Electronic Healthcare Systems Research Articles

Comparison of machine learning and traditional methods for prediction of adverse clinical events after percutaneous coronary intervention

Abstract Background Accurate prediction of clinical outcomes following percutaneous coronary intervention (PCI) is essential for mitigating risk and periprocedural planning. Traditional risk models have demonstrated modest predictive value. Machine learning (ML) models offer an alternative risk stratification that may provide improved predictive accuracy. We performed a systematic review and meta-analysis comparing ML models and traditional risk scores for prediction of clinical outcomes after PCI. Methods This study was reported according to PRISMA guidelines. PubMed, EMBASE, Web of Science and Cochrane databases were searched until 1st November, 2023 for studies comparing ML models with traditional statistical methods for event prediction after PCI. The primary outcome was comparative discrimination measured by C-statistics with 95% confidence intervals between ML models and traditional methods in estimating the risk of all-cause mortality, major bleeding and the composite outcome major adverse cardiovascular events (MACE). Results Thirty-four models were included across 13 observational studies (4105916 patients). The summary C-statistic all ML models across all clinical endpoints was 0.81 (95% CI, 0.78-0.85), compared to traditional methods 0.79 (95% CI, 0.74-0.85). The difference in C-statistic was 0.02 (95% CI, 0.002-0.04, p=0.56). Of included studies, only 1 model was externally validated and 10 models were calibrated. Conclusion ML models did not outperform traditional risk scores in the discrimination of all-cause mortality, major bleeding or MACE following PCI. While integration of ML algorithms into electronic healthcare systems may improve periprocedural risk stratification, immediate implementation in the clinical setting remains uncertain. Further research is required to overcome methodological and validation limitations.

Machine learning approaches for risk prediction after percutaneous coronary intervention: a systematic review and meta-analysis

Abstract Aims Accurate prediction of clinical outcomes following percutaneous coronary intervention (PCI) is essential for mitigating risk and peri-procedural planning. Traditional risk models have demonstrated a modest predictive value. Machine learning (ML) models offer an alternative risk stratification that may provide improved predictive accuracy. Methods and results This study was reported according to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses, Critical Appraisal and Data Extraction for Systematic Reviews of Prediction Modelling Studies and Transparent Reporting of a multivariable prediction model for Individual Prognosis or Diagnosis guidelines. PubMed, EMBASE, Web of Science, and Cochrane databases were searched until 1 November 2023 for studies comparing ML models with traditional statistical methods for event prediction after PCI. The primary outcome was comparative discrimination measured by C-statistics with 95% confidence intervals (CIs) between ML models and traditional methods in estimating the risk of all-cause mortality, major bleeding, and the composite outcome major adverse cardiovascular events (MACE). Thirty-four models were included across 13 observational studies (4 105 916 patients). For all-cause mortality, the pooled C-statistic for top-performing ML models was 0.89 (95%CI, 0.84–0.91), compared with 0.86 (95% CI, 0.80–0.93) for traditional methods (P = 0.54). For major bleeding, the pooled C-statistic for ML models was 0.80 (95% CI, 0.77–0.84), compared with 0.78 (95% CI, 0.77–0.79) for traditional methods (P = 0.02). For MACE, the C-statistic for ML models was 0.83 (95% CI, 0.75–0.91), compared with 0.71 (95% CI, 0.69–0.74) for traditional methods (P = 0.007). Out of all included models, only one model was externally validated. Calibration was inconsistently reported across all models. Prediction Model Risk of Bias Assessment Tool demonstrated a high risk of bias across all studies. Conclusion Machine learning models marginally outperformed traditional risk scores in the discrimination of MACE and major bleeding following PCI. While integration of ML algorithms into electronic healthcare systems has been hypothesized to improve peri-procedural risk stratification, immediate implementation in the clinical setting remains uncertain. Further research is required to overcome methodological and validation limitations.

