Digital transformation in healthcare - architectures of present and future information technologies

Testing scenarios for using telepresence robots in healthcare settings

The ageing global population puts heavy pressure on healthcare systems everywhere. Addressing ageing-related chronic conditions requires employment of novel innovative solutions. Telehealth technologies, including telepresence robots (TPRs), are being rapidly developed to provide healthcare services efficiently wherever needed. This article explores the role of TPRs in addressing the challenges of providing healthcare to an ageing population, emphasizing their potential advantages and drawbacks. Employing an exploratory research approach with qualitative data collection techniques, we tested three TPR usage scenarios in simulated healthcare settings: anamnesis, measurements, and falls and frailty. The study employed a non-random purposive sample comprising 25 participants, and was conducted at a medical facility in June 2023. The findings suggest that TPRs offer promising solutions for healthcare professionals and patients, especially in scenarios when physical presence is impossible or physical isolation is required to prevent contagion. However, the technology is not yet ready to substitute fully human medical workers, potentially causing patient reluctance and emphasizing the need for patient-centered approaches to technology adoption. In addition, more studies are needed to address ethical, privacy, and scalability concerns.

Effectiveness of EHR systems on decision support in primary healthcare: a technology acceptance model 3 perspective

PURPOSE

This study aims to evaluate healthcare practitioners' perceptions of electronic health record (EHR) systems and their effectiveness in supporting clinical decision-making in Tanzanian Primary Healthcare (PHC) facilities.

DESIGN/METHODOLOGY/APPROACH

A mixed-methods approach was employed, combining quantitative data from structured questionnaires and qualitative insights from open-ended responses. The study was conducted in the Dodoma region of Tanzania, focusing on a diverse representation of PHC facilities, including district hospitals, health centres and dispensaries. Data were analysed using multiple linear regression for quantitative data, and thematic analysis was applied to qualitative responses.

FINDINGS

The results revealed that while EHR systems are widely used in Tanzanian PHC facilities, their impact on clinical decision-making remains limited. Only a moderate portion of practitioners perceived EHR systems as effective in decision support, and frequent system use was negatively correlated with user satisfaction. Challenges such as inadequate training and support, system crashes, slow performance and poor usability and integration into clinical workflows were significant barriers to effectively utilising EHR systems.

ORIGINALITY/VALUE

This study contributes to the limited literature on EHR system implementation in low-resource settings, specifically Tanzania, by focusing on decision-support features within EHR systems. The findings offer valuable insights for healthcare policymakers, system designers and practitioners to optimise EHR implementation and improve healthcare outcomes in resource-constrained environments.

Designing digital patient experiences: The digital health design framework

BACKGROUND

Digital health (DH) brings considerable benefits, but it comes with potential risks. Human Factors (HF) play a critical role in providing high-quality and acceptable DH solutions. Consultation with designers is crucial for reflecting on and improving current DH design practices.

OBJECTIVES

We investigated the general DH design processes, challenges, and corresponding strategies that can improve the digital patient experience (PEx).

METHODS

A semi-structured interview study with 24 design professionals. All audio recordings were transcribed, deidentified, grammatically corrected, and imported into ATLAS.ti for data analysis. Three coders participated in data coding following the thematic analysis approach.

RESULTS

We identified eight DH design stages and grouped them into four phases: preparation, problem-thinking, problem-solving, and implementation. The analysis presented twelve design challenges associated with contextual, practical, managerial, and commercial aspects that can hinder the design process. We identified eight common strategies used by respondents to tackle these challenges.

CONCLUSIONS

We propose a Digital Health Design (DHD) framework to improve the digital PEx. It provides an overview of design deliverables, activities, stakeholders, challenges, and corresponding strategies for each design stage.

Navigating the future of health care with AI-driven digital therapeutics

Digital therapeutics (DTx) is a recently conceived idea in health care that aims to cure ailments and modify patient behavior by employing a range of digital technologies. Notably, when traditional medication is not entirely efficacious, DTx offers an innovative avenue for treatments linked to dysfunctional behaviors and lifestyle management. DTx involves extremely adaptable therapeutic devices that empower greater patient engagement in treating illness, using algorithms to collect, transfer and analyze the patient's data. Efficient clinical monitoring and supervision at the individual level by remote access and algorithms for a range of diseases is made possible by integrating machine learning and artificial intelligence with DTx. There is a potentially large worldwide market for DTx owing to its convenient, personalized therapies.

Patients' Expectations of Doctors' Clinical Competencies in the Digital Health Care Era: Qualitative Semistructured Interview Study Among Patients

BACKGROUND

Digital technologies have impacted health care delivery globally, and are increasingly being deployed in clinical practice. However, there is limited research on patients' expectations of doctors' clinical competencies when using digital health care technologies (DHTs) in medical care. Understanding these expectations can reveal competency gaps, enhance patient confidence, and contribute to digital innovation initiatives.

OBJECTIVE

This study explores patients' perceptions of doctors' use of DHTs in clinical care. Using Singapore as a case study, it examines patients' expectations regarding doctors' communication, diagnosis, and treatment skills when using telemedicine, health apps, wearable devices, electronic health records, and artificial intelligence.

METHODS

Findings were drawn from individual semistructured interviews with patients from outpatient clinics. Participants were recruited using purposive sampling. Data were analyzed qualitatively using thematic analysis.

RESULTS

Twenty-five participants from different backgrounds and with various chronic conditions participated in the study. They expected doctors to be adept in handling medical data from apps and wearable devices. For telemedicine, participants expected a level of assessment of their medical conditions akin to in-person consultations. In addition, they valued doctors recognizing when a physical examination was necessary. Interestingly, eye contact was appreciated but deemed nonessential by participants across all age bands when electronic health records were used, as they valued the doctor's efficiency more than eye contact. Nonetheless, participants emphasized the need for empathy throughout the clinical encounter regardless of DHT use. Furthermore, younger participants had a greater expectation for DHT use among doctors compared to older ones, who preferred DHTs as a complement rather than a replacement for clinical skills. The former expected doctors to be knowledgeable about the algorithms, principles, and purposes of DHTs such as artificial intelligence technologies to better assist them in diagnosis and treatment.

CONCLUSIONS

By identifying patients' expectations of doctors amid increasing health care digitalization, this study highlights that while basic clinical skills remain crucial in the digital age, the role of clinicians needs to evolve with the introduction of DHTs. It has also provided insights into how DHTs can be integrated effectively into clinical settings, aligning with patients' expectations and preferences. Overall, the findings offer a framework for high-income countries to harness DHTs in enhancing health care delivery in the digital era.

Implementation barriers and facilitators of remote monitoring, remote consultation and digital care platforms through the eyes of healthcare professionals: a review of reviews

OBJECTIVES

Digital transformation in healthcare is a necessity considering the steady increase in healthcare costs, the growing ageing population and rising number of people living with chronic diseases. The implementation of digital health technologies in patient care is a potential solution to these issues, however, some challenges remain. In order to navigate such complexities, the perceptions of healthcare professionals (HCPs) must be considered. The objective of this umbrella review is to identify key barriers and facilitators involved in digital health technology implementation, from the perspective of HCPs.

DESIGN

Systematic umbrella review following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses statement.

DATA SOURCES

Embase.com, PubMed and Web of Science Core Collection were searched for existing reviews dated up to 17 June 2022. Search terms included digital health technology, combined with terms related to implementation, and variations in terms encompassing HCP, such as physician, doctor and the medical discipline.

ELIGIBILITY CRITERIA

Quantitative and qualitative reviews evaluating digital technologies that included patient interaction were considered eligible. Three reviewers independently synthesised and assessed eligible reviews and conducted a critical appraisal.

DATA EXTRACTION AND SYNTHESIS

Regarding the data collection, two reviewers independently synthesised and interpreted data on barriers and facilitators.

RESULTS

Thirty-three reviews met the inclusion criteria. Barriers and facilitators were categorised into four levels: (1) the organisation, (2) the HCP, (3) the patient and (4) technical aspects. The main barriers and facilitators identified were (lack of) training (n=22/33), (un)familiarity with technology (n=17/33), (loss of) communication (n=13/33) and security and confidentiality issues (n=17/33). Barriers of key importance included increased workload (n=16/33), the technology undermining aspects of professional identity (n=11/33), HCP uncertainty about patients' aptitude with the technology (n=9/33), and technical issues (n=12/33).

