Improve Health Care Delivery Research Articles

Bridging innovation to implementation in artificial intelligence fracture detection

The deployment of AI in medical imaging, particularly in areas such as fracture detection, represents a transformative advancement in orthopaedic care. AI-driven systems, leveraging deep-learning algorithms, promise to enhance diagnostic accuracy, reduce variability, and streamline workflows by analyzing radiograph images swiftly and accurately. Despite these potential benefits, the integration of AI into clinical settings faces substantial barriers, including slow adoption across health systems, technical challenges, and a major lag between technology development and clinical implementation. This commentary explores the role of AI in healthcare, highlighting its potential to enhance patient outcomes through more accurate and timely diagnoses. It addresses the necessity of bridging the gap between AI innovation and practical application. It also emphasizes the importance of implementation science in effectively integrating AI technologies into healthcare systems, using frameworks such as the Consolidated Framework for Implementation Research and the Knowledge-to-Action Cycle to guide this process. We call for a structured approach to address the challenges of deploying AI in clinical settings, ensuring that AI’s benefits translate into improved healthcare delivery and patient care.Cite this article: Bone Joint J 2025;107-B(6):582–586.

Mixed reality holographic navigation for intracranial lesions using HoloLens 2: A pilot study and literature review.

Mixed reality holographic navigation for intracranial lesions using HoloLens 2: A pilot study and literature review.

Convergence of AI and Healthcare Administration: Transforming Patient Data Processing and Claims Management Through Intelligent Automation

This article examines the transformative impact of artificial intelligence and automation technologies on healthcare administrative workflows, focusing on patient data processing and claims management. Healthcare organizations face significant administrative inefficiencies that burden the system with excessive costs and divert clinical resources away from patient care. The article explores how AI-driven solutions are revolutionizing key administrative processes including patient application processing, hospital claims validation, regulatory compliance, and data security. Through article analysis of implementation data across multiple healthcare settings, the article demonstrates how these technologies substantially reduce processing times, minimize error rates, enhance fraud detection, strengthen compliance, and improve cybersecurity while simultaneously generating significant cost savings. The integration of AI into administrative workflows not only addresses immediate operational challenges but also enables healthcare professionals to redirect their focus toward patient care activities, ultimately leading to improved healthcare delivery and outcomes.

Monitoring patient pathways at a secondary healthcare services through process mining via Fuzzy Miner

BackgroundThis study explored workflow pathways followed by patients seeking secondary healthcare services at a local hospital in a rural part of Turkey using process mining to improve hospital resource management.MethodsThe study used process mining to discover process flows as patient pathways implied by hospital records for in-patient, out-patient, biochemical laboratory, and radiology services. Utilizing its flexibility, visualizations and robustness, authors implemented fuzzy-miner algorithm. First, we processed the relevant data from patient records. Then, this data was transformed into event and activity logs. Subsequently, all data components were collected into a data warehouse, and the process mining algorithm was applied. The process mining specified resource usage levels and workload, service waiting times, associated bottlenecks in hospital services, and related statistics/measures.ResultsThe results from the proposed process mining analysis offer insights and decision support to improve hospital resource management. For example, the resulting statistics indicate the high waiting times (e.g., median of waiting times around 2 h within the selected time period) in the General Surgery and Cardiology services, whose resources were highly utilized (2,699 and 6,162 times). Overloads at laboratories and radiological imaging seem to be contributing to these long waiting times, and capacities for the associated services may need to be improved. Waiting times and resource workloads are higher on specific dates related to local commercial and social activities.ConclusionsProcess mining successfully identified the real work flows, bottlenecks, and long waiting times at services within the considered local hospital and provided insights to the hospital management for improving their practices. Moreover, the analyses revealed unique challenges in providing care at a local hospital located far from the city center, emphasizing the potential of process mining to improve healthcare delivery tailored to the specific hospital environment.Clinical trial numberNot applicable.

