Why digital medicine depends on interoperability

Healthcare professionals' experiences of eHealth in palliative care for older people: challenges, compromises and the price of dignity

PURPOSE

To explore whether and how eHealth solutions support the dignity of healthcare professionals and patients in palliative care contexts.

METHOD

This qualitative study used phenomenographic analysis involving four focus group interviews, with healthcare professionals who provide palliative care to older people.

RESULTS

Analysis revealed four categories of views on working with eHealth in hierarchical order: Safeguarding the patient by documenting-eHealth is a grain of support, Treated as less worthy by authorities-double standards, Distrust in the eHealth solution-when the "solution" presents a danger; and Patient first-personal contact with patients endows more dignity than eHealth. The ability to have up-to-date patient information was considered crucial when caring for vulnerable, dying patients. eHealth solutions were perceived as essential technological support, but also as unreliable, even dangerous, lacking patient information, with critical information potentially missing or overlooked. This caused distrust in eHealth, introduced unease at work, and challenged healthcare professionals' identities, leading to embodied discomfort and feeling of a lack of dignity.

CONCLUSION

The healthcare professionals perceived work with eHealth solutions as challenging their sense of dignity, and therefore affecting their ability to provide dignified care for the patients. However, healthcare professionals managed to provide dignified palliative care by focusing on patient first.

FHIR Implementation Guide for Stroke: A dual focus on the patient's clinical pathway and value-based healthcare

BACKGROUND

Stroke management requires a coordinated strategy, adhering to clinical pathways (CP) and value-based healthcare (VBHC) principles from onset to rehabilitation. However, the discrepancies between these pathways and actual patient experiences highlight the need for ongoing monitoring and addressing interoperability issues across multiple institutions in stroke care. To address this, the Fast Healthcare Interoperability Resource (FHIR) Implementation Guide (IG) standardizes the information exchange among these systems, considering a specific context of use.

OBJECTIVE

Develop an FHIR IG for stroke care rooted in established stroke CP and VBHC principles.

METHOD

We represented the stroke patient journey by considering the core stroke CP, the International Consortium for Health Outcomes Measurement (ICHOM) dataset for stroke, and a Brazilian case study using the Business Process Model and Notation (BPMN). Next, we developed a data dictionary that aligns variables with existing FHIR resources and adapts profiling from the Brazilian National Health Data Network (BNHDN).

RESULTS

Our BPMN model encompassed three critical phases that represent the entire patient journey from symptom onset to rehabilitation. The stroke data dictionary included 81 variables, which were expressed as questionnaires, profiles, and extensions. The FHIR IG comprised nine pages: Home, Stroke-CP, Data Dictionary, FHIR, ICHOM, Artifacts, Examples, Downloads, and Security. We developed 96 artifacts, including 7 questionnaires, 27 profiles with corresponding example instances, 3 extensions, 18 value sets, and 14 code systems pertinent to ICHOM outcome measures.

CONCLUSION

The FHIR IG for stroke in this study represents a significant advancement in healthcare interoperability, streamlining the tracking of patient outcomes for quality enhancement, facilitating informed treatment choices, and enabling the development of dashboards to promote collaborative excellence in patient care.

Advancing governance for digital transformation in health: insights from Georgia's experience

Enhancing digital health governance is critical to healthcare systems in low-income and middle-income countries. However, implementing governance-enhancing reforms in these countries is often challenging due to the multiplicity of external players and insufficient operational guidance that is accessible. Using data from desktop research, in-depth interviews, focus group discussions and three stakeholder workshops, this paper aims to provide insights into Georgia's experience in advancing digital health governance reforms. It reveals how Georgia has progressed on this path by unpacking the general term 'governance' into operational domains, where stakeholders and involved institutions could easily relate their institutional and personal roles and responsibilities with the specific function needed for digital health. Based on this work, the country delineated institutional responsibilities and passed the necessary regulations to establish better governance arrangements for digital health. The Georgia experience provides practical insights into the challenges faced and solutions found for advancing digital health governance in a middle-income country setting. The paper highlights the usefulness of operational definitions for the digital health governance domains that helped (a) increase awareness among stakeholders about the identified domains and their meaning, (b) discuss possible governance and institutional arrangements relevant to a country context, and (c) design the digital health governance architecture that the government decreed. Finally, the paper offers a broad description of domains in which the governance arrangements could be considered and used for other settings where relevant. The paper points to the need for a comprehensive taxonomy for governance domains to better guide digital health governance enhancements in low-middle-income country settings.

A Responsible Framework for Applying Artificial Intelligence on Medical Images and Signals at the Point of Care: The PACS-AI Platform

The potential of artificial intelligence (AI) in medicine lies in its ability to enhance clinicians' capacity to analyse medical images, thereby improving diagnostic precision and accuracy and thus enhancing current tests. However, the integration of AI within health care is fraught with difficulties. Heterogeneity among health care system applications, reliance on proprietary closed-source software, and rising cybersecurity threats pose significant challenges. Moreover, before their deployment in clinical settings, AI models must demonstrate their effectiveness across a wide range of scenarios and must be validated by prospective studies, but doing so requires testing in an environment mirroring the clinical workflow, which is difficult to achieve without dedicated software. Finally, the use of AI techniques in health care raises significant legal and ethical issues, such as the protection of patient privacy, the prevention of bias, and the monitoring of the device's safety and effectiveness for regulatory compliance. This review describes challenges to AI integration in health care and provides guidelines on how to move forward. We describe an open-source solution that we developed that integrates AI models into the Picture Archives Communication System (PACS), called PACS-AI. This approach aims to increase the evaluation of AI models by facilitating their integration and validation with existing medical imaging databases. PACS-AI may overcome many current barriers to AI deployment and offer a pathway toward responsible, fair, and effective deployment of AI models in health care. In addition, we propose a list of criteria and guidelines that AI researchers should adopt when publishing a medical AI model to enhance standardisation and reproducibility.

"Covering provider": an effort to streamline clinical communication chaos

Objective

This report describes a root cause analysis of incorrect provider assignments and a standardized workflow developed to improve the clarity and accuracy of provider assignments.

Materials and Methods

A multidisciplinary working group involving housestaff was assembled. Key drivers were identified using value stream mapping and fishbone analysis. A report was developed to allow for the analysis of correct provider assignments. A standardized workflow was created and piloted with a single service line. Pre- and post-pilot surveys were administered to nursing staff and participating housestaff on the unit.

Results

Four key drivers were identified. A standardized workflow was created with an exclusive treatment team role in Epic held by a single provider at any given time, with a corresponding patient list column displaying provider information for each patient. Pre- and post-survey responses report decreased confusion, decreased provider identification errors, and increased user satisfaction among RNs and residents with sustained uptake over time.

Conclusion

This work demonstrates structured root cause analysis, notably engaging housestaff, to develop a standardized workflow for an understudied and growing problem. The development of tools and strategies to address the widespread burdens resulting from clinical communication failures is needed.