Preventing Overrides of Severe Drug Allergy Alerts Initiative: an Implemented System Upgrade

Administering medications to patients with documented drug hypersensitivity reactions (DHR) poses a significant risk for adverse events, ranging from mild reactions to life-threatening incidents. Electronic healthcare systems have revolutionized the modern clinical decision-making process, with built in warnings. However, as these alerts become a routine part of healthcare provider’s workflow, alert fatigue becomes a challenge. This study was conducted within the Ministry of National Guard Health Affairs (MNGHA), a government healthcare system in Saudi Arabia. A taskforce of experts was formed to develop an electronic path that would prevent unintentional overrides of severe drug allergy alerts. The system underwent rigorous testing, and monitoring parameters were established. We outline the implementation of a system upgrade designed to trigger an alternative interruption in the computerized physician order entry (CPOE) process, distinct from the regular allergy pop-up alerts. The alternate path is activated upon a CPOE with a drug-to-drug match and a documented severe drug allergy symptom, necessitating co-signature form another prescriber before proceeding. The adopted upgrade is a proactive approach to enhance medication safety in electronic healthcare systems, ensuring that serious allergy-related warnings are not overridden, ultimately enhancing patient safety. Further monitoring will confirm the safety and effectiveness of this measure. This study provides a model for institutions seeking to prevent allergy-related harm within their patient population.

Evaluating the Effectiveness of Using Change Management Methodologies to Improve Employee Adoption of New Electronic Healthcare Systems in the Kingdom of Saudi Arabia

Evaluating the Effectiveness of Using Change Management Methodologies to Improve Employee Adoption of New Electronic Healthcare Systems in the Kingdom of Saudi Arabia

Blockchain technology and raft consensus for secure physician prescriptions and improved diagnoses in electronic healthcare systems

With electronic healthcare systems undergoing rapid change, optimizing the crucial process of recording physician prescriptions is a task with major implications for patient care. The power of blockchain technology and the precision of the Raft consensus algorithm are combined in this article to create a revolutionary solution for this problem. In addition to addressing these issues, the proposed framework, by focusing on the challenges associated with physician prescriptions, is a breakthrough in a new era of security and dependability for the healthcare sector. The Raft algorithm is a cornerstone that improves the diagnostic decision-making process, increases confidence in patients, and sets a new standard for robust healthcare systems. In the proposed consensus algorithm, a weighted sum of two influencing factors including the physician acceptability and inter-physicians’ reliability is used for selecting the participating physicians. An investigation is conducted to see how well the Raft algorithm performs in overcoming prescription-related roadblocks that support a compelling argument for improved patient care. Apart from its technological benefits, the proposed approach seeks to revolutionize the healthcare system by fostering trust between patients and providers. Raft’s ability to communicate presents the proposed solution as an effective way to deal with healthcare issues and ensure security.

Implementation of Outpatient Automated Stewardship Information System (OASIS©) Audit and Feedback in Two Healthcare Systems

Background: Combating antibiotic resistance, exacerbated by widespread unnecessary outpatient antibiotic prescriptions, necessitates innovative stewardship solutions. Audit and feedback reports are effective but often resource heavy. We introduced a free, open-source system, Outpatient Automated Stewardship Information System (OASIS©), for automating the creation and distribution of recurring audit and feedback reports to clinicians to improve antibiotic prescribing. Methods: We used mixed methods to evaluate implementation of OASIS© across 11 clinics at Denver Health and Hospital Authority (DHHA) and Children’s Hospital Colorado (CHCO) from July 2022 to August 2023. Both sites host their own Epic® electronic healthcare and enterprise data warehouse systems. R statistical software was utilized to retrieve and process the data needed to create individual[HCM1] clinician audit and feedback reports with peer comparison. Reports were provided for 1) antibiotics prescribed for respiratory diagnoses, 2) antibiotics prescribed for respiratory diagnoses where antibiotics are never indicated, 3) first-line antibiotic prescribing for acute otitis media (AOM), and 4) five-day duration of antibiotics for children two years and older with AOM. Feedback reports for each metric were emailed to clinicians for three consecutive months. The primary outcome was adaptations needed to implement OASIS©. Secondary outcomes included fidelity (measured by email readership), time to set up and maintain the program, and barriers and facilitators to implementation (assessed by four qualitative interviews with OASIS© stakeholders). Results: The most significant adaptations made pertained to the automation of OASIS© reports for organizations not using R for data retrieval and reporting, setting up OASIS© specific email addresses, and validating clinician fidelity via read receipts. Fidelity was higher at DHHA (91-100%) compared to CHCO (10-30%). When interviewed, data analysts expressed that time for initial setup ranged from 1-6 hours. After reporting was automated, the estimated monthly time to send reports was 10 minutes. Views on setup complexity were split, but all recognized the readability of the reports and OASIS©’s value for improving prescribing behaviors. The greatest barriers to implementation included obtaining analytic resources for initial setup and the need to download additional R packages. No interviewee had prior experience creating audit and feedback reports. Conclusions: Implementing OASIS© requires addressing system diversity and knowledge gaps in outpatient informatics and antibiotic stewardship. Despite these challenges, the tool proved efficient and beneficial for monitoring and reporting antimicrobial prescribing. This free tool could likely be effectively disseminated to other health systems given the limited time and resources required for adaptations, setup, and monitoring. [HCM1]I