Advancing Patient Education:The Impact of Nurse- Led Health Promotion Program

Patient education is a crucial component of healthcare, empowering individuals to make informed decisions about their well-being. This study explores the impact of nurse-led health promotion programs in enhancing patient knowledge, self-care practices, and overall health outcomes. Using a mixed-methods approach, the research examines the effectiveness of these programs in various healthcare settings. Quantitative data is collected through patient surveys measuring knowledge retention and behavioral changes, while qualitative insights are gathered through interviews with nurses and patients. Findings highlight the role of nurses as primary educators, demonstrating that structured health promotion initiatives significantly improve patient engagement, adherence to treatment plans, and preventive health behaviors. The study also identifies challenges faced by nurses, such as time constraints and patient compliance issues, and offers recommendations for optimizing education strategies. Ultimately, this research underscores the importance of strengthening nurse-led patient education programs to enhance public health and improve healthcare delivery.

Improving comprehensive geriatric assessments with the clinical frailty scale: a focus group study

BackgroundThe purpose of this study is to exploratively evaluate the geriatric team’s views on the implementation of the Comprehensive Geriatric Assessment (CGA) and Clinical Frailty Scale (CFS) on frail older people with acute orthopaedic disorders who are cared for in two geriatric care wards in the southwest of Sweden.MethodsA qualitative design with focus groups was applied, based on a social constructivist research tradition. This approach differs from other qualitative methods, such as interviews, in that it encourages interaction between research participants and contributes to shedding light on a collective understanding of the world. This means that the analysis is based on the group’s collective input, not individual statements. The study group consisted of 21 professionals participating in four focus groups, with five to six participants per group. The participants in each group represented different professions within the geriatric team, including nurses, nursing assistants, physicians, occupational therapists, and physiotherapists.ResultsThe results underscore the importance of the CFS as the basis for CGA, emphasizing the effectiveness of the scale as a shared instrument promoting collaboration in healthcare. Our study uniquely points out the lack of research exploring the team-based use of tools for conducting a frailty assessment using the CFS. The study also highlights the importance of effective teamwork and a person-centred approach. The balance between person-centred care and what is feasible within the organization is crucial to providing the best possible care for patients.ConclusionsIn Sweden, as in other places, how healthcare staff experience their work is key to the quality of care. The study concludes that positive staff experiences with new approaches drive healthcare improvement, benefiting patients and society. This highlights the potential for further improvements in healthcare delivery through continued innovation and collaboration.Trial registrationClinical trial number: not applicable.

Exploring the Gender Preferences for Healthcare Providers and Their Influence on Patient Satisfaction.

Background: Patient satisfaction is a key indicator for improving healthcare delivery, yet the influence of gender preferences on healthcare providers remains underexplored. Cultural norms and gender perceptions often shape the patient preferences, affecting access to care, patient-provider relationships, and overall satisfaction. Thus, this study investigates the patients' gender preferences and their impact on satisfaction in Tanzania. Methods: The study utilized a cross-sectional design, collecting data from five health centres: Mikongeni, Konga, Mzumbe, Tangeni, and Mlali. A total of 240 randomly selected respondents participated in the study. Gender preferences were categorized as male, female, and both, and determinants were analyzed using a multivariate probit model (MPM), while satisfaction was analyzed using an ordered logit model (OLM). Results: Results reveal that female providers were preferred for empathy (58.30%), intimate care (50.00%), and receptionist roles (50.00%), while males were favored for surgery (50.00%), professionalism (0.86), and IT roles (41.70%). Professionalism (0.75) and communication (0.70) had the strongest positive effects on very high satisfaction. Male provider preference was strongly linked to higher satisfaction (0.84), while female preference showed a mild effect (0.23). Insurance (0.32) and care at Tangeni Health Centre (0.70) boosted satisfaction, while consultation fees (-0.26) reduced it. Conclusions: The study recommends that healthcare systems address gender stereotypes by equipping all providers with both technical and relational care skills, regardless of gender. It also highlights the need for culturally and religiously sensitive care practices that acknowledge how societal norms shape patient preferences and satisfaction. To enhance patient-centered care, policies should promote affordability, broaden insurance coverage, and integrate patient feedback on gender preferences into healthcare delivery models.

