scholarly journals Morton Filter-Based Security Mechanism for Healthcare System in Cloud Computing

Healthcare ◽  
2021 ◽  
Vol 9 (11) ◽  
pp. 1551
Author(s):  
Sugandh Bhatia ◽  
Jyoteesh Malhotra
Keyword(s):  
Electronic Health Records ◽  
Data Structures ◽  
Cloud Security ◽  
Healthcare Systems ◽  
Security And Privacy ◽  
Load Factor ◽  
Security Mechanism ◽  
Health Records ◽  
Reliability Factor ◽  
Electronic Health

Electronic health records contain the patient’s sensitive information. If these data are acquired by a malicious user, it will not only cause the pilferage of the patient’s personal data but also affect the diagnosis and treatment. One of the most challenging tasks in cloud-based healthcare systems is to provide security and privacy to electronic health records. Various probabilistic data structures and watermarking techniques were used in the cloud-based healthcare systems to secure patient’s data. Most of the existing studies focus on cuckoo and bloom filters, without considering their throughputs. In this research, a novel cloud security mechanism is introduced, which supersedes the shortcomings of existing approaches. The proposed solution enhances security with methods such as fragile watermark, least significant bit replacement watermarking, class reliability factor, and Morton filters included in the formation of the security mechanism. A Morton filter is an approximate set membership data structure (ASMDS) that proves many improvements to other data structures, such as cuckoo, bloom, semi-sorting cuckoo, and rank and select quotient filters. The Morton filter improves security; it supports insertions, deletions, and lookups operations and improves their respective throughputs by 0.9× to 15.5×, 1.3× to 1.6×, and 1.3× to 2.5×, when compared to cuckoo filters. We used Hadoop version 0.20.3, and the platform was Red Hat Enterprise Linux 6; we executed five experiments, and the average of the results has been taken. The results of the simulation work show that our proposed security mechanism provides an effective solution for secure data storage in cloud-based healthcare systems, with a load factor of 0.9. Furthermore, to aid cloud security in healthcare systems, we presented the motivation, objectives, related works, major research gaps, and materials and methods; we, thus, presented and implemented a cloud security mechanism, in the form of an algorithm and a set of results and conclusions.

scholarly journals Secure Exchange of Medical Data Using a Novel Real-Time Biometric-Based Protection and Recognition Method

Electronics ◽  
2020 ◽  
Vol 9 (12) ◽  
pp. 2013
Author(s):  
Shams Ud Din ◽  
Zahoor Jan ◽  
Muhammad Sajjad ◽  
Maqbool Hussain ◽  
Rahman Ali ◽  
...  
Keyword(s):  
Electronic Health Records ◽  
Real Time ◽  
Signal To Noise Ratio ◽  
Security And Privacy ◽  
The Internet ◽  
Healthcare Organizations ◽  
Health Records ◽  
Clinical Notes ◽  
Electronic Health ◽  
Value Decomposition

Security and privacy are essential requirements, and their fulfillment is considered one of the most challenging tasks for healthcare organizations to manage patient data using electronic health records. Electronic health records (clinical notes, images, and documents) become more vulnerable to breaching patients’ privacy when shared with an external organization in the current arena of the internet of medical things (IoMT). Various watermarking techniques were introduced in the medical field to secure patients’ data. Most of the existing techniques focus on an image or document’s imperceptibility without considering the watermark(logo). In this research, a novel technique of watermarking is introduced, which supersedes the shortcomings of existing approaches. It guarantees the imperceptibility of the image/document and takes care of watermark(biometric), which is further passed through a process of recognition for claiming ownership. It extracts suitable frequencies from the transform domain using specialized filters to increase the robustness level. The extracted frequencies are modified by adding the biomedical information while considering the strength factor according to the human visual system. The watermarked frequencies are further decomposed through a singular value decomposition technique to increase payload capacity up to (256 × 256). Experimental results over a variety of medical and official images demonstrate the average peak signal-to-noise ratio (PSNR 54.43), and the normal correlation (N.C.) value is 1. PSNR and N.C. of the watermark were calculated after attacks. The proposed technique is working in real-time for embedding, extraction, and recognition of biometrics over the internet, and its uses can be realized in various platforms of IoMT technologies.

