Deletion and Recovery Scheme of Electronic Health Records Based onMedical Certificate Blockchain

The trusted sharing of Electronic Health Records(EHRs)can realize the efficient use of medical data resources.Generally speaking,EHRs are widely used in blockchain-based medical data platforms.EHRs are valuable private assets of patients,and the ownership belongs to patients.While recent research has shown that patients can freely and effectively delete the EHRs stored in hospitals,it does not address the challenge of record sharing when patients revisit doctors.In order to solve this problem,this paper proposes a deletion and recovery scheme of EHRs based on Medical Certificate Blockchain.This paper uses cross-chain technology to connect the Medical Certificate Blockchain and the Hospital Blockchain to real-ize the recovery of deleted EHRs.At the same time,this paper uses the Medical Certificate Blockchain and the InterPlanetary File System(IPFS)to store Personal Health Records,which are generated by patients visiting different medical institutions.In addition,this paper also combines digital watermarking technology to ensure the authenticity of the restored electronic medical records.Under the combined effect of blockchain technology and digital watermarking,our proposal will not be affected by any other rights throughout the process.System analysis and security analysis illustrate the completeness and feasibility of the scheme.

Machine-Learning-Enabled Obesity Level Prediction Through Electronic Health Records

Obesity is a critical health condition that severely affects an individual’s quality of life andwell-being.The occurrence of obesity is strongly associated with extreme health conditions,such as cardiac diseases,diabetes,hypertension,and some types of cancer.Therefore,it is vital to avoid obesity and or reverse its occurrence.Incorporating healthy food habits and an active lifestyle can help to prevent obesity.In this regard,artificial intelligence(AI)can play an important role in estimating health conditions and detecting obesity and its types.This study aims to see obesity levels in adults by implementing AIenabled machine learning on a real-life dataset.This dataset is in the form of electronic health records(EHR)containing data on several aspects of daily living,such as dietary habits,physical conditions,and lifestyle variables for various participants with different health conditions(underweight,normal,overweight,and obesity type I,II and III),expressed in terms of a variety of features or parameters,such as physical condition,food intake,lifestyle and mode of transportation.Three classifiers,i.e.,eXtreme gradient boosting classifier(XGB),support vector machine(SVM),and artificial neural network(ANN),are implemented to detect the status of several conditions,including obesity types.The findings indicate that the proposed XGB-based system outperforms the existing obesity level estimation methods,achieving overall performance rates of 98.5%and 99.6%in the scenarios explored.

Using AI and Precision Nutrition to Support Brain Health during Aging

Artificial intelligence, often referred to as AI, is a branch of computer science focused on developing systems that exhibit intelligent behavior. Broadly speaking, AI researchers aim to develop technologies that can think and act in a way that mimics human cognition and decision-making [1]. The foundations of AI can be traced back to early philosophical inquiries into the nature of intelligence and thinking. However, AI is generally considered to have emerged as a formal field of study in the 1940s and 1950s. Pioneering computer scientists at the time theorized that it might be possible to extend basic computer programming concepts using logic and reasoning to develop machines capable of “thinking” like humans. Over time, the definition and goals of AI have evolved. Some theorists argued for a narrower focus on developing computing systems able to efficiently solve problems, while others aimed for a closer replication of human intelligence. Today, AI encompasses a diverse set of techniques used to enable intelligent behavior in machines. Core disciplines that contribute to modern AI research include computer science, mathematics, statistics, linguistics, psychology and cognitive science, and neuroscience. Significant AI approaches used today involve statistical classification models, machine learning, and natural language processing. Classification methods are widely applicable to problems in various domains like healthcare, such as informing diagnostic or treatment decisions based on patterns in data. Dean and Goldreich, 1998, define ML as an approach through which a computer has to learn a model by itself from the data provided but no specification on the sort of model is provided to the computer. They can then predict values for things that are different from the values used in training the models. NLP looks at two interrelated concerns, the task of training computers to understand human languages and the fact that since natural languages are so complex, they lend themselves very well to serving a number of very useful goals when used by computers.

