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.

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.

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.

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.

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. .

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.

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.

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.

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.

Impact of mobile health and medical applications on clinical practice in gastroenterology

Mobile health apps (MHAs) and medical apps (MAs) are becoming increasinglypopular as digital interventions in a wide range of health-related applications inalmost all sectors of healthcare. The surge in demand for digital medical solutionshas been accelerated by the need for new diagnostic and therapeutic methods inthe current coronavirus disease 2019 pandemic. This also applies to clinicalpractice in gastroenterology, which has, in many respects, undergone a recentdigital transformation with numerous consequences that will impact patients andhealth care professionals in the near future. MHAs and MAs are considered tohave great potential, especially for chronic diseases, as they can support the selfmanagementof patients in many ways. Despite the great potential associated withthe application of MHAs and MAs in gastroenterology and health care in general,there are numerous challenges to be met in the future, including both the ethicaland legal aspects of applying this technology. The aim of this article is to providean overview of the current status of MHA and MA use in the field ofgastroenterology, describe the future perspectives in this field and point out someof the challenges that need to be addressed.

The Effects of Information and Communication Technology on Health Service Delivery at Tafo Government Hospital

This study sought to find out the effects of Information and Communication Technology (ICT) on health service delivery at Tafo Government Hospital. A descriptive survey design was used. Data were collected through the use of semi-structured questionnaire and administered to 50 respondents where stratified random sampling technique was used by ranking position as strata. Data were analyzed using descriptive statistics. From the findings, 56% of the respondents overwhelmingly agreed to the fact that the applications of ICT provide quicker medical diagnoses, reduced workload among users, improvement in patients’ waiting time and information accessibility. Nonetheless, 72% bemoaned lack of ICT infrastructure, poor ICT network concerns coupled with that insufficient knowledge on the use of ICT could impede the impact of ICT in quality service delivery. This could be deduced from the findings that ICT improves collaboration and clinical decision support in facilitating clinical work flow integration among nurses and other medical professionals. Moreover, the findings above affirm the fact that without electricity, ICT infrastructure, insufficient skills and technical knowledge in dealing with ICT innovations, it is impossible to successfully adopt ICT resources in health care delivery. The above findings show that the majority of healthcare professional generally had a positive attitude towards ICT prospects as they rated their skill as fairly well. The study recommends that the Ministry of Health plays a supporting role by investing in health care ICT.

HealthyBlockchain for Global Patients

An emerging healthcare delivery model is enabling a new era of clinical care based on well-informed decision-making processes.Current healthcare information systems(HISs)fall short of adopting this model due to a conflict between information security needed to implement the new model and those already enforced locally to support traditional care models.Meanwhile,in recent times,the healthcare sector has shown a substantial interest in the potential of using blockchain technology for providing quality care to patients.No blockchain solution proposed so far has fully addressed emerging cross-organization information-sharing needs in healthcare.In this paper,we aim to study the use of blockchain in equipping struggling HISs to cope with the demands of the new healthcare delivery model,by proposing HealthyBlockchain as a granular patient-centered ledger that digitally tracks a patient’s medical transactions all along the treatment pathway to support the care teams.The patient-centered ledger is a neutral tamper-proof trail timestamp block sequence that governs distributed patient information across the decentralized discrete HISs.HealthyBlockchain connects patients,clinicians,and healthcare providers to facilitate a transparent,trustworthy,and secure supporting platform.

Fostering Patient Safety: Importance of Nursing Documentation

Background: Nurses are professionally accountable for assessing and documenting patients’ vital signs. Nurses failing to fulfill this responsibility position their patients at risk. This paper presents two real-life cases pertaining to patients’ safety resulting in fatal outcomes, leading to the professional, legal, and ethical liability of nurses as the providers of patient care. Objective: This paper focuses on the role of organizational culture in fostering patient safety specifically in monitoring and documentation of patients’ vital signs and early recognition of warning signs. Methodology: A comprehensive literature search was conducted using various databases, examining the significance of vital signs monitoring and documentation and early warning signs in patient safety. Relevant articles combining quantitative and qualitative data were analyzed. Results: By fostering an environment of honest reporting, healthcare organizations can enhance patient safety and improve the quality of care. This paper offers valuable insights and recommendations for developing effective strategies aligned with organizational policies and protocols. Conclusion: This paper serves as a valuable resource, encouraging healthcare professionals to reflect on their practices and the organizations to assess their contributions to creating a culture of safety. It also highlights the importance of reporting and disclosing adverse events as learning opportunities and outlines the role of ethics, professionalism, legislation, and organizational support in achieving patient safety.

