Personal Health Records Research Articles

A REVIEW: AN ENHANCED E-HEALTH SYSTEM USING PERMISSIONED BLOCKCHAIN-BASED IDENTITY MANAGEMENT AND USER AUTHENTICATION SCHEME

The health problems of this world's population are universal. Everyone nowadays values protecting their personal health information. No one likes to let people in on their vulnerabilities. Electronic Health Records (EHR) were developed as a solution to this issue. However, several writers have pointed out that there are drawbacks with EHR, including as costs, data loss, security threats, and others. In this study, we will explore how blockchain may replace EHR and other healthcare record keeping methods. Blockchain is a distributed ledger that records and verifies all transactions via the use of hash values and referencing of prior blocks. Since blockchain is becoming more popular in the medical industry. Blockchain ensures the confidentiality, availability, auditability, and manageability of health records conveniently. Keyword Blockchain, Smart contracts, PHR (Personal Health Records), healthcare, access control.

Privacy-Conflict Resolution for Integrating Personal and Electronic Health Records in Blockchain-Based Systems.

This paper suggests using blockchain technology for privacy concerns in integrating personal health records and electronic health records in decentralized e-healthcare systems. This report focuses on designing automatic conflict resolution techniques to ensure patient data ownership, transparency, and privacy in inter-organizational processes. This paper proposes designing automatic conflict resolution techniques in decentralized e-healthcare systems, which can improve inter-organizational processes in healthcare. Using blockchain technology to integrate personal and electronic health records can ensure patient data ownership and promote transparency in data usage, addressing privacy concerns in healthcare systems. This paper emphasizes the importance of data privacy and protection in healthcare systems, highlighting the need for compliance with laws and regulations. The research results, including the proof-of-concept prototype, can provide practical insights into implementing conflict resolution techniques in decentralized e-healthcare systems.

The Iowa Health Data Resource (IHDR): an innovative framework for transforming the clinical health data ecosystem.

This manuscript will be of interest to most Clinical and Translational Science Awards (CTSA) as they retool for the increasing emphasis on translational science from translational research. This effort is an extension of the EDW4R work that most CTSAs have done to deploy infrastructure and tools for researchers to access clinical data. The Iowa Health Data Resource (IHDR) is a strategic investment made by the University of Iowa to improve access to real-world health data. The goals of IHDR are to improve the speed of translational health research, to boost interdisciplinary collaboration, and to improve literacy about health data. The first objective toward this larger goal was to address gaps in data access, data literacy, lack of computational environments for processing Personal Health Information (PHI) and the lack of processes and expertise for creating transformative datasets. A three-pronged approach was taken to address the objective. The approach involves integration of an intercollegiate team of non-informatics faculty and staff, a data enclave for secure patient data analyses, and novel comprehensive datasets. To date, all five of the health science colleges (dentistry, medicine, nursing, pharmacy, and public health) have had at least one staff and one faculty member complete the two-month experiential learning curriculum. Over the first two years of this project, nine cohorts totaling 36 data liaisons have been trained, including 18 faculty and 18 staff. IHDR data enclave eliminated the need to duplicate computational infrastructure inside the hospital firewall which reduced infrastructure, hardware and human resource costs while leveraging the existing expertise embedded in the university research computing team. The creation of a process to develop and implement transformative datasets has resulted in the creation of seven domain specific datasets to date. The combination of people, process, and technology facilitates collaboration and interdisciplinary research in a secure environment using curated data sets. While other organizations have implemented individual components to address EDW4R operational demands, the IHDR combines multiple resources into a novel, comprehensive ecosystem IHDR enables scientists to use analysis tools with electronic patient data to accelerate time to science.

Patient Identification and Healthcare System

Abstract: Personal Health Records Identification is a system that allows an individual to store his/her health related information with doctor. The Personal Health Records Identification can control his/her data stored on the system using the fingerprint. This work aims to propose a privacy-preserved identification scheme to be used in the Personal Health Records Identification system during an emergency situation especially when the victim is unconscious. The fingerprint- based scheme under a Protected Biometric Template concept is applied to identify the victim without compromising the privacy of the victim. The usability and security discussions in the proposed scheme is practical under the current existing communication technology

Developing Training for MyChart Use for Older Persons with Mild Cognitive Impairment