The Effect of Health Information Technology Assets on the Sustainability of Health Management Information Systems Usage Moderated by Strategy in Kendari City

Background:Health policies in Indonesia facilitate public access to healthcare information in efforts to improve public health. These policies rely on the support of electronic healthcare information systems to provide health data and information. However, the usage of these systems faces various challenges that affect their sustainability. Objective:To determine the effect of healthcare information technology assets on the sustainability of healthcare management information systems and the role of healthcare information technology strategies as a moderating variable. Methods:This is a quantitative and explanatory research study. The sample consisted of 350 respondents, with a significance level of 0.05. Latent variables included the sustainability of healthcare management information system usage, healthcare information technology assets, and information system strategies, which also served as an intervening variable. Results:The direct effect test yielded a p-value of 0.000 (<0.05), leading to the rejection of the null hypothesis. Thus, healthcare information technology assets and healthcare information technology strategies have a positive and significant effect on the sustainability of healthcare management information system usage. The indirect effect test yielded a p-value of 0.003 (<0.05), also leading to the rejection of the null hypothesis. These results indicate that the healthcare information technology strategies variable moderates the effect of healthcare information technology assets on the sustainability of healthcare management information system usage. Conclusion:Healthcare information technology strategies strengthen the effect of healthcare information technology assets on the sustainability of healthcare management information system usage. Keywords:Health Policy; Health Information Systems; Information Technology; Indonesia

#1817 Decrease in hospital-acquired blood stream infection by using automated sensor technology to improve and keep a high level of hand hygiene compliance

Abstract Background and Aims Hospital-acquired infection (HAIs) is a problem for 6-8% of hospitalized patients. HAIs has personal costs, prolongs the hospital stay, increasing the risk of morbidity, mortality, time and staff consuming. Hand hygiene (HH) is essential for preventing infections. World Health Organization (WHO) recommends hand hygiene compliance (HHC) as a key performance indicator. Manual observations of HHC are time consuming and may affect compliance by the physical presence of the observers (Hawthorne effect). There is a need to identify the most effective long-term intervention to improve HHC and reduce HAI. The Danish Ministry of Health have chosen blood stream infections (HA-BSI) as a national indicator for the overall risk of HAIs expecting an incidence of no more than 8 cases per 10,000 patient days. The aim of the present study was to examine the use of an automated sensor technology to improve HHC and prevent HAIs in an inpatient department for nephrology and intern medicine. Nephrology patients have a high number of invasive catheters, which may increases the risk of HAI. Method The study comprised a 2 year cohort with no specific HHC interventions (July 2018-June 2020), a baseline period (February 2020-June 2020) to investigate HHC level by an electronic hand hygiene measuring system (EHHMS) without ongoing HHC interventions followed by a 3 year cohort period (July 2020-June 2023) with various EHHMS-based feedback interventions to staff. Intervention consisted primarily of group feedback and later individual feedback. All caregivers and doctors carried an anonymous Sani ID tag (Sani Nudge, DK) fixed to their nametag throughout the study period. EHHMS-based Sani Sensors were located in designated locations in the clinical department with the purpose of continuously capturing the caregiver patient contact including staff use of liquid alcohol based hand rub (ABHR). In Denmark, the national hygiene guidelines also require that ABHR follow hand wash. Local hygiene mentors regularly presented anonymous group data on boards and in letters to the staff. Soon after there was a demand on individual data, which was then added by individual results forwarded in a personal email every week. HA-BSI was defined as the presence of a pathogen in the blood and the prescription of an appropriate concomitant antimicrobial treatment and classified as hospital-acquired if the patient has been hospitalized for >48 hours. Data were obtained from various electronic health care systems in the hospital. Results We collected HHC data by monitoring 203 caregivers and 50 doctors for a period of 40 months. Caregivers HHC improved after implementation of an electronic hand hygiene measuring system from baseline to group feedback and finally individual feedback (39% vs 50% vs 71%). For doctors, the improvement from baseline to group feedback and finally individual feedback was 30% vs. 32% vs 52%. The overall improvement curve reached a stable plateau after 20 months. An incidence rate of 14 HA-BSI cases per 10,000 patient days during a previous two years control period (95% CI 8.8-21.2) was during the intervention period markedly reduced to 5 HA-BSI cases per 10,000 patient days (95% CI 2.3-9.0). This difference was statistically significant, P = 0.004. Conclusion The use and feedback of the electronic hand hygiene monitoring system improved successfully hand hygiene compliance in the groups of both caregivers, doctors, and furthermore reduced the rate of HA-BSI significantly. We observed a significant stepwise increase in compliance after group feedback data and later after reporting individual data. There continue to be a need to find effective long-term interventions to improve and keep a high level of hand hygiene compliance and a sustained reduction in hospital-acquired bloodstream infection.