Point-counterpoint: What is the best strategy for developing generative AI for hospital medicine?

Generative Artificial Intelligence (Gen AI) shows significant promise as a technology that could improve healthcare delivery, but its implementation will be influenced by the spheres in which it is studied and the limited resources of hospitals. The Point authors argue that we should focus on is the cognitive abilities of GenAI or we risk being left out of a technological leap that will change the way doctors practice. The Counterpoint argues that we should focus on using GenAI to ease system burdens and address workflow issues, focusing our efforts on fixing the problems that would improve doctors' quality of life and increase time spent with patients.

The role of training and development on enhancing work engagement and employee quality service delivery

This study examines the role of training and development in enhancing work engagement and service quality in rural public health facilities in Tanzania. Grounded in Social Exchange Theory, Job Demands-Resources Theory, and the SERVQUAL model, the study adopts a positivist paradigm, a deductive approach, and a cross-sectional survey design. Data were collected from 285 respondents across 63 health centers and dispensaries using self-administered questionnaires. Partial Least Squares Structural Equation Modeling (PLS-SEM) was employed for data analysis. The findings reveal that training and development significantly enhance work engagement, which in turn positively influences service quality. Furthermore, work engagement partially mediates the relationship between training and development and service quality, highlighting its pivotal role in translating employee development initiatives into improved service outcomes. These results underscore the importance of investing in training and development programs to foster employee work engagement and enhance service quality in rural health facilities. The study provides valuable insights for policymakers and healthcare administrators aiming to improve healthcare delivery in resource-constrained settings.

Development of computable guidelines: GIN McMaster guideline development checklist extension for computable guidelines

AbstractBackgroundTransforming Clinical Practice Guideline (CPG) recommendations into computer readable language is a complex and ongoing process that requires significant resources, including time, expertise, and funds. The objective is to provide an extension of the widely used GIN‐McMaster Guideline Development Checklist (GDC) and Tool for the development of computable guidelines (CGs).MethodsBased on an outcome from the Human Centered Design (HCD) workshop hosted by the Guidelines International Network North America (GIN‐NA), a team was formed to develop the checklist extension. The team included guideline developers, researchers, implementers, and informaticists who reviewed the GDC and developed a list of additional requirements to help guideline developers author clearer, more implementable narrative guideline recommendations (referred to as knowledge level 1, or L1 recommendations) and ensure conformance‐testable attributes of the different artifacts of clinical guideline recommendations. The team vetted this list with guideline development organizations and health informatics experts to validate it, for clarity, usability, and effectiveness. The team used an iterative process to determine the final extension components for CG development guidance.ResultsThe team identified nine components that complement the topics included in GDC for developing, implementing, and adopting CG recommendations.ConclusionThis study demonstrates that the defined principles in the L1 Checklist, grounded in current guideline development standards, may significantly enhance the writing, development, and implementation of computable recommendations. Collaboration among guideline developers, implementers, and informaticists from the outset is crucial for achieving effective integration of these guidelines into clinical workflows. Future work should focus on assessing this extension within various ongoing learning initiatives and point‐of‐care digitization efforts, including the scholarly communications ecosystem and learning health systems, to further improve healthcare delivery.