AbstractBackgroundMyChart, a mechanism for sharing personal health information and communication among patients and healthcare providers is well received but underutilized by older adults, especially those with Mild Cognitive Impairment (MCI). Difficulty using or accessing technology coupled with rapid changes in portals may negatively impact a patient’s ability to effectively manage their health concerns. We report the initial phase of a study in older adults using focus groups to 1) determine the nature of user difficulties and knowledge gaps, and explore the preferred training methods (in person, virtual, manual), 2) develop a MyChart training module based on focus group data, and 3) assess the feasibility of providing training to improve MyChart use amongst older adults with MCI.MethodWe conducted three focus groups with older adults with MCI. Groups were held from August‐November 2022 and consisted of 15 participants both familiar and unfamiliar with MyChart. Focus groups were conducted virtually, and participants were asked open ended questions regarding user difficulties, knowledge gaps, and preferred training methods. Sessions were recorded, transcribed, and later analyzed utilizing thematic analysis. Focus groups continued until saturation was reached.ResultWe approached 84 ADRC participants to get 15 needed to reach saturation. 1) Technology and Health: participants acknowledged technology could be used positively to control health 2) MyChart: participants reported positive view of MyChart as a way to communicate with doctors and manage medical information 3) Difficulty with MyChart: participants stated there was no clear way to get help if needed to make appointments or find medical information 4) Learning technology: participants reported a preference for verbal teaching methods such as a video (over solely manualized teaching) that could be slowed down and stressed the importance of repetition of information.ConclusionThe use of direct patient feedback was positive in that we discovered that older adults with MCI are open to use MyChart to monitor their health but have clear preferences on how to learn and get support with the technology.

Translating the consent form is the tip of the iceberg: using cognitive interviews to assess the barriers to informed consent in South African health facilities

The increasing digitisation of personal health data has led to an increase in the demand for onward health data. This study sought to develop local language scripts for use in public sector maternity clinics to capture informed consent for onward health data use. The script considered five possible health data uses: 1. Sending of general health information content via mobile phones; 2. Delivery of personalised health information via mobile phones; 3. Use of women’s anonymised health data; 4. Use of child’s anonymised health data; and 5. Use of data for recontact. Qualitative interviews (n = 54) were conducted among women attending maternity services in three public health facilities in Gauteng and Western Cape, South Africa. Using cognitive interviewing techniques, interviews sought to:(1) explore understanding of the consent script in five South African languages, (2) assess women’s understanding of what they were consenting to, and (3) improve the consent script. Multiple rounds of interviews were conducted, each followed by revisions to the consent script, until saturation was reached, and no additional cognitive failures identified. Cognitive failures were a result of: (1) words and phrases that did not translate easily in some languages, (2) cognitive mismatches that arose as a result of different world views and contexts, (3) linguistic gaps, and (4) asymmetrical power relations that influence how consent is understood and interpreted. Study activities resulted in the development of an informed consent script for onward health data use in five South African languages for use in maternity clinics.

Building Trust: Developing an Ethical Communication Framework for Navigating Artificial Intelligence Discussions and Addressing Potential Patient Concerns