Federated systems for automated infection surveillance: a perspective

Automation of surveillance of infectious diseases-where algorithms are applied to routine care data to replace manual decisions-likely reduces workload and improves quality of surveillance. However, various barriers limit large-scale implementation of automated surveillance (AS). Current implementation strategies for AS in surveillance networks include central implementation (i.e. collecting all data centrally, and central algorithm application for case ascertainment) or local implementation (i.e. local algorithm application and sharing surveillance results with the network coordinating center). In this perspective, we explore whether current challenges can be solved by federated AS. In federated AS, scripts for analyses are developed centrally and applied locally. We focus on the potential of federated AS in the context of healthcare associated infections (AS-HAI) and of severe acute respiratory illness (AS-SARI). AS-HAI and AS-SARI have common and specific requirements, but both would benefit from decreased local surveillance burden, alignment of AS and increased central and local oversight, and improved access to data while preserving privacy. Federated AS combines some benefits of a centrally implemented system, such as standardization and alignment of an easily scalable methodology, with some of the benefits of a locally implemented system including (near) real-time access to data and flexibility in algorithms, meeting different information needs and improving sustainability, and allowance of a broader range of clinically relevant case-definitions. From a global perspective, it can promote the development of automated surveillance where it is not currently possible and foster international collaboration.The necessary transformation of source data likely will place a significant burden on healthcare facilities. However, this may be outweighed by the potential benefits: improved comparability of surveillance results, flexibility and reuse of data for multiple purposes. Governance and stakeholder agreement to address accuracy, accountability, transparency, digital literacy, and data protection, warrants clear attention to create acceptance of the methodology. In conclusion, federated automated surveillance seems a potential solution for current barriers of large-scale implementation of AS-HAI and AS-SARI. Prerequisites for successful implementation include validation of results and evaluation requirements of network participants to govern understanding and acceptance of the methodology.

State-of-the-Art Fast Healthcare Interoperability Resources (FHIR)-Based Data Model and Structure Implementations: Systematic Scoping Review

BACKGROUND

Data models are crucial for clinical research as they enable researchers to fully use the vast amount of clinical data stored in medical systems. Standardized data and well-defined relationships between data points are necessary to guarantee semantic interoperability. Using the Fast Healthcare Interoperability Resources (FHIR) standard for clinical data representation would be a practical methodology to enhance and accelerate interoperability and data availability for research.

OBJECTIVE

This research aims to provide a comprehensive overview of the state-of-the-art and current landscape in FHIR-based data models and structures. In addition, we intend to identify and discuss the tools, resources, limitations, and other critical aspects mentioned in the selected research papers.

METHODS

To ensure the extraction of reliable results, we followed the instructions of the PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews) checklist. We analyzed the indexed articles in PubMed, Scopus, Web of Science, IEEE Xplore, the ACM Digital Library, and Google Scholar. After identifying, extracting, and assessing the quality and relevance of the articles, we synthesized the extracted data to identify common patterns, themes, and variations in the use of FHIR-based data models and structures across different studies.

RESULTS

On the basis of the reviewed articles, we could identify 2 main themes: dynamic (pipeline-based) and static data models. The articles were also categorized into health care use cases, including chronic diseases, COVID-19 and infectious diseases, cancer research, acute or intensive care, random and general medical notes, and other conditions. Furthermore, we summarized the important or common tools and approaches of the selected papers. These items included FHIR-based tools and frameworks, machine learning approaches, and data storage and security. The most common resource was "Observation" followed by "Condition" and "Patient." The limitations and challenges of developing data models were categorized based on the issues of data integration, interoperability, standardization, performance, and scalability or generalizability.

CONCLUSIONS

FHIR serves as a highly promising interoperability standard for developing real-world health care apps. The implementation of FHIR modeling for electronic health record data facilitates the integration, transmission, and analysis of data while also advancing translational research and phenotyping. Generally, FHIR-based exports of local data repositories improve data interoperability for systems and data warehouses across different settings. However, ongoing efforts to address existing limitations and challenges are essential for the successful implementation and integration of FHIR data models.

System interoperability and data linkage in the era of health information management: A bibliometric analysis

Background: Across the world, health data generation is growing exponentially. The continuous rise of new and diversified technology to obtain and handle health data places health information management and governance under pressure. Lack of data linkage and interoperability between systems undermines best efforts to optimise integrated health information technology solutions. Objective: This research aimed to provide a bibliometric overview of the role of interoperability and linkage in health data management and governance. Method: Data were acquired by entering selected search queries into Google Scholar, PubMed, and Web of Science databases and bibliometric data obtained were then imported to Endnote and checked for duplicates. The refined data were exported to Excel, where several levels of filtration were applied to obtain the final sample. These sample data were analysed using Microsoft Excel (Microsoft Corporation, Washington, USA), WORDSTAT (Provalis Research, Montreal, Canada) and VOSviewer software (Leiden University, Leiden, Netherlands). Results: The literature sample was retrieved from 3799 unique results and consisted of 63 articles, present in 45 different publications, both evaluated by two specific in-house global impact rankings. Through VOSviewer, three main clusters were identified: (i) e-health information stakeholder needs; (ii) e-health information quality assessment; and (iii) e-health information technological governance trends. A residual correlation between interoperability and linkage studies in the sample was also found. Conclusion: Assessing stakeholders' needs is crucial for establishing an efficient and effective health information system. Further and diversified research is needed to assess the integrated placement of interoperability and linkage in health information management and governance. Implications: This research has provided valuable managerial and theoretical contributions to optimise system interoperability and data linkage within health information research and information technology solutions.

From palm to practice: prescription digital therapeutics for mental and brain health at the National Institutes of Health

Prescription Digital Therapeutics (PDTs) are emerging as promising tools for treating and managing mental and brain health conditions within the context of daily life. This commentary distinguishes PDTs from other Software as Medical Devices (SaMD) and explores their integration into mental and brain health treatments. We focus on research programs and support from the National Institutes of Health (NIH), discussing PDT research supported by the NIH's National Institute on Child Health and Development (NICHD), National Institute of Mental Health (NIMH), and National Institute on Aging (NIA). We present a hierarchical natural language processing topic analysis of NIH-funded digital therapeutics research projects. We delineate the PDT landscape across different mental and brain health disorders while highlighting opportunities and challenges. Additionally, we discuss the research foundation for PDTs, the unique therapeutic approaches they employ, and potential strategies to improve their validity, reliability, safety, and effectiveness. Finally, we address the research and collaborations necessary to propel the field forward, ultimately enhancing patient care through innovative digital health solutions.

Predicting postoperative delirium assessed by the Nursing Screening Delirium Scale in the recovery room for non-cardiac surgeries without craniotomy: A retrospective study using a machine learning approach

Postoperative delirium (POD) contributes to severe outcomes such as death or development of dementia. Thus, it is desirable to identify vulnerable patients in advance during the perioperative phase. Previous studies mainly investigated risk factors for delirium during hospitalization and further used a linear logistic regression (LR) approach with time-invariant data. Studies have not investigated patients' fluctuating conditions to support POD precautions. In this single-center study, we aimed to predict POD in a recovery room setting with a non-linear machine learning (ML) technique using pre-, intra-, and postoperative data. The target variable POD was defined with the Nursing Screening Delirium Scale (Nu-DESC) ≥ 1. Feature selection was conducted based on robust univariate test statistics and L1 regularization. Non-linear multi-layer perceptron (MLP) as well as tree-based models were trained and evaluated-with the receiver operating characteristics curve (AUROC), the area under precision recall curve (AUPRC), and additional metrics-against LR and published models on bootstrapped testing data. The prevalence of POD was 8.2% in a sample of 73,181 surgeries performed between 2017 and 2020. Significant univariate impact factors were the preoperative ASA status (American Society of Anesthesiologists physical status classification system), the intraoperative amount of given remifentanil, and the postoperative Aldrete score. The best model used pre-, intra-, and postoperative data. The non-linear boosted trees model achieved a mean AUROC of 0.854 and a mean AUPRC of 0.418 outperforming linear LR, well as best applied and retrained baseline models. Overall, non-linear machine learning models using data from multiple perioperative time phases were superior to traditional ones in predicting POD in the recovery room. Class imbalance was seen as a main impediment for model application in clinical practice.

