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Heliyon Aug 2023While working alongside professional nurses, student nurses develop professional identity and learn the professional nursing role, a process known as professional...
While working alongside professional nurses, student nurses develop professional identity and learn the professional nursing role, a process known as professional socialisation. Professional nurses should model professional behaviour to be emulated by student nurses. We used a qualitative exploratory design to explore if professional nurses behave in a manner that supports professional socialisation of student nurses in a clinical learning environment. According to our observations, two main categories emerged regarding professional nurses' behaviour. The first category was unprofessional conduct with sub-categories that included disrespect, infringed patient privacy, breached confidentiality, inappropriate dress code and lack of punctuality. The second category was ward disorganisation which was related to delegating duties and structured orientation programmes for student nurses. In this study, professional nurses did not behave in a manner consistent with professional socialisation in the clinical learning environment. Student nurses may struggle to develop professional identity, leading to reduced confidence and poor patient quality care. Student nurses need to be professionally socialised in a clinical learning environment and professional nurses need to be empowered on how to carry out this process.
PubMed: 37576296
DOI: 10.1016/j.heliyon.2023.e18611 -
International Journal of Environmental... Aug 2023Federated learning (FL) provides a distributed machine learning system that enables participants to train using local data to create a shared model by eliminating the... (Review)
Review
Federated learning (FL) provides a distributed machine learning system that enables participants to train using local data to create a shared model by eliminating the requirement of data sharing. In healthcare systems, FL allows Medical Internet of Things (MIoT) devices and electronic health records (EHRs) to be trained locally without sending patients data to the central server. This allows healthcare decisions and diagnoses based on datasets from all participants, as well as streamlining other healthcare processes. In terms of user data privacy, this technology allows collaborative training without the need of sharing the local data with the central server. However, there are privacy challenges in FL arising from the fact that the model updates are shared between the client and the server which can be used for re-generating the client's data, breaching privacy requirements of applications in domains like healthcare. In this paper, we have conducted a review of the literature to analyse the existing privacy and security enhancement methods proposed for FL in healthcare systems. It has been identified that the research in the domain focuses on seven techniques: Differential Privacy, Homomorphic Encryption, Blockchain, Hierarchical Approaches, Peer to Peer Sharing, Intelligence on the Edge Device, and Mixed, Hybrid and Miscellaneous Approaches. The strengths, limitations, and trade-offs of each technique were discussed, and the possible future for these seven privacy enhancement techniques for healthcare FL systems was identified.
Topics: Humans; Privacy; Blockchain; Computer Communication Networks; Electronic Health Records; Delivery of Health Care
PubMed: 37569079
DOI: 10.3390/ijerph20156539 -
Journal of Medical Internet Research Aug 2023ChatGPT has promising applications in health care, but potential ethical issues need to be addressed proactively to prevent harm. ChatGPT presents potential ethical...
ChatGPT has promising applications in health care, but potential ethical issues need to be addressed proactively to prevent harm. ChatGPT presents potential ethical challenges from legal, humanistic, algorithmic, and informational perspectives. Legal ethics concerns arise from the unclear allocation of responsibility when patient harm occurs and from potential breaches of patient privacy due to data collection. Clear rules and legal boundaries are needed to properly allocate liability and protect users. Humanistic ethics concerns arise from the potential disruption of the physician-patient relationship, humanistic care, and issues of integrity. Overreliance on artificial intelligence (AI) can undermine compassion and erode trust. Transparency and disclosure of AI-generated content are critical to maintaining integrity. Algorithmic ethics raise concerns about algorithmic bias, responsibility, transparency and explainability, as well as validation and evaluation. Information ethics include data bias, validity, and effectiveness. Biased training data can lead to biased output, and overreliance on ChatGPT can reduce patient adherence and encourage self-diagnosis. Ensuring the accuracy, reliability, and validity of ChatGPT-generated content requires rigorous validation and ongoing updates based on clinical practice. To navigate the evolving ethical landscape of AI, AI in health care must adhere to the strictest ethical standards. Through comprehensive ethical guidelines, health care professionals can ensure the responsible use of ChatGPT, promote accurate and reliable information exchange, protect patient privacy, and empower patients to make informed decisions about their health care.
