we saw an increase from 67% to 89% of the post-intervention group obtaining labs at 3 months. Overall concordance to the system's evidence-based clinical care pathway increased from 45% to 83% of the post-intervention group. This QI initiative achieved measurable improvement in patient care and outcomes by leveraging the expertise of a forensic RNCC.

An optimized deep neural network with explainable artificial intelligence framework for brain tumour classification

ABSTRACT Brain tumour classification plays a significant role in improving patient care, treatment planning, and enhancing the overall healthcare system’s effectiveness. This article presents a ResNet framework optimized using Henry gas solubility optimization (HGSO) for the classification of brain tumours, resulting in improved classification performance in magnetic resonance images (MRI). Two variants of the deep residual neural network, namely ResNet-18 and ResNet-50, are trained on the MRI training dataset. The four critical hyperparameters of the ResNet model: momentum, initial learning rate, maximum epochs, and validation frequency are tuned to obtain optimal values using HGSO algorithm. Subsequently, the optimized ResNet model is evaluated using two separate databases: Database1, comprising four tumour classes, and Database2, with three tumour classes. The performance is assessed using accuracy, sensitivity, specificity, precision, and F-score. The highest classification accuracy of 0.9825 is attained using the proposed optimized ResNet-50 framework on Database1. Moreover, the Gradient-weighted Class Activation Mapping (GRAD-CAM) algorithm is utilized to enhance the understanding of deep neural networks by highlighting the regions that are influential in making a particular classification decision. Grad-CAM heatmaps confirm the model focuses on relevant tumour features, not image artefacts. This research enhances MRI brain tumour classification via deep learning optimization strategies.

Transforming Healthcare in India: The Role of Artificial Intelligence and Regulatory Frameworks for Sustainable Growth

ABSTRACTArtificial intelligence (AI) has quickly emerged as a game changer in healthcare, providing innovative ways to improve patient care, enhance processes, and reduce expenses. AI could solve important healthcare issues in India, including increasing service demand, a lack of trained medical personnel, and notable geographical differences, especially in rural areas. AI can aid in addressing this gap by offering scalable, affordable regulatory framework that enhance diagnosis, treatment planning, and suitable resource allocation. This paper examines the impact of AI on healthcare, considering its benefits, challenges, and ethical implications for improving the healthcare delivery system. The paper also explores global regulatory frameworks and their implications for AI in healthcare, focusing on the roles of the United States, United Kingdom, European Union, India, and other prominent organizations. Additionally, the paper explores the opportunity to develop a robust AI policy framework for healthcare in India, drawing from global approaches. The study emphasizes ethical, interoperable AI and outlines a roadmap for India's healthcare sector‐integrating risk‐based regulations, enhanced digital infrastructure, ethical AI policies, and private entrepreneurship via public–private partnerships—to position India as a leader in AI‐driven healthcare regulation. As India continues to invest in digital health infrastructure, a comprehensive, ethically sound regulatory framework will be crucial in ensuring that AI‐powered healthcare is accessible, affordable, and inclusive for all citizens. By learning from global best practices and focusing on equitable healthcare, India can lead the way in AI‐driven healthcare innovation.

Are we all singing from the same song sheet? Standardizing terminology used in inter-professional telehealth education and practice: a mixed method study

BackgroundTelehealth interventions have proven essential in maintaining healthcare delivery during the global pandemic. However, its broader adoption within different healthcare settings has been impacted by inconsistent and non-standardized terminology, which poses challenges to global implementation and stakeholder communication. This article addresses these barriers by analyzing telehealth-related terms and developing a detailed clinical guide to aid inter-professional health educators in adopting standardized terminology, improving clarity, and fostering collaboration.MethodsA mixed-methods approach was used, comprising four phases. Phase 1 included weekly online journal club sessions (February to August 2024) focused on digital health topics, where relevant terms were discussed and extracted. Phase 2 involved detailed transcription analysis to identify telehealth-related terms based on their frequency of use and relevance to digital health. Phase 3 was a systematic literature review to contextualize and refine the identified terms. Phase 4 entailed expert validation, where five digital health professionals reviewed the proposed terminology and provided refinements. Additionally, terms were cross-referenced with the Medical Subject Headings (MeSH) database to evaluate their existing definitions.ResultsA total of 314 telehealth terms were identified through discussions in the International Journal Club in Digital Health (IJC DH) and a literature review. Approximately 90.44% of these terms were sourced from 12 journal club sessions, covering topics such as Digital Health, Digital Psychiatry, Neurorehabilitation, and Robotic Surgery. The literature review contributed 30 unique terms, with further analysis revealing that 73% of the terms were not defined in the MeSH database. This finding underscores the evolving nature of telehealth and the need for terminology standardization. Expert reviews validated most proposed definitions, though specific terms required additional discussion.ConclusionsThe resulting standardized terminology guide enhances inter-professional collaboration in telehealth by providing clear and consistent definitions. This guide reduces miscommunication, facilitates interdisciplinary research and practice, and can be integrated into educational curricula to prepare future healthcare professionals for the complexities of digital health. By addressing terminology gaps, this study supports the advancement of telehealth education and improves patient care outcomes.