SkinIncept: an ensemble transfer learning-based approach for multiclass skin disease classification using InceptionV3 and InceptionResNetV2

Skin diseases are a significant public health challenge in Bangladesh, with prevalence rates soaring from 11.16 to 63% in recent years. The lack of access to dermatological expertise and resource constraints in rural areas exacerbate delayed or inaccurate diagnoses, leading to worsening conditions and higher treatment costs. This study addresses this critical issue by developing a robust and accurate system for classifying Bangladesh’s ten most common skin diseases using convolutional neural networks (CNNs)-based transfer learning models. Six pre-trained CNN models are implemented, and a novel ensemble model (i.e., SkinIncept) is proposed. Data collection incorporates primary data from the Damien Foundation Hospital and the Bangladesh Institute of Dermatology, STD, and AIDS (BIDSA), supplemented by additional images from reliable web portals. Before model training, extensive preprocessing techniques such as cropping, resizing, filtering, contrast enhancement, histogram equalization, CLAHE, gamma correction, segmentation, and data augmentation are applied to ensure the quality and consistency of the images. The quality of processed images is validated using statistical methods, including MSE, PSNR, SSIM, and RMSE. The performance of each model is rigorously evaluated using several performance metrics, instilling confidence in the study’s methodology and results. Among the six pre-trained models, InceptionResNet-V2 achieved 93.65% accuracy, and the proposed ensemble model achieved a remarkable classification accuracy of 96.52% based on six ablation studies to fine-tune the model and optimize the hyperparameters. These findings form the foundation for “Skin Medicare,” a mobile application providing AI-driven, accessible, and accurate skin disease diagnosis, offering a scalable solution to improve healthcare delivery in underserved and resource-constrained regions.

Utilization, challenges, and training needs of digital health technologies: Perspectives from healthcare professionals.

Utilization, challenges, and training needs of digital health technologies: Perspectives from healthcare professionals.

American Society for Metabolic and Bariatric Surgery: postoperative care pathway guidelines for Roux-en-Y gastric bypass.

American Society for Metabolic and Bariatric Surgery: postoperative care pathway guidelines for Roux-en-Y gastric bypass.

Precision nutrition for cardiometabolic diseases.

Precision nutrition is a vibrant and rapidly evolving field of scientific research and innovation with the potential to deliver health, societal and economic benefits by improving healthcare delivery and policies. Advances in deep phenotyping technologies, digital tools and artificial intelligence have made possible early proof-of-concept research that expands the understanding of within- and between-person variability in responses to diet. These studies illustrate the promise of precision nutrition to complement the traditional 'one size fits all' dietary guidelines, which, while considering broad life-stage and disease-specific nutritional requirements, often lack the granularity to account fully for individual variations in nutritional needs and dietary responses. Despite these developments, however, considerable challenges remain before precision nutrition can be implemented on a broader scale. This Review examines the current state of precision nutrition research, with a focus on its application to reducing the incidence and burden of cardiometabolic diseases. We critically examine the evidence base, explore the potential benefits and discuss the challenges and opportunities ahead.

Advancing healthcare through real-time AI diagnostics: Current innovations and applications

Advancing healthcare through real-time AI diagnostics: Current innovations and applications

Digital transformation in the healthcare sector: a novel strategic perspective.

This study aims to fill the gap in digital transformation (DT) literature, particularly within the healthcare sector, by investigating the effect of strategic reconfiguration (SREC), as an antecedent, on DT. Further, it also aims to investigate the effect of DT on strategic renewal (SR) as a strategic outcome of DT. Thereby, the current study explores the drivers and outcomes of DT from a new strategic perspective. The structural model is tested via the partial least squares structural equation modeling using a sample of 264 private Egyptian hospitals. SREC directly and positively affects SR. Besides, the SREC-SR relationship is partially mediated by DT. Accordingly, this study introduces a novel strategic perspective model of DT that depicts how Egyptian private hospitals could reconfigure themselves to transform toward digitalization, which ultimately enabled them to deliver new value propositions and diversified services. The sample is restricted to Egyptian private hospitals; thereby, the results may differ in other sectors and other countries. This study ignores the boundary conditions that may accelerate organizations' movement toward digitalization. Managers of private hospitals can leverage the findings of this study to manage their strategic resources through SREC and foster a culture of DT to enhance their renewal in an increasingly digitalized healthcare landscape. By demonstrating the positive effects of DT on SR, this study underscores the role of technology in improving healthcare delivery, patient outcomes and overall quality of care. To the best of the author's knowledge, this is the first empirical study to introduce a model of the strategic antecedents and consequences of DT within the healthcare sector. Unlike the existing DT literature, the current study goes beyond the traditional technological perspective for studying DT by concentrating on the strategic perspective. Therefore, the current study contributes to the existing DT literature by being the first empirical study to investigate the non-technological strategic antecedents that enable successful DT while propping the potential strategic outcome of DT.