Impact of Laboratory Value Flowsheet in Electronic Health Record (EHR) Documentation Time

Research on the use of EHR is contradictory since it presents contradicting results regarding the time spent documenting. There is research that supports the use of electronic records as a tool to speed documentation;and research that found that it is time consuming. The purpose of this quantitative retrospective before-after project was to measure the impact of using the laboratory value flowsheet within the EHR on documentation time. The research question was: “Does the use of a laboratory value flowsheet in the EHR impact documentation time by primary care providers (PCPs)?” The theoretical framework utilized in this project was the Donabedian Model. The population in this research was the two PCPs in a small primary care clinic in the northwest of Puerto Rico. The sample was composed of all the encounters during the months of October 2019 and December 2019. The data was obtained through data mining and analyzed using SPSS 27. The evaluative outcome of this project is that there is a decrease in documentation time after implementation of the use of the laboratory value flowsheet in the EHR. However, patients per day increase therefore having an impact on the number of patients seen per day/week/month. The implications for clinical practice include the use of templates to improve workflow and documentation as well as decreasing documentation time while also increasing the number of patients seen per day. .

Applications of Artificial Intelligence in Morocco’s Healthcare Sector: A Springboard to Medical Excellence

Artificial intelligence (AI) is revolutionizing the healthcare sector worldwide. In Morocco, several AI applications are being deployed in public and private healthcare establishments, improving appointment management, surgical operations, diagnostics, patient record tracking, biology and radiology, and OR organization. This article explores the main AI applications used in the Moroccan healthcare sector, their frequency of use, the types of establishments adopting them, as well as the main functionalities of each application and its contribution to the sector. The aim of this study is to analyze the impact of the main AI applications on quality of care and process efficiency in Moroccan healthcare facilities. This research focuses on several fundamental questions: Which AI applications are most frequently used? What types of establishments are adopting these technologies, and for which specific functionalities? What are the benefits and challenges of integrating AI into the Moroccan healthcare system, particularly in terms of territorial distribution and accessibility? The methodology is based on a quantitative analysis of data collected from selected healthcare establishments, combined with studies of reports from public health authorities and a sweep of their websites. The results show that 45% of hospitals use AI systems for appointment scheduling and 30% for medical diagnosis. The use of surgical robots, such as the Da Vinci system, increased by 30% between 2020 and 2024. Comparisons with other emerging countries highlight Morocco’s acceptable advances, while underlining the challenges, particularly in terms of the territorial distribution of these technological infrastructures generally centralized in the country’s major cities.

An Investigation on the Barriers and Facilitators of the Implementation of Electronic Health Records (EHR)

The application of technology in health care, in the form of electronic health records (EHR), is the most important and necessary issue in order to improve the quality of health care, and studies have shown that, not only is it a way to integrate information and represent the condition of patients, and a dynamic source for health care, however it leads to gain access to clinical information and records, electronic communications, comprehensive training and management, and ultimately enhancing the public health;the aim of this study is to investigate the factors influencing the implementation of EHR, which are known as barriers and facilitators. The research is conducted in the form of a review research, and with the help of the Keywords of EHR;barriers and facilitators, articles, from 2008 to 2013, were searched and studied in the Internet-databases. The results of the studies show that the most effective factors include: efficiency, motivation, management, and the participation of end users. Factors such as technical aspects ease of use, available resources, and human resources, have limited effects. And security and privacy, the expected output, lack of time, and workload have relative effects, and also the relation between the patient and clinical staff, has no effects in the process of implementing EHR.

A Secure Signcryption Scheme for Electronic Health Records Sharing in Blockchain

In the existing Electronic Health Records(EHRs),the medical information of patients is completely controlled by various medical institutions.As such,patients have no dominant power over their own EHRs.These personal data are not only inconvenient to access and share,but are also prone to cause privacy disclosure.The blockchain technology provides a new development direction in the medical field.Blockchain-based EHRs are characterized by decentralization,openness and non-tampering of records,which enable patients to better manage their own EHRs.In order to better protect the privacy of patients,only designated receivers can access EHRs,and receivers can authenticate the sharer to ensure that the EHRs are real and effective.In this study,we propose an identity-based signcryption scheme with multiple authorities for multiple receivers,which can resist N-1 collusion attacks among N authorities.In addition,the identity information of receivers is anonymous,so the relationship between them and the sharer is not disclosed.Under the random oracle model,it was proved that our scheme was secure and met the unforgeability and confidentiality requirements of signcryption.Moreover,we evaluated the performance of the scheme and found that it had the moderate signcryption efficiency and excellent signcryption attributes.