[Digital Healthcare: Requirements for a Successful Transformation]

Digital transformation of healthcare is the dominating discussion topic for all healthcare stakeholders. Digital transformation encompasses all areas of healthcare and is far more than digital healthcare applications (DiGA), digital care applications (DiPA), telemedicine applications, telematics infrastructure activities, and applications from the fields of eHealth, mHealth, or Telehealth. All existing care processes and structures in the healthcare system are undergoing an inventory in order to transfer analog components of care into a digital context. The digital transformation is not taking place exclusively in economic sectors such as healthcare, but is a process of change throughout society in the collection, use, provision, linking and evaluation of information (=data). For the healthcare sector, it is clear that different technical concepts are used, while digital healthcare takes place in different places and at different times, may include different user (groups) and retains, expands or changes the healthcare context. Established healthcare functions such as diagnostics, therapy, documentation and the management of healthcare services are retained and transferred to a digital context. In addition, new application areas will emerge, such as the overarching access to health data by different actors, real-time-driven monitoring systems of holistic health data, (clinical) decision systems, or the provision of data for health services. Even if the majority of the digital transformation has not yet taken place, it is assumed that these functions and application areas of healthcare will serve to sustainably improve the quality of care and benefit the well-being of all (future) patients. For the transfer of existing processes into a digital context and the establishment of new application areas, there are prerequisites for healthcare institutions and the healthcare system itself.

Designing medical artificial intelligence systems for global use: focus on interoperability, scalability, and accessibility

Advances in artificial intelligence (AI) and machine learning systems promise faster, more efficient, and more personalized care. While many of these models are built on the premise of improving access to the timely screening, diagnosis, and treatment of cardiovascular disease, their validity and accessibility across diverse and international cohorts remain unknown. In this mini-review article, we summarize key obstacles in the effort to design AI systems that will be scalable, accessible, and accurate across distinct geographical and temporal settings. We discuss representativeness, interoperability, quality assurance, and the importance of vendor-agnostic data types that will be available to end-users across the globe. These topics illustrate how the timely integration of these principles into AI development is crucial to maximizing the global benefits of AI in cardiology.

BART, the new robotic assistant: big data, artificial intelligence, robotics, and telemedicine integration for an ICU 4.0

We are in the era of Health 4.0 when novel technologies are providing tools capable of improving the quality and safety of the services provided. Our project involves the integration of different technologies (AI, big data, robotics, and telemedicine) to create a unique system for patients admitted to intensive care units suffering from infectious diseases capable of both increasing the personalization of care and ensuring a safer environment for caregivers.

Incorporating digital platforms into nutritional care in chronic kidney disease

Digital health is increasingly recognized for its value to enhance patient care and clinical care processes. People with chronic kidney disease often find dietary self-management challenging. There is promising evidence that digital health interventions can support people with chronic kidney disease to self-manage their diet, by providing more frequent access to nutritional information and dietitians and by facilitating regular monitoring and feedback. There is some emerging evidence of the impact of digital interventions in chronic kidney disease; however, more research is needed to provide meaningful interpretation of how digital interventions can enhance current practice. Importantly, a number of factors need to be considered when designing, developing, implementing, and evaluating the impact of digital interventions. Consideration of the nutrition service and patients' needs, motivation and digital literacy, type of digital intervention, and the ability to embed the digital intervention into current care processes are critical. This paper overviews the current literature on digital health and self-management, factors to consider when embedding digital interventions and platforms into nutrition care and practical considerations for designing and implementing digital health interventions to enhance the nutritional care of people with chronic kidney disease.

The Role of Informatics in Advancing Emergency Medicine: A Comprehensive Review

Emergency Medicine Informatics (EMI) is a rapidly advancing field that utilizes information technology to enhance the delivery of emergency medical services. This comprehensive literature review explores the key components, benefits, challenges, and future directions of EMI. By integrating Electronic Health Records, Clinical Decision Support Systems, telemedicine, data analytics, interoperability, and patient monitoring systems, EMI has the potential to significantly improve patient outcomes and operational efficiency in emergency departments. However, the implementation of these technologies faces several obstacles, including interoperability issues, data security concerns, usability challenges, and high costs. This review highlights how these technologies are transforming emergency care, discusses the barriers to their implementation, and provides perspectives on potential solutions and future progress in the field.

Climate change and artificial intelligence in healthcare: Review and recommendations towards a sustainable future

The rapid advancement of artificial intelligence (AI) in healthcare has revolutionized the industry, offering significant improvements in diagnostic accuracy, efficiency, and patient outcomes. However, the increasing adoption of AI systems also raises concerns about their environmental impact, particularly in the context of climate change. This review explores the intersection of climate change and AI in healthcare, examining the challenges posed by the energy consumption and carbon footprint of AI systems, as well as the potential solutions to mitigate their environmental impact. The review highlights the energy-intensive nature of AI model training and deployment, the contribution of data centers to greenhouse gas emissions, and the generation of electronic waste. To address these challenges, the development of energy-efficient AI models, the adoption of green computing practices, and the integration of renewable energy sources are discussed as potential solutions. The review also emphasizes the role of AI in optimizing healthcare workflows, reducing resource waste, and facilitating sustainable practices such as telemedicine. Furthermore, the importance of policy and governance frameworks, global initiatives, and collaborative efforts in promoting sustainable AI practices in healthcare is explored. The review concludes by outlining best practices for sustainable AI deployment, including eco-design, lifecycle assessment, responsible data management, and continuous monitoring and improvement. As the healthcare industry continues to embrace AI technologies, prioritizing sustainability and environmental responsibility is crucial to ensure that the benefits of AI are realized while actively contributing to the preservation of our planet.

Breaking down the Digital Fortress: The Unseen Challenges in Healthcare Technology-Lessons Learned from 10 Years of Research

Healthcare is undergoing a fundamental shift in which digital health tools are becoming ubiquitous, with the promise of improved outcomes, reduced costs, and greater efficiency. Healthcare professionals, patients, and the wider public are faced with a paradox of choice regarding technologies across multiple domains. Research is continuing to look for methods and tools to further revolutionise all aspects of health from prediction, diagnosis, treatment, and monitoring. However, despite its promise, the reality of implementing digital health tools in practice, and the scalability of innovations, remains stunted. Digital health is approaching a crossroads where we need to shift our focus away from simply looking at developing new innovations to seriously considering how we overcome the barriers that currently limit its impact. This paper summarises over 10 years of digital health experiences from a group of researchers with backgrounds in physical therapy-in order to highlight and discuss some of these key lessons-in the areas of validity, patient and public involvement, privacy, reimbursement, and interoperability. Practical learnings from this collective experience across patient cohorts are leveraged to propose a list of recommendations to enable researchers to bridge the gap between the development and implementation of digital health tools.

Creation of Standardized Common Data Elements for Diagnostic Tests in Infectious Disease Studies: Semantic and Syntactic Mapping

BACKGROUND

It is necessary to harmonize and standardize data variables used in case report forms (CRFs) of clinical studies to facilitate the merging and sharing of the collected patient data across several clinical studies. This is particularly true for clinical studies that focus on infectious diseases. Public health may be highly dependent on the findings of such studies. Hence, there is an elevated urgency to generate meaningful, reliable insights, ideally based on a high sample number and quality data. The implementation of core data elements and the incorporation of interoperability standards can facilitate the creation of harmonized clinical data sets.