Readiness towards artificial intelligence among medical and dental undergraduate students in Peshawar, Pakistan: a cross-sectional survey

IntroductionArtificial intelligence is a transformative tool for improving healthcare delivery and diagnostic accuracy in the medical and dental fields. This study aims to assess the readiness of future healthcare workers for artificial intelligence and address this gap by examining students’ perceptions, attitudes, and knowledge related to AI in Peshawar, Pakistan.MethodsA quantitative cross-sectional survey was conducted on 423 students from randomly chosen medical and dental colleges. The Medical AI Readiness Scale (MAIRS-MS) was used to perform a self-administered online questionnaire that was used to gather data. Using SPSS software, descriptive statistics and chi-square tests were used to evaluate the data. The level of significance was set at p ≤ 0.05.ResultsFrom multiple medical and dental colleges, 407 students participated in this survey. The survey showed that 29.7% of students had low, 62.2% had moderate, and only 8.1% had high readiness levels. Most medical and dental students in Peshawar, Pakistan, showed moderate readiness. There were significant gender discrepancies, showing males dominating females in readiness scores. There were only slight differences in the AI readiness scores and the academic years from the 1st to 5th year. Only a few non-Pakistani students responded, which may hinder conclusive determinations regarding national disparities.ConclusionThe study revealed moderate AI readiness among participants, with significant gender disparities favouring males. Overall, there were no significant differences between dentistry and medical fields. In-depth analysis by domain and knowledge areas might uncover further distinctions.Clinical trial numberNot Applicable.

Effectual Energy Optimization, Fault-Tolerant Attack Detection, and Data Aggregation in Healthcare IoT Using Enhanced Waterwheel Archimedes and Deep Siamese Maxout Forward Harmonic Networks

The Internet of Medical Things (IoMT) has emerged as a transformative technology for improving healthcare delivery and patient outcomes. However, IoMT systems face significant challenges, including high latency, energy inefficiency, and vulnerability to cyberattacks, which compromise data security and patient privacy. Existing methods for attack detection and secure routing in IoMT often suffer from high latency, limited fault tolerance, and insufficient accuracy in identifying sophisticated attacks. To address these challenges, this paper proposes two novel approaches: the Improved Waterwheel Archimedes Optimization Algorithm (WWAOA) for secure routing and the Deep Siamese Maxout Forward Harmonic Network (DSMFHN) for attack detection in healthcare IoT. The Improved WWAOA integrates the Waterwheel Plant Algorithm (WWPA) with the Archimedes Optimization Algorithm (AOA) to optimize cluster head (CH) selection and secure routing. It considers key fitness parameters such as energy consumption, link lifetime (LLT), trust, delay, distance, and fault tolerance to enhance network efficiency and resilience. The DSMFHN combines Siamese Neural Networks (SNN) and Deep Maxout Networks (DMN) with forward harmonic analysis to detect attacks with high accuracy and low false positive rates. Additionally, data aggregation is performed using Bidirectional Long Short-Term Memory (BiLSTM) with adaptive weightage based on fault and malicious node detection. Experimental results demonstrate that the proposed methods outperform existing techniques. The Improved WWAOA achieves a minimal delay of 0.557 ms, maximal energy efficiency of 0.182 J, a packet delivery ratio (PDR) of 93.894%, and a trust value of 87.152. Meanwhile, the DSMFHN achieves a high accuracy of 92.598%, a true positive rate (TPR) of 91.643%, and a low false positive rate (FPR) of 0.156. These results highlight the effectiveness of the proposed methods in addressing the critical challenges of latency, energy efficiency, and security in healthcare IoT systems.

