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Diagnostics (Basel, Switzerland) Sep 2023The prevalence of renal cell carcinoma (RCC) is increasing due to advanced imaging techniques. Surgical resection is the standard treatment, involving complex radical... (Review)
Review
The prevalence of renal cell carcinoma (RCC) is increasing due to advanced imaging techniques. Surgical resection is the standard treatment, involving complex radical and partial nephrectomy procedures that demand extensive training and planning. Furthermore, artificial intelligence (AI) can potentially aid the training process in the field of kidney cancer. This review explores how artificial intelligence (AI) can create a framework for kidney cancer surgery to address training difficulties. Following PRISMA 2020 criteria, an exhaustive search of PubMed and SCOPUS databases was conducted without any filters or restrictions. Inclusion criteria encompassed original English articles focusing on AI's role in kidney cancer surgical training. On the other hand, all non-original articles and articles published in any language other than English were excluded. Two independent reviewers assessed the articles, with a third party settling any disagreement. Study specifics, AI tools, methodologies, endpoints, and outcomes were extracted by the same authors. The Oxford Center for Evidence-Based Medicine's evidence levels were employed to assess the studies. Out of 468 identified records, 14 eligible studies were selected. Potential AI applications in kidney cancer surgical training include analyzing surgical workflow, annotating instruments, identifying tissues, and 3D reconstruction. AI is capable of appraising surgical skills, including the identification of procedural steps and instrument tracking. While AI and augmented reality (AR) enhance training, challenges persist in real-time tracking and registration. The utilization of AI-driven 3D reconstruction proves beneficial for intraoperative guidance and preoperative preparation. Artificial intelligence (AI) shows potential for advancing surgical training by providing unbiased evaluations, personalized feedback, and enhanced learning processes. Yet challenges such as consistent metric measurement, ethical concerns, and data privacy must be addressed. The integration of AI into kidney cancer surgical training offers solutions to training difficulties and a boost to surgical education. However, to fully harness its potential, additional studies are imperative.
PubMed: 37835812
DOI: 10.3390/diagnostics13193070 -
Journal of Medical Internet Research Aug 2023Thorough data stewardship is a key enabler of comprehensive health research. Processes such as data collection, storage, access, sharing, and analytics require... (Review)
Review
BACKGROUND
Thorough data stewardship is a key enabler of comprehensive health research. Processes such as data collection, storage, access, sharing, and analytics require researchers to follow elaborate data management strategies properly and consistently. Studies have shown that findable, accessible, interoperable, and reusable (FAIR) data leads to improved data sharing in different scientific domains.
OBJECTIVE
This scoping review identifies and discusses concepts, approaches, implementation experiences, and lessons learned in FAIR initiatives in health research data.
METHODS
The Arksey and O'Malley stage-based methodological framework for scoping reviews was applied. PubMed, Web of Science, and Google Scholar were searched to access relevant publications. Articles written in English, published between 2014 and 2020, and addressing FAIR concepts or practices in the health domain were included. The 3 data sources were deduplicated using a reference management software. In total, 2 independent authors reviewed the eligibility of each article based on defined inclusion and exclusion criteria. A charting tool was used to extract information from the full-text papers. The results were reported using the PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews) guidelines.
RESULTS
A total of 2.18% (34/1561) of the screened articles were included in the final review. The authors reported FAIRification approaches, which include interpolation, inclusion of comprehensive data dictionaries, repository design, semantic interoperability, ontologies, data quality, linked data, and requirement gathering for FAIRification tools. Challenges and mitigation strategies associated with FAIRification, such as high setup costs, data politics, technical and administrative issues, privacy concerns, and difficulties encountered in sharing health data despite its sensitive nature were also reported. We found various workflows, tools, and infrastructures designed by different groups worldwide to facilitate the FAIRification of health research data. We also uncovered a wide range of problems and questions that researchers are trying to address by using the different workflows, tools, and infrastructures. Although the concept of FAIR data stewardship in the health research domain is relatively new, almost all continents have been reached by at least one network trying to achieve health data FAIRness. Documented outcomes of FAIRification efforts include peer-reviewed publications, improved data sharing, facilitated data reuse, return on investment, and new treatments. Successful FAIRification of data has informed the management and prognosis of various diseases such as cancer, cardiovascular diseases, and neurological diseases. Efforts to FAIRify data on a wider variety of diseases have been ongoing since the COVID-19 pandemic.
CONCLUSIONS
This work summarises projects, tools, and workflows for the FAIRification of health research data. The comprehensive review shows that implementing the FAIR concept in health data stewardship carries the promise of improved research data management and transparency in the era of big data and open research publishing.
INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID)
RR2-10.2196/22505.
Topics: Humans; COVID-19; Pandemics; Big Data; Cardiovascular Diseases; Data Accuracy
PubMed: 37639292
DOI: 10.2196/45013 -
A systematic review of biobanks in Latin America: Strengths and limitations for biomedical research.The International Journal of Biological... Jun 2024Biobanks are valuable tools for developing and applying scientific research and international cooperation through the collection of biological materials and their... (Review)
Review
Biobanks are valuable tools for developing and applying scientific research and international cooperation through the collection of biological materials and their associated data. Systematic research following the Preferred Reporting Items for Systematic Review and Meta-Analysis guidelines was conducted in late 2022 in PubMed and Scopus, and generated 17 articles to be reviewed in depth and critically assessed using the Critical Appraisal Skills Programme Checklist due to the limited available data; 12 relevant health organizations and government websites outside of peer-reviewed journals were also included. Our research identified 44 biobanks in Latin America. In general, there is a lack of regulation and legislation guaranteeing the stored materials' quality and institutional collaboration. We believe a consensus needs to be reached regarding the terminology and definitions used for biobanks. The design for informed consent should also be agreed upon to ensure the privacy of the data shared among institutions. In conclusion, in Latin America, there is a clear need for government support in creating specific procedures for biobanks and providing further support for existing biobanks.
Topics: Latin America; Humans; Biological Specimen Banks; Biomedical Research
PubMed: 38613331
DOI: 10.1177/03936155241239672 -
Journal of Medical Internet Research May 2024In recent years, there has been an upwelling of artificial intelligence (AI) studies in the health care literature. During this period, there has been an increasing... (Review)
Review
BACKGROUND
In recent years, there has been an upwelling of artificial intelligence (AI) studies in the health care literature. During this period, there has been an increasing number of proposed standards to evaluate the quality of health care AI studies.
OBJECTIVE
This rapid umbrella review examines the use of AI quality standards in a sample of health care AI systematic review articles published over a 36-month period.
METHODS
We used a modified version of the Joanna Briggs Institute umbrella review method. Our rapid approach was informed by the practical guide by Tricco and colleagues for conducting rapid reviews. Our search was focused on the MEDLINE database supplemented with Google Scholar. The inclusion criteria were English-language systematic reviews regardless of review type, with mention of AI and health in the abstract, published during a 36-month period. For the synthesis, we summarized the AI quality standards used and issues noted in these reviews drawing on a set of published health care AI standards, harmonized the terms used, and offered guidance to improve the quality of future health care AI studies.
RESULTS
We selected 33 review articles published between 2020 and 2022 in our synthesis. The reviews covered a wide range of objectives, topics, settings, designs, and results. Over 60 AI approaches across different domains were identified with varying levels of detail spanning different AI life cycle stages, making comparisons difficult. Health care AI quality standards were applied in only 39% (13/33) of the reviews and in 14% (25/178) of the original studies from the reviews examined, mostly to appraise their methodological or reporting quality. Only a handful mentioned the transparency, explainability, trustworthiness, ethics, and privacy aspects. A total of 23 AI quality standard-related issues were identified in the reviews. There was a recognized need to standardize the planning, conduct, and reporting of health care AI studies and address their broader societal, ethical, and regulatory implications.
CONCLUSIONS
Despite the growing number of AI standards to assess the quality of health care AI studies, they are seldom applied in practice. With increasing desire to adopt AI in different health topics, domains, and settings, practitioners and researchers must stay abreast of and adapt to the evolving landscape of health care AI quality standards and apply these standards to improve the quality of their AI studies.
Topics: Artificial Intelligence; Humans; Delivery of Health Care; Quality of Health Care
PubMed: 38776538
DOI: 10.2196/54705 -
Frontiers in Health Services 2024The number of mHealth apps has increased rapidly during recent years. Literature suggests a number of problems and barriers to the adoption of mHealth apps, including...
INTRODUCTION
The number of mHealth apps has increased rapidly during recent years. Literature suggests a number of problems and barriers to the adoption of mHealth apps, including issues such as validity, usability, as well as data privacy and security. Continuous quality assessment and assurance systems might help to overcome these barriers. Aim of this scoping review was to collate literature on quality assessment tools and quality assurance systems for mHealth apps, compile the components of the tools, and derive overarching quality dimensions, which are potentially relevant for the continuous quality assessment of mHealth apps.