IntelliCare: Integrating IoT and Machine Learning for Remote Patient Monitoring in Healthcare: A Comprehensive Framework

The development of smart health monitoring systems has emerged as a consequence of the integration of Internet of Things (IoT) and Machine Learning (ML) technologies within the healthcare sector. This transformation has significantly reshaped patient care methodologies, shifting from traditional approaches to electronic healthcare systems. Leveraging IoT technology fosters a contemporary medical device ecosystem, fostering seamless communication among healthcare professionals, patients, and medical devices. Through the deployment of IoT devices, encompassing sensors and transmitters, coupled with Machine Learning algorithms, various applications have arisen, spanning from remote patient monitoring to real-time health assessment during ambulance transit to medical facilities. This proposed framework aims to monitor essential physiological parameters including body temperature, blood pressure, heart rate, sweat analysis, glucose levels, ECG, EEG, and pulse oximetry, transmitting pertinent data for tailored processing and analysis. Implantable IoT devices serve as conduits for wireless communication, data storage, centralized computation, and portable processing, facilitating connectivity among sensors, GPS-enabled ambulances, medical practitioners, and patients. To mitigate potential health risks, sensors are equipped with Machine Learning capabilities to promptly assess illness severity and recommend appropriate interventions, potentially triggering automated alerts to healthcare providers. This seamless exchange of information via IoT and wireless networks enables rapid communication between doctors and patients, facilitating personalized medical recommendations, prescription management, and hospital selection based on individual health profiles.

Revocable and Traceable Undeniable Attribute-Based Encryption in Cloud-Enabled E-Health Systems.

The emerging cloud storage technology has significantly improved efficiency and productivity in the traditional electronic healthcare field. However, it has also brought about many security concerns. Ciphertext policy attribute-based encryption (CP-ABE) holds immense potential in achieving fine-grained access control, providing robust security for electronic healthcare data in the cloud. However, current CP-ABE schemes still face issues such as inflexible attribute revocation, relatively lower computational capabilities, and key management. To address these issues, this paper introduces a revocable and traceable undeniable ciphertext policy attribute-based encryption scheme (MA-RUABE). MA-RUABE not only enables fast and accurate data traceability, effectively preventing malicious user key leakage, but also includes a direct revocation feature, significantly enhancing computational efficiency. Furthermore, the introduction of a multi-permission mechanism resolves the issue of centralization of power caused by single-attribute permissions. Furthermore, a security analysis demonstrates that our system ensures resilience against chosen plaintext attacks. Experimental results demonstrate that MA-RUABE incurs lower computational overhead, effectively enhancing system performance and ensuring data-sharing security in cloud-based electronic healthcare systems.

