Review of HIPAA, Part 1: History, Protected Health Information, and Privacy and Security Rules

For-profit versus non-profit cybersecurity posture: breach types and locations in healthcare organisations

BACKGROUND

The implementation of emerging technologies has resulted in an increase of data breaches in healthcare organisations, especially during the COVID-19 pandemic. Health information and cybersecurity managers need to understand if, and to what extent, breach types and locations are associated with their organisation's business type.

OBJECTIVE

To investigate if breach type and breach location are associated with business type, and if so, investigate how these factors affect information systems and protected health information in for-profit versus non-profit organisations.

METHOD

The quantitative study was performed using chi-square tests for association and post-hoc comparison of column proportions analysis on an archival data set of reported healthcare data breaches from 2020 to 2022. Data from the Department of Health and Human Services website was retrieved and each organisation classified as for-profit or non-profit.

RESULTS

For-profit organisations experienced a significantly higher number of breaches due to theft, and non-profit organisations experienced a significantly higher number of breaches due to unauthorised access. Furthermore, the number of breaches that occurred on laptops and paper/films was significantly higher in for-profit organisations.

CONCLUSION

While the threat level of hacking techniques is the same in for-profit and non-profit organisations, certain breach types are more likely to occur within specific breach locations based on the organisation's business type. To protect the privacy and security of medical information, health information and cybersecurity managers need to align with industry-leading frameworks and controls to prevent specific breach types that occur in specific locations within their environments.

Privacy in Community Pharmacies in Saudi Arabia: A Cross-Sectional Study

BACKGROUND

Privacy in healthcare is a fundamental right essential to maintain patient confidentiality and trust. Community pharmacies in Saudi Arabia (SA) play a critical role in the healthcare system by providing accessible services and serving as initial points of contact for medical advice. However, the open nature of these settings poses significant challenges in maintaining patient privacy.

METHODS

This cross-sectional study used electronic surveys distributed across various online platforms. The target sample included Saudi adults, with a sample size of 385 participants to achieve 80% statistical power at a 95% confidence interval. The survey comprised demographic questions and sections evaluating perceptions of privacy, the importance of privacy, and personal experiences regarding privacy in community pharmacies. Descriptive statistics and logistic regression models were used for the analysis.

RESULTS

A total of 511 responses were obtained. The mean age was 33.5 years, with an almost equal distribution of males (49.71%) and females (50.29%). Most participants held a bachelor's degree or higher (78.67%). Privacy perceptions varied, with only 9.0% strongly agreeing that there was a private space for consultations, while 64.0% felt that the design of community pharmacies did not adequately consider patient privacy, and 86.9% reported that conversations could be overheard. Privacy concerns were notable, with almost one-half of the participants (47.6%) having concerns about privacy and 56.6% doubting the confidentiality of their health information. Moreover, 17.6% reported being asked for unnecessary personal information when buying medication, and 56.2% admitted to avoiding discussing a health problem with the pharmacist due to privacy concerns. Experiences of privacy breaches were reported by 15.7% of respondents. Logistic regression analysis revealed that the availability of private space in the pharmacy and patients feeling that the pharmacy respects their privacy were associated with a lower likelihood of avoiding discussions with pharmacists due to privacy concerns (OR = 0.758, CI = 0.599-0.0957 and OR = 0.715, CI = 0.542-0.945 respectively) Conversely, greater privacy concerns and previous privacy breaches significantly increased the likelihood of avoiding discussions with pharmacists in the community pharmacy (OR = 1.657, CI = 1.317-2.102 and OR = 4.127, CI = 1.886-9.821 respectively).

CONCLUSIONS

This study highlights the significant concerns regarding privacy practices in community pharmacies in SA. Thus, there is a need for standards to improve privacy in community pharmacies, such as mandating the need for private consultation areas and enhanced staff training on handling privacy-related issues. Addressing the issue of privacy is crucial for maintaining patient trust, improving healthcare service quality, and ensuring effective patient-pharmacist interactions.

