Objective: To design and implement a tool that creates a secure, privacy preserving linkage of electronic health record (EHR) data across multiple sites in a large metropolitan area in the United States (Chicago, IL), for use in clinical research.
Methods: The authors developed and distributed a software application that performs standardized data cleaning, preprocessing, and hashing of patient identifiers to remove all protected health information. The application creates seeded hash code combinations of patient identifiers using a Health Insurance Portability and Accountability Act compliant SHA-512 algorithm that minimizes re-identification risk. The authors subsequently linked individual records using a central honest broker with an algorithm that assigns weights to hash combinations in order to generate high specificity matches.
Results: The software application successfully linked and de-duplicated 7 million records across 6 institutions, resulting in a cohort of 5 million unique records. Using a manually reconciled set of 11 292 patients as a gold standard, the software achieved a sensitivity of 96% and a specificity of 100%, with a majority of the missed matches accounted for by patients with both a missing social security number and last name change. Using 3 disease examples, it is demonstrated that the software can reduce duplication of patient records across sites by as much as 28%.
Conclusions: Software that standardizes the assignment of a unique seeded hash identifier merged through an agreed upon third-party honest broker can enable large-scale secure linkage of EHR data for epidemiologic and public health research. The software algorithm can improve future epidemiologic research by providing more comprehensive data given that patients may make use of multiple healthcare systems.
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http://dx.doi.org/10.1093/jamia/ocv038 | DOI Listing |
JMIR Ment Health
December 2024
Faculty of Applied Computer Science, Augsburg University, Augsburg, Germany.
Background: The rise of wearable sensors marks a significant development in the era of affective computing. Their popularity is continuously increasing, and they have the potential to improve our understanding of human stress. A fundamental aspect within this domain is the ability to recognize perceived stress through these unobtrusive devices.
View Article and Find Full Text PDFCogn Neurodyn
December 2024
Department of Electronic Engineering and Information Science, University of Science and Technology of China, Hefei, 230027 Anhui China.
Domain adaptation (DA) has been frequently used to solve the inter-patient variability problem in EEG-based seizure prediction. However, existing DA methods require access to the existing patients' data when adapting the model, which leads to privacy concerns. Besides, most of them treat the whole existing patients' data as one single source and attempt to minimize the discrepancy with the target patient.
View Article and Find Full Text PDFBMC Urol
December 2024
Department of Urology, 920th Hospital of Joint Logistic Support Force, Kunming, 650000, China.
Background: To analyze the safety and efficacy of microsurgical subinguinal varicocelectomy(MSV) performed with and without preservation of all testicular arteries and lymphatic system.
Methods: All of the 98 patients with varicocele who underwent MSV were included in the analysis. Fifty-eight male patients surgically underwent MSV with preservation of all testicular arteries and lymphatic system(Group 1).
Background: Artificial intelligence-driven Clinical Decision Support Systems (AI-CDSS) are increasingly being integrated into healthcare for various purposes, including resource allocation. While these systems promise improved efficiency and decision-making, they also raise significant ethical concerns. This study aims to explore healthcare professionals' perspectives on the ethical implications of using AI-CDSS for healthcare resource allocation.
View Article and Find Full Text PDFFront Big Data
December 2024
School of Business, Amrita Vishwa Vidyapeetham, Amritapuri, Kollam, Kerala, India.
Introduction: The rapid escalation of cyber threats necessitates innovative strategies to enhance cybersecurity and privacy measures. Artificial Intelligence (AI) has emerged as a promising tool poised to enhance the effectiveness of cybersecurity strategies by offering advanced capabilities for intrusion detection, malware classification, and privacy preservation. However, this work addresses the significant lack of a comprehensive synthesis of AI's use in cybersecurity and privacy across the vast literature, aiming to identify existing gaps and guide further progress.
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