Security and privacy of electronic health records: Concerns and challenges

Tamilarasi P., Akila D.

Resource allocation for Big data streams in cloud systems involves selecting the appropriate cloud resources. Since incorrect resource allocation results in either under provisioning or over provisioning, accurate resource allocation becomes challenging in Big data applications. Hence, the objective of this work is to design an optimal solution for resource allocation for minimizing the network bandwidth and response delay. In this paper, a task allocation and re-allocation mechanism for Big data applications is designed. It consists of two important agents: RE-allocation Agent (REA) and Resource Agent (RA). The RA is responsible for mapping the user requirements to the available VMs. The REA monitors the resources and chooses the VMs for resource reconfiguration. Then, it dispatches an allocation or de-allocation request to RA, running in the physical system, based on the varying requirements of virtual machines. Experimental results show that the proposed TARA has less execution time and achieves better utilization of resources, when compared to existing tool.

Alsalem M.A., Zaidan A.A., Zaidan B.B., Hashim M., Albahri O.S., Albahri A.S., Hadi A., Mohammed K.I.

This study aims to systematically review prior research on the evaluation and benchmarking of automated acute leukaemia classification tasks. The review depends on three reliable search engines: ScienceDirect, Web of Science and IEEE Xplore. A research taxonomy developed for the review considers a wide perspective for automated detection and classification of acute leukaemia research and reflects the usage trends in the evaluation criteria in this field. The developed taxonomy consists of three main research directions in this domain. The taxonomy involves two phases. The first phase includes all three research directions. The second one demonstrates all the criteria used for evaluating acute leukaemia classification. The final set of studies includes 83 investigations, most of which focused on enhancing the accuracy and performance of detection and classification through proposed methods or systems. Few efforts were made to undertake the evaluation issues. According to the final set of articles, three groups of articles represented the main research directions in this domain: 56 articles highlighted the proposed methods, 22 articles involved proposals for system development and 5 papers centred on evaluation and comparison. The other taxonomy side included 16 main and sub-evaluation and benchmarking criteria. This review highlights three serious issues in the evaluation and benchmarking of multiclass classification of acute leukaemia, namely, conflicting criteria, evaluation criteria and criteria importance. It also determines the weakness of benchmarking tools. To solve these issues, multicriteria decision-making (MCDM) analysis techniques were proposed as effective recommended solutions in the methodological aspect. This methodological aspect involves a proposed decision support system based on MCDM for evaluation and benchmarking to select suitable multiclass classification models for acute leukaemia. The said support system is examined and has three sequential phases. Phase One presents the identification procedure and process for establishing a decision matrix based on a crossover of evaluation criteria and acute leukaemia multiclass classification models. Phase Two describes the decision matrix development for the selection of acute leukaemia classification models based on the integrated Best and worst method (BWM) and VIKOR. Phase Three entails the validation of the proposed system.

Hussain M., Al-Haiqi A., Zaidan A.A., Zaidan B.B., Kiah M., Iqbal S., Iqbal S., Abdulnabi M.

Mobile Health (mHealth) applications are readily accessible to the average users of mobile devices, and despite the potential of mHealth applications to improve the availability, affordability and effectiveness of delivering healthcare services, they handle sensitive medical data, and as such, have also the potential to carry substantial risks to the security and privacy of their users. Developers of applications are usually unknown, and users are unaware of how their data are being managed and used. This is combined with the emergence of new threats due to the deficiency in mobile applications development or the design ambiguities of the current mobile operating systems. A number of mobile operating systems are available in the market, but the Android platform has gained the topmost popularity. However, Android security model is short of completely ensuring the privacy and security of users' data, including the data of mHealth applications. Despite the security mechanisms provided by Android such as permissions and sandboxing, mHealth applications are still plagued by serious privacy and security issues. These security issues need to be addressed in order to improve the acceptance of mHealth applications among users and the efficacy of mHealth applications in the healthcare systems. The focus of this research is on the security of mHealth applications, and the main objective is to propose a coherent, practical and efficient framework to improve the security of medical data associated with Android mHealth applications, as well as to protect the privacy of their users. The proposed framework provides its intended protection mainly through a set of security checks and policies that ensure protection against traditional as well as recently published threats to mHealth applications. The design of the framework comprises two layers: a Security Module Layer (SML) that implements the security-check modules, and a System Interface Layer (SIL) that interfaces SML to the Android OS. SML enforces security and privacy policies at different levels of Android platform through SIL. The proposed framework is validated via a prototypic implementation on actual Android devices to show its practicality and evaluate its performance. The framework is evaluated in terms of effectiveness and efficiency. Effectiveness is evaluated by demonstrating the performance of the framework against a selected set of attacks, while efficiency is evaluated by comparing the performance overhead in terms of energy consumption, memory and CPU utilization, with the performance of a mainline, stock version of Android. Results of the experimental evaluations showed that the proposed framework can successfully protect mHealth applications against a wide range of attacks with negligible overhead, so it is both effective and practical.