Introduction: In an era where technological advancements in large language models and generative artificial intelligence (AI) platforms like ChatGPT continually redefine the boundaries of medicine, the advent of Amazon Web Services (AWS) HealthScribe on July 26th, 2023, heralds a transformative moment in healthcare. This HIPAA-eligible generative AI service, capable of transcribing patient-provider conversations and automatically entering them into an electronic health record (EHR) system, represents a profound intersection of technology and medical practice. As this technology permeates clinical settings, addressing associated patient concerns with AI becomes paramount. Although providers may grasp the intricacies of such technology swiftly, patients are likely to harbor concerns. Patients may be unfamiliar with the technology or question its safety. Clear and precise communication will be essential for physicians to ease patient concerns. Our team interviewed forty-eight subjects to survey their understanding, concerns, and opinions on artificial intelligence. We then classified and visually charted their responses. Based on our data, we created a framework for ethical communication physicians can follow when talking to patients about using artificial intelligence in clinical settings. Method: A multidisciplinary team encompassing physicians, advanced practice providers specializing in hematology and oncology, and bioethicists engaged with forty-eight subjects. The study population represented a diverse cross-section of society, differing in aspects such as age, sex assigned at birth, political orientation, education, income, ethnicity, occupation, and religious affiliation. Our investigative process included a structured interview containing twenty-five foundational questions. No personal health information was collected during the questionnaire, and all questions sought only to garner subject opinion. Questions were designed to probe areas of interest such as: (1) the subject's general feelings about AI in healthcare, (2) their familiarity with AI technologies, (3) the existence of any specific concerns, and (4) a deeper exploration of those concerns. The responses were meticulously collected, categorized, and analyzed to discern emergent trends. Results: Our team developed The TRUST Framework based on our research findings by identifying and addressing the three primary concerns with AI in healthcare; transparency, confidentiality, and consent. The data presented a mosaic of varied opinions, with minimal discernible trends correlating with specific demographic attributes. One of the subtle patterns identified pertained to age. Subjects within the 12-35 age bracket demonstrated a familiarity with artificial intelligence, generally expressing indifference rather than concern or enthusiasm. Those aged 35-65 exhibited greater pronounced indifference than any age demographic and revealed an unfamiliarity with existing artificial intelligence tools. Conversely, respondents aged 65 and older expressed the highest level of concern and a prevalent unfamiliarity with the range of available artificial intelligence applications. Conclusion: Effective communication when introducing the use of artificial intelligence in healthcare settings is imperative. This conclusion is based on abstract reasoning and rooted in real-world feedback received from a diverse subject population. Our research revealed that subjects have varying feelings and concerns with artificial intelligence, irrespective of their background. Therefore, avoiding unconscious bias and having a framework for communicating how AI will be used is essential. As the frontier of AI continues to expand, the need for ethical and transparent communication will only grow. This study serves as a call to action for healthcare providers to commit to clear, honest, and empathetic communication about AI's role in patient care. Doing so will promote the overall acceptance of AI in healthcare, subsequently enhancing patient outcomes and alleviating patient concerns by building trust.

Data management in the digital health environment scale development study*

PurposeThis study aims to develop a scale that measures individuals' perceptions of privacy, security, use, sharing, benefit and satisfaction in the digital health environment. MethodWithin the scope of the study, in the scale development process; The stages of literature review, creation of items, getting expert opinion, conducting a pilot study, ensuring construct and criterion validity, and reliability analyses were carried out. The literature was searched for the formation of the question items. To evaluate the created question items, expert opinion was taken, and the question items were arranged according to the feedback from the experts. In line with the study's purpose and objectives, the focus group consisted of individuals aged 18 and above within the community. The convenience sampling method was employed for sample selection. Data were collected using an online survey conducted through Google Forms. Before commencing the survey, participants were briefed on the research's content. A pilot study was conducted with 30 participants, and as a result of the feedback from the participants, eliminations were made in the question items and the scale was made ready for application. The research was conducted by reference to 812 participants in the community. Expert evaluations of the question items were obtained, and a pilot study was conducted. A sociodemographic information form, a scale developed by the researcher, Norman and Skinner's e-Health Literacy Scale, and the Mobile Health and Personal Health Record Management Scale were used as data collection tools. Results: The content validity of the research was carried out by taking expert opinions and conducting a pilot study. Exploratory factor analysis and confirmatory factor analysis were performed to ensure construct validity. The total variance explained by the scale was 60.43%. The results of confirmatory factor analysis indicated that the 20-Item 5-factor structure exhibited good fit values. According to the analysis of criterion validity, there are significant positive correlations among the Data Management in the Digital Health Environment Scale, Norman and Skinner’s e-Health Literacy Scale and the Mobile Health and Personal Health Record Management Scale (p < 0.01; r = .669, .378). The Cronbach's alpha value of the scale is .856, and the test–retest reliability coefficient is .909. Conclusion: The Data Management in the Digital Health Environment Scale is a valid and reliable measurement tool that measures individuals' perceptions of privacy, security, use, sharing, benefit and satisfaction in the digital health environment.

A Decision Cloud Ranking Approach Based on Privacy and Security in Blockchain E-Health Industry 4.0 Systems

