Patient Privacy Research Articles

Testing the Feasibility of a Digital Point of Care Solution for the Trusted Near Real-Time Bidirectional Exchange of Novel and Informative Adverse Event Information

AbstractA digital point-of-care solution was implemented to test the feasibility of near-real-time bi-directional communication between pharmacovigilance experts (PVEs) and healthcare professionals (HCPs) for exchanging unique and informative adverse event (AE) information. The solution was implemented in a commercially available electronic health record (EHR) system/platform, no direct contact between PVEs and the HCPs was possible. The Clinical Affairs team of the EHR vendor was used as an intermediary to ensure appropriate information was exchanged while protecting HCP and patient privacy. The study yielded 9 drug-event pairs of interest (AEI), 2 of which were confirmed as AEs by the HCP. On average it took 20.6 h to receive initial AEI information and 58.8 h to receive follow-up information, which represents a 96% reduction in time compared to current methods. Both interactions provided unique data that would not have been collected otherwise leading to the PVE being able to appropriately determine a potential causal association. This study successfully demonstrated the feasibility of using a compliant, bi-directional, digitally enabled clinical communication channel at the point of care to complement existing pharmacovigilance activities.

Knowledge, attitudes, and practices of community pharmacists providing over-the-counter emergency hormonal contraception: a scoping review.

Reducing the rate and impact of unintended pregnancy through effective contraception is a public health goal. Since deregulation, globally, ease of access to community pharmacists has enabled them to play a key role in the provision of emergency hormonal contraceptive pills (ECP). The aim of this scoping review is to explore pharmacists' overall knowledge of and attitudes and practices towards the provision of emergency contraception. A systematic literature search for the period from 1999 to 2023 was conducted using Scopus, Medline (Ovid), CINAHL, Emcare, Web of Science, and Google Scholar. Keywords such as emergency contraception, emergency contraceptive, morning after pill, plan B, pharmacist, community pharmacist, and pharmacy were applied. Articles published only in English that described the knowledge, attitudes, and practices of community pharmacists providing emergency contraception were included in this review. Twenty studies met the inclusion criteria. Despite positive attitudes towards the provision of ECP, there is a clear deficit in both the knowledge and counselling practices of pharmacists, with some pharmacists lacking an open attitude towards the supply of ECP to adolescents and third parties. Usage of a private counselling area ranged from 0% to 90% highlighting privacy for patients seeking ECPs is lacking during consultations. In countries where practice guidelines are available, these are often not being optimally utilized. This review has highlighted gaps in pharmacists' knowledge and counselling practices, demonstrating shortcomings in pharmacists' education, training, and application of professional practice guidelines. Future research on ECP guidelines is recommended to improve implementation and usability in practice.

The role and impact of new technologies on healthcare systems

AbstractThe integration of new technologies into healthcare systems is revolutionizing the industry by significantly enhancing the capabilities of healthcare providers, improving patient outcomes, and optimizing administrative operations. This comprehensive examination critically assesses the impact of these technologies on various stakeholders within the healthcare ecosystem, highlighting both the benefits and challenges. Emerging technologies such as artificial intelligence (AI), which refers to the simulation of human intelligence by machines, the Internet of Things (IoT), which involves interconnected devices that collect and exchange health-related data in real time, Blockchain, which is a decentralized and tamper-proof ledger technology used to securely manage patient data, and wearable devices are explored for their potential applications in diagnostics, treatment, and patient monitoring. Despite their transformative potential, these technologies also bring forth a range of limitations and challenges, including ethical concerns regarding patient privacy, legal implications of data security, and technical hurdles related to interoperability and implementation. By addressing these issues, the healthcare industry can better navigate the complexities of technological advancement, ensuring that innovation contributes positively to patient care and operational efficiency.

