Cardiovascular health is of utmost importance, and the rapid evolution of monitoring devices and applications has revolutionized the way we manage it. From smartwatches to electrocardiogram (ECG) devices and implantable solutions, these technological advancements aim to improve patient outcomes. However, the integration of patients' health information with technology necessitates stringent measures to protect personal health information (PHI) and ensure compliance with the Health Insurance Portability and Accountability Act (HIPAA). This study investigates the compliance of various cardiovascular health management (CHM) software, applications, and systems with HIPAA regulations. Through an extensive literature review and systematic analysis, we assessed over 25 scholarly articles that discuss the intersection of cardiovascular health monitoring devices and HIPAA compliance. Our evaluation of the current state of compliance across different applications revealed both strengths and weaknesses in the industry’s privacy and security measures. Our findings indicate that although many CHM solutions demonstrate compliance with HIPAA regulations, there remain areas of concern, particularly regarding data encryption, storage, and access control. To address these gaps, we propose specific recommendations for enhancing privacy and security, including the adoption of robust encryption methods, strict access control policies, and regular security audits. Future research should focus on the development of standardized compliance frameworks for cardiovascular health management systems, as well as the assessment of emerging technologies and their potential impact on HIPAA compliance. By advancing our understanding of the challenges and opportunities in this field, we aim to contribute to the ongoing efforts to protect patients' PHI and promote secure, reliable, and effective health management solutions.

Skin cancer is a deadly disease. Melanoma is a type of skin cancer responsible for the high mortality rate. Early detection of skin cancer can enable patients to treat the disease and minimize the death rate. Skin cancer detection is challenging since different types of skin lesions share high similarities. This paper proposes a computer-based deep learning approach that will accurately identify different kinds of skin lesions. Deep learning approaches can detect skin cancer very accurately since the models learn each pixel of an image. Sometimes humans can get confused by the similarities of the skin lesions, which we can minimize by involving the machine. However, not all deep learning approaches can give better predictions. Some deep learning models have limitations, leading the model to a false-positive result. We have introduced several deep learning models to classify skin lesions to distinguish skin cancer from different types of skin lesions. Before classifying the skin lesions, data preprocessing and data augmentation methods are used. Finally, a Convolutional Neural Network (CNN) model and six transfer learning models such as Resnet-50, VGG-16, Densenet, Mobilenet, Inceptionv3, and Xception are applied to the publically available benchmark HAM10000 dataset to classify seven classes of skin lesions and to conduct a comparative analysis. The models will detect skin cancer by differentiating the cancerous cell from the non-cancerous ones. The models’ performance is measured using performance metrics such as precision, recall, f1 score, and accuracy. We receive accuracy of 90, 88, 88, 87, 82, and 77 percent for inceptionv3, Xception, Densenet, Mobilenet, Resnet, CNN, and VGG16, respectively. Furthermore, we develop five different stacking models such as inceptionv3-inceptionv3, Densenet-mobilenet, inceptionv3-Xception, Resnet50-Vgg16, and stack-six for classifying the skin lesions and found that the stacking models perform poorly. We achieve the highest accuracy of 78 percent among all the stacking models.

Interoperability remains one of the biggest challenges facing healthcare organizations today. Despite the advancements made through digital transformation and API that allow increased interoperability, patients still have to contend with a different patient portal for each provider they visit. Several health systems are unable to successfully exchange EHR data. API transfer and consolidate patient information including medical history and treatment records across the disparate health care systems. Mobile apps use API to gather data from various medical wearables and add the data to a patient's health record. However, API exposes application logic and sensitive data information giving patient data a window to the World Wide Web and has thus increasingly become a target for attackers. As the need for tighter API security grows, managing APIs becomes more important than ever. The goal of this paper is to provide an overview and discuss research questions that can aid in understanding and building the knowledge base on API data integration and interoperability.