CONCLUSIONS

The implementation strategy should address the key barriers highlighted by HCPs, for instance, by providing adequate training to familiarise HCPs with the technology, adapting the technology to the patient preferences and addressing technical issues. Barriers on both HCP and patient levels can be overcome by investigating the needs of the end-users. As we shift from traditional face-to-face care models towards new modes of care delivery, further research is needed to better understand the role of digital technology in the HCP-patient relationship.

Clinical Validation of Digital Healthcare Solutions: State of the Art, Challenges and Opportunities

Digital health technologies (DHTs) at the intersection of health, medical informatics, and business aim to enhance patient care through personalised digital approaches. Ensuring the efficacy and reliability of these innovations demands rigorous clinical validation. A PubMed literature review (January 2006 to July 2023) identified 1250 papers, highlighting growing academic interest. A focused narrative review (January 2018 to July 2023) delved into challenges, highlighting issues such as diverse regulatory landscapes, adoption issues in complex healthcare systems, and a plethora of evaluation frameworks lacking pragmatic guidance. Existing frameworks often omit crucial criteria, neglect empirical evidence, and clinical effectiveness is rarely included as a criterion for DHT quality. The paper underscores the urgency of addressing challenges in accreditation, adoption, business models, and integration to safeguard the quality, efficacy, and safety of DHTs. A pivotal illustration of collaborative efforts to address these challenges is exemplified by the Digital Health Validation Center, dedicated to generating clinical evidence of innovative healthcare technologies and facilitating seamless technology transfer. In conclusion, it is necessary to harmonise evaluation approaches and frameworks, improve regulatory clarity, and commit to collaboration to integrate rigorous clinical validation and empirical evidence throughout the DHT life cycle.

Machine learning-based clinical decision support using laboratory data

Artificial intelligence (AI) and machine learning (ML) are becoming vital in laboratory medicine and the broader context of healthcare. In this review article, we summarized the development of ML models and how they contribute to clinical laboratory workflow and improve patient outcomes. The process of ML model development involves data collection, data cleansing, feature engineering, model development, and optimization. These models, once finalized, are subjected to thorough performance assessments and validations. Recently, due to the complexity inherent in model development, automated ML tools were also introduced to streamline the process, enabling non-experts to create models. Clinical Decision Support Systems (CDSS) use ML techniques on large datasets to aid healthcare professionals in test result interpretation. They are revolutionizing laboratory medicine, enabling labs to work more efficiently with less human supervision across pre-analytical, analytical, and post-analytical phases. Despite contributions of the ML tools at all analytical phases, their integration presents challenges like potential model uncertainties, black-box algorithms, and deskilling of professionals. Additionally, acquiring diverse datasets is hard, and models' complexity can limit clinical use. In conclusion, ML-based CDSS in healthcare can greatly enhance clinical decision-making. However, successful adoption demands collaboration among professionals and stakeholders, utilizing hybrid intelligence, external validation, and performance assessments.

The unmet promise of trustworthy AI in healthcare: why we fail at clinical translation

Artificial intelligence (AI) has the potential to revolutionize healthcare, for example via decision support systems, computer vision approaches, or AI-based prevention tools. Initial results from AI applications in healthcare show promise but are rarely translated into clinical practice successfully and ethically. This occurs despite an abundance of "Trustworthy AI" guidelines. How can we explain the translational gaps of AI in healthcare? This paper offers a fresh perspective on this problem, showing that failing translation of healthcare AI markedly arises from a lack of an operational definition of "trust" and "trustworthiness". This leads to (a) unintentional misuse concerning what trust (worthiness) is and (b) the risk of intentional abuse by industry stakeholders engaging in ethics washing. By pointing out these issues, we aim to highlight the obstacles that hinder translation of Trustworthy medical AI to practice and prevent it from fulfilling its unmet promises.

Nutritional management recommendation systems in polycystic ovary syndrome: a systematic review

BACKGROUND

People with polycystic ovary syndrome suffer from many symptoms and are at risk of developing diseases such as hypertension and diabetes in the future. Therefore, the importance of self-care doubles. It is mainly to modify the lifestyle, especially following the principles of healthy eating. The purpose of this study is to review artificial intelligence-based systems for providing management recommendations, especially food recommendations.

MATERIALS AND METHODS

This study started by searching three databases: PubMed, Scopus, and Web of Science, from inception until 6 June 2023. The result was the retrieval of 15,064 articles. First, we removed duplicate studies. After the title and abstract screening, 119 articles remained. Finally, after reviewing the full text of the articles and considering the inclusion and exclusion criteria, 20 studies were selected for the study. To assess the quality of articles, we used criteria proposed by Malhotra, Wen, and Kitchenham. Out of the total number of included studies, seventeen studies were high quality, while three studies were moderate quality.

RESULTS

Most studies were conducted in India in 2021. Out of all the studies, diagnostic recommendation systems were the most frequently researched, accounting for 86% of the total. Precision, sensitivity, specificity, and accuracy were more common than other performance metrics. The most significant challenge or limitation encountered in these studies was the small sample size.

CONCLUSION

Recommender systems based on artificial intelligence can help in fields such as prediction, diagnosis, and management of polycystic ovary syndrome. Therefore, since there are no nutritional recommendation systems for these patients in Iran, this study can serve as a starting point for such research.

Development of a communication platform for patients with head and neck cancer for effective information delivery and improvement of doctor-patient relationship: application of treatment journey-based service blueprint

BACKGROUND

Effective communication and information delivery enhance doctor-patient relationships, improves adherence to treatment, reduces work burden, and supports decision-making. The study developed a head and neck cancer (HNC) communication platform to support effective delivery of information about HNC treatment and improve the doctor-patient relationship.

METHODS

This study was structured in three main phases: 1) The requirement elicitation phase sought an understanding of the HNC treatment journey and service failure points (FPs) obtained through patient/medical staff interviews and observations, along with a review of the electronic health record system; 2) The development phase involved core needs analysis, solutions development through a co-creation workshop, and validation of the solutions through focus groups; and 3) the proposed HNC communication platform was integrated with the current treatment system, and the flow and mechanism of the interacting services were structured using a service blueprint (SB).

RESULTS

Twenty-two service FPs identified through interviews and observations were consolidated into four core needs, and solutions were proposed to address each need: an HNC treatment journey map, cancer survivor stories, operation consent redesign with surgical illustrations, and a non-verbal communication toolkit. The communication platform was designed through the SB in terms of the stage at which the solution was applied and the actions and interactions of the service providers.

CONCLUSIONS

The developed platform has practical significance, reflecting a tangible service improvement for both patients and medical staff, making it applicable in hospital settings.

Digital health integration for noncommunicable diseases: Comprehensive process mapping for full-life-cycle management

AIM

To create a systematic digital health process mapping framework for full-life-cycle noncommunicable disease management grounded in key stakeholder engagement.

METHODS

A triphasic, qualitative methodology was employed to construct a process mapping framework for digital noncommunicable disease management in Shanghai, China. The first phase involved desk research to examine current guidance and practices. In the second phase, pivotal stakeholders participated in focus group discussions to identify prevalent digital touchpoints across lifetime noncommunicable disease management. In the final phase, the Delphi technique was used to refine the framework based on expert insights and obtain consensus.

RESULTS

We identified 60 digital touchpoints across five essential stages of full-life-cycle noncommunicable disease management. Most experts acknowledged the rationality and feasibility of these touchpoints.

CONCLUSIONS

This study led to the creation of a comprehensive digital health process mapping framework that encompasses the entire life cycle of noncommunicable disease management. The insights gained emphasize the importance of a systemic strategic, person-centered approach over a fragmented, purely technocentric approach. We recommend that healthcare professionals use this framework as a linchpin for efficient disease management and seamless technology incorporation in clinical practice.

Digital stressors and resources perceived by emergency physicians and associations to their digital stress perception, mental health, job satisfaction and work engagement

BACKGROUND

Digital technologies are increasingly being integrated into healthcare settings, including emergency departments, with the potential to improve efficiency and patient care. Although digitalisation promises many benefits, the use of digital technologies can also introduce new stressors and challenges among medical staff, which may result in the development of various negative work and health outcomes. Therefore, this study aims to identify existing digital stressors and resources among emergency physicians, examine associations with various work- and health-related parameters, and finally identify the potential need for preventive measures.