E-health Industry 4.0 systems ranking based on Blockchain is a multi-criteria decision-making (MCDM) problem, considering the multiple evaluation properties, their significance, and data variety. The final closeness between the evaluation sample and ideal solutions also constitutes an optimisation problem. To the authors’ knowledge, no study has provided a multi-privacy and security ranking approach solution for E-health Industry 4.0 systems based on Blockchain. Consequently, this study proposes and discusses a multi-privacy and security ranking approach solution for E-health Industry 4.0 systems, utilising the SFS-FWZIC method to address the significance of properties issue and the Grey-TOPSIS method to tackle data variation, multiple evaluation properties, and the ideal solutions optimisation problem. The methodology of the proposed approach consists of three sections. First, three decision matrices are constructed based on the intersection of E-health systems with the mentioned properties, intersecting Electronic Health Records (EHRs), Electronic Medical Records (EMRs), and Personal Health Records (PHRs) with seven properties. Second, the weights of each privacy and security property are calculated using the SFS-FWZIC method. Finally, the weights determined by the SFS-FWZIC method and the three decision matrices are input into the Grey-TOPSIS method to rank E-health systems across the three categories. The findings reveal the following: (1) The SFS-FWZIC method effectively assigned weights to privacy and security properties, with access control achieving the highest significance weight (0.1934) and Secure-search receiving the lowest weight (0.0603). (2) The Grey-TOPSIS method efficiently ranked E-health systems across the three categories based on various parameters, including α=0.1,α=0.3,α=0.5,α=0.7, and α=0.9. Sensitivity and correlation analysis were conducted to evaluate the results, revealing high correlation results based on each α value across all discussed scenarios of changing property weights. The implications of this study can lead to better decision-making, improved security and privacy, increased competition, and widespread adoption, ultimately contributing to more efficient and effective healthcare delivery.

Evaluating the Landscape of Personal Health Records in Korea: Results of the National Health Informatization Survey.

This study examined the adoption and utilization of personal health records (PHR) across Korean medical institutions using data from the 2020 National Health and Medical Informatization Survey. Spearheaded by the Ministry of Health and Welfare and prominent academic societies, this study surveyed PHR utilization in 574 medical institutions. Among these institutions, 84.9% (487 hospitals) maintained medical portals. However, just 14.1% (81 hospitals) had web-based or mobile PHRs, with 66.7% (28 of 42) of tertiary care hospitals adopting them. Tertiary hospitals led in PHR services: 87.8% offered certification issuance, 51.2% provided educational information, 63.4% supported online payment, and 95.1% managed appointment reservations. In contrast, general and smaller hospitals had lower rates. Online medical information viewing was prominent in tertiary hospitals (64.3%). Most patients accessed test results via PHRs, but other data types were less frequent, and only a few allowed downloads. Despite the widespread access to medical data through PHRs, integration with wearables and biometric data transfers to electronic medical records remained low, with limited plans for expansion in the coming three years. Approximately two-thirds of the surveyed medical institutions provided PHRs, but hospitals and clinics in charge of community care had very limited PHR implementation. Government-led leadership is required to invigorate the use of PHRs in medical institutions.

Benefits of Information Technology in Healthcare: Artificial Intelligence, Internet of Things, and Personal Health Records.

Systematic evaluations of the benefits of health information technology (HIT) play an essential role in enhancing healthcare quality by improving outcomes. However, there is limited empirical evidence regarding the benefits of IT adoption in healthcare settings. This study aimed to review the benefits of artificial intelligence (AI), the internet of things (IoT), and personal health records (PHR), based on scientific evidence. The literature published in peer-reviewed journals between 2016 and 2022 was searched for systematic reviews and meta-analysis studies using the PubMed, Cochrane, and Embase databases. Manual searches were also performed using the reference lists of systematic reviews and eligible studies from major health informatics journals. The benefits of each HIT were assessed from multiple perspectives across four outcome domains. Twenty-four systematic review or meta-analysis studies on AI, IoT, and PHR were identified. The benefits of each HIT were assessed and summarized from a multifaceted perspective, focusing on four outcome domains: clinical, psycho-behavioral, managerial, and socioeconomic. The benefits varied depending on the nature of each type of HIT and the diseases to which they were applied. Overall, our review indicates that AI and PHR can positively impact clinical outcomes, while IoT holds potential for improving managerial efficiency. Despite ongoing research into the benefits of health IT in line with advances in healthcare, the existing evidence is limited in both volume and scope. The findings of our study can help identify areas for further investigation.