An Anonymous Authenticated Key Agreement Scheme for Telecare Medical Information Systems

With the rapid development of information technology from one side and the experience of the COVID-19 pandemic from the other side, people presently prefer to access healthcare services remotely. Telecare Medical Information System (TMIS) provides more flexible, faster, and more convenient e-healthcare services available to all people, particularly those who lack access to physicians due to their geographical restrictions. However, due to the sensitivity of medical information, preventing unauthorized access to patient data and preserving patient privacy is crucial. In this paper, we propose an authenticated key agreement scheme for TMIS to preserve the privacy of the patient’s identity from all internal (even the health server and the physician) and external entities. Moreover, the physician’s identity is kept secret from all external entities. Formal and informal security analysis of the proposed scheme indicates that it is secure against all attacks in the context.

Shareable artificial intelligence to extract cancer outcomes from electronic health records for precision oncology research.

Databases that link molecular data to clinical outcomes can inform precision cancer research into novel prognostic and predictive biomarkers. However, outside of clinical trials, cancer outcomes are typically recorded only in text form within electronic health records (EHRs). Artificial intelligence (AI) models have been trained to extract outcomes from individual EHRs. However, patient privacy restrictions have historically precluded dissemination of these models beyond the centers at which they were trained. In this study, the vulnerability of text classification models trained directly on protected health information to membership inference attacks is confirmed. A teacher-student distillation approach is applied to develop shareable models for annotating outcomes from imaging reports and medical oncologist notes. 'Teacher' models trained on EHR data from Dana-Farber Cancer Institute (DFCI) are used to label imaging reports and discharge summaries from the Medical Information Mart for Intensive Care (MIMIC)-IV dataset. 'Student' models are trained to use these MIMIC documents to predict the labels assigned by teacher models and sent to Memorial Sloan Kettering (MSK) for evaluation. The student models exhibit high discrimination across outcomes in both the DFCI and MSK test sets. Leveraging private labeling of public datasets to distill publishable clinical AI models from academic centers could facilitate deployment of machine learning to accelerate precision oncology research.

Recent Advancements in Federated Learning: State of the Art, Fundamentals, Principles, IoT Applications and Future Trends

Federated learning (FL) is creating a paradigm shift in machine learning by directing the focus of model training to where the data actually exist. Instead of drawing all data into a central location, which raises concerns about privacy, costs, and delays, FL allows learning to take place directly on the device, keeping the data safe and minimizing the need for transfer. This approach is especially important in areas like healthcare, where protecting patient privacy is critical, and in industrial IoT settings, where moving large numbers of data is not practical. What makes FL even more compelling is its ability to reduce the bias that can occur when all data are centralized, leading to fairer and more inclusive machine learning outcomes. However, it is not without its challenges—particularly with regard to keeping the models secure from attacks. Nonetheless, the potential benefits are clear: FL can lower the costs associated with data storage and processing, while also helping organizations to meet strict privacy regulations like GDPR. As edge computing continues to grow, FL’s decentralized approach could play a key role in shaping how we handle data in the future, moving toward a more privacy-conscious world. This study identifies ongoing challenges in ensuring model security against adversarial attacks, pointing to the need for further research in this area.

Patient privacy and the need for private consultation areas in Kuwait’s polyclinic pharmacies