Verheij R.A., Curcin V., Delaney B.C., McGilchrist M.M.

Background Enormous amounts of data are recorded routinely in health care as part of the care process, primarily for managing individual patient care. There are significant opportunities to use these data for other purposes, many of which would contribute to establishing a learning health system. This is particularly true for data recorded in primary care settings, as in many countries, these are the first place patients turn to for most health problems. Objective In this paper, we discuss whether data that are recorded routinely as part of the health care process in primary care are actually fit to use for other purposes such as research and quality of health care indicators, how the original purpose may affect the extent to which the data are fit for another purpose, and the mechanisms behind these effects. In doing so, we want to identify possible sources of bias that are relevant for the use and reuse of these type of data. Methods This paper is based on the authors’ experience as users of electronic health records data, as general practitioners, health informatics experts, and health services researchers. It is a product of the discussions they had during the Translational Research and Patient Safety in Europe (TRANSFoRm) project, which was funded by the European Commission and sought to develop, pilot, and evaluate a core information architecture for the learning health system in Europe, based on primary care electronic health records. Results We first describe the different stages in the processing of electronic health record data, as well as the different purposes for which these data are used. Given the different data processing steps and purposes, we then discuss the possible mechanisms for each individual data processing step that can generate biased outcomes. We identified 13 possible sources of bias. Four of them are related to the organization of a health care system, whereas some are of a more technical nature. Conclusions There are a substantial number of possible sources of bias; very little is known about the size and direction of their impact. However, anyone that uses or reuses data that were recorded as part of the health care process (such as researchers and clinicians) should be aware of the associated data collection process and environmental influences that can affect the quality of the data. Our stepwise, actor- and purpose-oriented approach may help to identify these possible sources of bias. Unless data quality issues are better understood and unless adequate controls are embedded throughout the data lifecycle, data-driven health care will not live up to its expectations. We need a data quality research agenda to devise the appropriate instruments needed to assess the magnitude of each of the possible sources of bias, and then start measuring their impact. The possible sources of bias described in this paper serve as a starting point for this research agenda.

Muhammad G., Alhamid M.F., Alsulaiman M., Gupta B.

The advancement of next-generation network technologies provides a huge improvement in healthcare facilities. Technologies such as 5G, edge computing, cloud computing, and the Internet of Things realize smart healthcare that a client can have anytime, anywhere, and in real time. Edge computing offers useful computing resources at the edge of the network to maintain low-latency and real-time computing. In this article, we propose a smart healthcare framework using edge computing. In the framework, we develop a voice disorder assessment and treatment system using a deep learning approach. A client provides his or her voice sample captured by smart sensors, and the sample goes to the edge computing for initial processing. Then the edge computing sends data to a core cloud for further processing. The assessment and management are controlled by a service provider through a cloud manager. Once the automatic assessment is done, the decision is sent to specialists, who prescribe appropriate treatment to the clients. The proposed system achieves 98.5 percent accuracy and 99.3 percent sensitivity using the Saarbrucken Voice Disorder database.

Kisekka V., Giboney J.S.