Effective Privacy Protection Strategies for Pregnancy and Gestation Information From Electronic Medical Records: Retrospective Study in a National Health Care Data Network in China

BACKGROUND

Pregnancy and gestation information is routinely recorded in electronic medical record (EMR) systems across China in various data sets. The combination of data on the number of pregnancies and gestations can imply occurrences of abortions and other pregnancy-related issues, which is important for clinical decision-making and personal privacy protection. However, the distribution of this information inside EMR is variable due to inconsistent IT structures across different EMR systems. A large-scale quantitative evaluation of the potential exposure of this sensitive information has not been previously performed, ensuring the protection of personal information is a priority, as emphasized in Chinese laws and regulations.

OBJECTIVE

This study aims to perform the first nationwide quantitative analysis of the identification sites and exposure frequency of sensitive pregnancy and gestation information. The goal is to propose strategies for effective information extraction and privacy protection related to women's health.

METHODS

This study was conducted in a national health care data network. Rule-based protocols for extracting pregnancy and gestation information were developed by a committee of experts. A total of 6 different sub-data sets of EMRs were used as schemas for data analysis and strategy proposal. The identification sites and frequencies of identification in different sub-data sets were calculated. Manual quality inspections of the extraction process were performed by 2 independent groups of reviewers on 1000 randomly selected records. Based on these statistics, strategies for effective information extraction and privacy protection were proposed.

RESULTS

The data network covered hospitalized patients from 19 hospitals in 10 provinces of China, encompassing 15,245,055 patients over an 11-year period (January 1, 2010-December 12, 2020). Among women aged 14-50 years, 70% were randomly selected from each hospital, resulting in a total of 1,110,053 patients. Of these, 688,268 female patients with sensitive reproductive information were identified. The frequencies of identification were variable, with the marriage history in admission medical records being the most frequent at 63.24%. Notably, more than 50% of female patients were identified with pregnancy and gestation history in nursing records, which is not generally considered a sub-data set rich in reproductive information. During the manual curation and review process, 1000 cases were randomly selected, and the precision and recall rates of the information extraction method both exceeded 99.5%. The privacy-protection strategies were designed with clear technical directions.

CONCLUSIONS

Significant amounts of critical information related to women's health are recorded in Chinese routine EMR systems and are distributed in various parts of the records with different frequencies. This requires a comprehensive protocol for extracting and protecting the information, which has been demonstrated to be technically feasible. Implementing a data-based strategy will enhance the protection of women's privacy and improve the accessibility of health care services.

Privacy preservation for federated learning in health care

Artificial intelligence (AI) shows potential to improve health care by leveraging data to build models that can inform clinical workflows. However, access to large quantities of diverse data is needed to develop robust generalizable models. Data sharing across institutions is not always feasible due to legal, security, and privacy concerns. Federated learning (FL) allows for multi-institutional training of AI models, obviating data sharing, albeit with different security and privacy concerns. Specifically, insights exchanged during FL can leak information about institutional data. In addition, FL can introduce issues when there is limited trust among the entities performing the compute. With the growing adoption of FL in health care, it is imperative to elucidate the potential risks. We thus summarize privacy-preserving FL literature in this work with special regard to health care. We draw attention to threats and review mitigation approaches. We anticipate this review to become a health-care researcher's guide to security and privacy in FL.

#OrthoTwitter: Blending Information, Education, and Entertainment Online

BACKGROUND

Twitter has become a part of every medical field, including orthopaedics. #OrthoTwitter is the hashtag commonly used for orthopaedic-related tweets. Researchers have assessed the impact of Twitter in orthopaedics, but no study has evaluated the individual tweets under #OrthoTwitter. The purpose of the present study was to determine the nature of the content and interactions under #OrthoTwitter and to analyze the usefulness of #OrthoTwitter.