METHODS

In this quantitative cross-sectional study, an online questionnaire was used to examine the relationship between digital stressors (technostress creators), digital resources (technostress inhibitors), technostress perception as well as mental health, job satisfaction and work engagement among 204 physicians working in German emergency medicine departments. Data collection lasted from December 2022 to April 2023. Validated scales were used for the questionnaire (e.g. "Technostress"-scale and the Copenhagen Psychosocial Questionnaire (COPSOQ). Descriptive and multiple regression analyses were run to test explorative assumptions.

RESULTS

The study found medium levels of technostress perception among the participating emergency physicians as well as low levels of persisting technostress inhibitors. The queried physicians on average reported medium levels of exhaustion symptoms, high levels of work engagement and job satisfaction. Significant associations between digital stressors and work- as well as health-related outcomes were analyzed.

CONCLUSION

This study provides a preliminary assessment of the persistence of digital stressors, digital resources and technostress levels, and their potential impact on relevant health and work-related outcomes, among physicians working in German emergency departments. Understanding and mitigating these stressors is essential to promote the well-being of physicians and ensure optimal patient care. As digitisation processes will continue to increase, the need for preventive support measures in dealing with technology stressors is obvious and should be expanded accordingly in the clinics. By integrating such support into everyday hospital life, medical staff in emergency departments can better focus on patient care and mitigate potential stress factors associated with digital technologies.

The Implementation of an Electronic Medical Record in a German Hospital and the Change in Completeness of Documentation: Longitudinal Document Analysis

BACKGROUND

Electronic medical records (EMR) are considered a key component of the health care system's digital transformation. The implementation of an EMR promises various improvements, for example, in the availability of information, coordination of care, or patient safety, and is required for big data analytics. To ensure those possibilities, the included documentation must be of high quality. In this matter, the most frequently described dimension of data quality is the completeness of documentation. In this regard, little is known about how and why the completeness of documentation might change after the implementation of an EMR.

OBJECTIVE

This study aims to compare the completeness of documentation in paper-based medical records and EMRs and to discuss the possible impact of an EMR on the completeness of documentation.

METHODS

A retrospective document analysis was conducted, comparing the completeness of paper-based medical records and EMRs. Data were collected before and after the implementation of an EMR on an orthopaedical ward in a German academic teaching hospital. The anonymized records represent all treated patients for a 3-week period each. Unpaired, 2-tailed t tests, chi-square tests, and relative risks were calculated to analyze and compare the mean completeness of the 2 record types in general and of 10 specific items in detail (blood pressure, body temperature, diagnosis, diet, excretions, height, pain, pulse, reanimation status, and weight). For this purpose, each of the 10 items received a dichotomous score of 1 if it was documented on the first day of patient care on the ward; otherwise, it was scored as 0.

RESULTS

The analysis consisted of 180 medical records. The average completeness was 6.25 (SD 2.15) out of 10 in the paper-based medical record, significantly rising to an average of 7.13 (SD 2.01) in the EMR (t178=-2.469; P=.01; d=-0.428). When looking at the significant changes of the 10 items in detail, the documentation of diet (P<.001), height (P<.001), and weight (P<.001) was more complete in the EMR, while the documentation of diagnosis (P<.001), excretions (P=.02), and pain (P=.008) was less complete in the EMR. The completeness remained unchanged for the documentation of pulse (P=.28), blood pressure (P=.47), body temperature (P=.497), and reanimation status (P=.73).

CONCLUSIONS

Implementing EMRs can influence the completeness of documentation, with a possible change in both increased and decreased completeness. However, the mechanisms that determine those changes are often neglected. There are mechanisms that might facilitate an improved completeness of documentation and could decrease or increase the staff's burden caused by documentation tasks. Research is needed to take advantage of these mechanisms and use them for mutual profit in the interests of all stakeholders.

TRIAL REGISTRATION

German Clinical Trials Register DRKS00023343; https://drks.de/search/de/trial/DRKS00023343.

Investigating the role of Cybersecurity's perceived threats in the adoption of health information systems

Information technology is one of the most rapidly growing technologies globally. Over the last decade, its usage in healthcare has been remarkable. Over the last decade, its usage in healthcare has been remarkable. The study examines the impact of various factors as barriers to adopting the information system in healthcare. These factors are categorized into three major types: external attacks, which include phishing attacks and ransomware; employee factors, including lack of skills and the issue of information misuse; and technological factors, including complexity and vulnerability. The findings show that external attacks and technological factors are the main barriers to adopting information systems, while employee factors have no significant impact on the adoption of information systems in the healthcare industry of Pakistan. The study provides implications for healthcare policy makers, professionals and organziations regarding the successful adoption of health information system.

From digital health to learning health systems: four approaches to using data for digital health design

Digital health technologies, powered by digital data, provide an opportunity to improve the efficacy and efficiency of health systems at large. However, little is known about different approaches to the use of data for digital health design, or about their possible relations to system-level dynamics. In this contribution, we identify four existing approaches to the use of data for digital health design, namely the silent, the overt, the data-enabled, and the convergent. After characterising the approaches, we provide real-life examples of each. Furthermore, we compare the approaches in terms of selected desirable characteristics of the design process, highlighting relative advantages and disadvantages. Finally, we reflect on the system-level relevance of the differentiation between the approaches and point towards future research directions. Overall, the contribution provides researchers and practitioners with a broad conceptual framework to examine data-related challenges and opportunities in digital health design.

Opportunities for healthcare digitalization in Europe: Comparative analysis of inequalities in access to medical services

Digitalization of healthcare systems is a great opportunity to address inequalities in access to healthcare in the European Union. There is an urgent need to build on what we learned from the COVID-19 pandemic, where digital health technologies were integrated swiftly to limit challenges in healthcare delivery. We created a database for the 27 European Union countries from the European Health Interview Survey (EHIS), the Digital Economy and Society Index (DESI), and other Eurostat databases. We performed k-means cluster analysis to group EU countries along two dimensions: inequalities in access to medical services and level of digitalization. We identified five distinct clusters: two clusters with high, two clusters with moderate, and one cluster with low unmet need for healthcare. Regarding digitalization, only one cluster comprising the Nordic countries, Spain and Cyprus exhibit high digital readiness. A cluster comprising the most developed countries in Western Europe represents moderate levels of both unmet need for healthcare and digitalization. For most EU countries, there is still a need to build digital infrastructure for the healthcare industry, which in the long term may increase the number of digital solutions used by both patients and healthcare professionals. Policy makers across the EU need to consider investing in initiatives that would support digital health solutions as an effective means of healthcare provision and healthcare management.

From electronic health records to clinical management systems: how the digital transformation can support healthcare services

Healthcare systems worldwide are currently undergoing significant transformations in response to increasing costs, a shortage of healthcare professionals and the growing complexity of medical needs among the population. Value-based healthcare reimbursement systems are emerging as an attempt to incentivize patient-centricity and cost containment. From a technological perspective, the transition to digitalized services is intended to support these transformations. A Health Information System (HIS) is a technological solution designed to govern the data flow generated and consumed by healthcare professionals and administrative staff during the delivery of healthcare services. However, the exponential growth of digital capabilities and applied advanced analytics has expanded their traditional functionalities and brought the promise of automating administrative procedures and simple repetitive tasks, while enhancing the efficiency and outcomes of healthcare services by incorporating decision support tools for clinical management. The future of HIS is headed towards modular architectures that can facilitate implementation and adaptation to different environments and systems, as well as the integration of various tools, such as artificial intelligence (AI) models, in a seamless way. As an example, we present the experience and future developments of the European Clinical Database (EuCliD®). EuCliD is a multilingual HIS used by 20 000 nurses and physicians on a daily basis to manage 105 000 patients treated in 1100 clinics in 43 different countries. EuCliD encompasses patients' follow-up, automatic reporting and mobile applications while enabling efficient management of clinical processes. It is also designed to incorporate multiagent systems to automate repetitive tasks, AI modules and advanced dynamic dashboards.