METHODS
Literature searches were performed in Medline, EMBASE and PsycInfo. Articles in English or German language were included if they contained information on development, application, or validation of generic concepts of quality assessment or quality assurance of mHealth apps. Screening and extraction were carried out by two researchers independently. Identified quality criteria and aspects were extracted and clustered into quality dimensions.
RESULTS
A total of 70 publications met inclusion criteria. Included publications contain information on five quality assurance systems and further 24 quality assessment tools for mHealth apps. Of these 29 systems/tools, 8 were developed for the assessment of mHealth apps for specific diseases, 16 for assessing mHealth apps for all fields of health and another five are not restricted to health apps. Identified quality criteria and aspects were extracted and grouped into a total of 14 quality dimensions, namely "information and transparency", "validity and (added) value", "(medical) safety", "interoperability and compatibility", "actuality", "engagement", "data privacy and data security", "usability and design", "technology", "organizational aspects", "social aspects", "legal aspects", "equity and equality", and "cost(-effectiveness)".
DISCUSSION
This scoping review provides a broad overview of existing quality assessment and assurance systems. Many of the tools included cover only a few dimensions and aspects and therefore do not allow for a comprehensive quality assessment or quality assurance. Our findings can contribute to the development of continuous quality assessment and assurance systems for mHealth apps.
SYSTEMATIC REVIEW REGISTRATION
https://www.researchprotocols.org/2022/7/e36974/, International Registered Report Identifier, IRRID (DERR1-10.2196/36974).
PubMed: 38751854
DOI: 10.3389/frhs.2024.1372871 -
Epilepsia May 2024Computer vision (CV) shows increasing promise as an efficient, low-cost tool for video seizure detection and classification. Here, we provide an overview of the... (Review)
Review
Computer vision (CV) shows increasing promise as an efficient, low-cost tool for video seizure detection and classification. Here, we provide an overview of the fundamental concepts needed to understand CV and summarize the structure and performance of various model architectures used in video seizure analysis. We conduct a systematic literature review of the PubMed, Embase, and Web of Science databases from January 1, 2000 to September 15, 2023, to identify the strengths and limitations of CV seizure analysis methods and discuss the utility of these models when applied to different clinical seizure phenotypes. Reviews, nonhuman studies, and those with insufficient or poor quality data are excluded from the review. Of the 1942 records identified, 45 meet inclusion criteria and are analyzed. We conclude that the field has shown tremendous growth over the past 2 decades, leading to several model architectures with impressive accuracy and efficiency. The rapid and scalable detection offered by CV models holds the potential to reduce sudden unexpected death in epilepsy and help alleviate resource limitations in epilepsy monitoring units. However, a lack of standardized, thorough validation measures and concerns about patient privacy remain important obstacles for widespread acceptance and adoption. Investigation into the performance of models across varied datasets from clinical and nonclinical environments is an essential area for further research.
Topics: Humans; Seizures; Electroencephalography; Video Recording
PubMed: 38426252
DOI: 10.1111/epi.17926 -
BMC Medical Ethics Dec 2023Conventional consent practices face ethical challenges in continuously evolving digital health environments due to their static, one-time nature. Dynamic consent offers... (Review)
Review
BACKGROUND
Conventional consent practices face ethical challenges in continuously evolving digital health environments due to their static, one-time nature. Dynamic consent offers a promising solution, providing adaptability and flexibility to address these ethical concerns. However, due to the immaturity of the concept and accompanying technology, dynamic consent has not yet been widely used in practice. This study aims to identify the facilitators of and barriers to adopting dynamic consent in real-world scenarios.
METHODS
This scoping review, conducted in December 2022, adhered to the PRISMA Extension for Scoping Reviews guidelines, focusing on dynamic consent within the health domain. A comprehensive search across Web of Science, PubMed, and Scopus yielded 22 selected articles based on predefined inclusion and exclusion criteria.
RESULTS
The facilitators for the adoption of dynamic consent in digital health ecosystems were the provision of multiple consent modalities, personalized alternatives, continuous communication, and the dissemination of up-to-date information. Nevertheless, several barriers, such as consent fatigue, the digital divide, complexities in system implementation, and privacy and security concerns, needed to be addressed. This study also investigated current technological advancements and suggested considerations for further research aimed at resolving the remaining challenges surrounding dynamic consent.
CONCLUSIONS
Dynamic consent emerges as an ethically advantageous method for digital health ecosystems, driven by its adaptability and support for continuous, two-way communication between data subjects and consumers. Ethical implementation in real-world settings requires the development of a robust technical framework capable of accommodating the diverse needs of stakeholders, thereby ensuring ethical integrity and data privacy in the evolving digital health landscape.
