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In the traditional electronic health record (EHR) management system, each medical service center manages their own health records, respectively, which are difficult to share on the different medical platforms. Recently, blockchain technology is one of the popular alternatives to enable medical service centers based on different platforms to share EHRs. However, it is hard to store whole EHR data in blockchain because of the size and the price of blockchain. To resolve this problem, cloud computing is considered as a promising solution. Cloud computing offers advantageous properties such as storage availability and scalability. Unfortunately, the EHR system with cloud computing can be vulnerable to various attacks because the sensitive data is sent over a public channel. We propose the secure protocol for cloud-assisted EHR system using blockchain. In the proposed scheme, blockchain technology is used to provide data integrity and access control using log transactions and the cloud server stores and manages the patient's EHRs to provide secure storage resources. We use an elliptic curve cryptosystems (ECC) to provide secure health data sharing with cloud computing. We demonstrate that the proposed EHR system can prevent various attacks by using informal security analysis and automated validation of internet security protocols and applications (AVISPA) simulation. Furthermore, we prove that the proposed EHR system provides secure mutual authentication using BAN logic analysis. We then compare the computation overhead, communication overhead, and security properties with existing schemes. Consequently, the proposed EHR system is suitable for the practical healthcare system considering security and efficiency.
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http://dx.doi.org/10.3390/s20102913 | DOI Listing |
PLoS One
September 2025
Department of Medicine, Faculty of Medicine, Universiti Malaya, Kuala Lumpur, Malaysia.
There is a lack of longitudinal data on type 2 diabetes (T2D) in low- and middle-income countries. We leveraged the electronic health records (EHR) system of a publicly funded academic institution to establish a retrospective cohort with longitudinal data to facilitate benchmarking, surveillance, and resource planning of a multi-ethnic T2D population in Malaysia. This cohort included 15,702 adults aged ≥ 18 years with T2D who received outpatient care (January 2002-December 2020) from Universiti Malaya Medical Centre (UMMC), Kuala Lumpur, Malaysia.
View Article and Find Full Text PDFJMIR Cancer
September 2025
Department of Health Outcomes and Biomedical Informatics, University of Florida, 1889 Museum Road, Suite 7000, Gainesville, FL, 32611, United States, 1 352 294-5969.
Background: Disparities in cancer burden between transgender and cisgender individuals remain an underexplored area of research.
Objective: This study aimed to examine the cumulative incidence and associated risk factors for cancer and precancerous conditions among transgender individuals compared with matched cisgender individuals.
Methods: We conducted a retrospective cohort study using patient-level electronic health record (EHR) data from the University of Florida Health Integrated Data Repository between 2012 and 2023.
Front Med (Lausanne)
August 2025
Department of Oncology, Shanghai Lung Cancer Center, Shanghai Chest Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China.
Background: This study evaluates how AI enhances EHR efficiency by comparing a lung cancer-specific LLM with general-purpose models (DeepSeek, GPT-3.5) and clinicians across expertise levels, assessing accuracy and completeness in complex lung cancer pathology documentation and task load changes pre-/post-AI implementation.
Methods: This study analyzed 300 lung cancer cases (Shanghai Chest Hospital) and 60 TCGA cases, split into training/validation/test sets.
Front Public Health
September 2025
King's Daughters Medical Center, University of Kentucky, Ashland, KY, United States.
Using precision analytics approaches with population health data helps identify localized patterns of social determinants and comorbidities, supporting the design of tailored interventions. The University of Kentucky College of Public Health (UKCPH) and UK King's Daughters (UKKD) have partnered to create a Precision Public Health Alliance (PPHA) applying precision analytics to UKKD electronic health records (EHR) as well as secondary datasets to map social, demographic, and clinical comorbidity factors onto colorectal cancer (CRC) screening data in UKKD's rural service area (the northeastern Kentucky counties of Boyd, Carter, Greenup, and Lawrence and southeast Ohio county of Lawrence). In addition to UKKD and UKCPH clinicians and researchers, PPHA includes a community-based Action Team of local social services, behavioral health, and public health agencies and Cooperative Extension agents responsible for translating findings into quality improvement priorities.
View Article and Find Full Text PDFCurr Opin Nephrol Hypertens
September 2025
Division of Nephrology, Hypertension, and Renal Transplantation, Department of Medicine, College of Medicine.
Purpose Of Review: Artificial intelligence is continuously and rapidly evolving. Artificial intelligence has the potential to address several clinical challenges associated with the prevention, detection, and management of acute kidney injury (AKI). This review provides an overview of the state of artificial intelligence for AKI decision-making, highlighting key recent developments, trends, and innovations towards real-world bedside deployment.
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