METHODS

After a pilot study, an analysis of #OrthoTwitter tweets was conducted from May 1, 2021, to June 30, 2021. Data were extracted in 2 stages. In Stage I, data were manually extracted at 8 p . m . IST (Indian Standard Time) on alternate days. In Stage II, data were collected using the web-scraping tool Octoparse. Data were analyzed on the basis of 3 characteristics-topic, purpose, and format of the tweet-with each characteristic comprising 10, 6, and 7 categories, respectively. An association analysis was performed using SPSS software.

RESULTS

One thousand and twenty-three tweets were analyzed. Five hundred and fifty-three (54%) of the 1,023 tweets were from orthopaedic surgeons and 123 (12%) were from orthopaedic residents. Medical students aspiring to be orthopaedic surgeons contributed 31 tweets (3%). #OrthoTwitter was also used by non-orthopaedic departments, most frequently radiology. Tweets that were educational or informative were the most common, as compared with tweets of other purposes. Two hundred and forty-six (24%) of the 1,023 tweets were educational (e.g., discussions of cases or journals) and 368 (36%) were informative (e.g., conference announcements and advertisements). Notable tweet subcategories included those related to COVID-19 (71 tweets; 7%), those of a motivational nature (41 tweets; 4%), and those containing some type of graphic content (644 tweets; 63%), for the topic, purpose, and format characteristics, respectively. We noted significantly more likes for tweets with an educational purpose (p = 0.017) and for tweets with images (p < 0.001). We also noted a significant number of retweets of educational tweets (p < 0.001).

CONCLUSIONS

#OrthoTwitter provides a unique environment in which education, news, collegial interaction, social responsibility, and entertainment thrive, making Twitter a virtual community. Tweets with an educational purpose and those that included images generated more interactions. Orthopaedic surgeons should consider using #OrthoTwitter in their orthopaedic-related tweets for a broader reach.

Screening for Risk of Fall-Related Inpatient Trauma in a US Acute Care Setting

Introduction Falls during hospitalization are a leading cause of preventable trauma-related injuries. Factors associated with fall risk include an unfamiliar environment, changes in health status, and efficacy based on the home environment. Assessing fall efficacy with an individualized prevention plan can decrease falls. The primary aim of this study was to estimate the effect of implementing a fall efficacy screening and intervention on reducing patient falls. Methods The study utilized a quasi-experimental, cross-sectional design with a convenience sample of patients admitted to an in-patient adult medical unit within a community hospital over a twelve-month period. Sampling times included pre-implementation, immediately post-implementation, and a second post-implementation phase. The intervention consisted of an admission fall efficacy screening tool and an individualized educational initiative. Statistical analysis included descriptive statistics of central tendency and dispersion, along with inferential statistics using independent sample t-tests, chi-square tests, correlations, and binary logistic regression. Results Among the study participants (n=2,074), the total sample had an average age of 67.7 (+/- 17.4) years and had mean scores of 13.3 (6.9) on the Short Falls Efficacy Scale-International and 51.8 (20.3) on the Morse Fall Scale. Fifty-two percent of the study population were female; 16.2% of the patients were diagnosed with cerebrovascular accident (CVA) or CVA-like symptoms. Fall rates decreased with a rate of change of -4.15% after efficacy screening and intervention. Males demonstrated higher efficacy in avoiding falls compared to females (t(828) = 3.369, p <0.001). Patients with a CVA diagnosis demonstrated higher efficacy scores compared to non-CVA patients (t(2071) = -3.348, p <0.001). FES risk groups (OR of 5.632, 95% CI (2.171-7.892)) and age over 65 (OR 1.21, 95% CI (1.006-1.442)) were significant predictors of a fall when patients with a primary CVA diagnosis were omitted from the sample (p= 0.022 and 0.046 respectively). Conclusion The findings suggest that efficacy screening may be associated with decreased falls for acute care non-CVA inpatient populations over 65 years of age. Further research into the predictive utility of fall efficacy screening in acute care CVA and non-CVA hospitalized patient populations aged 65 years and above is recommended.