The diffusion of health information technologies (HITs) within the health care sector continues to grow. However, there is no theory explaining how success of HITs influences patient care outcomes. With the increase in data breaches, HITs' success now hinges on the effectiveness of data protection solutions. Still, empirical research has only addressed privacy concerns, with little regard for other factors of information assurance.The objective of this study was to study the effectiveness of HITs using the DeLone and McLean Information Systems Success Model (DMISSM). We examined the role of information assurance constructs (ie, the role of information security beliefs, privacy concerns, and trust in health information) as measures of HIT effectiveness. We also investigated the relationships between information assurance and three aspects of system success: attitude toward health information exchange (HIE), patient access to health records, and perceived patient care quality.Using structural equation modeling, we analyzed the data from a sample of 3677 cancer patients from a public dataset. We used R software (R Project for Statistical Computing) and the Lavaan package to test the hypothesized relationships.Our extension of the DMISSM to health care was supported. We found that increased privacy concerns reduce the frequency of patient access to health records use, positive attitudes toward HIE, and perceptions of patient care quality. Also, belief in the effectiveness of information security increases the frequency of patient access to health records and positive attitude toward HIE. Trust in health information had a positive association with attitudes toward HIE and perceived patient care quality. Trust in health information had no direct effect on patient access to health records; however, it had an indirect relationship through privacy concerns.Trust in health information and belief in the effectiveness of information security safeguards increases perceptions of patient care quality. Privacy concerns reduce patients' frequency of accessing health records, patients' positive attitudes toward HIE exchange, and overall perceived patient care quality. Health care organizations are encouraged to implement security safeguards to increase trust, the frequency of health record use, and reduce privacy concerns, consequently increasing patient care quality.

Albahri O.S., Albahri A.S., Mohammed K.I., Zaidan A.A., Zaidan B.B., Hashim M., Salman O.H.

The new and ground-breaking real-time remote monitoring in triage and priority-based sensor technology used in telemedicine have significantly bounded and dispersed communication components. To examine these technologies and provide researchers with a clear vision of this area, we must first be aware of the utilised approaches and existing limitations in this line of research. To this end, an extensive search was conducted to find articles dealing with (a) telemedicine, (b) triage, (c) priority and (d) sensor; (e) comprehensively review related applications and establish the coherent taxonomy of these articles. ScienceDirect, IEEE Xplore and Web of Science databases were checked for articles on triage and priority-based sensor technology in telemedicine. The retrieved articles were filtered according to the type of telemedicine technology explored. A total of 150 articles were selected and classified into two categories. The first category includes reviews and surveys of triage and priority-based sensor technology in telemedicine. The second category includes articles on the three-tiered architecture of telemedicine. Tier 1 represents the users. Sensors acquire the vital signs of the users and send them to Tier 2, which is the personal gateway that uses local area network protocols or wireless body area network. Medical data are sent from Tier 2 to Tier 3, which is the healthcare provider in medical institutes. Then, the motivation for using triage and priority-based sensor technology in telemedicine, the issues related to the obstruction of its application and the development and utilisation of telemedicine are examined on the basis of the findings presented in the literature.

Kruse C.S., Beane A.

Background: Health information technology (HIT) has been introduced into the health care industry since the 1960s when mainframes assisted with financial transactions, but questions remained about HIT’s contribution to medical outcomes. Several systematic reviews since the 1990s have focused on this relationship. This review updates the literature. Objective: The purpose of this review was to analyze the current literature for the impact of HIT on medical outcomes. We hypothesized that there is a positive association between the adoption of HIT and medical outcomes. Methods: We queried the Cumulative Index of Nursing and Allied Health Literature (CINAHL) and Medical Literature Analysis and Retrieval System Online (MEDLINE) by PubMed databases for peer-reviewed publications in the last 5 years that defined an HIT intervention and an effect on medical outcomes in terms of efficiency or effectiveness. We structured the review from the Primary Reporting Items for Systematic Reviews and Meta-Analysis (PRISMA), and we conducted the review in accordance with the Assessment for Multiple Systematic Reviews (AMSTAR). Results: We narrowed our search from 3636 papers to 37 for final analysis. At least one improved medical outcome as a result of HIT adoption was identified in 81% (25/37) of research studies that met inclusion criteria, thus strongly supporting our hypothesis. No statistical difference in outcomes was identified as a result of HIT in 19% of included studies. Twelve categories of HIT and three categories of outcomes occurred 38 and 65 times, respectively. Conclusions: A strong majority of the literature shows positive effects of HIT on the effectiveness of medical outcomes, which positively supports efforts that prepare for stage 3 of meaningful use. This aligns with previous reviews in other time frames.

Kruse C.S., Smith B., Vanderlinden H., Nealand A.

