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BMC Health Services Research Oct 2023With the spread of Covid-19 disease, health interventions related to the control, prevention, and treatment of this disease and other diseases were given real attention....
BACKGROUND
With the spread of Covid-19 disease, health interventions related to the control, prevention, and treatment of this disease and other diseases were given real attention. The purpose of this systematic review is to express facilitators and barriers of using mobile health (mHealth) interventions during the Covid-19 pandemic.
METHODS
In this systematic review, original studies were searched using keywords in the electronic database of PubMed until August 2022. The objectives and outcomes of these studies were extracted. Finally, to identify the facilitators and barriers of mHealth interventions, a qualitative content analysis was conducted based on the strengths, weaknesses, opportunities, and threats (SWOT) analysis method with Atlas.ti 8 software. We evaluated the studies using the Mixed Methods Appraisal Tool (MMAT).
RESULTS
In total, 1598 articles were identified and 55 articles were included in this study. Most of the studies used mobile applications to provide and receive health services during the Covid-19 pandemic (96.4%). The purpose of the applications was to help prevention (17), follow-up (15), treatment (12), and diagnosis (8). Using SWOT analysis, 13 facilitators and 18 barriers to patients' use of mHealth services were identified.
CONCLUSION
Mobile applications are very flexible technologies that can be customized for each person, patient, and population. During the Covid-19 pandemic, the applications designed due to lack of interaction, lack of time, lack of attention to privacy, and non-academic nature have not met their expectations of them.
Topics: Humans; COVID-19; Mobile Applications; Pandemics; Telemedicine
PubMed: 37898755
DOI: 10.1186/s12913-023-10171-w -
Cureus Sep 2023Blockchain technology has gained attention as a potential solution for improving data security, privacy, and interoperability in various industries, including... (Review)
Review
Blockchain technology has gained attention as a potential solution for improving data security, privacy, and interoperability in various industries, including healthcare. In the field of dentistry, the implementation of blockchain holds promise for transforming dental practice and management. However, a comprehensive evaluation of the existing literature regarding the implementation of blockchain technology in dental practice is lacking. This systematic review aimed to assess the current evidence on the implementation of blockchain technology in dental practice and management. A systematic literature search was conducted using major databases to identify relevant studies. The search strategy included keywords related to blockchain technology and dentistry. The investigation was performed as per the PRISMA guidelines. Studies reporting on the implementation, adoption, and outcomes of blockchain technology in dental practice and management were included. Quality assessment and data extraction were performed following predefined criteria. The initial search yielded a multitude of articles, and after applying the inclusion and exclusion criteria, six studies were included in the systematic review. The studies explored various aspects of blockchain technology implementation in dental practice, including data security, interoperability, supply chain management, and patient consent management. Furthermore, the use of blockchain-based systems showed potential benefits in enhancing supply chain management efficiency and patient consent authentication. This systematic review provided insights into the current state of blockchain technology implementation in dental practice and management. The findings suggested that blockchain technology has the potential to enhance data security, privacy, and interoperability in dental practices. However, further research and real-world implementation studies are needed to fully understand the impact of blockchain technology on dental practice and to address the existing challenges.
PubMed: 37868487
DOI: 10.7759/cureus.45512 -
Journal of Medical Internet Research Oct 2023Frailty syndrome (FS) is one of the most common noncommunicable diseases, which is associated with lower physical and mental capacities in older adults. FS diagnosis is... (Review)
Review
BACKGROUND
Frailty syndrome (FS) is one of the most common noncommunicable diseases, which is associated with lower physical and mental capacities in older adults. FS diagnosis is mostly focused on biological variables; however, it is likely that this diagnosis could fail owing to the high biological variability in this syndrome. Therefore, artificial intelligence (AI) could be a potential strategy to identify and diagnose this complex and multifactorial geriatric syndrome.
OBJECTIVE
The objective of this scoping review was to analyze the existing scientific evidence on the use of AI for the identification and diagnosis of FS in older adults, as well as to identify which model provides enhanced accuracy, sensitivity, specificity, and area under the curve (AUC).
METHODS
A search was conducted using PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews) guidelines on various databases: PubMed, Web of Science, Scopus, and Google Scholar. The search strategy followed Population/Problem, Intervention, Comparison, and Outcome (PICO) criteria with the population being older adults; intervention being AI; comparison being compared or not to other diagnostic methods; and outcome being FS with reported sensitivity, specificity, accuracy, or AUC values. The results were synthesized through information extraction and are presented in tables.