Abstract Introduction In Kuwait, pharmacists provide essential medication counseling and education. However, the open layout of polyclinic pharmacies often compromises patient privacy, deterring individuals from seeking necessary consultations. Objective This study aims to assess how patients perceive the level of privacy in Kuwait’s polyclinic pharmacies and evaluate patients’ need for a private consultation area. Method A cross-sectional study was conducted among patients at polyclinic pharmacies in Kuwait. Convenience sampling was used to select the polyclinics. Patients were approached after collecting their medications and invited to participate in the study. Those who consented completed a questionnaire that included sections on demographics, six items assessing the privacy construct, four items evaluating the need for a consultation area, and an open-ended question for additional comments. Ethical approval was obtained from the Human Ethical Committee, Health Sciences Center, Kuwait University. Results The study included 210 participants, with a majority residing in the Capital Governorate (55.2%), and being female (70%). The median age was 43 years old (IQR=29-55). Over two-thirds of the participants held a university degree (69%). Most participants were collecting medication for themselves (83.8%), and were exempt from fees (84.3%). When measuring patients’ privacy satisfaction, a score was calculated and a scale was divided into good to excellent (24 - 30), fair (18 - 23), and poor (6 -17). In addition, when measuring the need for a private consultation area score, a scale was divided into high need (16-20), fair need (12-15), and no need (4-11). Regarding perceived privacy, only 20% of participants had a good to excellent score, while 41.9% had a fair score and 38.1% had a poor score. When asked about the need for a private consultation room, 63.8% expressed a high need, while 15.2% believed it was unnecessary. Participants who had lower privacy scores reported a higher need for private consultation areas. Discussion The findings provide valuable insights into patient perspectives and highlight the necessity of improving pharmacy practices in Kuwait. Enhancing privacy in pharmacies could significantly improve the public’s perception of pharmacists as essential healthcare providers. However, several limitations should be noted. The sample is not representative of the population, as the proportion of non-Kuwaiti participants is less than the actual population ratio. Consequently, the results cannot be generalized to the entire population. Future studies should aim for a more representative sample and consider a longer data collection period to address these limitations. Conclusion The results of this study reveal that a significant number of patients perceive privacy in Kuwait's polyclinic pharmacies as fair, with many expressing a strong need for private consultation areas. These findings underscore the importance of enhancing privacy measures in these settings1. Implementing private consultation rooms in polyclinic pharmacies could greatly improve patient satisfaction and the overall quality of healthcare services provided by pharmacists. Reference 1. Waheedi S, Marshall V, Alharbia H, Farris KB. Patients’ perceptions of privacy and the need for a private consultation area in community pharmacy. Poster Abstracts. International Journal of Pharmacy Practice. 2016;24:33–60

Pharmacogenetic testing: current state of the issue

Aim. The purpose of this work is to emphasize the importance and practical benefits of pharmacogenetics in medicine, demonstrating how the integration of pharmacogenetics into clinical practice can improve treatment outcomes and enhance the safety of therapies. Pharmacogenetics is a rapidly developing branch of pharmacology that studies how genetic variation affects an individual’s response to drugs. By understanding these genetic differences, healthcare providers can tailor drug therapy to maximize efficacy and minimize adverse drug reactions. The field aims to move away from the traditional one-size-fits-all approach and instead personalize medical treatment based on a person’s genetic makeup. The ability to predict a person’s response to medication based on their genetic profile has profound implications. For example, pharmacogenetic testing can identify patients at risk for life-threatening adverse drug reactions, such as drug-induced liver injury. This proactive approach can prevent adverse events, improving patient safety and the overall quality of care. In addition, pharmacogenetics aids in the development of new drugs by identifying genetic markers associated with drug responses, optimizing the drug development process, and reducing the time and costs associated with bringing new drugs to market. Key advances in pharmacogenetics include the identification of genetic polymorphisms in enzymes, such as cytochrome P450, that affect drug metabolism, thereby influencing both safety and efficacy. Pharmacogenetic testing allows doctors to predict the best drug and dosage for a patient, improving treatment outcomes and reducing the risk of adverse drug events. Despite its potential, the integration of pharmacogenetics into clinical practice faces challenges, including a lack of reliable clinical evidence, inadequate physician education, and the need for a comprehensive health IT infrastructure to support the use of genetic data. Ethical and legal issues, such as patient privacy and the risk of genetic discrimination, also present significant obstacles. However, continued research, the development of genetic testing technologies, and interdisciplinary collaborations are paving the way for more widespread adoption of pharmacogenetics, which promises to significantly improve patient care and healthcare efficiency. Key resources such as PharmGKB, CPIC, and the NIH provide valuable information and guidance for clinicians, researchers, and students, helping bridge the gap between genetics research and clinical application. Conclusions. Pharmacogenetics represents a significant advancement in personalized medicine, offering the potential to tailor drug therapies to individual genetic profiles. Although the field has made substantial progress, challenges remain in the form of insufficient clinical evidence, implementation barriers, and ethical concerns. Continued research and collaboration among stakeholders are essential to fully realize the benefits of pharmacogenetic testing in clinical practice.