Quality Improvement Intervention Targeting Ventilator Management GAMUT Metric Improves Documentation and Patient Care Practices

ABSTRACT OBJECTIVES To evaluate the effect of a bundled quality improvement (QI) intervention targeting implementation of the Ground & Air Medical qUality in Transport (GAMUT) metric plateau pressure (Pplat) < 30 mmHg on the incidence of Pplat documentation and rate of lung protective ventilation (LPV). METHODS A QI-bundle encompassing education, spaced repetition, and signposting was implemented in a university affiliated, physician/nurse-staffed critical care transport (CCT) program. Our primary aim was to improve the rate of LPV during CCT with the secondary aim of improving the documentation rate of Pplat. Statistical analysis was performed pre- and post-QI intervention using a Pearson chi square, 95% confidence intervals (CI), nonparametric tests of medians, and two-sample t-test as appropriate. Cohen’s effect size was calculated to evaluate the magnitude of difference. Control charts assess changes over time. RESULTS Before QI bundle implementation, Pplat was documented in 11.8% of charts compared to 96.2% post-implementation (p = <0.001, difference 84.4%, 95% CI [49.1%, 66.2%]). There was a statistically significant increase in the proportion of patients ventilated with both Pplat < 30 mmHg and driving pressure (DP) < 15 mmHg post-QI bundle implementation (p = <0.001, difference 20.9%, 95% CI 3.20%, 38.6% and p = <0.001, difference 45.3%, 95% CI 14.8%, 57.8%, respectively). CONCLUSIONS To our knowledge, this is the first report of a QI initiative targeting implementation of a GAMUT metric to improve both documentation and patient care practices. Our findings indicate that our QI intervention improved documentation of Pplat by nearly nine-fold. We also demonstrated sustained improvement in patient care with higher rates of LPV as defined by Pplat < 30 mmHg and DP < 15 mmHg post-implementation.

Deep learning (DL)‐based advancements in prostate cancer imaging: Artificial intelligence (AI)‐based segmentation of 68Ga‐PSMSA PET for tumor volume assessment

AbstractPositron emission tomography (PET) with gallium‐68 prostate‐specific membrane antigen (68Ga‐PSMA) has emerged as a promising imaging modality for evaluating prostate cancer (PC). Quantification of tumor volume is crucial for staging, radiotherapy treatment planning, response assessment, and prognosis in PC patients. This review provides an overview of the current methods and challenges in the assessment of regional and total tumor volumes using 68Ga‐PSMA PET. Traditional manual segmentation methods are time‐consuming processes that are further challenged by inter‐observer variability. Artificial intelligence (AI)‐based segmentation techniques offer a promising solution to these challenges. AI algorithms, such as deep learning‐based models, have shown remarkable performance in automating tumor segmentation tasks with high accuracy and efficiency. This review discusses the principles underlying AI‐based segmentation algorithms, including convolutional neural networks, and their applications in PC imaging. Furthermore, the advantages of AI‐based segmentation are highlighted, such as improved reproducibility, faster processing times, and potential for personalized medicine. Despite these advancements, AI‐based segmentation faces significant challenges, including the need for standardized protocols, extensive validation studies, and seamless integration into clinical workflows. Addressing these limitations is essential for the widespread adoption of AI‐based segmentation in 68Ga‐PSMA PET for PC, ultimately advancing the field and improving patient care.