The privacy of patients and the security of their information is the most imperative barrier to entry when considering the adoption of electronic health records in the healthcare industry. Considering current legal regulations, this review seeks to analyze and discuss prominent security techniques for healthcare organizations seeking to adopt a secure electronic health records system. Additionally, the researchers sought to establish a foundation for further research for security in the healthcare industry. The researchers utilized the Texas State University Library to gain access to three online databases: PubMed (MEDLINE), CINAHL, and ProQuest Nursing and Allied Health Source. These sources were used to conduct searches on literature concerning security of electronic health records containing several inclusion and exclusion criteria. Researchers collected and analyzed 25 journals and reviews discussing security of electronic health records, 20 of which mentioned specific security methods and techniques. The most frequently mentioned security measures and techniques are categorized into three themes: administrative, physical, and technical safeguards. The sensitive nature of the information contained within electronic health records has prompted the need for advanced security techniques that are able to put these worries at ease. It is imperative for security techniques to cover the vast threats that are present across the three pillars of healthcare.

Carey D.J., Fetterolf S.N., Davis F.D., Faucett W.A., Kirchner H.L., Mirshahi U., Murray M.F., Smelser D.T., Gerhard G.S., Ledbetter D.H.

Geisinger Health System (GHS) provides an ideal platform for Precision Medicine. Key elements are the integrated health system, stable patient population, and electronic health record (EHR) infrastructure. In 2007, Geisinger launched MyCode, a system-wide biobanking program to link samples and EHR data for broad research use.Patient-centered input into MyCode was obtained using participant focus groups. Participation in MyCode is based on opt-in informed consent and allows recontact, which facilitates collection of data not in the EHR and, since 2013, the return of clinically actionable results to participants. MyCode leverages Geisinger's technology and clinical infrastructure for participant tracking and sample collection.MyCode has a consent rate of >85%, with more than 90,000 participants currently and with ongoing enrollment of ~4,000 per month. MyCode samples have been used to generate molecular data, including high-density genotype and exome sequence data. Genotype and EHR-derived phenotype data replicate previously reported genetic associations.The MyCode project has created resources that enable a new model for translational research that is faster, more flexible, and more cost-effective than traditional clinical research approaches. The new model is scalable and will increase in value as these resources grow and are adopted across multiple research platforms.Genet Med 18 9, 906-913.

Miotto R., Li L., Kidd B.A., Dudley J.T.

Secondary use of electronic health records (EHRs) promises to advance clinical research and better inform clinical decision making. Challenges in summarizing and representing patient data prevent widespread practice of predictive modeling using EHRs. Here we present a novel unsupervised deep feature learning method to derive a general-purpose patient representation from EHR data that facilitates clinical predictive modeling. In particular, a three-layer stack of denoising autoencoders was used to capture hierarchical regularities and dependencies in the aggregated EHRs of about 700,000 patients from the Mount Sinai data warehouse. The result is a representation we name “deep patient”. We evaluated this representation as broadly predictive of health states by assessing the probability of patients to develop various diseases. We performed evaluation using 76,214 test patients comprising 78 diseases from diverse clinical domains and temporal windows. Our results significantly outperformed those achieved using representations based on raw EHR data and alternative feature learning strategies. Prediction performance for severe diabetes, schizophrenia, and various cancers were among the top performing. These findings indicate that deep learning applied to EHRs can derive patient representations that offer improved clinical predictions, and could provide a machine learning framework for augmenting clinical decision systems.

Hussain M., Al-Haiqi A., Zaidan A.A., Zaidan B.B., Kiah M.L., Anuar N.B., Abdulnabi M.

To survey researchers' efforts in response to the new and disruptive technology of smartphone medical apps, mapping the research landscape form the literature into a coherent taxonomy, and finding out basic characteristics of this emerging field represented on: motivation of using smartphone apps in medicine and healthcare, open challenges that hinder the utility, and the recommendations to improve the acceptance and use of medical apps in the literature.We performed a focused search for every article on (1) smartphone (2) medical or health-related (3) app, in four major databases: MEDLINE, Web of Science, ScienceDirect, and IEEE Xplore. Those databases are deemed broad enough to cover both medical and technical literature.The final set included 133 articles. Most articles (68/133) are reviews and surveys that refer to actual apps or the literature to describe medical apps for a specific specialty, disease, or purpose; or to provide a general overview of the technology. Another group (43/133) carried various studies, from evaluation of apps to exploration of desired features when developing them. Few researchers (17/133) presented actual attempts to develop medical apps, or shared their experiences in doing so. The smallest portion (5/133) proposed general frameworks addressing the production or operation of apps.Since 2010, researchers followed the trend of medical apps in several ways, though leaving areas or aspect for further attention. Regardless of their category, articles focus on the challenges that hinder the full utility of medical apps and do recommend mitigations to them.Research on smartphone medical apps is active and various. We hope that this survey contribute to the understanding of the available options and gaps for other researchers to join this line of research.