Topics: Humans; Ecosystem; Communication; Privacy; Technology; Informed Consent
PubMed: 38041034
DOI: 10.1186/s12910-023-00988-9 -
The Journal of Hospital Infection May 2024Outpatient parenteral antimicrobial therapy (OPAT) has been expanding in recent years and serves as a viable solution in reducing the shortage of hospital beds. However,... (Review)
Review
Outpatient parenteral antimicrobial therapy (OPAT) has been expanding in recent years and serves as a viable solution in reducing the shortage of hospital beds. However, the wider implementation of OPAT faces numerous challenges. This review aimed to assess implementation barriers and facilitators of OPAT services. Studies describing barriers and facilitators of the OPAT service were retrieved from PubMed, Scopus, MEDLINE, EMBASE, CINAHL, Cochrane Library, Web of Science Proceedings, International Pharmaceutical Abstracts and PsycINFO. All types of study designs published in the English language were included. Studies that did not mention any barrier or facilitator, did not differentiate OPAT and inpatient, focused on specific antimicrobials or diseases, and made no distinction between parenteral and other treatments were excluded. Qualitative analysis was performed using the 'best-fit' framework approach and the Consolidated Framework for Implementation Research (CFIR). The review was PROSPERO registered (CRD42023441083). A total of 8761 studies were screened for eligibility and 147 studies were included. Problems in patient selection, lack of awareness, poor communication and co-ordination, lack of support, lack of structured service and inappropriate prescriptions were identified. OPAT provides safe, effective and efficient treatment while maintaining patients' privacy and comfort, resulting in less daily life disruption, and reducing the risk of infection. Satisfaction and preference for OPAT were very high. Initiatives in strengthening OPAT such as antimicrobial stewardship and telemedicine are beneficial. Challenges to and facilitators of OPAT were identified among patients, health professionals, OPAT service providers and healthcare administrators. Understanding them is crucial to designing targeted initiatives for successful OPAT service implementation.
Topics: Humans; Ambulatory Care; Anti-Infective Agents; Outpatients; Infusions, Parenteral
PubMed: 38423135
DOI: 10.1016/j.jhin.2024.02.006 -
Telemedicine Journal and E-health : the... May 2024(Review)
Review
Topics: Telemedicine; Humans; India; Health Services Accessibility; Stakeholder Participation; Attitude of Health Personnel
PubMed: 38206654
DOI: 10.1089/tmj.2023.0297 -
Journal of Medical Internet Research May 2024Health care organizations worldwide are faced with an increasing number of cyberattacks and threats to their critical infrastructure. These cyberattacks cause... (Review)
Review
BACKGROUND
Health care organizations worldwide are faced with an increasing number of cyberattacks and threats to their critical infrastructure. These cyberattacks cause significant data breaches in digital health information systems, which threaten patient safety and privacy.
OBJECTIVE
From a sociotechnical perspective, this paper explores why digital health care systems are vulnerable to cyberattacks and provides sociotechnical solutions through a systematic literature review (SLR).
METHODS
An SLR using the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) was conducted by searching 6 databases (PubMed, Web of Science, ScienceDirect, Scopus, Institute of Electrical and Electronics Engineers, and Springer) and a journal (Management Information Systems Quarterly) for articles published between 2012 and 2022 and indexed using the following keywords: "(cybersecurity OR cybercrime OR ransomware) AND (healthcare) OR (cybersecurity in healthcare)." Reports, review articles, and industry white papers that focused on cybersecurity and health care challenges and solutions were included. Only articles published in English were selected for the review.
RESULTS
In total, 5 themes were identified: human error, lack of investment, complex network-connected end-point devices, old legacy systems, and technology advancement (digitalization). We also found that knowledge applications for solving vulnerabilities in health care systems between 2012 to 2022 were inconsistent.
CONCLUSIONS
This SLR provides a clear understanding of why health care systems are vulnerable to cyberattacks and proposes interventions from a new sociotechnical perspective. These solutions can serve as a guide for health care organizations in their efforts to prevent breaches and address vulnerabilities. To bridge the gap, we recommend that health care organizations, in partnership with educational institutions, develop and implement a cybersecurity curriculum for health care and intelligence information sharing through collaborations; training; awareness campaigns; and knowledge application areas such as secure design processes, phase-out of legacy systems, and improved investment. Additional studies are needed to create a sociotechnical framework that will support cybersecurity in health care systems and connect technology, people, and processes in an integrated manner.
Topics: Computer Security; Humans; Delivery of Health Care; Patient Safety
PubMed: 38820579
DOI: 10.2196/46904