OBJECTIVE

This study's objective was to compare, harmonize, and standardize variables focused on diagnostic tests used as part of CRFs in 6 international clinical studies of infectious diseases in order to, ultimately, then make available the panstudy common data elements (CDEs) for ongoing and future studies to foster interoperability and comparability of collected data across trials.

METHODS

We reviewed and compared the metadata that comprised the CRFs used for data collection in and across all 6 infectious disease studies under consideration in order to identify CDEs. We examined the availability of international semantic standard codes within the Systemized Nomenclature of Medicine - Clinical Terms, the National Cancer Institute Thesaurus, and the Logical Observation Identifiers Names and Codes system for the unambiguous representation of diagnostic testing information that makes up the CDEs. We then proposed 2 data models that incorporate semantic and syntactic standards for the identified CDEs.

RESULTS

Of 216 variables that were considered in the scope of the analysis, we identified 11 CDEs to describe diagnostic tests (in particular, serology and sequencing) for infectious diseases: viral lineage/clade; test date, type, performer, and manufacturer; target gene; quantitative and qualitative results; and specimen identifier, type, and collection date.

CONCLUSIONS

The identification of CDEs for infectious diseases is the first step in facilitating the exchange and possible merging of a subset of data across clinical studies (and with that, large research projects) for possible shared analysis to increase the power of findings. The path to harmonization and standardization of clinical study data in the interest of interoperability can be paved in 2 ways. First, a map to standard terminologies ensures that each data element's (variable's) definition is unambiguous and that it has a single, unique interpretation across studies. Second, the exchange of these data is assisted by "wrapping" them in a standard exchange format, such as Fast Health care Interoperability Resources or the Clinical Data Interchange Standards Consortium's Clinical Data Acquisition Standards Harmonization Model.

Discharge process for patients experiencing homelessness in the emergency department: A thematic qualitative study

People experiencing homelessness are more likely to utilize emergency departments than their non-homeless counterparts. However, obtaining a bed in a homeless shelter for patients can be complex. To better understand the challenges of finding a safe discharge plan for homeless patients in the emergency department, our team conducted interviews with emergency department social workers and homeless shelter case managers in the Boston area. We identified and mapped the stages in the processes performed by both parties, identifying challenges with successful placement into a shelter. Furthermore, we assembled a data dictionary of key factors considered when assessing a patient's fit for a homeless shelter. By identifying bottlenecks and areas of opportunity, this study serves as a first step in enabling homeless individuals to receive the post-discharge assistance they require.

Deep learning and predictive modelling for generating normalised muscle function parameters from signal images of mandibular electromyography

Challenges arise in accessing archived signal outputs due to proprietary software limitations. There is a notable lack of exploration in open-source mandibular EMG signal conversion for continuous access and analysis, hindering tasks such as pattern recognition and predictive modelling for temporomandibular joint complex function. To Develop a workflow to extract normalised signal parameters from images of mandibular muscle EMG and identify optimal clustering methods for quantifying signal intensity and activity durations. A workflow utilising OpenCV, variational encoders and Neurokit2 generated and augmented 866 unique EMG signals from jaw movement exercises. k-means, GMM and DBSCAN were employed for normalisation and cluster-centric signal processing. The workflow was validated with data collected from 66 participants, measuring temporalis, masseter and digastric muscles. DBSCAN (0.35 to 0.54) and GMM (0.09 to 0.24) exhibited lower silhouette scores for mouth opening, anterior protrusion and lateral excursions, while K-means performed best (0.10 to 0.11) for temporalis and masseter muscles during chewing activities. The current study successfully developed a deep learning workflow capable of extracting normalised signal data from EMG images and generating quantifiable parameters for muscle activity duration and general functional intensity.

Artificial Intelligence in Orthodontics: Critical Review

With increasing digitalization in orthodontics, certain orthodontic manufacturing processes such as the fabrication of indirect bonding trays, aligner production, or wire bending can be automated. However, orthodontic treatment planning and evaluation remains a specialist's task and responsibility. As the prediction of growth in orthodontic patients and response to orthodontic treatment is inherently complex and individual, orthodontists make use of features gathered from longitudinal, multimodal, and standardized orthodontic data sets. Currently, these data sets are used by the orthodontist to make informed, rule-based treatment decisions. In research, artificial intelligence (AI) has been successfully applied to assist orthodontists with the extraction of relevant data from such data sets. Here, AI has been applied for the analysis of clinical imagery, such as automated landmark detection in lateral cephalograms but also for evaluation of intraoral scans or photographic data. Furthermore, AI is applied to help orthodontists with decision support for treatment decisions such as the need for orthognathic surgery or for orthodontic tooth extractions. One major challenge in current AI research in orthodontics is the limited generalizability, as most studies use unicentric data with high risks of bias. Moreover, comparing AI across different studies and tasks is virtually impossible as both outcomes and outcome metrics vary widely, and underlying data sets are not standardized. Notably, only few AI applications in orthodontics have reached full clinical maturity and regulatory approval, and researchers in the field are tasked with tackling real-world evaluation and implementation of AI into the orthodontic workflow.

Sharing sensitive data in life sciences: an overview of centralized and federated approaches

Biomedical data are generated and collected from various sources, including medical imaging, laboratory tests and genome sequencing. Sharing these data for research can help address unmet health needs, contribute to scientific breakthroughs, accelerate the development of more effective treatments and inform public health policy. Due to the potential sensitivity of such data, however, privacy concerns have led to policies that restrict data sharing. In addition, sharing sensitive data requires a secure and robust infrastructure with appropriate storage solutions. Here, we examine and compare the centralized and federated data sharing models through the prism of five large-scale and real-world use cases of strategic significance within the European data sharing landscape: the French Health Data Hub, the BBMRI-ERIC Colorectal Cancer Cohort, the federated European Genome-phenome Archive, the Observational Medical Outcomes Partnership/OHDSI network and the EBRAINS Medical Informatics Platform. Our analysis indicates that centralized models facilitate data linkage, harmonization and interoperability, while federated models facilitate scaling up and legal compliance, as the data typically reside on the data generator's premises, allowing for better control of how data are shared. This comparative study thus offers guidance on the selection of the most appropriate sharing strategy for sensitive datasets and provides key insights for informed decision-making in data sharing efforts.

Deep Learning and High-Resolution Anoscopy: Development of an Interoperable Algorithm for the Detection and Differentiation of Anal Squamous Cell Carcinoma Precursors-A Multicentric Study

High-resolution anoscopy (HRA) plays a central role in the detection and treatment of precursors of anal squamous cell carcinoma (ASCC). Artificial intelligence (AI) algorithms have shown high levels of efficiency in detecting and differentiating HSIL from low-grade squamous intraepithelial lesions (LSIL) in HRA images. Our aim was to develop a deep learning system for the automatic detection and differentiation of HSIL versus LSIL using HRA images from both conventional and digital proctoscopes. A convolutional neural network (CNN) was developed based on 151 HRA exams performed at two volume centers using conventional and digital HRA systems. A total of 57,822 images were included, 28,874 images containing HSIL and 28,948 LSIL. Partial subanalyses were performed to evaluate the performance of the CNN in the subset of images acetic acid and lugol iodine staining and after treatment of the anal canal. The overall accuracy of the CNN in distinguishing HSIL from LSIL during the testing stage was 94.6%. The algorithm had an overall sensitivity and specificity of 93.6% and 95.7%, respectively (AUC 0.97). For staining with acetic acid, HSIL was differentiated from LSIL with an overall accuracy of 96.4%, while for lugol and after therapeutic manipulation, these values were 96.6% and 99.3%, respectively. The introduction of AI algorithms to HRA may enhance the early diagnosis of ASCC precursors, and this system was shown to perform adequately across conventional and digital HRA interfaces.

