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. .

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.

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.

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.

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.

Correlation between an Independent Electronic Health Record &External Ranking of Children’s Hospitals

Introduction: To evaluate the correlation between the presence of an independent EHR (compared to a shared EHR system within an adult hospital system) and an externally-derived third party ranking of children’s hospitals. Methods: Children’s hospitals that ranked in the top fifty of the 2019-2020 US News and World Report (USNWR) were included in the analysis. The mean and median ranking of children’s hospitals with independent versus a shared EHR was evaluated. The 2019-2020 USNWR rankings of the top twenty adult hospitals in the United States were then evaluated. For each children’s hospital with an associated adult hospital that was both ranked, it was noted as to whether the EHR for the children’s hospital was independent or shared and statistical differences in rankings compared. Results: Among the top 50 children’s hospitals included, the median USNWR ranking for hospitals was statistically different with an independent EHR than with a shared EHR (13 vs. 30.0) (p = 0.002). The 21 top ranked adult hospitals were associated with 17 children’s hospitals ranked in the top 50. The median ranking for those with an independent EHR was statistically different for those with independent EHR versus shared EHR (7 vs. 28) (p = 0.002). Conclusion: Children’s hospitals with an independent EHR are associated with higher scores on an independent external ranking of hospital quality compared to those which share an EHR with a partner adult hospital.

Effects of Health Record Computerization in the Context of the COVID-19 Pandemic in Burundi’ Hospitals: Propensity Score Matching

Introduction: A great number of software are currently used to digitalize the patient records in order to optimize the quality of services offered to patients. The objective of this study was to evaluate the effects of Electronic Health Records use in Burundi’s hospitals, taking into account the COVID-19 pandemic context. Methods: This was a quasi-experimental study based on difference in difference method. Ten district hospitals were included in the study, five of them had the Electronic Health Records and five of which did not yet have the Electronic Health Records. The hospital’s control group were chosen using the propensity score matching method. The period before the project’s implementation was 2014 and the period after were 2019 and 2020. Results: After 5 years of the Electronic Health Record’s implementation, the results showed an increase in outpatient consultation (70%), deliveries (more than 100%), caesarean sections (56%) and major surgeries (43%) indicators. The overall quality score of hospitals’ care had a regressive effect of 37% and the income from performance-based funding had an increase by 31%. The indicators which were affected by the context of the COVID-19 pandemic were especially outpatient consultation, caesarian section, income from performance-based funding decreased by 3%, 5% and 20% respectively. Conclusion: The effects of Electronic Health Records use are effective. As the COVID-19 pandemic impacted the hospital’s indicators negatively, the resilient strategies alongside the potential shocks are recommended.

Evaluation of the HIV Case-Based Surveillance System: A Pilot of the Electronic Health Record System in Mutare District, Zimbabwe, 2021

Background: Zimbabwe started HIV case-based surveillance in April 2017. Rapid testing for HIV recent infection was introduced into routine HIV and testing services in 2019 along with the Impilo Electronic Health Record System. For the period January-June 2020, only 1 out of 13 health facilities in Mutare district reported seven newly diagnosed HIV patients through the electronic health record system compared to 483 in the District Health Information System (DHIS-2) recorded from paper-based registers. We evaluated the case-based surveillance system attributes, usefulness and reasons for under-reporting from January-December 2020. Methods: We conducted a descriptive cross-sectional study using updated Centres for Disease Control guidelines for evaluating public health surveillance systems. Questionnaires were administered to 36 health workers involved in HIV testing services. Facility checklists were used to collect data on knowledge, system attributes and usefulness of the system. Completed HIV case-based surveillance forms were assessed for completeness. Epi Info Version 7 was used to generate frequencies, means and proportions. Results: The reasons for under-reporting of patients in the electronic health record system were lack of reporting guidelines 26/36 (72%), limited coordination between technical staff and health facilities 24/36 (67%) and limited competency on the Electronic health record system 22/36 (61%). Timeliness, completeness, and validity were 88%, 82% and 100% respectively. The stability of the system was affected by the lack of standard operating procedures during system interruptions. Overall representativeness was 45% despite increasing from 3/226 (1%) to 224/303 (73%) between Quarter-1 and Quarter-4 of 2020. Acceptability was 100% due to reduced paperwork and the ability to generate simple reports. The information generated was used to identify new infection hotspots 28/36 (78%). Conclusion: The HIV cases based surveillance system was timely, acceptable with good data quality. Representativeness was poor due to limited competency on the electronic health record system. As a result, health workers received further training.