Bibliometric Analysis of Context, Trends, and Contents of Digital Health Technology Used in Dental Health

Digital tools and apps are revolutionizing healthcare and provide creative answers to urgent problems. Through teamwork and the incorporation of digital technologies, dentistry has experienced a remarkable revolution. A large body of scholarly research backs up this trend. The context, trends, and content of digital health technology in oral and dental health are examined in our bibliometric analysis. Using targeted keywords and synonyms, an organized searching technique was used in the Scopus database, yielding 1942 articles that were extracted into a CSV file. To acquire insights into the content, trends, and context, visualization using VOSviewer 1.6.18 and a variety of analyses-including coauthorship, citation, cooccurrence of author keywords, bibliographic coupling, and cocitation-were executed. The analysis revealed that the USA and the UK contributed to a significant quantity of the literature, with newer contributions coming from nations like India. Cone Beam Computed Tomography, Dental Caries, and Artificial Intelligence were prominent keywords. It is important to note that BMC Oral Health was associated with a sizable number of the papers. This bibliometric analysis provides insightful information about the context, content, and trends of digital health in the field of oral and dental health. By implementing the right technology, policymakers can use this information to increase oral health, encourage dental literacy, and improve access to dental treatment. It is vital to take into account the wide variety of technologies and their classifications based on dental services and contextual variables.

Impact of a mobile-app assisted self-management educational intervention on the scores of asthma control test (ACT) questionnaire among young asthmatic patients

BACKGROUND

Mobile phone apps have reached almost all sectors of everyday modern human life, including health promotion and patient education intervention. Regarding asthma self-management programs, apps are considered to be a potential learning medium for patients with asthma, especially young patients, due to their effectiveness in improving patients' knowledge and, consequently, the level of asthma control.

OBJECTIVE

The aim of this study was to evaluate the effectiveness of the AsmaDroid® mobile app, as compared with conventional educational methods, as a self-management educational intervention tool for improving asthma control among young patients.

METHODS

To determine the app's effectiveness, the study involved 140 participants from various backgrounds and applied a quasi-experimental method using a two-group pretest and posttest with a control group design. Specifically, the treatment groups received the AsmaDroid® app as a learning medium, while the control groups used conventional methods (e.g., books, posters, videos, and social media). Before and after a 4-week intervention period, all the participants of both groups were asked to complete the Asthma Control Test (ACT) questionnaire.

RESULTS

The results of this study revealed a significant difference of +1.4 (p < 0.0001) in the pretest and posttest scores of the ACT questionnaire from the intervention group, while no difference was found in the control group.

CONCLUSIONS

Therefore, this study concluded that mobile app-assisted self-management educational intervention significantly improved the scores of the ACT questionnaire among young asthmatic patients.

Challenges and opportunities of big data analytics in healthcare

Data science is an interdisciplinary discipline that employs big data, machine learning algorithms, data mining techniques, and scientific methodologies to extract insights and information from massive amounts of structured and unstructured data. The healthcare industry constantly creates large, important databases on patient demographics, treatment plans, results of medical exams, insurance coverage, and more. The data that IoT (Internet of Things) devices collect is of interest to data scientists. Data science can help with the healthcare industry's massive amounts of disparate, structured, and unstructured data by processing, managing, analyzing, and integrating it. To get reliable findings from this data, proper management and analysis are essential. This article provides a comprehensive study and discussion of process data analysis as it pertains to healthcare applications. The article discusses the advantages and disadvantages of using big data analytics (BDA) in the medical industry. The insights offered by BDA, which can also aid in making strategic decisions, can assist the healthcare system.

Integrating Digital Innovation Mechanisms in Digital Infrastructures: The Case of Digital Remote Care

Digital innovation (DIN) is crucial for managing the growth of resource use in the hospital sector and for providing citizens with services aligned with the requirements of the modern world. DIN includes the co-creation of novel services, such as digital remote care (DRC) solutions. The healthcare sector, with a plethora of applications, is an example of a large digital infrastructure. Our study aims to explore how DRC initiatives can be integrated in large-scale digital infrastructures. Our in-depth case study, which explores 72 different DRC trajectories at 9 hospital health trusts in Norway, reveals the dynamic interplay among 3 key mechanisms - idealistic entrepreneurship, anchoring and remote infrastructure. Our contribution to the DIN literature is a model that shows the interplay among these key mechanisms, which increases the innovation pace, improves the innovations' scalability and provides a robust organisation that constantly implements innovations. As a contributions to DRC practice, lessons learned to speed up the innovation pace are offered: (1) Create a DRC organisational structure. (2) Ensure financial predictability. (3) secure anchoring upward in the governance structure. (4) Make the remote infrastructure appropriate for integration with the current digital infrastructure. (5) Advocate the success across the organisation to spur others to innovate.

Artificial intelligence in medicine: A comprehensive survey of medical doctor's perspectives in Portugal

Artificial Intelligence (AI) is increasingly influential across various sectors, including healthcare, with the potential to revolutionize clinical practice. However, risks associated with AI adoption in medicine have also been identified. Despite the general understanding that AI will impact healthcare, studies that assess the perceptions of medical doctors about AI use in medicine are still scarce. We set out to survey the medical doctors licensed to practice medicine in Portugal about the impact, advantages, and disadvantages of AI adoption in clinical practice. We designed an observational, descriptive, cross-sectional study with a quantitative approach and developed an online survey which addressed the following aspects: impact on healthcare quality of the extraction and processing of health data via AI; delegation of clinical procedures on AI tools; perception of the impact of AI in clinical practice; perceived advantages of using AI in clinical practice; perceived disadvantages of using AI in clinical practice and predisposition to adopt AI in professional activity. Our sample was also subject to demographic, professional and digital use and proficiency characterization. We obtained 1013 valid, fully answered questionnaires (sample representativeness of 99%, confidence level (p< 0.01), for the total universe of medical doctors licensed to practice in Portugal). Our results reveal that, in general terms, the medical community surveyed is optimistic about AI use in medicine and are predisposed to adopt it while still aware of some disadvantages and challenges to AI use in healthcare. Most medical doctors surveyed are also convinced that AI should be part of medical formation. These findings contribute to facilitating the professional integration of AI in medical practice in Portugal, aiding the seamless integration of AI into clinical workflows by leveraging its perceived strengths according to healthcare professionals. This study identifies challenges such as gaps in medical curricula, which hinder the adoption of AI applications due to inadequate digital health training. Due to high professional integration in the healthcare sector, particularly within the European Union, our results are also relevant for other jurisdictions and across diverse healthcare systems.

Development of an Evidence-Based Conceptual Model of the Health Care Sector Under Digital Transformation: Integrative Review

BACKGROUND

Digital transformation is currently one of the most influential developments. It is fundamentally changing consumers' expectations and behaviors, challenging traditional firms, and disrupting numerous markets. Recent discussions in the health care sector tend to assess the influence of technological implications but neglect other factors needed for a holistic view on the digital transformation. This calls for a reevaluation of the current state of digital transformation in health care. Consequently, there is a need for a holistic view on the complex interdependencies of digital transformation in the health care sector.

OBJECTIVE

This study aimed to examine the effects of digital transformation on the health care sector. This is accomplished by providing a conceptual model of the health care sector under digital transformation.

METHODS

First, the most essential stakeholders in the health care sector were identified by a scoping review and grounded theory approach. Second, the effects on these stakeholders were assessed. PubMed, Web of Science, and Dimensions were searched for relevant studies. On the basis of an integrative review and grounded theory methodology, the relevant academic literature was systematized and quantitatively and qualitatively analyzed to evaluate the impact on the value creation of, and the relationships among, the stakeholders. Third, the findings were synthesized into a conceptual model of the health care sector under digital transformation.

RESULTS

A total of 2505 records were identified from the database search; of these, 140 (5.59%) were included and analyzed. The results revealed that providers of medical treatments, patients, governing institutions, and payers are the most essential stakeholders in the health care sector. As for the individual stakeholders, patients are experiencing a technology-enabled growth of influence in the sector. Providers are becoming increasingly dependent on intermediaries for essential parts of the value creation and patient interaction. Payers are expected to try to increase their influence on intermediaries to exploit the enormous amounts of data while seeing their business models be challenged by emerging technologies. Governing institutions regulating the health care sector are increasingly facing challenges from new entrants in the sector. Intermediaries increasingly interconnect all these stakeholders, which in turn drives new ways of value creation. These collaborative efforts have led to the establishment of a virtually integrated health care ecosystem.