Topics: Humans; Artificial Intelligence; Reproducibility of Results; Data Collection; Disclosure; Patient Compliance
PubMed: 37566454
DOI: 10.2196/48009 -
Journal of Medical Internet Research Aug 2023The health care industry has faced various challenges over the past decade as we move toward a digital future where services and data are available on demand. The... (Review)
Review
The health care industry has faced various challenges over the past decade as we move toward a digital future where services and data are available on demand. The systems of interconnected devices, users, data, and working environments are referred to as the Internet of Health Care Things (IoHT). IoHT devices have emerged in the past decade as cost-effective solutions with large scalability capabilities to address the constraints on limited resources. These devices cater to the need for remote health care services outside of physical interactions. However, IoHT security is often overlooked because the devices are quickly deployed and configured as solutions to meet the demands of a heavily saturated industry. During the COVID-19 pandemic, studies have shown that cybercriminals are exploiting the health care industry, and data breaches are targeting user credentials through authentication vulnerabilities. Poor password use and management and the lack of multifactor authentication security posture within IoHT cause a loss of millions according to the IBM reports. Therefore, it is important that health care authentication security moves toward adaptive multifactor authentication (AMFA) to replace the traditional approaches to authentication. We identified a lack of taxonomy for data models that particularly focus on IoHT data architecture to improve the feasibility of AMFA. This viewpoint focuses on identifying key cybersecurity challenges in a theoretical framework for a data model that summarizes the main components of IoHT data. The data are to be used in modalities that are suited for health care users in modern IoHT environments and in response to the COVID-19 pandemic. To establish the data taxonomy, a review of recent IoHT papers was conducted to discuss the related work in IoHT data management and use in next-generation authentication systems. Reports, journal articles, conferences, and white papers were reviewed for IoHT authentication data technologies in relation to the problem statement of remote authentication and user management systems. Only publications written in English from the last decade were included (2012-2022) to identify key issues within the current health care practices and their management of IoHT devices. We discuss the components of the IoHT architecture from the perspective of data management and sensitivity to ensure privacy for all users. The data model addresses the security requirements of IoHT users, environments, and devices toward the automation of AMFA in health care. We found that in health care authentication, the significant threats occurring were related to data breaches owing to weak security options and poor user configuration of IoHT devices. The security requirements of IoHT data architecture and identified impactful methods of cybersecurity for health care devices, data, and their respective attacks are discussed. Data taxonomy provides better understanding, solutions, and improvements of user authentication in remote working environments for security features.
Topics: Humans; Confidentiality; Telemedicine; Pandemics; COVID-19; Internet; Computer Security
PubMed: 37490633
DOI: 10.2196/44114 -
JMIR Human Factors Jul 2023Patient portals can facilitate patient engagement in care management. Driven by national efforts over the past decade, patient portals are being implemented by hospitals...
Adult Patients' Experiences of Using a Patient Portal With a Focus on Perceived Benefits and Difficulties, and Perceptions on Privacy and Security: Qualitative Descriptive Study.
BACKGROUND
Patient portals can facilitate patient engagement in care management. Driven by national efforts over the past decade, patient portals are being implemented by hospitals and clinics nationwide. Continuous evaluation of patient portals and reflection of feedback from end users across care settings are needed to make patient portals more user-centered after the implementation.
OBJECTIVE
The aim of this study was to investigate the lived experience of using a patient portal in adult patients recruited from a variety of care settings, focusing on their perceived benefits and difficulties of using the patient portal, and trust and concerns about privacy and security.
METHODS
This qualitative descriptive study was part of a cross-sectional digital survey research to examine the comprehensive experience of using a patient portal in adult patients recruited from 20 care settings from hospitals and clinics of a large integrated health care system in the mid-Atlantic area of the United States. Those who had used a patient portal offered by the health care system in the past 12 months were eligible to participate in the survey. Data collected from 734 patients were subjected to descriptive statistics and content analysis.
RESULTS
The majority of the participants were female and non-Hispanic White with a mean age of 53.1 (SD 15.34) years. Content analysis of 1589 qualitative comments identified 22 themes across 4 topics: beneficial aspects (6 themes) and difficulties (7 themes) in using the patient portal; trust (5 themes) and concerns (4 themes) about privacy and security of the patient portal. Most of the participants perceived the patient portal functions as beneficial for communicating with health care teams and monitoring health status and care activities. At the same time, about a quarter of them shared difficulties they experienced while using those functions, including not getting eMessage responses timely and difficulty finding information in the portal. Protected log-in process and trust in health care providers were the most mentioned reasons for trusting privacy and security of the patient portal. The most mentioned reason for concerns about privacy and security was the risk of data breaches such as hacking attacks and identity theft.