Suitability of the Current Health Technology Assessment of Innovative Artificial Intelligence-Based Medical Devices: Scoping Literature Review

BACKGROUND

Artificial intelligence (AI)-based medical devices have garnered attention due to their ability to revolutionize medicine. Their health technology assessment framework is lacking.

OBJECTIVE

This study aims to analyze the suitability of each health technology assessment (HTA) domain for the assessment of AI-based medical devices.

METHODS

We conducted a scoping literature review following the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) methodology. We searched databases (PubMed, Embase, and Cochrane Library), gray literature, and HTA agency websites.

RESULTS

A total of 10.1% (78/775) of the references were included. Data quality and integration are vital aspects to consider when describing and assessing the technical characteristics of AI-based medical devices during an HTA process. When it comes to implementing specialized HTA for AI-based medical devices, several practical challenges and potential barriers could be highlighted and should be taken into account (AI technological evolution timeline, data requirements, complexity and transparency, clinical validation and safety requirements, regulatory and ethical considerations, and economic evaluation).

CONCLUSIONS

The adaptation of the HTA process through a methodological framework for AI-based medical devices enhances the comparability of results across different evaluations and jurisdictions. By defining the necessary expertise, the framework supports the development of a skilled workforce capable of conducting robust and reliable HTAs of AI-based medical devices. A comprehensive adapted HTA framework for AI-based medical devices can provide valuable insights into the effectiveness, cost-effectiveness, and societal impact of AI-based medical devices, guiding their responsible implementation and maximizing their benefits for patients and health care systems.

Converge or Collide? Making Sense of a Plethora of Open Data Standards in Health Care

Practitioners of digital health are familiar with disjointed data environments that often inhibit effective communication among different elements of the ecosystem. This fragmentation leads in turn to issues such as inconsistencies in services versus payments, wastage, and notably, care delivered being less than best-practice. Despite the long-standing recognition of interoperable data as a potential solution, efforts in achieving interoperability have been disjointed and inconsistent, resulting in numerous incompatible standards, despite the widespread agreement that fewer standards would enhance interoperability. This paper introduces a framework for understanding health care data needs, discussing the challenges and opportunities of open data standards in the field. It emphasizes the necessity of acknowledging diverse data standards, each catering to specific viewpoints and needs, while proposing a categorization of health care data into three domains, each with its distinct characteristics and challenges, along with outlining overarching design requirements applicable to all domains and specific requirements unique to each domain.

Discussion paper: implications for the further development of the successfully in emergency medicine implemented AUD2IT-algorithm

The AUD2IT-algorithm is a tool to structure the data, which is collected during an emergency treatment. The goal is on the one hand to structure the documentation of the data and on the other hand to give a standardised data structure for the report during handover of an emergency patient. AUD2IT-algorithm was developed to provide residents a documentation aid, which helps to structure the medical reports without getting lost in unimportant details or forgetting important information. The sequence of anamnesis, clinical examination, considering a differential diagnosis, technical diagnostics, interpretation and therapy is rather an academic classification than a description of the real workflow. In a real setting, most of these steps take place simultaneously. Therefore, the application of the AUD2IT-algorithm should also be carried out according to the real processes. A big advantage of the AUD2IT-algorithm is that it can be used as a structure for the entire treatment process and also is entirely usable as a handover protocol within this process to make sure, that the existing state of knowledge is ensured at each point of a team-timeout. PR-E-(AUD2IT)-algorithm makes it possible to document a treatment process that, in principle, does not have to be limited to the field of emergency medicine. Also, in the outpatient treatment the PR-E-(AUD2IT)-algorithm could be used and further developed. One example could be the preparation and allocation of needed resources at the general practitioner. The algorithm is a standardised tool that can be used by healthcare professionals of any level of training. It gives the user a sense of security in their daily work.

Revolutionizing Oncology: A Comprehensive Review of Digital Health Applications

Digital health is poised to revolutionize the field of oncology, offering innovative solutions that enhance diagnostics, treatment, and patient care. This comprehensive review delves into the multifaceted landscape of digital health in oncology, encompassing its definition, significance, applications, benefits, challenges, ethical considerations, and future trends. Key findings highlight the potential for early detection, personalized treatment, enhanced care coordination, patient empowerment, accelerated research, and cost efficiency. Ethical concerns surrounding privacy, equitable access, and responsible data use are discussed. Looking ahead, the future of digital health in oncology is bright, driven by advancements in artificial intelligence, virtual and augmented reality, predictive analytics, global collaboration, and evolving regulations. This review underscores the need for collaboration among stakeholders and a patient-centered approach to harness the transformative power of digital health, promising a future where the burden of cancer is lessened through innovation and compassionate care.

Current Status and Future Directions: The Application of Artificial Intelligence/Machine Learning for Precision Medicine

Technological innovations, such as artificial intelligence (AI) and machine learning (ML), have the potential to expedite the goal of precision medicine, especially when combined with increased capacity for voluminous data from multiple sources and expanded therapeutic modalities; however, they also present several challenges. In this communication, we first discuss the goals of precision medicine, and contextualize the use of AI in precision medicine by showcasing innovative applications (e.g., prediction of tumor growth and overall survival, biomarker identification using biomedical images, and identification of patient population for clinical practice) which were presented during the February 2023 virtual public workshop entitled "Application of Artificial Intelligence and Machine Learning for Precision Medicine," hosted by the US Food and Drug Administration (FDA) and University of Maryland Center of Excellence in Regulatory Science and Innovation (M-CERSI). Next, we put forward challenges brought about by the multidisciplinary nature of AI, particularly highlighting the need for AI to be trustworthy. To address such challenges, we subsequently note practical approaches, viz., differential privacy, synthetic data generation, and federated learning. The proposed strategies - some of which are highlighted presentations from the workshop - are for the protection of personal information and intellectual property. In addition, methods such as the risk-based management approach and the need for an agile regulatory ecosystem are discussed. Finally, we lay out a call for action that includes sharing of data and algorithms, development of regulatory guidance documents, and pooling of expertise from a broad-spectrum of stakeholders to enhance the application of AI in precision medicine.

The Role of AI in Hospitals and Clinics: Transforming Healthcare in the 21st Century

As healthcare systems around the world face challenges such as escalating costs, limited access, and growing demand for personalized care, artificial intelligence (AI) is emerging as a key force for transformation. This review is motivated by the urgent need to harness AI's potential to mitigate these issues and aims to critically assess AI's integration in different healthcare domains. We explore how AI empowers clinical decision-making, optimizes hospital operation and management, refines medical image analysis, and revolutionizes patient care and monitoring through AI-powered wearables. Through several case studies, we review how AI has transformed specific healthcare domains and discuss the remaining challenges and possible solutions. Additionally, we will discuss methodologies for assessing AI healthcare solutions, ethical challenges of AI deployment, and the importance of data privacy and bias mitigation for responsible technology use. By presenting a critical assessment of AI's transformative potential, this review equips researchers with a deeper understanding of AI's current and future impact on healthcare. It encourages an interdisciplinary dialogue between researchers, clinicians, and technologists to navigate the complexities of AI implementation, fostering the development of AI-driven solutions that prioritize ethical standards, equity, and a patient-centered approach.