Prototipe Rekam Kesehatan Personal pada Pasien Diabetes Mellitus (Literature Review)

Personal Health Record (PHR) is a collection of health-related data that is archived and managed by an individual or parties associated with the PHR. Its purpose is to provide a comprehensive and accurate summary of a person's health history that can be accessed online. This research is a Literature Review study which aims to determine the features and implementation of Personal Health Records in Patients with Diabetes Mellitus. The method used is the Literature Review method from 7 journals which uses several methods including the prototype method, qualitative methods, heuristic evaluation methods, applied development methods, participatory action research methods, and randomized controlled trial methods with data collection techniques through google scholar, direct science and pubmed. The results of the research are seen in the features and implementation of the Personal Health Record. The Personal Health Record feature for diabetes mellitus with dialysis measures has 25 features on the main page, the PHR feature for diabetes mellitus has 7 main features. PHR implementation allows users to access their personal health records anytime and anywhere. Thus, the PHR system can increase accessibility and allow users to access PHR without time restrictions and also the Personal Health Record can assist in monitoring the health condition of Diabetes Mellitus patients. Research conclusion According to the journals analyzed, PHR is effective because it can encourage patient participation in individual health controls, and can access information about diabetes mellitus through monitoring physical activity, diet, weight, and glucose levels.

Digital Sovereignty of Older Citizens for a Self-Determined Use of Personal Health Records: E-Learning Design and Study Results from the EPA-Coach Project

Promoting digital literacy and digital sovereignty has become one of the key policies in Europe. Digital sovereignty has been defined as the ability to act and exercise freedom of choice in the digital world. While digital services and applications offer great opportunities for the older generations to live independently and communicate with family and friends, the digital sovereignty of citizens including older adults has become a concern of policy-makers. Hence fostering digital sovereignty has been seen as an important objective for a self-determined use of digital media including control over own data. This paper presents an approach to designing an e-learning application for senior learners, which is aimed at fostering the digital sovereignty of older citizens in using Personal Health Records (PHR) in Germany. This e-learning application, called ePA-Coach, is designed in a research project funded by the Federal German Ministry of Education and Research to offer an opportunity for citizens 65+ to acquire digital skills needed for the self-determined use of PHR. The paper presents the digital literacy framework for exercising digital sovereignty related to PHR, which was developed in the ePA-Coach project based on the European Digital Competence Framework. We describe how this framework was applied in the design of micro-learning units and combined with gamification techniques from the Octalysis framework. Finally, we outline results from two exploratory studies with older citizens which revealed strong preferences for design elements related to digital sovereignty such as choice and control of the learning progress.

Improvement in medical consultation by using an AI-powered symptom assessment application (SAA)

Abstract Introduction The shortage of healthcare workers affects countries worldwide and primarily affects people in rural areas. Although Romania trains a high number of physicians, the absence of effective retention and motivation policies leads many of them to opt for higher-paying jobs in other countries. As a result, patients experience long travel and wait times. Our study assessed if using an AI-powered SAA, Ada, in a waiting room is feasible for patients, can improve consultation efficiency, and enhance patient-physician communication. Methods This study was conducted in the waiting room of a GP in Cluj-Napoca, Romania, from May to July 2022. Consenting patients completed a symptom assessment while waiting for their consultation. A survey was administered to patients, the study nurse, and physician to gather feedback on their experience. Results The study enrolled 49 patients (29 women, 20 men). Most of the patients found the questions in Ada to be of appropriate length (94%), understandable (90%), and engaging (86%). Patients using Ada felt that they could express all their health issues (79%) and explain their symptoms better to the doctor (84%). They also felt more confident to discuss symptoms that their doctor may have missed (86%) and would use Ada again (84%). Most patients completed the assessment independently (65%). The study physician reported that 70% of patients who used Ada answered questions quickly and asked more questions related to their symptoms. Discussion By providing personalized health information, Ada prepared patients for their consultation, resulting in improved patient engagement and facilitation of clinical conversation. Incorporating a SAA into routine patient care has the potential to enhance the quality of medical care and reduce consultation time, thus, compensating for physician shortages. Further studies to determine the efficacy of integrating SAA with telemedicine can further help tackle the issue of uneven health workforce distribution. Key messages • Patients found Ada, a mobile AI-powered symptom assessment application, easy to use, understandable, and would use it again. The app helped prepare patients for their doctor’s consultation. • Doctors reported increased patient engagement in consultation among users of Ada. These apps can help save time when used by patients in the waiting room and the results are shared with the doctors.