scholarly journals The portability of natural language processing methods to detect suicidality from unstructured clinical text in US and UK electronic health records

2021 ◽  
Author(s):  
Marika Cusick ◽  
Sumithra Velupillai ◽  
Johnny Downs ◽  
Thomas Campion ◽  
Rina Dutta ◽  
...  
Keyword(s):  
Natural Language Processing ◽  
Electronic Health Records ◽  
Natural Language ◽  
Language Processing ◽  
Healthcare Systems ◽  
Risk Identification ◽  
Health Records ◽  
Clinical Text ◽  
Academic Medical ◽  
Electronic Health

Abstract In the global effort to prevent death by suicide, many academic medical institutions are implementing natural language processing (NLP) approaches to detect suicidality from unstructured clinical text in electronic health records (EHRs), with the hope of targeting timely, preventative interventions to individuals most at risk of suicide. Despite the international need, the development of these NLP approaches in EHRs has been largely local and not shared across healthcare systems. In this study, we developed a process to share NLP approaches that were individually developed at King’s College London (KCL), UK and Weill Cornell Medicine (WCM), US - two academic medical centers based in different countries with vastly different healthcare systems. After a successful technical porting of the NLP approaches, our quantitative evaluation determined that independently developed NLP approaches can detect suicidality at another healthcare organization with a different EHR system, clinical documentation processes, and culture, yet do not achieve the same level of success as at the institution where the NLP algorithm was developed (KCL approach: F1-score 0.85 vs. 0.68, WCM approach: F1-score 0.87 vs. 0.72). Shared use of these NLP approaches is a critical step forward towards improving data-driven algorithms for early suicide risk identification and timely prevention.

scholarly journals A Landscape Study Highlights the Urgent Need for Evidence Based Strategies to Enable Electronic Health Records Integration in the National Healthcare Systems of Low- and Middle-Income Countries

2021 ◽  
Vol 16 (3) ◽  
pp. 140-142
Author(s):  
Joanne M. Muellenbach
Keyword(s):  
Electronic Health Records ◽  
Grey Literature ◽  
Healthcare Systems ◽  
Health Records ◽  
Middle Income ◽  
Middle Income Countries ◽  
National Healthcare ◽  
Electronic Health ◽  
International Experts ◽  
Low And Middle Income

A Review of: Kumar, M., & Mostafa, J. (2020). Electronic health records for better health in lower- and middle-income countries: A landscape study. Library Hi Tech, 38(4), 751–767. https://doi.org/10.1108/LHT-09-2019-0179  Abstract Objective – To identify how low- and middle-income countries (LMICs) approached the development of national and subnational electronic health records (EHRs) and to understand the challenges related to EHR research priorities and sustainability. Design – Landscape study consisting of a review of the scientific literature, country-focused grey literature, and consultation with international experts.   Setting – Hospitals and healthcare systems within LMICs.  Subjects – The 402 publications retrieved through a systematic search of four scientific electronic databases along with 49 publications found through a country-focused analysis of grey literature and 14 additional publications found through consultation with two international experts.  Methods – On 15 May 2019, the authors comprehensively searched four major scientific databases: Global Health, PubMed, Scopus, and Web of Science. They also searched the grey literature and repositories in consultation with country-based international digital health experts. The authors subsequently used Mendeley reference management software to organize and remove duplicate publications. Peer-reviewed publications that focused on developing national EHRs within LMIC healthcare systems were included for the title and abstract screening. Data analysis was mainly qualitative, and the results were organized to highlight stakeholders, health information architecture (HIA), and sustainability. Main Results – The results were presented in three subsections. The first two described critical stakeholders for developing national and subnational EHRs and HIA, including country eHealth foundations, EHRs, and subsystems. The third section presented and discussed pressing challenges related to EHR sustainability. The findings of the three subsections were further explored through the presentation of three LMIC case studies that described stakeholders, HIA, and sustainability challenges. Conclusion – The results of this landscape study highlighted the scant evidence available to develop and sustain national and subnational EHRs within LMICs. The authors noted that there appears to be a gap in understanding how EHRs impact patient-level and population outcomes within the LMICs. The study revealed that EHRs were primarily designed to support monitoring and evaluating health programs focused on a particular disease or group of diseases rather than common health problems. While national governments and international donors focused on the role of EHRs to improve patient care, the authors highlighted the urgent need for further research on the development of EHRs, with a focus on efficiency, evaluation, monitoring, and quality within the national healthcare enterprise.