In response to the COVID-19 pandemic, the White House and a coalition of leading research groups have prepared the COVID-19 Open Research Dataset (CORD-19) containing over 51,000 scholarly articles, including over 40,000 with full text, about COVID-19, SARS-CoV-2, and related coronaviruses. Medical professional including physicians frequently seek answers to specific questions to improve guidelines and decisions. The huge resource of medical literature is important sources to generate new insights that can help medical communities to provide relevant knowledge and overall fight against the infectious disease. There are ongoing attempts to develop intelligent systems to automatically extract relevant knowledge from many unstructured documents. In this paper, we propose an efficient question answering framework based on automatically analyzing thousands of articles to generate both long text answers (sections/ paragraphs) in response to the questions that are posed by medical communities. In the process of developing the framework, we explored natural language processing techniques like query expansion, data preprocessing, and vector space models early. We show the initial results of an example query answering for the incubation period.

Alzheimer's disease, the most common type of dementia, is ranked sixth amongst the leading causes of death in the United States. As the disease progresses, individuals affected will experience challenges with memory loss, vision impairment, word-finding, and reasoning. When riddled with such symptoms, password memorization can pose a problem. Even though there are several authentication systems in play, none considers all signs and symptoms Alzheimer's disease can cause. We propose a two-step password authentication system that would utilize geolocation and fingerprint biometric screening to assist this specific population by providing a more secure way to access their information.

Fall is a major concern among elderly population. Accidental fall if unattended for long time, may lead to severe injuries and disability. Prompt detection of fall is an important research problem, particularly in the homecare settings for elderly citizens, where not enough service providers are available to monitor their health and welfare daily. There are some available fall detection approaches, however, they are either expensive or the accuracy of fall detection is not satisfactory. In this paper, we develop a low cost fall detection framework using Android phone's built in sensors with the goal of detecting fall and notifying to emergency responders. We generate a dataset and train 3 popular machine learning algorithms to detect fall events: Logistic Regression, Naïve Bayes and Neural Network. Our study shows the performance comparison of the learning algorithms. The evaluation results show that our approach can successfully detect fall and neural network-based technique can perform better than other learning techniques.

Electronic Health Records have proven to be indispensable yet continue to present a host of problems. One of the most pressing concerns is how to share patient information freely and efficiently. Hospitals and clinics may share data internally, but there is an inability due to the lack of infrastructure or an unwillingness to share data between systems. Implementing a peer-to-peer distributed digital ledger, known as a blockchain, to record and transmit transactional data in conjunction with Cloud based technologies may be a solution to bridge the communication gap. This paper sets forth a new approach for a blockchain and cloud computing network utilizing Amazon Web Services and Ethereum blockchain to facilitate semantic level interoperability of Electronic Health Records systems without standardized data forms and formatting.

Deriving the benefits of electronic information resources as provided by electronic medical record systems (EMR) on a global scale is critically dependent on physicians' adoption and continued use of such resources. Yet, there is little known about the factors that motivate physicians to adopt and continue to use electronic information resources. The purpose of this article is to investigate the motivational factors leading to adoption and usage of electronic information resources in diverse regions of the world including developing countries (India and Egypt) and developed countries (the US). Based on the socio-cognitive theory and the decomposed theory of planned behavior, the authors surveyed 314 physicians in three countries in order to assess their engagement with electronic information resources. Data was analyzed via PLS for direct and indirect effects of socio-cognitive constructs and their impact on electronic information resources' use intentions. The authors' results suggest there are similarities as well as differences in factors impacting adoption and usage of electronic information resources pre and post EMR implementation in both developing and developed countries. They found that physicians' perceptions of effort expectations, technological infrastructure and support, and computer self-efficacy were the strongest direct drivers influencing intentions to use electronic information resources both in pre and post-EMR implementations in all three countries that were studied. However, a richer set of factors contributed to physicians' intentions to continue to use electronic information resources, post-EMR, in developed countries as compared to pre-EMR in developing countries. Social influences had a strong indirect effects, influencing physicians' perceptions of effort expectations post-EMR as well as perceptions of performance expectations pre-EMR implementation. Computer self-efficacy was a significant predictor of effort expectations of an electronic information resource both pre and post-EMR implementation while compatibility with physicians' practices significantly influenced performance expectations in both pre and post EMR implementations in all three countries studied. The authors' study provides important theoretical and practical implications for successful management and implementation of electronic information resources such that they are adopted and used in the healthcare environment.