Use of content management systems to address nursing workflow

Nurses are at the forefront of providing healthcare services to individuals of all age groups and with varying medical conditions.Aside from the critical knowledge and technical skills from nursing science,advancement in technology has assisted nurses in delivering quality nursing care by streamlining workflow processes and ensuring that data can easily be retrieved or modified.Electronic health records dramatically changed the landscape of the healthcare practice by providing an electronic means to store data and for healthcare professionals to retrieve and manipulate health information in a secured and collaborative environment.But with the nature of data being stored in the electronic health records,nurses still need to organize and process these data into relevant information,knowledge or wisdom so they can provide better holistic care to patients.This discussion paper details the role of content management systems in addressing nursing workflow by providing a mechanism for nurses to be developers themselves,and not just users or consumers of health innovative technologies.By using content management systems as platform for application development,nurses or other healthcare professionals,may be able to address problems with internal workflow without having to incur huge amounts in software development,or having to extensively learn programming languages.

Predictive modeling of 30-day readmission risk of diabetes patients by logistic regression,artificial neural network,and EasyEnsemble

Objective:To determine the most influential data features and to develop machine learning approaches that best predict hospital readmissions among patients with diabetes.Methods:In this retrospective cohort study,we surveyed patient statistics and performed feature analysis to identify the most influential data features associated with readmissions.Classification of all-cause,30-day readmission outcomes were modeled using logistic regression,artificial neural network,and Easy Ensemble.F1 statistic,sensitivity,and positive predictive value were used to evaluate the model performance.Results:We identified 14 most influential data features(4 numeric features and 10 categorical features)and evaluated 3 machine learning models with numerous sampling methods(oversampling,undersampling,and hybrid techniques).The deep learning model offered no improvement over traditional models(logistic regression and Easy Ensemble)for predicting readmission,whereas the other two algorithms led to much smaller differences between the training and testing datasets.Conclusions:Machine learning approaches to record electronic health data offer a promising method for improving readmission prediction in patients with diabetes.But more work is needed to construct datasets with more clinical variables beyond the standard risk factors and to fine-tune and optimize machine learning models.

Federated Learning Based on Extremely Sparse Series Clinic Monitoring Data

Decentralized machine learning frameworks,e.g.,federated learning,are emerging to facilitate learning with medical data under privacy protection.It is widely agreed that the establishment of an accurate and robust medical learning model requires a large number of continuous synchronous monitoring data of patients from various types of monitoring facilities.However,the clinic monitoring data are usually sparse and imbalanced with errors and time irregularity,leading to inaccurate risk prediction results.To address this issue,this paper designs a medical data resampling and balancing scheme for federated learning to eliminate model biases caused by sample imbalance and provide accurate disease risk prediction on multi-center medical data.Experimental results on a real-world clinical database MIMIC-Ⅳ demonstrate that the proposed method can improve AUC(the area under the receiver operating characteristic) from 50.1% to 62.8%,with a significant performance improvement of accuracy from 76.8% to 82.2%,compared to a vanilla federated learning artificial neural network(ANN).Moreover,we increase the model’s tolerance for missing data from 20% to 50% compared with a stand-alone baseline model.

An Efficient Ensemble Model for Various Scale Medical Data

Electronic Health Records(EHRs)are the digital form of patients’medical reports or records.EHRs facilitate advanced analytics and aid in better decision-making for clinical data.Medical data are very complicated and using one classification algorithm to reach good results is difficult.For this reason,we use a combination of classification techniques to reach an efficient and accurate classification model.This model combination is called the Ensemble model.We need to predict new medical data with a high accuracy value in a small processing time.We propose a new ensemble model MDRL which is efficient with different datasets.The MDRL gives the highest accuracy value.It saves the processing time instead of processing four different algorithms sequentially;it executes the four algorithms in parallel.We implement five different algorithms on five variant datasets which are Heart Disease,Health General,Diabetes,Heart Attack,and Covid-19 Datasets.The four algorithms are Random Forest(RF),Decision Tree(DT),Logistic Regression(LR),and Multi-layer Perceptron(MLP).In addition to MDRL(our proposed ensemble model)which includes MLP,DT,RF,and LR together.From our experiments,we conclude that our ensemble model has the best accuracy value for most datasets.We reach that the combination of the Correlation Feature Selection(CFS)algorithm and our ensemble model is the best for giving the highest accuracy value.The accuracy values for our ensemble model based on CFS are 98.86,97.96,100,99.33,and 99.37 for heart disease,health general,Covid-19,heart attack,and diabetes datasets respectively.

Real-World Data for the Drug Development in the Digital Era

Randomized clinical trials(RCTs)have long been recognized the gold standard for regulatory approval in the drug development.However,RCTs may not be feasible in some diseases and/or under certain situations,and findings from RCTs may not be generalized to real-world patients in routine clinical practice.Real-world evidence(RWE),which is generated from various real-world data(RWD),has become more and more important for the drug development and clinical decision-making in the digital era.This paper described RWD and real-world data studies(RWDSs),followed by the characteristics and differences between RCTs and RWDSs.Furthermore,the challenges and limitations of RWD and RWE were discussed.Finally,this paper highlights that the efforts must be made during RWE generation from data collection/database selection,study design,statistical analysis,and interpretation of the results to minimize the biases and confounding effects.