Hyperledger Fabric Blockchain: Secure and Efficient Solution for Electronic Health Records

Background:Electronic Health Record(EHR)systems are used as an efficient and effective technique for sharing patient’s health records among different hospitals and various other key stakeholders of the healthcare industry to achieve better diagnosis and treatment of patients globally.However,the existing EHR systems mostly lack in providing appropriate security,entrusted access control and handling privacy and secrecy issues and challenges in current hospital infrastructures.Objective:To solve this delicate problem,we propose a Blockchain-enabled Hyperledger Fabric Architecture for different EHR systems.Methodology:In our EHR blockchain system,Peer nodes from various organizations(stakeholders)create a ledger network,where channels are created to enable secure and private communication between different stakeholders on the ledger network.Individual patients and other stakeholders are identified and registered on the network by unique digital certificates issued by membership service provider(MSP)component of the fabric architecture.Results:We created and implemented different Chaincodes to handle the business logic for executing separate EHR transactions on the network.The proposed fabric architecture provides a secure,transparent and immutable mechanism to store,share and exchange EHRs in a peer-to-peer network of different healthcare stakeholders.It ensures interoperability,scalability and availability in adapting the existing EHRs for strengthening and providing an effective and secure method to integrate and manage patient records among medical institutions in the healthcare ecosystem.

The Importance of Health Records

Health records have played an increasingly important role throughout history as an important legal document for the exercise of individuals’ rights. However, domestic legislation fails to define health records as a legally important collection of health data and documents. Recording facts and storing legally important documents are therefore the tasks of the operator. Using the prescriptive method we will determine which laws are governing the management of medical records, their safety and accessibility. Based on the descriptive method, we will describe the process of handling health records by the provider of health treatment, focusing on exposed regulatory gaps in the area of the protection of the rights of an individual. Through the analysis of the laws governing the management of health records, even after death and operator terminating the service, we will carry out inductive reasoning and provide conclusions regarding the attitude towards health records. Considering different results we can conclude that health information, especially documents relevant to the protection of individual’s rights, is not transparent. Above all, the documents in the collection are not recorded properly, thus allowing for their removal. Even the transfer of health records by the provider of health treatment is not defined, which could result in the disposal of the entire health documentation.

Psychiatric electronic health records privacy in Jordan: A policy brief

Psychiatric health records are highly sensitive data which requires special policy to maintain its privacy,without affecting data accessibility.The current authors reviewed social,ethical and legal underpinnings for psychiatric electronic health records(EHR),and suggests a policy to maintain privacy and confidentiality of the psychiatric data,without affecting data accessibility.The purpose of this policy brief is to discuss and provide alternatives regarding psychiatric electronic health records privacy and information access.The current policy applied in Jordan still immature to ensure high levels of reliability,as the psychiatric data is openly accessed to the non-specialized personnel.Sensitive personal data policy is recommended in this paper with developing overriding mechanisms to counteract obstacles to data accessibility.

Opportunities for nurses in the era of electronic health records

Nursing leaders are currently faced with opportunities to advance nursing’s role in the use of electronic health records (EHRs). Nurse leaders can advance the design of EHRs with nurse informaticists to improve health outcomes of individual and populations of patients.

Securing Patient-Centric Personal Health Records Sharing System in Cloud Computing

Personal health record （PHR） enables patients to manage their own electronic medical records （EMR） in a centralized way, and it is oRen outsourced to be stored in a third-party server. In this paper we propose a novel secure and scalable system for sharing PHRs. We focus on the multiple data owner scenario, and divide the users in the system into multiple security domains that greatly reduce the key management complexity for owners and users. A high degree of patient privacy is guaranteed by exploiting hierarchical and multi- authority attribute-sets based encryption （HM- ASBE）. Our system not only supports compound attributes due to flexible attribute sets combinations, but also achieves fine-grained access control. Our scheme supports efficient on-demand user/attribute revocation and break-glass access under emergency scenarios.