CONCLUSIONS
This study provides an empirical understanding of the lived experience of using a patient portal in adult patient users across care settings with a focus on the beneficial aspects and difficulties in using the patient portal, and trust and concerns about privacy and security. Our study findings can serve as a valuable reference for health care institutions and software companies to implement more user-centered, secure, and private patient portals. Future studies may consider targeting other patient portal programs and patients with infrequent or nonuse of patient portals.
PubMed: 37490316
DOI: 10.2196/46044 -
Indian Journal of Medical Ethics 2023Mobile phone-based interventions are being increasingly used in community health work in India. The extensive use of mobile phones in community health work is associated... (Review)
Review
BACKGROUND
Mobile phone-based interventions are being increasingly used in community health work in India. The extensive use of mobile phones in community health work is associated with several ethical issues. This review was conducted to identify the ethical issues related to mHealth applications in community health work in India.
METHODS
We performed a scoping review of literature in PubMed and Google Scholar using a search strategy that we developed. We included studies that mentioned ethical issues in mHealth applications that involved community health work and community health workers in India, published in peer reviewed English language journals between 2011 and 2021. All three authors screened the articles, shortlisted them, read them, and extracted the data. We then synthesised the data into a conceptual framework.
RESULTS
Our search yielded 1125 papers, from which we screened and shortlisted 121, after reading which we included 58 in the final scoping review. The main ethical issues identified from review of these papers included benefits of mHealth applications such as improved quality of care, increased awareness about health and illness, increased accountability of the health system, accurate data capture and timely data driven decision making. The risks of mHealth applications identified were impersonal communication of community health worker, increased workload, potential breach in privacy, confidentiality, and stigmatisation. The inherent inequities in access to mobile phones in the community due to gender and class led to exclusion of women and the poor from the benefits of mHealth interventions. Though mHealth interventions increased access to healthcare by taking healthcare to remote areas through tele-health, unless we contextualise mHealth to local rural settings through community engagement, it is likely to remain inequitable.
CONCLUSION
This scoping review revealed that there is a lack of well conducted empirical studies which explore the ethical issues related to mHealth applications in community health work.
Topics: Humans; Female; Public Health; Delivery of Health Care; Cell Phone; Telemedicine; India; Mobile Applications
PubMed: 37310008
DOI: 10.20529/IJME.2023.037 -
Surgical Endoscopy Aug 2023Laparoscopic videos are increasingly being used for surgical artificial intelligence (AI) and big data analysis. The purpose of this study was to ensure data privacy in...
BACKGROUND
Laparoscopic videos are increasingly being used for surgical artificial intelligence (AI) and big data analysis. The purpose of this study was to ensure data privacy in video recordings of laparoscopic surgery by censoring extraabdominal parts. An inside-outside-discrimination algorithm (IODA) was developed to ensure privacy protection while maximizing the remaining video data.
METHODS
IODAs neural network architecture was based on a pretrained AlexNet augmented with a long-short-term-memory. The data set for algorithm training and testing contained a total of 100 laparoscopic surgery videos of 23 different operations with a total video length of 207 h (124 min ± 100 min per video) resulting in 18,507,217 frames (185,965 ± 149,718 frames per video). Each video frame was tagged either as abdominal cavity, trocar, operation site, outside for cleaning, or translucent trocar. For algorithm testing, a stratified fivefold cross-validation was used.
RESULTS
The distribution of annotated classes were abdominal cavity 81.39%, trocar 1.39%, outside operation site 16.07%, outside for cleaning 1.08%, and translucent trocar 0.07%. Algorithm training on binary or all five classes showed similar excellent results for classifying outside frames with a mean F1-score of 0.96 ± 0.01 and 0.97 ± 0.01, sensitivity of 0.97 ± 0.02 and 0.0.97 ± 0.01, and a false positive rate of 0.99 ± 0.01 and 0.99 ± 0.01, respectively.
CONCLUSION
IODA is able to discriminate between inside and outside with a high certainty. In particular, only a few outside frames are misclassified as inside and therefore at risk for privacy breach. The anonymized videos can be used for multi-centric development of surgical AI, quality management or educational purposes. In contrast to expensive commercial solutions, IODA is made open source and can be improved by the scientific community.
Topics: Humans; Artificial Intelligence; Privacy; Laparoscopy; Algorithms; Neural Networks, Computer; Video Recording
PubMed: 37145173
DOI: 10.1007/s00464-023-10078-x