scholarly journals Blockchain Architecture to Meet Challenges in Management of Electronic Health Records in IoT based Healthcare Systems

2021 ◽  
Author(s):  
Maria Arif ◽  
Megha Kuliha ◽  
Sunita Varma
Keyword(s):  
Electronic Health Records ◽  
Health Care Providers ◽  
Financial Services ◽  
Healthcare Systems ◽  
Wireless Networking ◽  
Wearable Devices ◽  
Care Providers ◽  
Consensus Protocol ◽  
Health Records ◽  
Electronic Health

Secure, immutable and transparent feature of blockchain has led researchers to find ways to harness its potential in sectors other than financial services. Blockchain is emerging as a popular tool to help solve some of the healthcare industry's age-old problems that have resulted in delayed treatments, inaccessible health records in emergency, wasteful spending and higher costs for doctors, health care providers, insurers and patients. Applying blockchain in healthcare brings a new challenge of integrating blockchain with Internet of Things (IoT) networks as sensor based medical and wearable devices are now used to gather information about the health of a patient and provide it to medical applications using wireless networking. This paper proposes an architecture that would provide a decentralized, secure, immutable, transparent, scalable and traceable system for management and access control of electronic health records (EHRs) through the use of consortium blockchain, smart contracts, proof-ofauthentication (PoAh) consensus protocol and decentralized cloud.

scholarly journals BSF-EHR: Blockchain Security Framework for Electronic Health Records of Patients

Sensors ◽  
2021 ◽  
Vol 21 (8) ◽  
pp. 2865
Author(s):  
Ibrahim Abunadi ◽  
Ramasamy Lakshmana Kumar
Keyword(s):  
Electronic Health Records ◽  
Medical Information ◽  
Health Sector ◽  
Personal Data ◽  
Security And Privacy ◽  
Healthcare Sector ◽  
Security Framework ◽  
Health Records ◽  
Blockchain Technology ◽  
Electronic Health

In the current epoch of smart homes and cities, personal data such as patients’ names, diseases and addresses are often violated. This is frequently associated with the safety of the electronic health records (EHRs) of patients. EHRs have numerous benefits worldwide, but at present, EHR information is subject to considerable security and privacy issues. This paper proposes a way to provide a secure solution to these issues. Previous sophisticated techniques dealing with the protection of EHRs usually make data inaccessible to patients. These techniques struggle to balance data confidentiality, patient demand and constant interaction with provider data. Blockchain technology solves the above problems since it distributes information in a transactional and decentralized manner. The usage of blockchain technology could help the health sector to balance the accessibility and privacy of EHRs. This paper proposes a blockchain security framework (BSF) to effectively and securely store and keep EHRs. It presents a safe and proficient means of acquiring medical information for doctors, patients and insurance agents while protecting the patient’s data. This work aims to examine how our proposed framework meets the security needs of doctors, patients and third parties and how the structure addresses safety and confidentiality concerns in the healthcare sector. Simulation outcomes show that this framework efficiently protects EHR data.

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