Ethical considerations in telehealth and artificial intelligence for work related musculoskeletal disorders: A scoping review.

Artificial Intelligence (AI) refers to the simulation of human intelligence in machines that are programmed to perform tasks that typically require human intelligence. The integration of AI and telehealth applications in healthcare raises ethical concerns such as bias, transparency, data privacy, and accountability for errors. Several studies have assessed this topic, particularly with regard to musculoskeletal disorders, which will be the focus of this manuscript. We will examine key ethical concepts including informed consent, data protection, confidentiality, physician malpractice, liability, and telemedicine regulations. Ethical issues pertaining to the topic were explored through a review paper. The primary objective of this scoping review was to map and synthesize the existing literature concerning ethical considerations in telehealth and AI for work-related musculoskeletal disorders. Research demonstrates that medication effectiveness, patient and physician satisfaction, and accessibility costs are higher with telemedicine and AI methods compared to in-person approaches, particularly for work-related musculoskeletal disorders. Therefore, addressing ethical issues, including patient data privacy and security, is crucial in this field. By considering these factors, the adoption of emerging AI and telemedicine applications, especially for work-related musculoskeletal disorders, is likely to increase. AI and telemedicine offer significant advantages, particularly in addressing work-related musculoskeletal disorders. However, ethical and legal issues surrounding their practice require standardized rules to ensure equitable access, quality care, sustainable costs, professional liability, patient privacy, data protection, and confidentiality. Further practical research studies are needed to address these considerations more effectively.

An Efficient EHR Secure Exchange Among Healthcare Servers Using Light Weight Scheme

This work addresses the critical need for secure and patient-controlled Electronic Health Records (EHR) migration among healthcare hospitals' cloud servers (HHS). The relevant approaches often lack robust access control and leave data vulnerable during transfer. Our proposed scheme empowers patients to delegate EHR migration to a trusted Third-Party Hospital (TTPH); which is the Certification Authority (CA) while enforcing access control. The system leverages asymmetric encryption utilizing the Elliptic Curve Digital Signature Algorithm (ECDSA), EEC and ECDSA added robust security and lightness EHR sharing. Patient and user privacy is managed due to anonymity through cryptographic hashing for data protection and utilizes mutual authentication for secure communication. Formal security analysis using the Scyther tool and informal analysis was conducted to validate the system's robustness. The proposed scheme achieved EHR integrity due to the verification of the communicated HHS and ensuring the integrity of the HHS digital certificate during EHR migration. Ultimately, the result achieved in the proposed work demonstrated the scheme's high balance between data security and accuracy of communication, where the best result obtained represented 7.7/ ms as computational cost and 1248 /bits as communication cost compared with the relevant approaches.

Maintaining Integrity and Confidentiality of Patients’ Records Using an Enhanced Security Technique

Medical record storage on paper is currently being phased out and replaced by more sophisticated Electronic Health Record (EHR) systems. However, the ease of access to data and the digitization of medical records also come with the potential of misuse of personally identifiable information and healthcare data breaches. How technology will protect patient privacy is one of the primary concerns about EHRs. Network connectivity is another major issue, therefore keeping private health information online may expose it to major information leaks to external parties. Around the world, data management and privacy are major concerns. The main concerns that prevent businesses from using cloud computing are security and information authenticity, as they fear that data may be lost to unauthorized parties. The quantity of patient-oriented data in the healthcare system is continuously increasing, and the current medical systems are confronted with security issues such as weak access control, data breaches or unauthorized disclosure, and insufficient authorization and authentication. An Enhanced Security Model was created in this study to secure and protect Electronic Health Records. The records were encrypted for security and privacy utilizing the Advanced Encryption Standard (AES), One Time Password (OTP), and National Identity Number (NIN). The system was designed using Object Oriented Analysis and Design Methodology (OOADM). The New System improved patient record security by utilizing NIN for cloud identity management, OTP for authentication, and AES for privacy and security. While the MySQL server was used to implement the database, HTML, CSS, PHP, and JavaScript were used to program the system. Comparing the new system to the old system, the results of the performance assessment analysis based on security, user-friendliness, dependability, and privacy show that the new system has very good performance efficiency and integrity.