RESULTS
We identified 26 studies that met the inclusion criteria, 6 of which had a data set over 2000 and 3 with data sets below 100. Machine learning was the most widely used type of AI, employed in 18 studies. Moreover, of the 26 included studies, 9 used clinical data, with clinical histories being the most frequently used data type in this category. The remaining 17 studies used nonclinical data, most frequently involving activity monitoring using an inertial sensor in clinical and nonclinical contexts. Regarding the performance of each AI model, 10 studies achieved a value of precision, sensitivity, specificity, or AUC ≥90.
CONCLUSIONS
The findings of this scoping review clarify the overall status of recent studies using AI to identify and diagnose FS. Moreover, the findings show that the combined use of AI using clinical data along with nonclinical information such as the kinematics of inertial sensors that monitor activities in a nonclinical context could be an appropriate tool for the identification and diagnosis of FS. Nevertheless, some possible limitations of the evidence included in the review could be small sample sizes, heterogeneity of study designs, and lack of standardization in the AI models and diagnostic criteria used across studies. Future research is needed to validate AI systems with diverse data sources for diagnosing FS. AI should be used as a decision support tool for identifying FS, with data quality and privacy addressed, and the tool should be regularly monitored for performance after being integrated in clinical practice.
Topics: Humans; Aged; Artificial Intelligence; Frail Elderly; Frailty; Machine Learning; Area Under Curve
PubMed: 37862082
DOI: 10.2196/47346 -
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 Oct 2023Studies have shown that mobile apps have the potential to serve as nonpharmacological interventions for dementia care, improving the quality of life of people living... (Review)
Review
BACKGROUND
Studies have shown that mobile apps have the potential to serve as nonpharmacological interventions for dementia care, improving the quality of life of people living with dementia and their informal caregivers. However, little is known about the needs for and privacy aspects of these mobile apps in dementia care.
OBJECTIVE
This review seeks to understand the landscape of existing mobile apps in dementia care for people living with dementia and their caregivers with respect to app features, usability testing, privacy, and security.
METHODS
ACM Digital Library, Cochrane Central Register of Controlled Trials, Compendex, Embase, Inspec, Ovid MEDLINE, PsycINFO, and Scopus were searched. Studies were included if they included people with dementia living in the community, their informal caregivers, or both; focused on apps in dementia care using smartphones or tablet computers; and covered usability evaluation of the app. Records were independently screened, and 2 reviewers extracted the data. The Centre for Evidence-Based Medicine critical appraisal tool and Mixed Methods Appraisal Tool were used to assess the risk of bias in the included studies. Thematic synthesis was used, and the findings were summarized and tabulated based on each research aim.
RESULTS
Overall, 44 studies were included in this review, with 39 (89%) published after 2015. In total, 50 apps were included in the study, with more apps developed for people living with dementia as end users compared with caregivers. Most studies (27/44, 61%) used tablet computers. The most common app feature was cognitive stimulation. This review presented 9 app usability themes: user interface, physical considerations, screen size, interaction challenges, meeting user needs, lack of self-awareness of app needs, stigma, technological inexperience, and technical support. In total, 5 methods (questionnaires, interviews, observations, logging, and focus groups) were used to evaluate usability. There was little focus on the privacy and security aspects, including data transfer and protection, of mobile apps for people living with dementia.
CONCLUSIONS
The limitations of this review include 1 reviewer conducting the full-text screening, its restriction to studies published in English, and the exclusion of apps that lacked empirical usability testing. As a result, there may be an incomplete representation of the available apps in the field of dementia care. However, this review highlights significant concerns related to the usability, privacy, and security of existing mobile apps for people living with dementia and their caregivers. The findings of this review provide a valuable framework to guide app developers and researchers in the areas of privacy policy development, app development strategies, and the importance of conducting thorough usability testing for their apps. By considering these factors, future work in this field can be advanced to enhance the quality and effectiveness of dementia care apps.
TRIAL REGISTRATION
PROSPERO CRD42020216141; https://www.crd.york.ac.uk/prospero/display_record.php?RecordID=216141.
INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID)
RR2-10.1159/000514838.