CONCLUSIONS

The conceptual model provides a novel and evidence-based perspective on the interrelations among actors in the health care sector, indicating that individual stakeholders need to recognize their role in the system. The model can be the basis of further evaluations of strategic actions of actors and their effects on other actors or the health care ecosystem itself.

Digital Transformation of Cancer Care in the Era of Big Data, Artificial Intelligence and Data-Driven Interventions: Navigating the Field

OBJECTIVES

To navigate the field of digital cancer care and define and discuss key aspects and applications of big data analytics, artificial intelligence (AI), and data-driven interventions.

DATA SOURCES

Peer-reviewed scientific publications and expert opinion.

CONCLUSION

The digital transformation of cancer care, enabled by big data analytics, AI, and data-driven interventions, presents a significant opportunity to revolutionize the field. An increased understanding of the lifecycle and ethics of data-driven interventions will enhance development of innovative and applicable products to advance digital cancer care services.

IMPLICATIONS FOR NURSING PRACTICE

As digital technologies become integrated into cancer care, nurse practitioners and scientists will be required to increase their knowledge and skills to effectively use these tools to the patient's benefit. An enhanced understanding of the core concepts of AI and big data, confident use of digital health platforms, and ability to interpret the outputs of data-driven interventions are key competencies. Nurses in oncology will play a crucial role in patient education around big data and AI, with a focus on addressing any arising questions, concerns, or misconceptions to foster trust in these technologies. Successful integration of data-driven innovations into oncology nursing practice will empower practitioners to deliver more personalized, effective, and evidence-based care.

Digital transformation towards the clinical laboratory of the future. Perspectives for the next decade

The transformation of clinical laboratories towards digitalization requires processes that improve digital maturity. This requires establishing connectivity, end-to-end workflow, and advanced analytical technologies and techniques. Digital technologies have the key role here, directing laboratory personnel and scientists to move their focus from routine to more complex and meaningful work. This requires their empowerment in working with new instruments and software. Strategies leading clinical laboratories through this transformation are not without challenges, but different models are being developed to overcome them. The essential is the role of interoperability.

A Hybrid Stacked CNN and Residual Feedback GMDH-LSTM Deep Learning Model for Stroke Prediction Applied on Mobile AI Smart Hospital Platform

Artificial intelligence (AI) techniques for intelligent mobile computing in healthcare has opened up new opportunities in healthcare systems. Combining AI techniques with the existing Internet of Medical Things (IoMT) will enhance the quality of care that patients receive at home remotely and the successful establishment of smart living environments. Building a real AI for mobile AI in an integrated smart hospital environment is a challenging problem due to the complexities of receiving IoT medical sensors data, data analysis, and deep learning algorithm complexity programming for mobile AI engine implementation AI-based cloud computing complexities, especially when we tackle real-time environments of AI technologies. In this paper, we propose a new mobile AI smart hospital platform architecture for stroke prediction and emergencies. In addition, this research is focused on developing and testing different modules of integrated AI software based on XAI architecture, this is for the mobile health app as an independent expert system or as connected with a simulated environment of an AI-cloud-based solution. The novelty is in the integrated architecture and results obtained in our previous works and this extended research on hybrid GMDH and LSTM deep learning models for the proposed artificial intelligence and IoMT engine for mobile health edge computing technology. Its main goal is to predict heart-stroke disease. Current research is still missing a mobile AI system for heart/brain stroke prediction during patient emergency cases. This research work implements AI algorithms for stroke prediction and diagnosis. The hybrid AI in connected health is based on a stacked CNN and group handling method (GMDH) predictive analytics model, enhanced with an LSTM deep learning module for biomedical signals prediction. The techniques developed depend on the dataset of electromyography (EMG) signals, which provides a significant source of information for the identification of normal and abnormal motions in a stroke scenario. The resulting artificial intelligence mHealth app is an innovation beyond the state of the art and the proposed techniques achieve high accuracy as stacked CNN reaches almost 98% for stroke diagnosis. The GMDH neural network proves to be a good technique for monitoring the EMG signal of the same patient case with an average accuracy of 98.60% to an average of 96.68% of the signal prediction. Moreover, extending the GMDH model and a hybrid LSTM with dense layers deep learning model has improved significantly the prediction results that reach an average of 99%.

Exploring the views of Singapore junior doctors on medical curricula for the digital age: A case study

This study aims to explore the perspectives of medical trainees on the impartation of digital competencies in Singapore's medical school curricula. It also considers how the medical school experience can be strengthened in order to bridge potential gaps in the integration of these competencies in the local curricula. Findings were drawn from individual interviews with 44 junior doctors from Singapore's public healthcare institutions including hospitals and national specialty centers. House officers and residents from different medical and surgical specialties were recruited using purposive sampling. Data was interpreted using qualitative thematic analysis. The doctors were in their first to tenth year of post-graduate training. Thirty of them graduated from the three local medical schools whereas 14 others were trained overseas. Overall, they felt insufficiently prepared to utilize digital technologies in view of their limited exposure to such technologies in medical school. Six key reasons were identified: lack of flexibility and dynamism within the curriculum, dated learning style, limited access to electronic health records, gradual uptake of digital technologies in the healthcare sector, lack of an ecosystem that promotes innovation, and lack of guidance from qualified and available mentors. Equipping medical students with skills relevant to the digital age would benefit from a concerted effort from multiple stakeholders: medical schools, medical educators and innovators, as well as the government. This study bears important implications for countries that seek to bridge the 'transformation gap' brought about by the digital age, which is defined as the sharp divergence between innovations that healthcare providers recognize as important but for which they feel insufficiently prepared.

Clinical informatics training in medical school education curricula: a scoping review

OBJECTIVES

This scoping review evaluates the existing literature on clinical informatics (CI) training in medical schools. It aims to determine the essential components of a CI curriculum in medical schools, identify methods to evaluate the effectiveness of a CI-focused education, and understand its delivery modes.

MATERIALS AND METHODS

This review was informed by the methodological guidance of the Joanna Briggs Institute. Three electronic databases including PubMed, Scopus, and Web of Science were searched for articles discussing CI between January 2010 and December 2021.

RESULTS

Fifty-nine out of 3055 articles were included in our final analysis. Components of CI education include its utilization in clinical practice, ethical implications, key CI-related concepts, and digital health. Evaluation of educational effectiveness entails external evaluation by organizations external to the teaching institute, and internal evaluation from within the teaching institute. Finally, modes of delivery include various pedagogical strategies and teaching CI using a multidisciplinary approach.

DISCUSSION

Given the broad discussion on the required competencies, we propose 4 recommendations in CI delivery. These include situating CI curriculum within specific contexts, developing evidence-based guidelines for a robust CI education, developing validated assessment techniques to evaluate curriculum effectiveness, and equipping educators with relevant CI training.

CONCLUSION

The literature reveals that CI training in the core curricula will complement if not enhance clinical skills, reiterating the need to equip students with relevant CI competencies. Furthermore, future research needs to comprehensively address current gaps in CI training in different contexts, evaluation methodologies, and delivery modes to facilitate structured training.

Digital Transformation in Healthcare: Technology Acceptance and Its Applications

Technological innovation has become an integral aspect of our daily life, such as wearable and information technology, virtual reality and the Internet of Things which have contributed to transforming healthcare business and operations. Patients will now have a broader range and more mindful healthcare choices and experience a new era of healthcare with a patient-centric culture. Digital transformation determines personal and institutional health care. This paper aims to analyse the changes taking place in the field of healthcare due to digital transformation. For this purpose, a systematic bibliographic review is performed, utilising Scopus, Science Direct and PubMed databases from 2008 to 2021. Our methodology is based on the approach by Wester and Watson, which classify the related articles based on a concept-centric method and an ad hoc classification system which identify the categories used to describe areas of literature. The search was made during August 2022 and identified 5847 papers, of which 321 fulfilled the inclusion criteria for further process. Finally, by removing and adding additional studies, we ended with 287 articles grouped into five themes: information technology in health, the educational impact of e-health, the acceptance of e-health, telemedicine and security issues.