Patient Autonomy in Medical Education: Navigating Ethical Challenges in the Age of Artificial Intelligence

The increasing integration of Artificial Intelligence (AI) in the medical domain signifies a transformative era in healthcare, with promises of improved diagnostics, treatment, and patient outcomes. However, this rapid technological progress brings a concomitant surge in ethical challenges permeating medical education. This paper explores the crucial role of medical educators in adapting to these changes, ensuring that ethical education remains a central and adaptable component of medical curricula. Medical educators must evolve alongside AI's advancements, becoming stewards of ethical consciousness in an era where algorithms and data-driven decision-making play pivotal roles in patient care. The traditional paradigm of medical education, rooted in foundational ethical principles, must adapt to incorporate the complex ethical considerations introduced by AI. This pedagogical approach fosters dynamic engagement, cultivating a profound ethical awareness among students. It empowers them to critically assess the ethical implications of AI applications in healthcare, including issues related to data privacy, informed consent, algorithmic biases, and technology-mediated patient care. Moreover, the interdisciplinary nature of AI's ethical challenges necessitates collaboration with fields such as computer science, data ethics, law, and social sciences to provide a holistic understanding of the ethical landscape.

Metaverse Wearables for Immersive Digital Healthcare: A Review

The recent exponential growth of metaverse technology has been instrumental in reshaping a myriad of sectors, not least digital healthcare. This comprehensive review critically examines the landscape and future applications of metaverse wearables toward immersive digital healthcare. The key technologies and advancements that have spearheaded the metamorphosis of metaverse wearables are categorized, encapsulating all-encompassed extended reality, such as virtual reality, augmented reality, mixed reality, and other haptic feedback systems. Moreover, the fundamentals of their deployment in assistive healthcare (especially for rehabilitation), medical and nursing education, and remote patient management and treatment are investigated. The potential benefits of integrating metaverse wearables into healthcare paradigms are multifold, encompassing improved patient prognosis, enhanced accessibility to high-quality care, and high standards of practitioner instruction. Nevertheless, these technologies are not without their inherent challenges and untapped opportunities, which span privacy protection, data safeguarding, and innovation in artificial intelligence. In summary, future research trajectories and potential advancements to circumvent these hurdles are also discussed, further augmenting the incorporation of metaverse wearables within healthcare infrastructures in the post-pandemic era.

Which information locations in covered entities under HIPAA must be secured first? A multi-criteria decision-making approach

Creating adequate safeguards for physical and online locations (e.g., desktop computers, network servers) where protected health information (PHI) may be breached is critical for management within entities compliant with the Health Information Portability and Accountability Act (HIPAA). With the increasing complexity of cyber breaches and budgetary issues, prioritizing which locations require the most immediate attention by top management through a data-driven model is more important than ever. Using CORAS threat modeling and five methods for multi-criteria decision-making, these locations were ranked from greatest to least risk of data breaches. Statistical methods were subsequently used for consistency and robustness checks. The findings illustrate that each type of covered entity under HIPAA must prioritize a different set of locations to safeguard first: health care providers must focus on the security of network servers, other portable electronic devices, and category of others (i.e., miscellaneous locations); health plans must focus on the security of paper and films, network servers, and others; and business associates must focus on the security of category of others, network servers, and other portable electronic devices. Combined with data on the source of the breaches (external vs. internal) and type of threats (e.g., hacking, theft), these findings provide recommendations for risk identification for privacy officers across health care.

Federated generalized linear mixed models for collaborative genome-wide association studies

Federated association testing is a powerful approach to conduct large-scale association studies where sites share intermediate statistics through a central server. There are, however, several standing challenges. Confounding factors like population stratification should be carefully modeled across sites. In addition, it is crucial to consider disease etiology using flexible models to prevent biases. Privacy protections for participants pose another significant challenge. Here, we propose distributed Mixed Effects Genome-wide Association study (dMEGA), a method that enables federated generalized linear mixed model-based association testing across multiple sites without explicitly sharing genotype and phenotype data. dMEGA employs a reference projection to correct for population-stratification and utilizes efficient local-gradient updates among sites, incorporating both fixed and random effects. The accuracy and efficiency of dMEGA are demonstrated through simulated and real datasets. dMEGA is publicly available at https://github.com/Li-Wentao/dMEGA.