Integration of Patient-Reported Outcome Data Collected Via Web Applications and Mobile Apps Into a Nation-Wide COVID-19 Research Platform Using Fast Healthcare Interoperability Resources: Development Study

BACKGROUND

The Network University Medicine projects are an important part of the German COVID-19 research infrastructure. They comprise 2 subprojects: COVID-19 Data Exchange (CODEX) and Coordination on Mobile Pandemic Apps Best Practice and Solution Sharing (COMPASS). CODEX provides a centralized and secure data storage platform for research data, whereas in COMPASS, expert panels were gathered to develop a reference app framework for capturing patient-reported outcomes (PROs) that can be used by any researcher.

OBJECTIVE

Our study aims to integrate the data collected with the COMPASS reference app framework into the central CODEX platform, so that they can be used by secondary researchers. Although both projects used the Fast Healthcare Interoperability Resources (FHIR) standard, it was not used in a way that data could be shared directly. Given the short time frame and the parallel developments within the CODEX platform, a pragmatic and robust solution for an interface component was required.

METHODS

We have developed a means to facilitate and promote the use of the German Corona Consensus (GECCO) data set, a core data set for COVID-19 research in Germany. In this way, we ensured semantic interoperability for the app-collected PRO data with the COMPASS app. We also developed an interface component to sustain syntactic interoperability.

RESULTS

The use of different FHIR types by the COMPASS reference app framework (the general-purpose FHIR Questionnaire) and the CODEX platform (eg, Patient, Condition, and Observation) was found to be the most significant obstacle. Therefore, we developed an interface component that realigns the Questionnaire items with the corresponding items in the GECCO data set and provides the correct resources for the CODEX platform. We extended the existing COMPASS questionnaire editor with an import function for GECCO items, which also tags them for the interface component. This ensures syntactic interoperability and eases the reuse of the GECCO data set for researchers.

CONCLUSIONS

This paper shows how PRO data, which are collected across various studies conducted by different researchers, can be captured in a research-compatible way. This means that the data can be shared with a central research infrastructure and be reused by other researchers to gain more insights about COVID-19 and its sequelae.

Navigating the Future: The Transformative Impact of Artificial Intelligence on Hospital Management- A Comprehensive Review

This comprehensive review explores the transformative impact of artificial intelligence (AI) on hospital management, delving into its applications, challenges, and future trends. Integrating AI in administrative functions, clinical operations, and patient engagement holds significant promise for enhancing efficiency, optimizing resource allocation, and revolutionizing patient care. However, this evolution is accompanied by ethical, legal, and operational considerations that necessitate careful navigation. The review underscores key findings, emphasizing the implications for the future of hospital management. It calls for a proactive approach, urging stakeholders to invest in education, prioritize ethical guidelines, foster collaboration, advocate for thoughtful regulation, and embrace a culture of innovation. The healthcare industry can successfully navigate this transformative era through collective action, ensuring that AI contributes to more effective, accessible, and patient-centered healthcare delivery.

New regulatory thinking is needed for AI-based personalised drug and cell therapies in precision oncology

Until recently the application of artificial intelligence (AI) in precision oncology was confined to activities in drug development and had limited impact on the personalisation of therapy. Now, a number of approaches have been proposed for the personalisation of drug and cell therapies with AI applied to therapy design, planning and delivery at the patient's bedside. Some drug and cell-based therapies are already tuneable to the individual to optimise efficacy, to reduce toxicity, to adapt the dosing regime, to design combination therapy approaches and, preclinically, even to personalise the receptor design of cell therapies. Developments in AI-based healthcare are accelerating through the adoption of foundation models, and generalist medical AI models have been proposed. The application of these approaches in therapy design is already being explored and realistic short-term advances include the application to the personalised design and delivery of drugs and cell therapies. With this pace of development, the limiting step to adoption will likely be the capacity and appropriateness of regulatory frameworks. This article explores emerging concepts and new ideas for the regulation of AI-enabled personalised cancer therapies in the context of existing and in development governance frameworks.

Implementation of cloud computing in the German healthcare system

With the advent of artificial intelligence and Big Data - projects, the necessity for a transition from analog medicine to modern-day solutions such as cloud computing becomes unavoidable. Even though this need is now common knowledge, the process is not always easy to start. Legislative changes, for example at the level of the European Union, are helping the respective healthcare systems to take the necessary steps. This article provides an overview of how a German university hospital is dealing with European data protection laws on the integration of cloud computing into everyday clinical practice. By describing our model approach, we aim to identify opportunities and possible pitfalls to sustainably influence digitization in Germany.

Web-based Prototype for Graphical Exploration of FHIR® Questionnaire Responses

The evaluation of clinical questionnaires is an important part of gaining knowledge in empirical research. The electronically captured responses are encoded in a standard format such as HL7 FHIR® that facilitates data exchange and systems interoperability. However, this also complicates access of the information to explore and interpret the results without appropriate tools. In this work, we present the design of a web-based graphical exploration tool for categorical questionnaire response data that can interact with FHIR-conformant HTTP endpoints. The web app enables non-technical users with simplified, direct visual access to highly structured FHIR questionnaire response data and preserves the applicability in arbitrary data exploration tasks. We describe the abstract feature design with the derived technical implementation to allow a universal, user-configurable data subselection mechanism to generate conditional one- and two-data-dimensional charts. The applicability of our developed prototype is demonstrated on synthetic FHIR data with the source code available at https://github.com/frankkramer-lab/FHIR-QR-Explorer.

Towards a framework for interoperability and reproducibility of predictive models

The development and deployment of machine learning (ML) models for biomedical research and healthcare currently lacks standard methodologies. Although tools for model replication are numerous, without a unifying blueprint it remains difficult to scientifically reproduce predictive ML models for any number of reasons (e.g., assumptions regarding data distributions and preprocessing, unclear test metrics, etc.) and ultimately, questions around generalizability and transportability are not readily answered. To facilitate scientific reproducibility, we built upon the Predictive Model Markup Language (PMML) to capture essential information. As a key component of the PREdictive Model Index and Exchange REpository (PREMIERE) platform, we present the Automated Metadata Pipeline (AMP) for conversion of a given predictive ML model into an extended PMML file that autocompletes an ML-based checklist, assessing model elements for interoperability and reproducibility. We demonstrate this pipeline on multiple test cases with three different ML algorithms and health-related datasets, providing a foundation for future predictive model reproducibility, sharing, and comparison.

The Necessity of Interoperability to Uncover the Full Potential of Digital Health Devices

Personalized health care can be optimized by including patient-reported outcomes. Standardized and disease-specific questionnaires have been developed and are routinely used. These patient-reported outcome questionnaires can be simple paper forms given to the patient to fill out with a pen or embedded in digital devices. Regardless of the format used, they provide a snapshot of the patient's feelings and indicate when therapies need to be adjusted. The advantage of digitizing these questionnaires is that they can be automatically analyzed, and patients can be monitored independently of doctor visits. Although the questions of most clinical patient-reported outcome questionnaires follow defined standards and are evaluated by clinical trials, these standards do not exist for data processing. Interoperable data formats and structures would benefit multilingual and cross-study data exchange. Linking questionnaires to standardized terminologies such as the Systematized Nomenclature of Medicine Clinical Terms (SNOMED CT) and Logical Observation Identifiers, Names, and Codes (LOINC) would improve this interoperability. However, linking clinically validated patient-reported outcome questionnaires to clinical terms available in SNOMED CT or LOINC is not as straightforward as it sounds. Here, we report our approach to link patient-reported outcomes from health applications to SNOMED CT or LOINC codes. We highlight current difficulties in this process and outline ways to minimize them.