Digital transformation of healthcare during the COVID-19 pandemic: Patients’ teleconsultation acceptance and trusting beliefs

The COVID-19 pandemic boosted the digital transformation of many services, including healthcare, and access to medical care using teleconsultation has increased rapidly. Thus, a growing number of online platforms have been developed to accommodate patients’ needs. This paper examines the factors that predict the intention to use medical teleconsultation by extending the unified theory of acceptance and use of technology (UTAUT2) with the three dimensions of trusting beliefs and self-efficacy. A survey was administered to patients who had used a teleconsultation platform during the pandemic period. As one of the largest studies to date, a sample of 1233 respondents was collected and analyzed using a partial least squares approach, often mobilized in the information systems (IS) domain. Furthermore, a deep analysis using all recommended metrics was performed. The results highlight the significance of trusting beliefs, and self-efficacy in the adoption of digital healthcare services. These findings contribute to both theory and practice in COVID-19 research.

Preliminary investigation of the diagnosis and gene function of deep learning PTPN11 gene mutation syndrome deafness

Syndromic deafness caused by PTPN11 gene mutation has gradually come into the public's view. In the past, many people did not understand its application mechanism and role and only focused on non-syndromic deafness, so the research on syndromic deafness is not in-depth and there is a large degree of lack of research in this area. In order to let the public know more about the diagnosis and gene function of deafness caused by PTPN11 gene mutation syndrome, this paper used deep learning technology to study the diagnosis and gene function of deafness caused by syndrome with the concept of intelligent medical treatment, and finally drew a feasible conclusion. This paper provided a theoretical and practical basis for the diagnosis of deafness caused by PTPN11 gene mutation syndrome and the study of gene function. This paper made a retrospective analysis of the clinical data of 85 deaf children who visited Hunan Children's Hospital,P.R. China from January 2020 to December 2021. The conclusion were as follows: Children aged 1-6 years old had multiple syndrome deafness, while children under 1 year old and children aged 6-12 years old had relatively low probability of complex deafness; girls were not easy to have comprehensive deafness, but there was no specific basis to prove that the occurrence of comprehensive deafness was necessarily related to gender; the hearing loss of patients with Noonan Syndrome was mainly characterized by moderate and severe damage and abnormal inner ear and auditory nerve; most of the mutation genes in children were located in Exon1 and Exon3, with a total probability of 57.65%. In the course of the experiment, it was found that deep learning was effective in the diagnosis of deafness with PTPN11 gene mutation syndrome. This technology could be applied to medical diagnosis to facilitate the diagnosis and treatment of more patients with deafness with syndrome. Intelligent medical treatment was also becoming a hot topic nowadays. By using this concept to analyze and study the pathological characteristics of deafness caused by PTPN11 gene mutation syndrome, it not only promoted patients to find diseases in time, but also helped doctors to diagnose and treat such diseases, which was of great significance to patients and doctors. The study of PTPN11 gene mutation syndrome deafness was also of great significance in genetics. The analysis of its genes not only enriched the gene pool, but also provided reference for future research.

Acceptability of wearable devices for measuring mobility remotely: Observations from the Mobilise-D technical validation study

Background

This study aimed to explore the acceptability of a wearable device for remotely measuring mobility in the Mobilise-D technical validation study (TVS), and to explore the acceptability of using digital tools to monitor health.

Methods

Participants (N = 106) in the TVS wore a waist-worn device (McRoberts Dynaport MM + ) for one week. Following this, acceptability of the device was measured using two questionnaires: The Comfort Rating Scale (CRS) and a previously validated questionnaire. A subset of participants (n = 36) also completed semi-structured interviews to further determine device acceptability and to explore their opinions of the use of digital tools to monitor their health. Questionnaire results were analysed descriptively and interviews using a content analysis.

Results

The device was considered both comfortable (median CRS (IQR; min-max) = 0.0 (0.0; 0-20) on a scale from 0-20 where lower scores signify better comfort) and acceptable (5.0 (0.5; 3.0-5.0) on a scale from 1-5 where higher scores signify better acceptability). Interviews showed it was easy to use, did not interfere with daily activities, and was comfortable. The following themes emerged from participants' as being important to digital technology: altered expectations for themselves, the use of technology, trust, and communication with healthcare professionals.

Conclusions

Digital tools may bridge existing communication gaps between patients and clinicians and participants are open to this. This work indicates that waist-worn devices are supported, but further work with patient advisors should be undertaken to understand some of the key issues highlighted. This will form part of the ongoing work of the Mobilise-D consortium.

Health professionals' sentiments towards implemented information technologies in psychiatric hospitals: a text-mining analysis

BACKGROUND

Psychiatric hospitals are increasingly being digitalised. Digitalisation often requires changes at work for health professionals. A positive attitude from health professionals towards technology is crucial for a successful and sustainable digital transformation at work. Nevertheless, insufficient attention is being paid to the health professionals' sentiments towards technology.

OBJECTIVE

This study aims to identify the implemented technologies in psychiatric hospitals and to describe the health professionals' sentiments towards these implemented technologies.

METHODS

A text-mining analysis of semi-structured interviews with nurses, physicians and psychologists was conducted. The analysis comprised word frequencies and sentiment analyses. For the sentiment analyses, the SentimentWortschatz dataset was used. The sentiments ranged from -1 (strongly negative sentiment) to 1 (strongly positive sentiment).

RESULTS

In total, 20 health professionals (nurses, physicians and psychologists) participated in the study. When asked about the technologies they used, the participating health professionals mainly referred to the computer, email, phone and electronic health record. Overall, 4% of the words in the transcripts were positive or negative sentiments. Of all words that express a sentiment, 73% were positive. The discussed technologies were associated with positive and negative sentiments. However, of all sentences that described technology at the workplace, 69.4% were negative.

CONCLUSIONS

The participating health professionals mentioned a limited number of technologies at work. The sentiments towards technologies were mostly negative. The way in which technologies are implemented and the lack of health professionals' involvement seem to be reasons for the negative sentiments.

MSmonitor-plus program and video calling care (MPVC) for multidisciplinary care and self-management in multiple sclerosis: study protocol of a single-center randomized, parallel-group, open label, non-inferiority trial

BACKGROUND

We designed a new multi-modal version of the MSmonitor, called the MSmonitor-Plus and Video calling Care (MPVC), a self-management and education program with e-health interventions that combines frequent use of specific questionnaires with video calling in treating multiple sclerosis (MS) patients.

OBJECTIVE

To assess the effectiveness, cost-effectiveness and feasibility of MPVC compared to care as usual (CAU), with the goal of achieving equal or better quality of life for MS patients and their partners/informal caregivers. Our hypothesis is that by using MPVC, monitoring will become more efficient, that patients' self-efficacy, quality of life, and adherence to treatment will improve, and that they will be able to live their lives more autonomously.

METHODS

A randomized, parallel-group, open label, non-inferiority trial will be conducted to compare MPVC with CAU in MS patients and their partners/informal caregivers. A total of 208 patients will be included with follow-up measurements for 2 years (at baseline and every 3 months). One hundred four patients will be randomized to MPVC and 104 patients to CAU. Partners/informal caregivers of both groups will be asked to participate. The study will consist of three parts: 1) a clinical effectiveness study, 2) an economic evaluation, and 3) a process evaluation. The primary outcome relates to equal or improved disease-specific physical and mental quality of life of the MS patients. Secondary outcomes relate to self-efficacy, efficiency, cost-effectiveness, autonomy, satisfaction with the care provided, and quality of life of partners/informal caregivers.

DISCUSSION

The idea behind using MPVC is that MS patients will gain more insight into the individual course of the disease and get a better grip on their symptoms. This knowledge should increase their autonomy, give patients more control of their condition and enable them to better and proactively interact with health care professionals. As the consulting process becomes more efficient with the use of MPVC, MS-related problems could be detected earlier, enabling earlier multidisciplinary care, treatment or modification of the treatment. This could have a positive effect on the quality of life for both the MS patient and his/her partner/informal caregiver, reducing health and social costs.

TRIAL REGISTRATION

NCT05242731 Clinical Trials.gov. Date of registration: 16 February 2022 retrospectively registered.