Asynchronous Federated Learning for Improved Cardiovascular Disease Prediction Using Artificial Intelligence

Healthcare professionals consider predicting heart disease an essential task and deep learning has proven to be a promising approach for achieving this goal. This research paper introduces a novel method called the asynchronous federated deep learning approach for cardiac prediction (AFLCP), which combines a heart disease dataset and deep neural networks (DNNs) with an asynchronous learning technique. The proposed approach employs a method for asynchronously updating the parameters of DNNs and incorporates a temporally weighted aggregation technique to enhance the accuracy and convergence of the central model. To evaluate the effectiveness of the proposed AFLCP method, two datasets with various DNN architectures are tested, and the results demonstrate that the AFLCP approach outperforms the baseline method in terms of both communication cost and model accuracy.

Ten simple rules for providing bioinformatics support within a hospital

Bioinformatics has become a key aspect of the biomedical research programmes of many hospitals' scientific centres, and the establishment of bioinformatics facilities within hospitals has become a common practice worldwide. Bioinformaticians working in these facilities provide computational biology support to medical doctors and principal investigators who are daily dealing with data of patients to analyze. These bioinformatics analysts, although pivotal, usually do not receive formal training for this job. We therefore propose these ten simple rules to guide these bioinformaticians in their work: ten pieces of advice on how to provide bioinformatics support to medical doctors in hospitals. We believe these simple rules can help bioinformatics facility analysts in producing better scientific results and work in a serene and fruitful environment.

Design and implementation of a New Blockchain-based digital health passport: A Moroccan case study

In the context of COVID-19 pandemic, the Moroccan Interior and Health Ministries have proposed to use the health pass with a QR code to identify vaccinated people. Additionally, the government suggested a mobile application to control the health passport authenticity. However, the key problem is the possibility of anyone scanning the QR code and figuring out citizens' private information, causing severe issues about individual privacy. In this work, the main contribution is integrating a private Blockchain-based digital health passport to ensure high protection of sensitive information, security and privacy among all the actors (Government, Ministry of Interior, Ministry of Health, verifiers) that comply with the CNDP (National Commission for the Control of Personal Data Protection) and the Moroccan Law 09–08. In our proposed architectural framework solution, we identify two types of actors: authorized and unauthorized, to limit and control access to the citizens' personal information. Besides, to preserve individuals' privacy, we adopt on-chain and off-chain storage (Interplanetary File Systems IPFS). In our case, smart contracts improve security and privacy in the health passport verification process. Our system implementation describes the proposed solution to grant individual privacy. To verify and validate our approach, we used Remix-IDE and Ethereum Blockchain to build smart contracts.

Trustworthy AI: Closing the gap between development and integration of AI systems in ophthalmic practice

An increasing number of artificial intelligence (AI) systems are being proposed in ophthalmology, motivated by the variety and amount of clinical and imaging data, as well as their potential benefits at the different stages of patient care. Despite achieving close or even superior performance to that of experts, there is a critical gap between development and integration of AI systems in ophthalmic practice. This work focuses on the importance of trustworthy AI to close that gap. We identify the main aspects or challenges that need to be considered along the AI design pipeline so as to generate systems that meet the requirements to be deemed trustworthy, including those concerning accuracy, resiliency, reliability, safety, and accountability. We elaborate on mechanisms and considerations to address those aspects or challenges, and define the roles and responsibilities of the different stakeholders involved in AI for ophthalmic care, i.e., AI developers, reading centers, healthcare providers, healthcare institutions, ophthalmological societies and working groups or committees, patients, regulatory bodies, and payers. Generating trustworthy AI is not a responsibility of a sole stakeholder. There is an impending necessity for a collaborative approach where the different stakeholders are represented along the AI design pipeline, from the definition of the intended use to post-market surveillance after regulatory approval. This work contributes to establish such multi-stakeholder interaction and the main action points to be taken so that the potential benefits of AI reach real-world ophthalmic settings.