Digital child health: opportunities and obstacles. A joint statement of European Academy of Paediatrics and European Confederation of Primary Care Paediatricians

The advancement of technology and the increasing digitisation of healthcare systems have opened new opportunities to transform the delivery of child health services. The importance of interoperable electronic health data in enhancing healthcare systems and improving child health care is evident. Interoperability ensures seamless data exchange and communication among healthcare entities, providers, institutions, household and systems. Using standardised data formats, coding systems, and terminologies is crucial in achieving interoperability and overcoming the barriers of different systems, formats, and locations. Paediatricians and other child health stakeholders can effectively address data structure, coding, and terminology inconsistencies by promoting interoperability and improving data quality and accuracy of children and youth, according to guidelines of the World Health Organisation. Thus, ensure comprehensive health assessments and screenings for children, including timely follow-up and communication of results. And implement effective vaccination schedules and strategies, ensuring timely administration of vaccines and prompt response to any concerns or adverse events. Developmental milestones can be continuously monitored. This can improve care coordination, enhance decision-making, and optimise health outcomes for children. In conclusion, using interoperable electronic child health data holds great promise in advancing international child healthcare systems and enhancing the child's care and well-being. By promoting standardised data exchange, interoperability enables timely health assessments, accurate vaccination schedules, continuous monitoring of developmental milestones, coordination of care, and collaboration among child healthcare professionals and the individual or their caregiver. Embracing interoperability is essential for creating a person-centric and data-driven healthcare ecosystem where the potential of digitalisation and innovation can be fully realized.

Holistic Human-Serving Digitization of Health Care Needs Integrated Automated System-Level Assessment Tools

Digital health tools, platforms, and artificial intelligence- or machine learning-based clinical decision support systems are increasingly part of health delivery approaches, with an ever-greater degree of system interaction. Critical to the successful deployment of these tools is their functional integration into existing clinical routines and workflows. This depends on system interoperability and on intuitive and safe user interface design. The importance of minimizing emergent workflow stress through human factors research and purposeful design for integration cannot be overstated. Usability of tools in practice is as important as algorithm quality. Regulatory and health technology assessment frameworks recognize the importance of these factors to a certain extent, but their focus remains mainly on the individual product rather than on emergent system and workflow effects. The measurement of performance and user experience has so far been performed in ad hoc, nonstandardized ways by individual actors using their own evaluation approaches. We propose that a standard framework for system-level and holistic evaluation could be built into interacting digital systems to enable systematic and standardized system-wide, multiproduct, postmarket surveillance and technology assessment. Such a system could be made available to developers through regulatory or assessment bodies as an application programming interface and could be a requirement for digital tool certification, just as interoperability is. This would enable health systems and tool developers to collect system-level data directly from real device use cases, enabling the controlled and safe delivery of systematic quality assessment or improvement studies suitable for the complexity and interconnectedness of clinical workflows using developing digital health technologies.

Testing and Practical Implementation of a User-Friendly Personalized and Long-Term Electronic Informed Consent Prototype in Clinical Research: Mixed Methods Study

BACKGROUND

Over the years, there has been increasing interest in electronic informed consent (eIC) in clinical research. The user-friendliness of an eIC application and its acceptance by stakeholders plays a central role in achieving successful implementation.

OBJECTIVE

This study aims to identify insights for the design and implementation of a user-friendly, personalized, and long-term eIC application based on a usability study with (potential) research participants and semistructured interviews with stakeholders on the practical integration of such an application into their daily practice.

METHODS

An eIC prototype was evaluated and refined through usability testing among Belgian citizens and iterative redesign. On the basis of a digital literacy questionnaire, a heterogeneous sample of participants was established. Participants needed to complete a series of usability tasks related to personalization and long-term interaction with the research team while using the "think aloud" technique. In addition, usability tests involved completing the System Usability Scale questionnaire and taking part in a semistructured feedback interview. Furthermore, semistructured interviews were conducted with ethics committee members, health care professionals, and pharmaceutical industry representatives active in Belgium and involved in clinical research. Thematic analysis was undertaken using the NVivo software (Lumivero).

RESULTS

In total, 3 iterations of usability tests were conducted with 10 participants each. Each cycle involved some participants who reported having low digital skills. The System Usability Scale scores related to the tasks on personalization and long-term interaction increased after each iteration and reached 69.5 (SD 8.35) and 71.3 (SD 16.1) out of 100, respectively, which represents above-average usability. Semistructured interviews conducted with health care professionals (n=4), ethics committee members (n=8), and pharmaceutical industry representatives (n=5) identified the need for an eIC system that can be easily set up. For example, a library could be established enabling stakeholders to easily provide background information about a clinical study, presented in the second layer of the interface. In contrast, some functionalities, such as informing participants about new studies through an eIC system, were not considered useful by stakeholders.

CONCLUSIONS

This study provides insights for the implementation of a user-friendly personalized and long-term eIC application. The study findings showed that usability testing is key to assessing and increasing the user-friendliness of an eIC application. Although this eIC system has the potential to be usable by a wide audience, participants with low digital literacy may not be able to use it successfully, highlighting the need for additional support for participants or other alternatives to an eIC system. In addition, key lessons emerging from the interviews included ensuring that the application is easy to implement in practice and is interoperable with other established systems.

Geographical and practical challenges in the implementation of digital health passports for cross-border COVID-19 pandemic management: a narrative review and framework for solutions

The rapid global spread of infectious diseases, epitomized by the recent COVID-19 pandemic, has highlighted the critical need for effective cross-border pandemic management strategies. Digital health passports (DHPs), which securely store and facilitate the sharing of critical health information, including vaccination records and test results, have emerged as a promising solution to enable safe travel and access to essential services and economic activities during pandemics. However, the implementation of DHPs faces several significant challenges, both related to geographical disparities and practical considerations, necessitating a comprehensive approach for successful global adoption. In this narrative review article, we identify and elaborate on the critical geographical and practical barriers that hinder global adoption and the effective utilization of DHPs. Geographical barriers are complex, encompassing disparities in vaccine access, regulatory inconsistencies, differences across countries in data security and users' privacy policies, challenges related to interoperability and standardization, and inadequacies in technological infrastructure and limited access to digital technologies. Practical challenges include the possibility of vaccine contraindications and breakthrough infections, uncertainties surrounding natural immunity, and limitations of standard tests in assessing infection risk. To address geographical disparities and enhance the functionality and interoperability of DHPs, we propose a framework that emphasizes international collaboration to achieve equitable access to vaccines and testing resources. Furthermore, we recommend international cooperation to establish unified vaccine regulatory frameworks, adopting globally accepted standards for data privacy and protection, implementing interoperability protocols, and taking steps to bridge the digital divide. Addressing practical challenges requires a meticulous approach to assessing individual risk and augmenting DHP implementation with rigorous health screenings and personal infection prevention measures. Collectively, these initiatives contribute to the development of robust and inclusive cross-border pandemic management strategies, ultimately promoting a safer and more interconnected global community in the face of current and future pandemics.