Fog-Based Secure Framework for Personal Health Records Systems

The rapid development of personal health records(PHR)systems enables an individual to collect,create,store and share his PHR to authorized entities.Health care systems within the smart city environment require a patient to share his PRH data with a multitude of institutions’repositories located in the cloud.The cloud computing paradigm cannot meet such a massive transformative healthcare systems due to drawbacks including network latency,scalability and bandwidth.Fog computing relieves the load of conventional cloud computing by availing intermediate fog nodes between the end users and the remote servers.Assuming a massive demand of PHR data within a ubiquitous smart city,we propose a secure and fog assisted framework for PHR systems to address security,access control and privacy concerns.Built under a fog-based architecture,the proposed framework makes use of efficient key exchange protocol coupled with ciphertext attribute based encryption(CP-ABE)to guarantee confidentiality and fine-grained access control within the system respectively.We also make use of digital signature combined with CP-ABE to ensure the system authentication and users privacy.We provide the analysis of the proposed framework in terms of security and performance.

Nursing decision support system:application in electronic health records

The clinical decision support system makes electronic health records(EHRs)structured,intelligent,and knowledgeable.The nursing decision support system(NDSS)is based on clinical nursing guidelines and nursing process to provide intelligent suggestions and reminders.The impact on nurses’work is mainly in shortening the recording time,improving the quality of nursing diagnosis,reducing the incidence of nursing risk events,and so on.However,there is no authoritative standard for the NDSS at home and abroad.This review introduces development and challenges of EHRs and recommends the application of the NDSS in EHRs,namely the nursing assessment decision support system,the nursing diagnostic decision support system,and the nursing care planning decision support system(including nursing intervene),hoping to provide a new thought and method to structure impeccable EHRs.

Ensuring Information Security in Electronic Health Record System Using Cryptography and Cuckoo Search Algorithm

In the contemporary era,the abundant availability of health information through internet and mobile technology raises concerns.Safeguarding and maintaining the confidentiality of patients’medical data becomes paramount when sharing such information with authorized healthcare providers.Although electronic patient records and the internet have facilitated the exchange of medical information among healthcare providers,concerns persist regarding the security of the data.The security of Electronic Health Record Systems(EHRS)can be improved by employing the Cuckoo Search Algorithm(CS),the SHA-256 algorithm,and the Elliptic Curve Cryptography(ECC),as proposed in this study.The suggested approach involves usingCS to generate the ECCprivate key,thereby enhancing the security of data storage in EHR.The study evaluates the proposed design by comparing encoding and decoding times with alternative techniques like ECC-GA-SHA-256.The research findings indicate that the proposed design achieves faster encoding and decoding times,completing 125 and 175 iterations,respectively.Furthermore,the proposed design surpasses other encoding techniques by exhibiting encoding and decoding times that are more than 15.17%faster.These results imply that the proposed design can significantly enhance the security and performance of EHRs.Through the utilization of CS,SHA-256,and ECC,this study presents promising methods for addressing the security challenges associated with EHRs.

支持用户撤销的可搜索电子健康记录共享方案

随着物联网与云存储技术的快速发展和广泛应用,每年都有大量的传感器设备被部署到医疗物联网(IoMT)系统,虽然这促进了电子健康记录(EHR)应用的普及,但EHR的安全存储与检索尚未得到妥善的解决。针对以上问题,基于可搜索加密构造长度固定的陷门用于对密文的搜索验证,减小了用户所需的通信开销;采用在线/离线加密技术,减小了用户端在线加密所需的计算开销;同时基于变色龙哈希函数,构造具有抗碰撞、语义安全等特点的私钥,避免了未撤销用户私钥频繁更新的问题,极大地减小了用户的计算开销。理论分析与实验结果表明所提方案在DBDH(Decisional Bilinear Diffie-Hellman)假设下是选择明文攻击是安全的,且与类似属性基加密方案相比,所提方案效率更高,在功能上支持在线加密、高效的用户撤销并具有更低的计算开销和存储开销。