Cifuentes M., Davis M., Fernald D., Gunn R., Dickinson P., Cohen D.J.

Amer K.

Mythili J., Gopalakrishnan R.

Shahriar S., Dara R., Akalu R.

Khan R.A., Mushtaq S., Lone S.A., Gupta R., Moon A.H.

Ozcelik M.M., Kok I., Ozdemir S.

ABSTRACTInternet of Medical Things (IoMT) paradigm refers to the process of collection, transmission and analysis of healthcare data using communication and information systems over the internet. IoMT consist of medical devices that can link to the internet or other networks, including wearables, sensors, monitoring tools and other medical appliances. IoMT data can be utilised to lower costs, increase the effectiveness of healthcare delivery and improve the patient health status. In addition to the potential benefits IoMT may provide, the impact of COVID19 pandemic has also strengthened the desire to collect patient data remotely and pushed a lot of medical professionals to utilise IoMT applications such as telemedicine, telehealth, remote patient monitoring, remote patient diagnostics and distant consultations etc. The expectation is that IoMT market size and the usage will increase dramatically and IoMT will change the conventional healthcare systems significantly in the upcoming years. Motivated with that growth expectation, this study aims to analyse the IoMT, its components, enabling technologies and applications by emphasising the fundamental pillars (sensing, communication, data analytics, and security) essential for developing a reliable, dependable, and secure IoMT ecosystem. Furthermore, this study conducts a detailed analysis of recent major cyberattacks targeting the healthcare industry, evaluating their impact and discussing the key lessons derived from these incidents by employing DOTMLPFI approach. Additionally, this survey offers a concise overview of the emerging technologies that complement IoMT in the development of smart healthcare systems and explores potential future directions within this evolving field.

Moulaei K., Akhlaghpour S., Fatehi F.

Alhasan T.K.

AbstractHealth Information Exchanges (HIEs) are revolutionizing healthcare by facilitating secure and timely patient data sharing across diverse organizations. However, their rapid expansion has introduced significant legal and ethical challenges, particularly regarding privacy, informed consent, and liability risks. This paper critically assesses the effectiveness of existing legal frameworks, including Health Insurance Portability and Accountability Act (HIPAA) and General Data Protection Regulation (GDPR), in addressing these challenges, revealing gaps in their application within HIEs. It argues that current consent models fail to provide meaningful control for patients, while privacy protections are weakened by issues such as re‐identification and jurisdictional inconsistencies. Moreover, liability in data breaches remains complex due to ambiguous responsibility among stakeholders. The study concludes that reforms are needed, including dynamic consent models, standardized liability frameworks, and enhanced data governance structures, to ensure secure, ethical, and effective data sharing. These changes are essential to fostering patient trust, improving healthcare delivery, and aligning with Sustainable Development Goal (SDG) 3—ensuring healthy lives and promoting well‐being for all.

Matlin S.A., Hanefeld J., Corte-Real A., da Cunha P.R., de Gruchy T., Manji K.N., Netto G., Nunes T., Şanlıer İ., Takian A., Zaman M.H., Saso L.

Han J., Kanelli M., Liu Y., Daristotle J.L., Pardeshi A., Forster T.A., Karchin A., Folk B., Murmann L., Tostanoski L.H., Carrasco S.E., Alsaiari S.K., Wang E.Y., Tran K., Zhang L., et. al.

Abstract Medical interventions often require timed series of doses, thus necessitating accurate medical record-keeping. In many global settings, these records are unreliable or unavailable at the point of care, leading to less effective treatments or disease prevention. Here we present an invisible-to-the-naked-eye on-patient medical record-keeping technology that accurately stores medical information in the patient skin as part of microneedles that are used for intradermal therapeutics. We optimize the microneedle design for both a reliable delivery of messenger RNA (mRNA) therapeutics and the near-infrared fluorescent microparticles that encode the on-patient medical record-keeping. Deep learning-based image processing enables encoding and decoding of the information with excellent temporal and spatial robustness. Long-term studies in a swine model demonstrate the safety, efficacy and reliability of this approach for the co-delivery of on-patient medical record-keeping and the mRNA vaccine encoding severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). This technology could help healthcare workers make informed decisions in circumstances where reliable record-keeping is unavailable, thus contributing to global healthcare equity.

Tyagi L.K., Gupta A., Sisodia V.S.