Barriers to Implementation of Teleretinal Diabetic Retinopathy Screening Programs Across the University of California

Aim: To describe barriers to implementation of diabetic retinopathy (DR) teleretinal screening programs and artificial intelligence (AI) integration at the University of California (UC). Methods: Institutional representatives from UC Los Angeles, San Diego, San Francisco, Irvine, and Davis were surveyed for the year of their program's initiation, active status at the time of survey (December 2021), number of primary care clinics involved, screening image quality, types of eye providers, image interpretation turnaround time, and billing codes used. Representatives were asked to rate perceptions toward barriers to teleretinal DR screening and AI implementation using a 5-point Likert scale. Results: Four UC campuses had active DR teleretinal screening programs at the time of survey and screened between 246 and 2,123 patients at 1-6 clinics per campus. Sites reported variation between poor-quality photos (<5% to 15%) and average image interpretation time (1-5 days). Patient education, resource availability, and infrastructural support were identified as barriers to DR teleretinal screening. Cost and integration into existing technology infrastructures were identified as barriers to AI integration in DR screening. Conclusions: Despite the potential to increase access to care, there remain several barriers to widespread implementation of DR teleretinal screening. More research is needed to develop best practices to overcome these barriers.

Harnessing the power of clinical decision support systems: challenges and opportunities

Clinical decision support systems (CDSSs) are increasingly integrated into healthcare settings to improve patient outcomes, reduce medical errors and enhance clinical efficiency by providing clinicians with evidence-based recommendations at the point of care. However, the adoption and optimisation of these systems remain a challenge. This review aims to provide an overview of the current state of CDSS, discussing their development, implementation, benefits, limitations and future directions. We also explore the potential for enhancing their effectiveness and provide an outlook for future developments in this field. There are several challenges in CDSS implementation, including data privacy concerns, system integration and clinician acceptance. While CDSS have demonstrated significant potential, their adoption and optimisation remain a challenge.

A hackathon as a tool to enhance research and practice on electronic health record systems' interoperability for chronic disease management and prevention

Objectives

The development of a standardized technical framework for exchanging electronic health records is widely recognized as a challenging endeavor that necessitates appropriate technological, semantic, organizational, and legal interventions to support the continuity of health and care. In this context, this study delineates a pan-European hackathon aimed at evaluating the efforts undertaken by member states of the European Union to develop a European electronic health record exchange format. This format is intended to facilitate secure cross-border healthcare and optimize service delivery to citizens, paving the way toward a unified European health data space.

Methods

The hackathon was conducted within the scope of the X-eHealth project. Interested parties were initially presented with a representative clinical scenario and a set of specifications pertaining to the European electronic health record exchange format, encompassing Laboratory Results Reports, Medical Imaging and Reports, and Hospital Discharge Reports. In addition, five onboarding webinars and two professional training events were organized to support the participating entities. To ensure a minimum acceptable quality threshold, a set of inclusion criteria for participants was outlined for the interested teams.

Results

Eight teams participated in the hackathon, showcasing state-of-the-art applications. These teams utilized technologies such as Health Level Seven-Fast Healthcare Interoperability Resources (HL7 FHIR) and Clinical Document Architecture (CDA), alongside pertinent IHE integration profiles. They demonstrated a range of complementary uses and practices, contributing substantial inputs toward the development of future-proof electronic health record management systems.

Conclusions

The execution of the hackathon demonstrated the efficacy of such approaches in uniting teams from diverse backgrounds to develop state-of-the-art applications. The outcomes produced by the event serve as proof-of-concept demonstrators for managing and preventing chronic diseases, delivering value to citizens, companies, and the research community.

Experts' Perspectives on Use of Fast Healthcare Interoperable Resources for Computerized Clinical Decision Support

Barriers to improving the US healthcare system include a lack of interoperability across digital health information and delays in seeking preventative and recommended care. Interoperability can be seen as the lynch pin to reducing fragmentation and improving outcomes related to digital health systems. The prevailing standard for information exchange to enable interoperability is the Health Level Seven International Fast Healthcare Interoperable Resources standard. To better understand Fast Healthcare Interoperable Resources within the context of computerized clinical decision support expert interviews of health informaticists were conducted and used to create a modified force field analysis. Current barriers and future recommendations to scale adoption of Fast Healthcare Interoperable Resources were explored through qualitative analysis of expert interviews. Identified barriers included variation in electronic health record implementation, limited electronic health record vendor support, ontology variation, limited workforce knowledge, and testing limitations. Experts recommended research funders require Fast Healthcare Interoperable Resource usage, development of an "app store," incentives for clinical organizations and electronic health record vendors, and Fast Healthcare Interoperable Resource certification development.

Leveraging physiology and artificial intelligence to deliver advancements in health care

Artificial intelligence in health care has experienced remarkable innovation and progress in the last decade. Significant advancements can be attributed to the utilization of artificial intelligence to transform physiology data to advance health care. In this review, we explore how past work has shaped the field and defined future challenges and directions. In particular, we focus on three areas of development. First, we give an overview of artificial intelligence, with special attention to the most relevant artificial intelligence models. We then detail how physiology data have been harnessed by artificial intelligence to advance the main areas of health care: automating existing health care tasks, increasing access to care, and augmenting health care capabilities. Finally, we discuss emerging concerns surrounding the use of individual physiology data and detail an increasingly important consideration for the field, namely the challenges of deploying artificial intelligence models to achieve meaningful clinical impact.

Implications of immersive technologies in healthcare sector and its built environment

Objectives

This research focuses on how built environment experts can contribute to the MXR-enabled digital innovation as part of the multidisciplinary team effort to ensure post-pandemic resilience in healthcare built environment. The goal of this research is to help healthcare providers, built environment experts, and policy makers respectively: (1) Advocate the benefits of MXR for innovating health and social care; (2) Spark debate across networks of expertise to create health-promoting environment; and (3) Understand the overriding priorities in making effective pathways to the implementation of MXR.

Methods

To highlight the novelty of this research, the study relies on two qualitative methodologies: exploratory literature review and semi-structured interviews. Based on the evaluation of prior works and cross-national case studies, hypotheses are formulated from three arenas: (1) Cross-sectional Initiatives for Post-pandemic Resilience; (2) Interoperability and Usability of Next-gen Medicines; and (3) Metaverse and New Forms of Value in Future Healthcare Ecosystems. To verify those hypotheses, empirical findings are derived from in-depth interviews with nine key informants.

Results

The main findings are summarized under the following three themes: (1) Synergism between Architecture and Technology; (2) Patient Empowerment and Staff Support; and (3) Scalable Health and Wellbeing in Non-hospital and Therapeutic Settings. Firstly, both built environment and healthcare sectors can benefit from the various capabilities of MXR through cross-sectional initiatives, evidence-based practices, and participatory approaches. Secondly, a confluence of knowledge and methods of HCI and HBI can increase the interoperability and usability of MXR for the patient-centered and value-based healthcare models. Thirdly, the MXR-enabled technological regime will largely affect the new forms of value in healthcare premises by fostering more decentralized, preventive, and therapeutic characteristics in the future healthcare ecosystems.

Conclusion

Whether it's virtual or physical, our healthcare systems have placed great emphasis on the rigor of evidence-based approach linking health outcome to a clinical environment. Henceforth, built environment experts should seek closer ties with the MXR ecosystems for the co-production of scalable health and wellbeing in non-hospital and therapeutic settings. Ultimately, this is to improve resource efficiency in the healthcare sector while considering the transition of health resources towards in silico status by increasing the implementation of MXR.