Topics: Humans; Mobile Applications; Caregivers; Quality of Life; Smartphone; Dementia
PubMed: 37824187
DOI: 10.2196/46188 -
BMC Medical Informatics and Decision... Oct 2023An awareness of antecedents of acceptance of digital contact tracing (DCT) can enable healthcare authorities to design appropriate strategies for fighting COVID-19 or... (Meta-Analysis)
Meta-Analysis
An awareness of antecedents of acceptance of digital contact tracing (DCT) can enable healthcare authorities to design appropriate strategies for fighting COVID-19 or other infectious diseases that may emerge in the future. However, mixed results about these antecedents are frequently reported. Most prior DCT acceptance review studies lack statistical synthesis of their results. This study aims to undertake a systematic review and meta-analysis of antecedents of DCT acceptance and investigate potential moderators of these antecedents. By searching multiple databases and filtering studies by using both inclusion and exclusion criteria, 76 and 25 studies were included for systematic review and meta-analysis, respectively. Random-effects models were chosen to estimate meta-analysis results since Q, I , and H index signified some degree of heterogeneity. Fail-safe N was used to assess publication bias. Most DCT acceptance studies have focused on DCT related factors. Included antecedents are all significant predictors of DCT acceptance except for privacy concerns and fear of COVID-19. Subgroup analysis showed that individualism/collectivism moderate the relationships between norms/privacy concerns and intention to use DCT. Based on the results, the mean effect size of antecedents of DCT acceptance and the potential moderators may be more clearly identified. Appropriate strategies for boosting the DCT acceptance rate can be proposed accordingly.
Topics: Humans; Contact Tracing; COVID-19; Databases, Factual; Group Processes; Health Facilities
PubMed: 37821864
DOI: 10.1186/s12911-023-02313-1 -
Health Promotion Perspectives 2023ChatGPT is an artificial intelligence based tool developed by OpenAI (California, USA). This systematic review examines the potential of ChatGPT in patient care and its...
BACKGROUND
ChatGPT is an artificial intelligence based tool developed by OpenAI (California, USA). This systematic review examines the potential of ChatGPT in patient care and its role in medical research.
METHODS
The systematic review was done according to the PRISMA guidelines. Embase, Scopus, PubMed and Google Scholar data bases were searched. We also searched preprint data bases. Our search was aimed to identify all kinds of publications, without any restrictions, on ChatGPT and its application in medical research, medical publishing and patient care. We used search term "ChatGPT". We reviewed all kinds of publications including original articles, reviews, editorial/ commentaries, and even letter to the editor. Each selected records were analysed using ChatGPT and responses generated were compiled in a table. The word table was transformed in to a PDF and was further analysed using ChatPDF.
RESULTS
We reviewed full texts of 118 articles. ChatGPT can assist with patient enquiries, note writing, decision-making, trial enrolment, data management, decision support, research support, and patient education. But the solutions it offers are usually insufficient and contradictory, raising questions about their originality, privacy, correctness, bias, and legality. Due to its lack of human-like qualities, ChatGPT's legitimacy as an author is questioned when used for academic writing. ChatGPT generated contents have concerns with bias and possible plagiarism.
CONCLUSION
Although it can help with patient treatment and research, there are issues with accuracy, authorship, and bias. ChatGPT can serve as a "clinical assistant" and be a help in research and scholarly writing.
PubMed: 37808939
DOI: 10.34172/hpp.2023.22 -
Cuadernos de Bioetica : Revista Oficial... 2023As health-related big data research (HRBDR) has drastically increased over the last years due to the rapid development of big data analytics, a range of important...
As health-related big data research (HRBDR) has drastically increased over the last years due to the rapid development of big data analytics, a range of important ethical issues are raised. In this study, a systematic literature review was conducted. Several and interesting results emerged from this review. The term ″big data″ has not yet been clearly defined. The already existing ethical principles and concepts need to be revisited in the new HRBDR context. Traditional research ethics notions like privacy and informed consent are to be reconsidered. HRBDR creates new ethical issues such those related to trust / trustworthiness and public values such as reciprocity, transparency, inclusivity and common good. The implementation of dynamic consent rather than broad consent is currently highlighted as the more satisfying solution. Ethical review committees in their current form are ill-suited to provide exclusive ethical oversight on HRBDR projects. Expanding Ethical Review Committees' purview and members' expertise, as well as creating novel oversight bodies by promoting a co-governance system including public and all the stakeholders involved are strongly recommended. The mechanism of ″social licence″, that is, informal permissions granted to researchers by society, can serve as a guideline. High-stakes decisions are often made under uncertainty. Machine learning algorithms are highly complex and in some cases opaque, and may yield biased decisions or discrimination. Improved interdisciplinary dialogue along with considering aspects like auditing, benchmarking, confidence / trust and explainability /interpretability may address concerns about HRBDR ethics. Finally and most importantly, research ethics shifts towards a population-based model of ethics.