The Role of Artificial Intelligence in Revolutionizing Mental Health Services: A Data-Driven Approach

The potential of artificial intelligence (AI) to transform mental health care through individualized treatment approaches is significant. This research investigates the application of AI and extensive healthcare datasets to improve the precision of mental health diagnoses and treatments. The study employs various machine learning techniques, such as Random Forest, CatBoost, K-Nearest Neighbors, XGBoost, Convolutional Neural Networks, and Long Short-Term Memory networks, to analyze diverse data sources including electronic health records, neuroimaging, genetic information, and demographic data. Following thorough data preprocessing, model training, and evaluation using metrics like accuracy, precision, recall, F1 score, and Cohen's Kappa, the Random Forest algorithm emerges as the top performer with 98.7% accuracy. The research highlights the importance of addressing ethical considerations, such as protecting patient privacy, ensuring data security, and mitigating algorithmic bias when implementing AI in mental health services. The findings indicate that AI-based approaches can markedly improve diagnostic accuracy, provide tailored treatment suggestions, and facilitate early relapse detection, thus promoting proactive mental health management. Key Words: Artificial Intelligence, Mental Health, Data- Driven approach, Personalized Treatment, Machine Learning, Natural Language Processing.

Patient Health Record Protection Beyond the Health Insurance Portability and Accountability Act: Mixed Methods Study

Background The security and privacy of health care information are crucial for maintaining the societal value of health care as a public good. However, governance over electronic health care data has proven inefficient, despite robust enforcement efforts. Both federal (HIPAA [Health Insurance Portability and Accountability Act]) and state regulations, along with the ombudsman rule, have not effectively reduced the frequency or impact of data breaches in the US health care system. While legal frameworks have bolstered data security, recent years have seen a concerning increase in breach incidents. This paper investigates common breach types and proposes best practices derived from the data as potential solutions. Objective The primary aim of this study is to analyze health care and hospital breach data, comparing it against HIPAA compliance levels across states (spatial analysis) and the impact of the Omnibus Rule over time (temporal analysis). The goal is to establish guidelines for best practices in handling sensitive information within hospitals and clinical environments. Methods The study used data from the Department of Health and Human Services on reported breaches, assessing the severity and impact of each breach type. We then analyzed secondary data to examine whether HIPAA’s storage and retention rule amendments have influenced security and privacy incidents across all 50 states. Finally, we conducted a qualitative analysis of textual data from vulnerability and breach reports to identify actionable best practices for health care settings. Results Our findings indicate that hacking or IT incidents have the most significant impact on the number of individuals affected, highlighting this as a primary breach category. The overall difference-in-differences trend reveals no significant reduction in breach rates (P=.50), despite state-level regulations exceeding HIPAA requirements and the introduction of the ombudsman rule. This persistence in breach trends implies that even strengthened protections and additional guidelines have not effectively curbed the rising number of affected individuals. Through qualitative analysis, we identified 15 unique values and associated best practices from industry standards. Conclusions Combining quantitative and qualitative insights, we propose the “SecureSphere framework” to enhance data security in health care institutions. This framework presents key security values structured in concentric circles: core values at the center and peripheral values around them. The core values include employee management, policy, procedures, and IT management. Peripheral values encompass the remaining security attributes that support these core elements. This structured approach provides a comprehensive security strategy for protecting patient health information and is designed to help health care organizations develop sustainable practices for data security.