The predictive role of identifying frailty in assessing the need for palliative care in the elderly: the application of machine learning algorithm

BackgroundPalliative care is a key component of integrated care to improve care quality and reduce hospitalization costs for patients with chronic obstructive pulmonary disease (COPD). This study aims to use machine learning algorithms to create an effective approach to the early recognition and identification of frailty as a long-term condition in COPD patients.MethodsThe level of frailty in a sample of patients (total n = 140) was assessed using the checklist of frailty assessment, which encompasses five questions: measured decrease in body mass index (BMI), fatigue status, physical activity status, and walking speed. The last question assessed disability through forced expiratory volume in the first second (FEV1) measured using spirometry results. The next checklist was the Palliative Care Needs Assessment Tool, taken from the assessment checklist for palliative care needs in patients with COPD by Thoenesen et al. [28]. We used different machine learning algorithms, with performance assessed using an area under the receiver-operating characteristic curve, sensitivity, and specificity, to develop a validated set of criteria for frailty using machine learning.ResultsStudy findings revealed that the palliative care needs assessment tool categorized 74% of all patients into two groups: those requiring palliative care and those not requiring it. Furthermore, the influential variables that contributed to predicting the need for palliative care included measured BMI reduction, fatigue status, physical activity level, slow walking, and FEV1. The super-learning model demonstrated higher accuracy (92%) than other machine-learning algorithms.ConclusionThe study highlights the need for more collaboration between clinicians and data scientists to use the potential of data collected from COPD patients in clinical settings with the purpose of early identification of frailty as a long-term condition. Predicting palliative care needs accurately is critical in these contexts, as it can lead to better resource allocation, improved healthcare delivery, and enhanced patient outcomes.

Syndromic Surveillance in Tribal Health: Perspectives from Three Tribal Epidemiology Centers on Access and Utilization.

Syndromic surveillance has evolved into a vital public health tool, providing near real-time data to detect and respond to health threats. While states administer syndromic surveillance systems, Tribal Epidemiology Centers (TECs) serve American Indian and Alaska Native (AIAN) communities across multistate regions, often encountering significant barriers to data access and utilization. This manuscript explores how TECs access and use syndromic surveillance data to address health disparities in AIAN populations, highlighting successes, innovations, and ongoing challenges. The Alaska Native Epidemiology Center (ANEC), Great Plains Tribal Epidemiology Center (GPTEC), and Northwest Tribal Epidemiology Center (NWTEC) provide insights into their syndromic surveillance practices. This includes data access methods, the creation of dashboards and reports, technical assistance for Tribal Health Organizations (THOs), and strategies for overcoming jurisdictional and data-sharing barriers. TECs have successfully leveraged syndromic surveillance to monitor critical health issues, including respiratory illnesses, substance misuse, behavioral health, and maternal care. Collaborative efforts have addressed race misclassification and data gaps, enabling targeted interventions such as air purifier distribution and improving health care delivery for tribal veterans. However, TECs can face restrictive data use agreements, jurisdictional misalignments, and limited access to granular data, hindering their ability to serve AIAN communities comprehensively. Syndromic surveillance offers transformative potential for improving public health in AIAN communities. To fully realize this potential, systemic changes are needed to streamline data-sharing agreements and improve data accuracy. These efforts, along with strong collaborations between TECs and state health departments, are critical to advancing health equity, respecting tribal sovereignty, and ensuring timely, actionable insights for AIAN populations.