Topics: Big Data; Ethics Committees, Research; Informed Consent; Ethics, Research; Ethical Review
PubMed: 37804492
DOI: 10.30444/CB.153 -
Frontiers in Public Health 2023Blockchain technology includes numerous elements such as distributed ledgers, decentralization, authenticity, privacy, and immutability. It has progressed past the hype...
Blockchain technology includes numerous elements such as distributed ledgers, decentralization, authenticity, privacy, and immutability. It has progressed past the hype to find actual use cases in industries like healthcare. Blockchain is an emerging area that relies on a consensus algorithm and the idea of a digitally distributed ledger to eliminate any intermediary risks. By enabling them to trace data provenance and any changes made, blockchain technology can enable different healthcare stakeholders to share access to their networks without violating data security and integrity. The healthcare industry faces challenges like fragmented data, security and privacy concerns, and interoperability issues. Blockchain technology offers potential solutions by ensuring secure, tamper-proof storage across multiple network nodes, improving interoperability and patient privacy. Encrypting patient data further enhances security and reduces unauthorized access concerns. Blockchain technology, deployed over the Internet, can potentially use the current healthcare data by using a patient-centric approach and removing the intermediaries. This paper discusses the effective utilization of blockchain technology in the healthcare industry. In contrast to other applications, the exoteric evaluation in this paper shows that the innovative technology called blockchain technology has a major role to play in the existing and future applications of the healthcare industry and has significant benefits.
Topics: Humans; Blockchain; Electronic Health Records; Computer Security; Delivery of Health Care; Confidentiality
PubMed: 37790716
DOI: 10.3389/fpubh.2023.1229386 -
Frontiers in Public Health 2023Given the increased availability of data sources such as hospital information systems, electronic health records, and health-related registries, a novel approach is...
BACKGROUND
Given the increased availability of data sources such as hospital information systems, electronic health records, and health-related registries, a novel approach is required to develop artificial intelligence-based decision support that can assist clinicians in their diagnostic decision-making and shorten rare disease patients' diagnostic odyssey. The aim is to identify key challenges in the process of mapping European rare disease databases, relevant to ML-based screening technologies in terms of organizational, FAIR and legal principles.
METHODS
A scoping review was conducted based on the PRISMA-ScR checklist. The primary article search was conducted in three electronic databases (MEDLINE/Pubmed, Scopus, and Web of Science) and a secondary search was performed in Google scholar and on the organizations' websites. Each step of this review was carried out independently by two researchers. A charting form for relevant study analysis was developed and used to categorize data and identify data items in three domains - organizational, FAIR and legal.
RESULTS
At the end of the screening process, 73 studies were eligible for review based on inclusion and exclusion criteria with more than 60% ( = 46) of the research published in the last 5 years and originated only from EU/EEA countries. Over the ten-year period (2013-2022), there is a clear cycling trend in the publications, with a peak of challenges reporting every four years. Within this trend, the following dynamic was identified: except for 2016, organizational challenges dominated the articles published up to 2018; legal challenges were the most frequently discussed topic from 2018 to 2022. The following distribution of the data items by domains was observed - (1) organizational ( = 36): data accessibility and sharing (20.2%); long-term sustainability (18.2%); governance, planning and design (17.2%); lack of harmonization and standardization (17.2%); quality of data collection (16.2%); and privacy risks and small sample size (11.1%); (2) FAIR ( = 15): findable (17.9%); accessible sustainability (25.0%); interoperable (39.3%); and reusable (17.9%); and (3) legal ( = 33): data protection by all means (34.4%); data management and ownership (22.9%); research under GDPR and member state law (20.8%); trust and transparency (13.5%); and digitalization of health (8.3%). We observed a specific pattern repeated in all domains during the process of data charting and data item identification - in addition to the outlined challenges, good practices, guidelines, and recommendations were also discussed. The proportion of publications addressing only good practices, guidelines, and recommendations for overcoming challenges when mapping RD databases in at least one domain was calculated to be 47.9% ( = 35).
CONCLUSION
Despite the opportunities provided by innovation - automation, electronic health records, hospital-based information systems, biobanks, rare disease registries and European Reference Networks - the results of the current scoping review demonstrate a diversity of the challenges that must still be addressed, with immediate actions on ensuring better governance of rare disease registries, implementing FAIR principles, and enhancing the EU legal framework.
Topics: Humans; Data Management; Rare Diseases; Artificial Intelligence; Registries; Privacy
PubMed: 37780450
DOI: 10.3389/fpubh.2023.1214766