Singh C., Singh R., Kamini, Kumar Y.

Healthcare industries have boomed in the last couple of years. Due to that, cyber security is the main key concern for medical systems. The objective of this systematic review is to identify the common cyber hacker attacks and to identify the best possible solution. In this Chapter, we have used the PRISMA model to select multiple articles from the web of science, EBSCO, and Google Scholar between 2010 and 2021. In this study, Types of Healthcare Data Types, Common Hacker attacks, and types of Security parameters concerning the healthcare sector has discussed to understand healthcare security issues. Also highlighted some Healthcare Cyber Security challenges and Healthcare Cyber Security Tools. The main role of Blockchain-Based security in Healthcare and Cyber Security in Medical Devices has also discussed in this study. Additionally, we have provided a comparative analysis of various authors by highlighting their work based on multiple healthcare components and challenges existing systems face. We have also provided the five investigations study along with their solutions. In the future, developing a cohesive plan in healthcare security is recommended to prevent severe cyber-attacks.

Anisha J.R., Arul Teen Y.P.

M H C., S V.

Background: The Cloud model is one of the most realistic frameworks with a vast range of social networking interactions. In medical data, security is a major constraint as it incorporates information about the patients. The cloud environment subjected to mobility and openness is exposed to security issues and limits authorization levels for data transmission. Objective: This paper aims to propose a security model for attack prevention within the healthcare environment. Method: The proposed Cryptographic Attribute-based Machine Learning (CAML) scheme incorporates three stages. Initially, the homomorphic encryption escrow is performed for secure data transmission in the cloud. Secondly, the information of the users is evaluated based on the consideration of users' authorization. The authorization process for the users is carried out with the attribute-based ECC technique. Finally, the ML model with the classifier is applied for the detection and classification of attacks in the medical network. Results: The detected attack is computed and processed with the CNN model. Simulation analysis is performed for the proposed CAML with conventional ANN, CNN, and RNN models. The simulation analysis of proposed CAML achieves a higher accuracy of 0.96 while conventional SVM, RF, and DT achieve an accuracy of 0.82, 0.89 and 0.93, respectively. Conclusion: Conclusion: With the analysis, it is concluded that the proposed CAML model achieves higher classification accuracy for attack detection and prevention in the cloud computing environment.

Odeh A.

Lee H.J., Kook S., Kim K., Ryu J., Lee Y., Won D.

Medical Internet of Things (IoT) systems can be used to monitor and treat patient health conditions. Security and privacy issues in medical IoT services are more important than those in any other IoT-enabled service. Therefore, various mutual authentication and key-distribution schemes have been proposed for secure communication in medical IoT services. We analyzed Hu et al.’s scheme and found that an attacker can impersonate legitimate sensor nodes and generate illegitimate session keys using the information stored in the sensor node and the information transmitted over the public channel. To overcome these vulnerabilities, we propose a scheme that utilizes physically unclonable functions to ensure a secure session key distribution and increase the computational efficiency of resource-limited sensor nodes. In addition, the proposed scheme enhances privacy protection using pseudonyms, which we prove using a formal security analysis tool, ProVerif 2.05.

Ibrahim M., Mahmoud M.A., Al-Sharafi M.A., Hassan A.

This study analyzes global research trends in electronic health records through a bibliometric approach, using the Scopus database, 18,555 articles published between 2014 and 2024 were retrieved, focusing on author co-authorships, publication outputs, the co-occurrence of author keywords, and affiliated countries. The findings show a steady increase in publications from 2014 to 2023. Researchers from the United States, China, and India made the most significant contributions to global publications. Affiliation institutions of the authors include Harvard Medical School, which leads with an impressive publication count is followed by Mayo Clinic which ranks second in terms of publications, while Columbia University come third in publications. In terms of co-authorship link strength, Gadekallu Thippa Reddy emerged as the author with the highest total link strength of 146. This Followed by Zhang Tao securing the second position with a total link strength of 42, other authors like Tang Buzhou, Wang Jiaqi and Xu Hui were also identified with their significance contribution in the field of EHR implementation each with their substantial link strengths of 17, 18 and 19 respectively. In terms of authors’ citations, Ghayvat Hemant leads with 67 citations, followed by Zhang Tao with 42 citations, and Xie Feng with 33 citations. The study concludes by providing broader summary of electronic health record trends, using data from the Scopus database. The findings indicate that in the past decade there is a significant growth, there is projections in the future it will continue to increase.