A scoping review of active, participant-centred, digital adverse events following immunization (AEFI) surveillance: A Canadian immunization research network study

BACKGROUND

Post-licensure adverse events following immunization (AEFI) surveillance is conducted to monitor vaccine safety, such as identifying batch/brand issues and rare reactions, which consequently improves community confidence. The integration of technology has been proposed to improve AEFI surveillance, however, there is an absence of description regarding which digital solutions are successfully being used and their unique characteristics.

OBJECTIVES

The objectives of this scoping review were to 1) map the research landscape on digital systems used for active, participant-centred, AEFI surveillance and 2) describe their core components.

METHODS

We conducted a scoping review informed by the PRISMA Extension for Scoping Reviews (PRSIMA-ScR) guideline. OVID-Medline, Embase Classic + Embase, and Medrxiv were searched by a medical librarian from January 1, 2000 to January 28th, 2021. Two independent reviewers determined which studies met inclusion based on pre-specified eligibility criteria. Data extraction was conducted using pre-made tables with specific variables by one investigator and verified by a second.

RESULTS

Twenty-seven publications met inclusion, the majority of which came from Australia (n = 15) and Canada (n = 6). The most studied active, participant-centred, digital AEFI surveillance systems were SmartVax (n = 8) (Australia), Vaxtracker (n = 7) (Australia), and Canadian National Vaccine Safety (CANVAS) Network (Canada) (n = 6). The two most common methods of communicating with vaccinees reported were short-message-service (SMS) (n = 15) and e-mail (n = 14), with online questionnaires being the primary method of data collection (n = 20).

CONCLUSION

Active, participant-centred, digital AEFI surveillance is an area actively being researched as depicted by the literature landscape mapped by this scoping reviewWe hypothesize that the AEFI surveillance approach herein described could become a primary method of collecting self-reported subjective symptoms and reactogenicity from vaccinees, complementing existing systems. Future evaluation of identified digital solutions is necessary to bring about improvements to current vaccine surveillance systems to meet contemporary and future public health needs.

Vascular access teams: a global outlook on challenges, benefits, opportunities, and future perspectives

BACKGROUND

Specialized vascular access training for medical professionals organized into vascular access teams (VATs) was shown to improve patient outcomes, clinical efficiency, and cost savings. Professional perspectives on VAT benefits, organization, challenges, and opportunities on a global scale remain inadequately explored. Using detailed perspectives, in this study, we explored the global VAT landscape, including challenges faced, clinical and clinico-economic impacts of VATs, with emphasis on underresearched facets of VAT initiation, data dissemination, and metrics or benchmarks for VAT success.

METHODS

Semistructured in-depth interviews of 14 VAT professionals from 9 countries and 5 continents were used to elicit qualitative and quantitative information.

RESULTS

Catheter insertions (100%) and training (86%) were the most performed VAT functions. Based on a 1-7 scale evaluating observed impacts of VATs, patient satisfaction (6.5) and institutional costs (6.2) were ranked the highest. VAT co-initiatives, advanced technology utilization (6.6), and ongoing member training (6.3) distinctly impacted VAT endeavors. Most institutions (64%) did not have routine mechanisms for recording VAT-related data; however, all participants (100%) stated the importance of sharing data to demonstrate VAT impacts. Time constraints (57%) emerged as one of the major deterrents to data collection or dissemination. The majority (64%) experienced an increased demand or workload for VAT services during the COVID-19 pandemic.

CONCLUSIONS

Despite the global variances in VATs and gaps in VAT-related data, all participants unanimously endorsed the benefits of VAT programs. Evaluating the impact of VATs, disseminating VAT-related data, and forging specialized institutional partnerships for data sharing and training are potential strategies to tackle the hurdles surrounding VAT formation and sustenance.

Deep convolutional neural network-based automated segmentation of the maxillofacial complex from cone-beam computed tomography - A validation study

OBJECTIVES

The present study investigated the accuracy, consistency, and time-efficiency of a novel deep CNN-based model for the automated maxillofacial bone segmentation from CBCT images.

METHOD

A dataset of 144 scans was acquired from two CBCT devices and randomly divided into three subsets: training set (n= 110), validation set (n= 10) and testing set (n=24). A three-dimensional (3D) U-Net (CNN) model was developed, and the achieved automated segmentation was compared with a manual approach.

RESULTS

The average time required for automated segmentation was 39.1 seconds with a 204-fold decrease in time consumption compared to manual segmentation (132.7 minutes). The model is highly accurate for identification of the bony structures of the anatomical region of interest with a dice similarity coefficient (DSC) of 92.6%. Additionally, the fully deterministic nature of the CNN model was able to provide 100% consistency without any variability. The inter-observer consistency for expert-based minor correction of the automated segmentation observed an excellent DSC of 99.7%.

CONCLUSION

The proposed CNN model provided a time-efficient, accurate, and consistent CBCT-based automated segmentation of the maxillofacial complex.

CLINICAL SIGNIFICANCE

Automated segmentation of the maxillofacial complex could act as a potent alternative to the conventional segmentation techniques for improving the efficiency of the digital workflows. This approach could deliver an accurate and ready-to-print three dimensional (3D) models that are essential to patient-specific digital treatment planning for orthodontics, maxillofacial surgery, and implant placement.

Integration of artificial intelligence and plasma steroidomics with laboratory information management systems: application to primary aldosteronism

Objectives

Mass spectrometry-based steroidomics combined with machine learning (ML) provides a potentially powerful approach in endocrine diagnostics, but is hampered by limitations in the conveyance of results and interpretations to clinicians. We address this shortcoming by integration of the two technologies with a laboratory information management systems (LIMS) model.

Methods

The approach involves integration of ML algorithm-derived models with commercially available mathematical programming software and a web-based LIMS prototype. To illustrate clinical utility, the process was applied to plasma steroidomics data from 22 patients tested for primary aldosteronism (PA).

Results

Once mass spectrometry data are uploaded into the system, automated processes enable generation of interpretations of steroid profiles from ML models. Generated reports include plasma concentrations of steroids in relation to age- and sex-specific reference intervals along with results of ML models and narrative interpretations that cover probabilities of PA. If PA is predicted, reports include probabilities of unilateral disease and mutations of KCNJ5 known to be associated with successful outcomes of adrenalectomy. Preliminary results, with no overlap in probabilities of disease among four patients with and 18 without PA and correct classification of all four patients with unilateral PA including three of four with KCNJ5 mutations, illustrate potential utility of the approach to guide diagnosis and subtyping of patients with PA.

Conclusions

The outlined process for integrating plasma steroidomics data and ML with LIMS may facilitate improved diagnostic-decision-making when based on higher-dimensional data otherwise difficult to interpret. The approach is relevant to other diagnostic applications involving ML.

Digital tools in allergy and respiratory care

Patient care in the allergy and respiratory fields is advancing rapidly, offering the possibility of the inclusion of a variety of digital tools that aim to improve outcomes of care. Impaired access to several health care facilities during the COVID-19 pandemic has considerably increased the appetite and need for the inclusion of e-health tools amongst end-users. Consequently, a multitude of different e-health tools have been launched worldwide with various registration and access options, and with a wide range of offered benefits. From the perspective of both patients and healthcare providers (HCPs), as well as from a legal and device-related perspective, several features are important for the acceptance, effectiveness,and long-term use of e-health tools. Patients and physicians have different needs and expectations of how digital tools might be of help in the care pathway. There is a need for standardization by defining quality assurance criteria. Therefore, the Upper Airway Diseases Committee of the World Allergy Organization (WAO) has taken the initiative to define and propose criteria for quality, appeal, and applicability of e-health tools in the allergy and respiratory care fields from a patient, clinician, and academic perspective with the ultimate aim to improve patient health and outcomes of care.