Harnessing Soft Logic to Represent the Privacy Paradox

The digital era introduces a significant issue concerning the preservation of individuals’ privacy. Each individual has two autonomous traits, privacy concern which indicates how anxious that person is about preserving privacy, and privacy behavior which refers to the actual actions the individual takes to preserve privacy. The significant gap between these two traits is called the privacy paradox. While the existence and the extensive distribution of the privacy paradox is widely-considered in both academic and public discussion, no convincing explanation of the phenomenon has been provided. In this study we harness a new mathematical approach, “soft logic,” to better represent the reality of the privacy paradox. Soft numbers extend zero from a singularity to an infinite one-dimensional axis, thus enabling the representation of contradictory situations that exist simultaneously, i.e., a paradox. We develop a mathematical model for representing the privacy paradox with soft numbers, and demonstrate its application empirically. This new theory has the potential to address domains that mix soft human reality with robust technological reality.

A single-center, cross-sectional study of cross-professional faculties' perception to virtual class under different scenarios: A stepwise approach

BACKGROUND

Virtual teaching in medical education is rising with the increased need caused by the recent pandemic. However, evaluations of the perception of clinical teachers across professions for setting a virtual class in different teaching scenarios are limited. This study aims to identify cross-professional clinical teachers' perception of virtual classes and the acceptability of the virtual class-specific checklist for setting a virtual class.

METHODS

We conducted a cross-sectional study to investigate clinical teachers' need to set and teach a virtual class and then designed a virtual class-specific checklist with five essential steps and a related training program in July 2021. After the training, 186 participants were randomly enrolled in October 2021 to evaluate their perceptions about setting virtual classes and the acceptability of the virtual class-specific checklist using an online assessment questionnaire.

RESULTS

In our institution, the number of faculty-led virtual classes has recently been on the increase. Our study revealed that most teachers agreed that virtual classes could break space and time limitations, but that the Internet environment could affect the fluency of the virtual class. They further agreed that the essential five steps in the checklist should vary depending on the type of teaching scenario. Most clinical teachers, with the exception of those who teach in the operating room, considered the operating room as the most difficult scenario for setting virtual classes.

CONCLUSION

Faculty training for setting virtual classes is essential, and the essential virtual class-specific five steps are suitable for different teachers and teaching scenarios. However, the virtual class-specific checklist should be further adjusted according to the limitations caused by emerging innovative virtual teaching technology.

Experiences of handovers between shifts among nurses in small and medium-sized hospitals: a focus-group study

Nursing handovers represent an important and complex form of communication in healthcare organizations that involve the exchange of patient-related information between nurses. This qualitative descriptive study aimed to identify the intershift handover experiences among nurses working in small and medium-sized hospitals. Focus-group interviews were conducted with 30 nurses who directly participated in patient care in such hospitals in South Korea. The reporting of the study findings adhered to the Consolidated Criteria for Reporting Qualitative Research checklist. The data were analysed using content analysis. Under the main theme of "baton touch in a relay", 6 categories, 17 subcategories and 45 codes (concepts) were derived. The six categories were "procedural rituals for shifts", "non-standardized handover training", "inconsistent handover style", "stress due to handovers", "coping strategies for handovers" and "interruptions of handovers". Nurses in small and medium-sized hospitals strive to improve the quality of handovers by preparing individual-level coping strategies under difficult conditions. This indicates that standardized handover education strategies need to be developed for nurses that are suitable for the personnel systems of small and medium-sized hospitals.