Privacy-Preserving Data Analytics in Internet of Medical Things

The healthcare sector has changed dramatically in recent years due to depending more and more on big data to improve patient care, enhance or improve operational effectiveness, and forward medical research. Protecting patient privacy in the era of digital health records is a major challenge, as there could be a chance of privacy leakage during the process of collecting patient data. To overcome this issue, we propose a secure, privacy-preserving scheme for healthcare data to ensure maximum privacy of an individual while also maintaining their utility and allowing for the performance of queries based on sensitive attributes under differential privacy. We implemented differential privacy on two publicly available healthcare datasets, the Breast Cancer Prediction Dataset and the Nursing Home COVID-19 Dataset. Moreover, we examined the impact of varying privacy parameter (ε) values on both the privacy and utility of the data. A significant part of this study involved the selection of ε, which determines the degree of privacy protection. We also conducted a computational time comparison by performing multiple complex queries on these datasets to analyse the computational overhead introduced by differential privacy. The outcomes demonstrate that, despite a slight increase in query processing time, it remains within reasonable bounds, ensuring the practicality of differential privacy for real-time applications.

AI-Driven Cardiomegaly Detection Using Cutting-Edge Chest X–ray imagery

This project aims to develop an innovative approach for detecting cardiomegaly, a condition characterized by an enlarged heart, using machine learning techniques, specifically Support Vector Machines (SVM), and state-of-the-art chest X-ray imagery. Cardiomegaly serves as a significant indicator of various cardiac diseases, and early detection through accurate imaging analysis can lead to timely intervention and improved patient outcomes. The proposed machine learning solution utilizes SVM algorithms trained on a diverse dataset of chest X-ray images to automatically identify signs of cardiomegaly with high precision and reliability. By harnessing the power of SVM, this system offers a non-invasive and cost-effective method for screening large populations for cardiac abnormalities, facilitating early diagnosis and appropriate medical management. Key features of the developed solution include robust feature extraction techniques, real-time analysis, and integration with existing healthcare infrastructure for seamless implementation in clinical settings. Moreover, the system prioritizes patient privacy and data security by adhering to relevant regulations and standards governing medical imaging and information management. Overall, this project represents a significant advancement in the field of cardiac health monitoring, providing healthcare professionals with a valuable tool for early detection and management of cardiomegaly, ultimately improving patient care and reducing the burden of cardio vascular disease

Vision language models in ophthalmology.

Vision Language Models are an emerging paradigm in artificial intelligence that offers the potential to natively analyze both image and textual data simultaneously, within a single model. The fusion of these two modalities is of particular relevance to ophthalmology, which has historically involved specialized imaging techniques such as angiography, optical coherence tomography, and fundus photography, while also interfacing with electronic health records that include free text descriptions. This review then surveys the fast-evolving field of Vision Language Models as they apply to current ophthalmologic research and practice. Although models incorporating both image and text data have a long provenance in ophthalmology, effective multimodal Vision Language Models are a recent development exploiting advances in technologies such as transformer and autoencoder models. Vision Language Models offer the potential to assist and streamline the existing clinical workflow in ophthalmology, whether previsit, during, or post-visit. There are, however, also important challenges to be overcome, particularly regarding patient privacy and explainability of model recommendations.

AI in microbiome-related healthcare.

Artificial intelligence (AI) has the potential to transform clinical practice and healthcare. Following impressive advancements in fields such as computer vision and medical imaging, AI is poised to drive changes in microbiome-based healthcare while facing challenges specific to the field. This review describes the state-of-the-art use of AI in microbiome-related healthcare. It points out limitations across topics such as data handling, AI modelling and safeguarding patient privacy. Furthermore, we indicate how these current shortcomings could be overcome in the future and discuss the influence and opportunities of increasingly complex data on microbiome-based healthcare.

Enhancing insights in sexually transmitted infection mapping: Syphilis in Forsyth County, North Carolina, a case study.