Personal health record storage and sharing using searchable encryption and blockchain: A comprehensive survey

AbstractPersonal Health Records (PHRs) allow patients to have full control over their health data. However, storage and sharing of PHRs still remains a difficult but necessary task, especially when health data is one of the major targets of cyber attacks worldwide. Searchable Encryption (SE) is a feasible solution for this problem and can be augmented by Blockchain to address some of its issues, such as verifiability. Therefore, SE using blockchain is a promising technologies to tackle the challenge of PHR storage and sharing. In this survey, we have explored the research works that use SE and blockchain technology for the same. The work starts with an introduction of cloud, searchable encryption and blockchain. Subsequently, we present a literature survey of the corresponding technologies. We then describe SE in detail and how it fits with blockchain. This is followed by description of noteworthy existing solutions for secure storage and sharing of PHRs. Even though there have been a number of surveys related to SE, none of them have surveyed the use of blockchain with SE or use of SE and blockchain in PHR sharing. The work concludes with a comparative study of these existing solutions and future scope in this direction.

Subjective Cognitive Load Evaluation of a Mobile Personal Health Record Application.

Mobile Personal Health Records (mPHRs), which make it possible to track and manage users' health information, can be an important aid in improving people's health. Despite its potential benefits, poor usability of systems can hinder the adoption and use of mPHRs. This study aims to evaluate the usability of a mobile health application in terms of perceived cognitive workload and performance. The cognitive workload experienced by 30 volunteers (15 experienced and 15 inexperienced), was measured while performing the given tasks with the NASA-Task Load Index (NASA-RTLX) scale, and the duration of the fulfillment of the tasks by eye tracking device. While there was no significant difference between the two user groups in the completion time of the tasks, a significant difference was found in the perceived cognitive load. "Making an appointment", which could take much longer to complete than other tasks, resulted in the highest cognitive load for all users. Further usability research using think-aloud protocols and user interviews could provide insights into design improvements for reducing cognitive load and enhancing performance.

Towards a universal patient-centric health record sharing platform

ObjectivesThis paper provides a practical approach to evaluate health record sharing platforms in terms of their ability to deliver interoperable healthcare of quality at a systems level. We use our previously published interoperability evaluation framework to evaluate our proposed System-level Record Sharing (SLRS) platform against four other common categories of health record sharing platforms in Canada, the United States, and Norway. MethodsIn this paper, we compare the SLRS platform architecture that we previously developed against 4 health record sharing platform categories. We conducted this comparative evaluation of 5 categories of healthcare platforms: SLRS, Commercial-Multi (CM), Commercial-Independent (CI), Governmental-Multi (GM), and Governmental-Independent (GI) using our proposed evaluation framework that is built upon quadruple aim, triple aim and Canadian Institute for Health Information (CIHI) health platform evaluation frameworks. ResultsSLRS and platforms managed by government organizations that provide a technology-independent or compatible platform were the most effective in terms of satisfying data interoperability, providing meaningful and effective information exchange, being compliant with health privacy regulations across a range of contexts, and having many of the costs paid for at a central level. All platforms struggled with context and process interoperability requirements, as well as providing evidence-based information across an entire health system. ConclusionTo optimize health management, both clinicians and patients need sharing of personal health information (PHI) across applications. Our findings indicate that commercial platforms in this study need to improve their governance structure and employ a consistent ontology that can be adopted by all EHR applications across a health system. Our proposed SLRS platform can support the sharing of health data across multiple health care organizations at a system-level, allowing clinicians to access patient health data to inform treatment and care decisions. Public interest abstractHealthcare organizations face barriers when exchanging information across their boundaries. Many obstacles are caused by varying technical requirements of the EHR applications they have, and their contextual regulations. Most healthcare organizations have a portal in which the patients can view their care history. However, the breadth of the information provided limits to the number of healthcare providers subscribed to that specific platform. Viewing the full history across the entire health system, requires maintaining multiple accounts.In this paper we refer to a new framework for sharing health information and its prototyped platform (SLRS) that we have previously developed in our lab. In this paper we report on how we evaluated the SLRS platform against four prominent practices of Health Information Exchange. We used our interoperability evaluation framework that was developed based on well-acknowledged criteria from literature and practice. We found areas of improvement for existing health information platforms so they can share their records across an entire health system.

Heart disease severity level identification system on Hyperledger consortium network.