Training Undergraduate Students in HIPAA Compliance

The Health Insurance Portability and Accountability Act (HIPAA) has radically changed the way healthcare is conducted, and its relevance continues to expand as healthcare technology evolves. This article describes a method for training inexperienced undergraduate students to become HIPAA-compliant clinical research volunteers in a pediatric traumatic brain injury (TBI) study. Volunteers are trained to use the hospital's electronic health records system (EHR) to identify potential study candidates for approach, and they develop this skill set through google classroom modules/quizzes along with routine zoom calls to solidify their consenting approach. Since the inception of this study in 2018, there have been over one hundred different undergraduate research volunteers involved, and there has not been a single HIPAA violation to date. This compliance success rate is indicative of the efficacy of this training protocol. This paper serves as a guide to implementing HIPAA compliance training and ensuring accountability in new and existing clinical research studies.

#Healthcare: Patient and Family Uses and Perceptions of Health Care Social Media

BACKGROUND/INTRODUCTION

Although social media is becoming a primary resource for information and support in all aspects of life, including health care, limited information is available describing social media use in parents whose child undergoes surgical care.

OBJECTIVES/AIMS

The aims of this study were to describe how patients/families use social media to address health care needs and understand their perceptions of social media privacy and reliability.

METHODS

A descriptive survey of 39 questions, both fixed choice and open ended, was distributed to a convenience sample of parents during their child's preoperative visit. Descriptive statistics were used to summarize fixed-choice responses. Content analysis was used to assess open-ended responses and comments.

RESULTS

A total of 205 completed surveys were available for review. Overall, 195 (95.6%) reported using social media, with 70 (35%) using social media up to 5 times a day and another 61 (30.5%) using it 6 to 40 times a day. Respondents used social media for medical information (122/60.1%), to make health care decisions (53/26.5%), after a diagnosis (104/52%), after a medical visit (88/44%), and to update friends and family (129/65.5%). Most respondents were undecided (111/58.1%) when asked how reliable medical information was on social media sites, with 33 (17.3%) believing medical information to be "reliable to very reliable" on social media sites. Among the 61 comments received, 4 themes emerged: Spectrum of Social Media Use, Social Media and Health Care Interaction, Social Media as a Source of Support and Peer Experience, and Reliability of Social Media.

DISCUSSION

Most respondents utilized social media for health care information while reporting feeling undecided on the reliability of the information. Understanding the multiple ways patients and families utilize social media provides health care members opportunities to discuss medical information, inform health care decision making, and support patient and family needs.

A Systematic Review of Federated Learning in the Healthcare Area: From the Perspective of Data Properties and Applications

Recent advances in deep learning have shown many successful stories in smart healthcare applications with data-driven insight into improving clinical institutions’ quality of care. Excellent deep learning models are heavily data-driven. The more data trained, the more robust and more generalizable the performance of the deep learning model. However, pooling the medical data into centralized storage to train a robust deep learning model faces privacy, ownership, and strict regulation challenges. Federated learning resolves the previous challenges with a shared global deep learning model using a central aggregator server. At the same time, patient data remain with the local party, maintaining data anonymity and security. In this study, first, we provide a comprehensive, up-to-date review of research employing federated learning in healthcare applications. Second, we evaluate a set of recent challenges from a data-centric perspective in federated learning, such as data partitioning characteristics, data distributions, data protection mechanisms, and benchmark datasets. Finally, we point out several potential challenges and future research directions in healthcare applications.

Intravenous versus inhalational anesthesia trial for outcome following intracranial aneurysm surgery: A prospective randomized controlled study

Background

For maintenance of anesthesia for intracranial aneurysmal neck clipping, both intravenous and inhalational anesthetics are in vogue. We aimed to evaluate the superiority of one agent over the other for long-term neurological outcomes in these patients.