In 2008-2011 Forsyth County, North Carolina experienced a four-fold increase in syphilis rising to over 35 cases per 100,000 mirroring the 2021 state syphilis rate. Our methodology extends current models with: 1) donut geomasking to enhance resolution while protecting patient privacy; 2) a moving window uniform grid to control the modifiable area unit problem, edge effect and remove kriging islands; and 3) mitigating the "small number problem" with Uniform Model Bayesian Maximum Entropy. Data is 2008-2011 early syphilis cases reported to the NC Department of Health and Human Services for Forsyth County. Results were assessed using latent rate theory cross validation. We show combining a moving window and a UMBME analysis with geomasked data effectively predicted the true or latent syphilis rate 5% to 26% more accurate than the traditional, geopolitical boundary method. It removed kriging islands, reduced background incidence rate to 0, relocated nine outbreak hotspots to more realistic locations, and elucidated hotspot connectivity producing more realistic geographical patterns for targeted insights. Using the Forsyth outbreak as a case study showed how the outbreak emerged from endemic areas spreading through sexual core transmitters and contextualizing the outbreak to current and past outbreaks. As the dynamics of sexually transmitted infections spread have changed to online partnership selection and demographically to include more women, partnership selection continues to remain highly localized. Furthermore, it is important to present methods to increase interpretability and accuracy of visual representations of data.

Nurses' perception of threats to human dignity in caring for patients with COVID-19: A qualitative study

PurposeIdentifying threats to patients' dignity is crucial for maintaining and promoting their dignity. Preserving patient dignity should be regarded as both a patient’s right and a moral obligation. This study was conducted to evaluate nurses' understanding of threats to human dignity in the care of patients with COVID-19. MethodsThis qualitative study was conducted using conventional content analysis in various clinical departments in Iran. Clinical nurses from different departments were selected through purposeful sampling. Data were collected via in-depth semi-structured interviews with 15 clinical nurses over two months in 2021 and analyzed using qualitative content analysis. ResultsDuring data analysis, two main categories and ten subcategories were identified: 1) Unethical behaviors (lack of respect for patient privacy, lack of patient involvement in decision-making procedures, ignoring the patient as an individual, aggressive behaviors, stigmatization, and failure to provide palliative and end-of-life care to patients and their families); 2) Organizational factors (inadequate financial support, lack of patient-care facilities, strict restrictions, and employing less experienced nurses in COVID wards). ConclusionThe present study showed that most participants expressed concerns about violations of patients' dignity and identified many threats to it. Based on the findings, it is suggested that other factors threatening patients' dignity be identified, the threats eliminated, and healthy treatment environments created to promote patients' dignity.

Artificial Intelligence-based Enhanced Brain Tumor Prediction: A Comprehensive Investigation of Machine Learning and Deep Learning

Brain tumors pose a significant threat to patients’ quality of life and survival rates, with traditional diagnostic methods often falling short due to their time-consuming nature and susceptibility to high misdiagnosis rates. Recent progress in Artificial Intelligence (AI), especially in Machine Learning (ML) and Deep Learning (DL), offer promising alternatives for the prediction and diagnosis of brain tumors. AI models, including Support Vector Machine (SVM), Random Forests, Logistic Regression, Artificial Neural Network (ANN), Convolutional Neural Network (CNN), and Long Short-Term Memory (LSTM) models networks, have demonstrated superior performance in processing complex medical data, thereby enhancing predictive accuracy and aiding clinical decision-making. This review systematically evaluates the application of these AI techniques in brain tumor prediction, highlighting their strengths and limitations, as well as the challenges faced in terms of model interpretability, data applicability, and patient privacy. Furthermore, this paper explored future prospects for improving model interpretability, transfer learning and domain adaptation for enhancing model applicability, and federated learning for preserving patient privacy. By addressing these issues, Artificial Intelligence can significantly advance brain tumor diagnosis and treatment, ultimately improving patient outcomes.