心房颤动及心房扑动智能评分系统在门诊的应用情况及来诊患者特点

目的:评价心房颤动及心房扑动智能评分系统在门诊的应用情况及来诊患者特点。方法:收集2020年8月至2022年6月于中国医学科学院阜外医院门诊就诊的25 223例心房颤动及心房扑动患者的所有就诊记录。比较系统更新(2021年9月)前后来诊患者的特点。另从25 223例患者中筛选出系统更新前后1年内相邻2次就诊的患者404例,比较2次评分的变化趋势及抗凝药物使用情况。结果:与系统更新前比,系统更新后CHA_(2)DS_(2)-VASc评分中男性≥2分或女性≥3分的患者比例差异无统计学意义(P>0.05),这些患者中抗凝治疗比例升高(25.2%vs. 35.9%,P<0.001)。无不评分理由患者比例系统更新前为37.9%,更新后下降至21.2%。比较404例系统更新前后1年内相邻2次就诊记录发现,与更新前比,更新后患者的CHA_(2)DS_(2)-VASc评分升高[(2.12±1.48)分vs.(2.35±1.51)分,P=0.029],同时CHA_(2)DS_(2)-VASc评分中男性≥2分或女性≥3分的患者比例升高(49.5%vs. 58.7%,P=0.011),这些患者中抗凝治疗比例也升高(28.5%vs. 41.4%,P<0.001)。HAS-BLED评分及HAS-BLED评分≥3分患者的比例在系统更新前后差异均无统计学意义(P均>0.05)。结论:本院门诊所应用的智能评分系统可有效帮助医生掌握来诊心房颤动及心房扑动患者的血栓栓塞和出血风险及相应的变化趋势,并帮助改善抗凝药物的使用。但还需进一步优化评分流程,规范患者评估及治疗。

基于个人健康记录的全程管理模式干预对老年糖尿病患者的效果评价

目的 分析基于个人健康记录(PHR)的全程管理模式干预对老年糖尿病患者认知水平、心理痛苦、自我管理行为、生活质量及血糖、血脂的影响。方法 将100例老年糖尿病患者按照随机数字表法分成常规组和干预组,各50例。常规组患者采用常规管理措施进行干预,干预组患者采用基于PHR的糖尿病全程管理模式进行干预。两组患者均干预6个月,各失访1例,最后分别纳入49例患者。干预前后采用自制糖尿病认知问卷、糖尿病痛苦量表(DDS)、糖尿病患者自我管理行为量表(SDSCA)、生存质量特异性量表(DSQL)评定两组患者认知水平、心理痛苦、自我管理行为、生存质量,比较干预前后两组患者血糖、血脂水平。结果 干预后,两组患者糖尿病认知水平均优于干预前,且干预组优于常规组(P<0.01)。干预后,两组患者DDS、DSQL各维度评分均低于干预前,且干预组低于常规组(P<0.05或0.01);两组患者SDSCA各维度评分均高于干预前,且干预组高于常规组(P<0.05或0.01)。干预后,两组患者空腹血糖、糖化血红蛋白、餐后2 h血糖、总胆固醇、低密度脂蛋白、三酰甘油水平均低于干预前,且干预组低于常规组(P<0.01);高密度脂蛋白水平高于干预前,且干预组高于常规组(P<0.01)。结论 基于PHR的糖尿病全程管理模式不仅有助于提升老年糖尿病患者的认知水平,缓解其心理痛苦,增强患者自我管理行为,提升患者生存质量,还能够改善老年糖尿病患者的糖脂控制情况。

基于CNN和双向LSTM的房颤预测模型

现有基于CNN的模型无法提取患者数据中的时序特征,而基于RNN的模型忽略了各医学变量的差异性特征。针对这种情况,提出一种结合CNN和RNN的房颤预测模型,利用一个独立CNN模块捕获电子病历数据中各医学变量间的差异性特征,同时使用一个独立的RNN模块捕获电子病历数据中时序性特征以及各医学变量间的相关性特征。在真实医院数据集上的实验结果表明,与最新的一些基于电子病历数据的疾病预测方法相比,该模型在房颤的预测方面表现得更加突出,F1值提高了2.14%,AUC值提高了1.32%。

基于关联规则挖掘的健康信息学主题分析——以dHealth会议为例

本研究旨在析出当前健康信息学领域的研究主题和方向、研究热点和前沿问题,以2014—2020年共七年的dHealth会议文献作为研究对象,运用关联规则方法对会议文献的关键词进行分析,归纳总结出健康信息学的主要研究方向,并对相关主题的会议文献进行回溯介绍。分析结果表明,近年来dHealth会议文献在健康信息学领域有六个主要研究方向,包括标准与标准化研究、电子健康档案研究、互操作性研究、计算机技术与应用研究、移动医疗与远程医疗研究及临床信息学研究。研究结论有助于进一步明晰健康信息学的研究热点与主流话题,并探索未来发展的方向。