Fischer machine learning for mobile cloud computing in eHealth systems using blockchain mechanism

The Electronic Healthcare (eHealth) systems are competent to ensure effective care engineering and intensified healthcare quality which are user-friendly cache and administration, in Electronic Health Records (EHRs). For secure EHRs of Mobile Cloud-based eHealth systems, ensuring high security and data privacy, Interplanetary File System in healthcare has traditionally been concentrated. However, there has been a recent push towards achieving high quality of e-health services because blockchain-based health care applications require QoS guarantees in terms of requirements such as network latency and end-to-end delay. In this work, an Extended Validation Certification-based Fischer Neural Network Optimization (EVC-FNNO) method for secured Mobile Cloud-based E-Health Systems is proposed. With the identity being the digital certificate, the EVC is provided with the identity to the mobile cloud user who will transact in the network. In this way, the mobile cloud user is being ensured to access the ledger for the transaction. Therefore, both data privacy and security is said to be provided. Next, with Fischer Neural Network Optimization (FNNO), every authenticated mobile cloud user via EVC then possess a copy of shared ledger, therefore resolving data acquisition in cloud server and hence solving network latency. The proposed method is verified by some demonstrative examples in addressing QoS. The empirical results show that the EVC-FNNO method provides an efficient solution by validating the mobile cloud user sensitive health information with digital certificate. Security analysis proves that the EVC-FNNO method is secure. We also conduct comprehensive performance evaluations that demonstrate the high efficiency of the EVC-FNNO method in terms of end-to-end delay, network latency and data privacy, compared to the existing data sharing methods.

Developing a reflection and analysis tool (We-ReAlyse) for readmissions to the intensive care unit: A quality improvement project

BackgroundReadmissions to the intensive care unit are associated with poorer patient outcomes and health prognoses, alongside increased lengths of stay and mortality risk. To improve quality of care and patients’ safety, it is essential to understand influencing factors relevant to specific patient populations and settings. A standardized tool for systematic retrospective analysis of readmissions would help healthcare professionals understand risks and reasons affecting readmissions; however, no such tool exists. PurposeThis study’s purpose was to develop a tool (We-ReAlyse) to analyze readmissions to the intensive care unit from general units by reflecting on affected patients’ pathways from intensive care discharge to readmission. The results will highlight case-specific causes of readmission and potential areas for departmental- and institutional-level improvements. MethodA root cause analysis approach guided this quality improvement project. The tool’s iterative development process included a literature search, a clinical expert panel, and a testing in January and February 2021. ResultsThe We-ReAlyse tool guides healthcare professionals to identify areas for quality improvement by reflecting the patient's pathway from the initial intensive care stay to readmission. Ten readmissions were analyzed by using the We-ReAlyse tool, resulting in key insights about possible root causes like the handover process, patient's care needs, the resources on the general unit and the use of different electronic healthcare record systems. ConclusionsThe We-ReAlyse tool provides a visualization/objectification of issues related to intensive care readmissions, gathering data upon which to base quality improvement interventions. Based on the information on how multi-level risk profiles and knowledge deficits contribute to readmission rates, nurses can target specific quality improvements to reduce those rates. Implications for clinical practice and researchWith the We-ReAlyse tool, we have the opportunity to collect detailed information about ICU readmissions for an in-depth analysis. This will allow health professionals in all involved departments to discuss and either correct or cope with the identified issues. In the long term, this will allow continuous, concerted efforts to reduce and prevent ICU readmissions. To obtain more data for analysis and to further refine and simplify the tool, it may be applied to larger samples of ICU readmissions. Furthermore, to test its generalizability, the tool should be applied to patients from other departments and other hospitals. Adapting it to an electronic version would facilitate the timely and comprehensive collection of necessary information. Finally, the tool’s emphasis comprises reflecting on and analyzing ICU readmissions, allowing clinicians to develop interventions targeting the identified problems. Therefore, future research in this area will require the development and evaluation of potential interventions.