Electronic Health Records (EHRs) play a vital role in the healthcare domain for the patient survival system. They can include detailed information such as medical histories, medications, allergies, immunizations, vital signs, and more. It can help to reduce medical errors, improve patient safety, and increase efficiency in healthcare delivery. EHR approaches are proven to be an efficient and successful way of sharing patients' personal health information. These kinds of highly sensitive information are vulnerable to privacy and security associated threats. As a result, new solutions must develop to meet the privacy and security concerns in health information systems. Blockchain technology has the potential to revolutionize the way electronic health records (EHRs) are stored, accessed, and utilized by healthcare providers. By utilizing a distributed ledger, blockchain technology can help ensure that data is immutable and secure from tampering. In this article, a Hyperledger consortium network has been developed for sharing health records with enhanced privacy and security. The attribute based access control (ABAC) mechanism is used for controlling access to electronic health records. The use of ABAC on the network provides EHRs with an extra layer of security and control, ensuring that only authorized users have access to sensitive data. By using attributes such as user identity, role, and health condition, it is possible to precisely control access to records on blockchain. Besides, a Gaussian naïve Bayes algorithm has been integrated with this consortium network for prediction of cardiovascular disease. The prediction of cardiovascular is difficult due to its correlated risk factors. This system is beneficial for both patients and physicians as it allows physicians to quickly identify high-risk patients and easily provide them with patient severity level using feature weight prediction algorithms. Dynamic emergency access control privileges are used for the emergency team and will be withdrawn once the emergency has been resolved, depending on the severity score. The system is implemented with the following medical datasets: the heart disease dataset, the Pima Indian diabetes dataset, the stroke prediction dataset, and the body fat prediction dataset. The above datasets are obtained from the Kaggle repository. This system evaluates system performance by simulating various operations using the Hyperledger Caliper benchmarking tool. The performance metrics such as latency, transaction rate, resource utilization, etc. are measured and compared with the benchmark.

Survey on security and privacy in Internet of Things‐based eHealth applications: Challenges, architectures, and future directions

AbstractThe integration of Internet of Things (IoT) technology into electronic health (eHealth) applications has revolutionized the healthcare landscape, enabling real‐time patient monitoring, personalized care, and improved patient outcomes. However, this convergence of IoT and healthcare also introduces critical security and privacy challenges, needing careful consideration. This survey comprehensively explores the multifaceted realm of security and privacy issues in IoT‐based eHealth applications. First, we taxonomize the diverse security threats that arise due to the interconnected nature of IoT medical devices. Additionally, we highlight privacy concerns stemming from the collection and sharing of personal health information, while reconciling them with the need for accessible and collaborative healthcare ecosystems. Second, we synthesize functional, ethical, and regulatory perspectives to pick up the major requirements needed in the context of eHealth data during their whole lifecycle, from creation to destruction. Third, we identify emerging research strategies employed to address security and privacy concerns, such as cloud‐based solutions, decentralized technologies such as blockchain technology and InterPlanetary File System (IPFS), cryptographic approaches, fine‐grained access control strategies, and so forth. Additionally, we examine the impact of these approaches on computational efficiency, latency, and energy consumption, critically evaluating their suitability in the healthcare context. Building upon this comprehensive assessment, we outline potential future research directions aimed at advancing security and privacy measures in IoT‐based eHealth applications.

Wearable technology data-based sleep and chronic disease relationship

Objective: The aim of the study was to examine the cross-sectional relationship between sleep duration and 12 chronic diseases&#x0D; (obesity risk, diabetes, asthma, renal failure, hypertensive diseases, chronic obstructive pulmonary disease, cardiovascular diseases,&#x0D; ischaemic heart disease, pulmonary heart disease, immunodeficiencies and immune system disorders) by transferring the data to the&#x0D; national electronic patient record system through wearable device technology.&#x0D; Materials and Methods: The data of the study were obtained from the Ministry of Health “Turkey National Personal Health Record&#x0D; System” (The “e-Nabız”). Between 30.03.2023 and 28.05.2023, 315448 data from 27847 people (15167 male) were collected and&#x0D; analysed on the basis of province, rural status, age group, gender and presence of chronic diseases and average, minimum (min),&#x0D; maximum (max) sleep duration. Descriptive statistics, chi-square analysis, Independent Samples t-Test, One-way Analysis of Variance&#x0D; and Pearson’s correlation coefficient were used.&#x0D; Results: The max. and average sleep duration were significantly shorter in men. Max. sleep duration decreased while the average sleep&#x0D; duration increased with increasing age. Participants with asthma, chronic renal failure and cerebrovascular diseases had decreased&#x0D; average sleep duration. The frequency of those who slept for 7-8 hours, which is the ideal sleep duration, is lower in all chronic diseases.&#x0D; Conclusion: Most of the common chronic diseases may affect the sleep duration times and quality, which may further affect the&#x0D; prognosis of these patients.