Methods

This prospective assessor-blind randomized study was conducted in 106 patients of 18-65 years of age with World Federation of Neurosurgeons Grade I-II of subarachnoid hemorrhage. After written informed consent, the patients were randomized into - intravenous group (Propofol) and inhalational group (Desflurane). The primary outcome was to study neurological outcome using Glasgow outcome scale (GOS) at 3 months following discharge while secondary outcomes included intraoperative brain condition, intraoperative hemodynamics, duration of hospital stay, Modified Rankin Score (MRS) at discharge, MRS, and Barthel's index at 3 months following discharge and estimation of perioperative biomarkers of brain injury.

Results

The GOS at 3 months was 5 (5.00-5.00) in the propofol group and 5 (4.00-5.00) in the desflurane group (P = 0.24). Both the anesthetics were similar in terms of intraoperative hemodynamics, brain relaxation, duration of hospital stay, MRS at discharge and 3 months, and Barthel Index at 3 months (P > 0.05). The perioperative serum interleukin-6 and S100B were comparable among the groups (P > 0.05).

Conclusion

The long-term neurological outcome of good grade aneurysm patients undergoing craniotomy and clipping remains comparable with the use of either propofol or desflurane. The effect of the two anesthetic agents on the various clinical parameters and the biomarkers of brain injury is also similar.

#OrthoTwitter: social media as an educational tool

Aims

The increased use of social media creates opportunity for new, effective methods of delivering medical and clinical education. Twitter is a popular social media platform where users can post frequent updates and create threads containing related content using hashtags. This study aims to investigate and analyse the type of content relating to orthopaedic surgery that is being posted on the platform of Twitter.

Methods

A retrospective search was performed for tweets containing the words ‘orthopaedic surgery’ or ‘orthopedic surgery’ or the use of the hashtag ‘#OrthoTwitter’ between November 2018 to November 2019. A total of 5243 tweets were included.

Results

Tweets containing ‘orthopaedic surgery’ or ‘orthopedic surgery’ most frequently contained promotional or marketing content (30% promotional, 21% marketing), and private organisations were the category of author to which the greatest number of tweets belonged (30%). Tweets containing educational or research content were the least common among all tweets containing ‘orthopaedic surgery’ or ‘orthopedic surgery’ (11%). In contrast, of the tweets containing the hashtag ‘#OrthoTwitter’, 44% contained educational or research content, 15% contained promotional content and no tweets containing marketing content. Furthermore, 87% of all tweets using the hashtag ‘#OrthoTwitter’ were from orthopaedic surgeons, and the least number of tweets were from private organisations (2%).

Conclusion

Twitter is a widely used social media platform regarding orthopaedic surgery. We propose that the hashtag ‘#OrthoTwitter’ can be used to create an online community of orthopaedic surgeons where members can assist one another through sharing reliable and educational content.

Development of a semi-structured, multifaceted, computer-aided questionnaire for outbreak investigation: e-Outbreak Platform

Aggressive tracing of contacts of confirmed cases is crucial to Taiwan's successful control of the early spread of COVID-19. As the pandemic lingers, an epidemiological investigation that can be conducted efficiently in a timely manner can help decrease the burden on the health personnel and increase the usefulness of such information in decision making. To develop a new tool that can improve the current practice of epidemiological investigation by incorporating new technologies in digital platform and knowledge graphs. To meet the various needs of the epidemiological investigation, we decided to develop an e-Outbreak Platform that provides a semi-structured, multifaceted, computer-aided questionnaire for outbreak investigation. There are three major parts of the platform: (1) a graphic portal that allows users to have an at-glance grasp of the functions provided by the platform and then choose the one they need; (2) disease-specific questionnaires that can accommodate different formats of the information, including text typing, button selection, and pull-down menu; and (3) functions to utilize the stored information, including report generation, statistical analyses, and knowledge graphs displaying contact tracing. When the number of outbreak investigation increases, the knowledge graphs can be extended to encompass other persons appearing in the same location at the same time, i.e., constituting a potential contact cluster. The information extracted can also be used to display the tracing on a map in animation. Overall, this system can provide a basis for further refinement that can be generalized to a variety of outbreak investigations.