Systematic Process to Determine Clinical Harm From Delayed Communication Between Primary and Secondary Healthcare

Introduction: Timely written communication between primary and secondary healthcare providers is paramount to ensure effective patient care. In 2020, there was a technical issue between two interconnected electronic patient record (EPR) systems that were used by a large hospital trust and the local community partners. The trust provides healthcare to a diverse multiethnic inner-city population across three inner-city London boroughs from two extremely busy acute district general hospitals. Consequently, over a four-month period, 58,521 outpatient clinic letters were not electronically sent to general practitioners following clinic appointments. This issue affected 27.9% of the total number of outpatient clinic letters sent during this period and 42,251 individual patients. This paper describes the structure, methodological process, and outcomes of the review process established to examine the harm that may have resulted due to the delay. Methodology: Senior clinicians examined the letters following training to ensure a standardized consistent approach to the evaluation. They searched whether any actions that had been requested to be undertaken by primary care had been completed in a timely fashion. Thereafter, they indicated whether in their opinion there was any potential “predefined” harm. All letters that were identified as “potential” harm were reexamined by the leads to determine that the harm or inaction was truly accurate. The trust then contacted the patient to apologize and urgently expedite the outstanding action. Patients were not contacted in those situations where no actions were required or already undertaken (99.5%), as this could potentially cause unnecessary anxiety. If an actual harm was detected, it would then be declared as a serious incident and investigated appropriately, including a duty of candor (if the harm was moderate or severe). A “clinical harm review panel” convened regularly to monitor the quality of this process and thereby provide quality assurance. Governance of the process of review was assured by this panel being overseen by a regularly convened regionwide group. Results: 58,521 letters were evaluated over three months by 36 evaluators. No serious untoward incidents were identified, but 1,323 inactions were identified from these letters. These were then all cross-checked with information from EPR. Consequently, only 327 were deemed to be inactions that required further contact with the patient (of the 58,521 letters evaluated, this constituted 0.56%). Certain departments made more requests compared to others (e.g., cardiology, dermatology, and gastroenterology). Most surgical specialties did not generate any actions. Reassuringly, no letters related to cancer had any outstanding actions. The frequency of actions not enacted due to the delay was as follows: did not attend (n=3), medication change (n=173), blood tests (n=73), other investigations (n=31), onward referral (n=47). An audit trail of all outstanding actions has been maintained to allow monitoring in case there was any query in future. We also reviewed those patients who had died to investigate whether the death could be in any way linked to nonreceipt of the letter. There were 367 deaths, and an independent review revealed that no deaths were linked to the nonreceipt of the letter. Ten percent of the deaths (n=36) had a full structured clinical review to further validate the process. Discussion: This paper has described a systematic process of analyzing a large cohort of electronic correspondence to determine any potential harm to patients that may occur due to the delay in communication between primary and secondary care. The structured methodology, well supported by relevant community stakeholders and closely monitored by the clinical harm review panel, could serve as a template to other organizations that may face similar incidents in future.

Application of industry 4.0 technology in containing Covid-19 spread and its challenges

The pandemic of Covid-19, an epidemic outbreak created many challenges and increased the demand for medical equipments, medicines, and related accessories and most of them had to be imported from abroad. The advanced information technology (Industry 4.0) was considered imperative to track and monitor the spread of the SARS-2 Virus that is Covid-19. A detailed review of literature is done to understand the challenges and the remedial action taken so far during the Covid-19 epidemic outbreak had been gone through using appropriate search engines and databases like Google-search, Science Direct, Scopus, Research Gate, and relevant blogs. The case reports were also considered in this study. We have found ten significant challenges (barriers) and identified several useful technology of industry 4.0 to control and manage the Covid-19 pandemic. This research paper is an attempt to examine and discuss the application of 4.0 technologies in containing the pandemic outbreak. Ten challenges were identified and those could be overcome by promptly applying appropriate technologies of industry 4.0 to control the spread of virus. These technologies help to educate and communicate the public and make them aware of the hazardous attack of Covid-19 virus when properly used.

Research on the Relationship between Digital Transformation and Performance of SMEs

Objective: Through an empirical analysis of the performance of SMEs undergoing digital transformation, this study attempts to identify the influencing factors that determine their sustainable development to provide reference for academic researchers and industrial decision makers. Method: This study first uses an interview method to investigate the impact of SMEs’ three main resources on digital transformation: digital technology, employee digital skills, and digital transformation strategy. Second, we assess the impact of digital transformation on financial performance. Using the structural equation model, 335 valid questionnaires were recovered through the questionnaire method, and the key factors were identified using SPSS and SPSSAU tools. Results: In the Chinese context, digital transformation affects SME performance, and the three resources mentioned above are positively correlated with SMEs’ digital transformation. Digital transformation is positively correlated with performance, and it is the mediator of the impact of digital transformation strategies on performance. Conclusion: For SMEs, focusing on investing in digital technologies, employee digital skills, and digital transformation strategies are three key factors that are beneficial for digital transformation, thus helping to improve performance and maintain their sustainable development.

Challenges and Drawbacks of the EU Medical System Generated by the COVID-19 Pandemic in the Field of Health Systems' Digitalization

The COVID-19 pandemic and the digitalization of medical services present significant challenges for the medical sector of the European Union, with profound implications for health systems and the provision of high-performance public health services. The sustainability and resilience of health systems are based on the introduction of information and communication technology in health processes and services, eliminating the vulnerability that can have significant consequences for health, social cohesion, and economic progress. This research aims to assess the impact of digitalization on several dimensions of health, introducing specific implications of the COVID-19 pandemic. The research methodology consists of three procedures: cluster analysis performed through vector quantization, agglomerative clustering, and an analytical approach consisting of data mapping. The main results highlight the importance of effective national responses and provide recommendations, various priorities, and objectives to strengthen health systems at the European level. Finally, the results reveal the need to reduce the gaps between the EU member states and a new approach to policy, governance, investment, health spending, and the performing provision of digital services.

Medical-Blocks: A Platform for Exploration, Management, Analysis, and Sharing of Data in Biomedical Research

BACKGROUND

Biomedical research requires healthcare institutions to provide sensitive clinical data to leverage data science and artificial intelligence technologies. However, providing healthcare data to researchers simple and secure, proves to be challenging for healthcare institutions.

OBJECTIVE

We describe and introduce Medical-Blocks, a platform for data exploration, data management, data analysis, and data sharing in biomedical research.

METHODS

The specification requirements for Medical-Blocks included: i) Connection to data sources of healthcare institutions with an interface for data exploration, ii) management of data in an internal file storage system, iii) data analysis through visualization and classification of data, and iv) data sharing via a file hosting service for collaboration. Medical-Blocks should be simple to use via a web-based user interface and extensible with new functionalities by a modular design via microservices ("blocks"). The scalability of the platform should be ensured by containerization. Security and legal regulations were considered during the development.

RESULTS

Medical-Blocks is a web application that runs in the cloud or as a local instance at a healthcare institution. Local instances of Medical-Blocks access data sources such as electronic health records and picture archiving and communications system (PACS) at healthcare institutions. Researchers and clinicians can explore, manage, and analyze the available data through Medical-Blocks. The data analysis involves classification of data for metadata extraction and the formation of cohorts. In collaborations, metadata (e.g., number of patients per cohort) and/or the data itself can be shared through Medical-Blocks locally or via a cloud instance to other researchers and clinicians.

CONCLUSIONS

Medical-Blocks facilitates biomedical research by providing a centralized platform to interact with medical data in collaborative research projects. The access to and management of medical data is simplified. Data can be swiftly analyzed to form cohorts for research and be shared among researchers. The modularity of Medical-Blocks makes the platform feasible for biomedical research where heterogenous medical data is needed.

CLINICALTRIAL

System 2 Diagnostic Process for the Next Generation of Physicians: "Inside" and "Outside" Brain-The Interplay between Human and Machine

Improving diagnosis has been one of the most critical issues in medicine for the last two decades. In the context of the rise of digital health and its augmentation and human diagnostic thinking, it has become necessary to integrate the concept of digital diagnosis into dual-process theory (DPT), which is the fundamental axis of the diagnostic thinking process physicians. Particularly, since the clinical decision support system (CDSS) corresponds to analytical thinking (system 2) in DPT, it is necessary to redefine system 2 to include the CDSS. However, to the best of my knowledge there has been no concrete conceptual model based on this need. The innovation and novelty of this paper are that it redefines system 2 to include new concepts and shows the relationship among the breakdown of system 2. In this definition, system 2 is divided into “inside” and “outside” brains, where “inside” includes symptomatologic, anatomical, biomechanical–physiological, and etiological thinking approaches, and “outside” includes CDSS. Moreover, this paper discusses the actual and possible future interplay between “inside” and “outside.” The author envisions that this paper will serve as a cornerstone for the future development of system 2 diagnostic thinking strategy.