Constructing Large Scale Cohort for Clinical Study on Heart Failure with Electronic Health Record in Regional Healthcare Platform:Challenges and Strategies in Data Reuse

Regional healthcare platforms collect clinical data from hospitals in specific areas for the purpose of healthcare management.It is a common requirement to reuse the data for clinical research.However,we have to face challenges like the inconsistence of terminology in electronic health records (EHR) and the complexities in data quality and data formats in regional healthcare platform.In this paper,we propose methodology and process on constructing large scale cohorts which forms the basis of causality and comparative effectiveness relationship in epidemiology.We firstly constructed a Chinese terminology knowledge graph to deal with the diversity of vocabularies on regional platform.Secondly,we built special disease case repositories (i.e.,heart failure repository) that utilize the graph to search the related patients and to normalize the data.Based on the requirements of the clinical research which aimed to explore the effectiveness of taking statin on 180-days readmission in patients with heart failure,we built a large-scale retrospective cohort with 29647 cases of heart failure patients from the heart failure repository.After the propensity score matching,the study group (n=6346) and the control group (n=6346) with parallel clinical characteristics were acquired.Logistic regression analysis showed that taking statins had a negative correlation with 180-days readmission in heart failure patients.This paper presents the workflow and application example of big data mining based on regional EHR data.

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.

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.

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.

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.

Practical use of electronic health records among patients with diabetes in scientific research

Electronic health(medical)records,which are also considered as patients*information that are routinely collected,provide a great chance for researchers to develop an epidemiological understanding of disease.Electronic health records systems cannot develop without the advance of computer industries.While conducting clinical trials that are always costly,feasible and reasonable analysis of routine patients5 information is more cost-effective and reflective of clinical practice,which is also called real world study.Real world studies can be well supported by big data in healthcare industry.Real world studies become more and more focused and important with the development of evidence-based medicine.These big data will definitely help in making decisions,making policies and guidelines,monitoring of effectiveness and safety on new drugs or technologies.Extracting,cleaning,and analyzing such big data will be a great challenge for clinical researchers.Successful applications and developments of electronic health record in western countries(eg,disease registries,health insurance claims,etc)have provided a clear direction for Chinese researchers.However,it is still at primary stages in China.This review tries to provide a full perspective on how to translate the electronic health records into scientific achievements,for example,among patients with diabetes.As a summary in the end,resource sharing and collaborations are highly recommended among hospitals and healthcare groups.

Deep Learning with Heterogeneous Graph Embeddings for Mortality Prediction from Electronic Health Records

Computational prediction of in-hospital mortality in the setting of an intensive care unit can help clinical practitioners to guide care and make early decisions for interventions. As clinical data are complex and varied in their structure and components, continued innovation of modelling strategies is required to identify architectures that can best model outcomes. In this work, we trained a Heterogeneous Graph Model(HGM) on electronic health record(EHR) data and used the resulting embedding vector as additional information added to a Convolutional Neural Network(CNN) model for predicting in-hospital mortality. We show that the additional information provided by including time as a vector in the embedding captured the relationships between medical concepts, lab tests, and diagnoses, which enhanced predictive performance. We found that adding HGM to a CNN model increased the mortality prediction accuracy up to 4%. This framework served as a foundation for future experiments involving different EHR data types on important healthcare prediction tasks.