Mortality Prediction of Various Cancer Patients via Relevant Feature Analysis and Machine Learning

Breast, lung, prostate, and stomach cancers are the most frequent cancer types globally. Early-stage detection and diagnosis of these cancers pose a challenge in the literature. When dealing with cancer patients, physicians must select among various treatment methods that have a risk factor. Since the risks of treatment may outweigh the benefits, treatment schedule is critical in clinical decision making. Manually deciding which medications and treatments are going to be successful takes a lot of expertise and can be hard. In this paper, we offer a computational solution to predict the mortality of various types of cancer patients. The solution is based on the analysis of diagnosis, medication, and treatment parameters that can be easily acquired from electronic healthcare systems. A classification-based approach introduced to predict the mortality outcome of cancer patients. Several classifiers evaluated on the Medical Information Mart in Intensive Care IV (MIMIC-IV) dataset. Diagnosis, medication, and treatment features extracted for breast, lung, prostate, and stomach cancer patients and relevant feature selection done with Logistic Regression. Best F1 scores were 0.74 for breast, 0.73 for lung, 0.82 for prostate, and 0.79 for stomach cancer. Best AUROC scores were 0.94 for breast, 0.91 for lung, 0.96 for prostate, and 0.88 for stomach cancer. In addition, using relevant features, results were very similar to the baseline for each cancer type. Using less features and a robust machine-learning model, the proposed approach can be easily implemented in hospitals when there are limited data and resources available.

A systematic review of trustworthy and explainable artificial intelligence in healthcare: Assessment of quality, bias risk, and data fusion

In the last few years, the trend in health care of embracing artificial intelligence (AI) has dramatically changed the medical landscape. Medical centres have adopted AI applications to increase the accuracy of disease diagnosis and mitigate health risks. AI applications have changed rules and policies related to healthcare practice and work ethics. However, building trustworthy and explainable AI (XAI) in healthcare systems is still in its early stages. Specifically, the European Union has stated that AI must be human-centred and trustworthy, whereas in the healthcare sector, low methodological quality and high bias risk have become major concerns. This study endeavours to offer a systematic review of the trustworthiness and explainability of AI applications in healthcare, incorporating the assessment of quality, bias risk, and data fusion to supplement previous studies and provide more accurate and definitive findings. Likewise, 64 recent contributions on the trustworthiness of AI in healthcare from multiple databases (i.e., ScienceDirect, Scopus, Web of Science, and IEEE Xplore) were identified using a rigorous literature search method and selection criteria. The considered papers were categorised into a coherent and systematic classification including seven categories: explainable robotics, prediction, decision support, blockchain, transparency, digital health, and review. In this paper, we have presented a systematic and comprehensive analysis of earlier studies and opened the door to potential future studies by discussing in depth the challenges, motivations, and recommendations. In this study a systematic science mapping analysis in order to reorganise and summarise the results of earlier studies to address the issues of trustworthiness and objectivity was also performed. Moreover, this work has provided decisive evidence for the trustworthiness of AI in health care by presenting eight current state-of-the-art critical analyses regarding those more relevant research gaps. In addition, to the best of our knowledge, this study is the first to investigate the feasibility of utilising trustworthy and XAI applications in healthcare, by incorporating data fusion techniques and connecting various important pieces of information from available healthcare datasets and AI algorithms. The analysis of the revised contributions revealed crucial implications for academics and practitioners, and then potential methodological aspects to enhance the trustworthiness of AI applications in the medical sector were reviewed. Successively, the theoretical concept and current use of 17 XAI methods in health care were addressed. Finally, several objectives and guidelines were provided to policymakers to establish electronic health-care systems focused on achieving relevant features such as legitimacy, morality, and robustness. Several types of information fusion in healthcare were focused on in this study, including data, feature, image, decision, multimodal, hybrid, and temporal.

Machine-learning versus traditional approaches for atherosclerotic cardiovascular risk prognostication in primary prevention cohorts: a systematic review and meta-analysis.

Cardiovascular disease (CVD) risk prediction is important for guiding the intensity of therapy in CVD prevention. Whilst current risk prediction algorithms use traditional statistical approaches, machine learning (ML) presents an alternative method that may improve risk prediction accuracy. This systematic review and meta-analysis aimed to investigate whether ML algorithms demonstrate greater performance compared with traditional risk scores in CVD risk prognostication. MEDLINE, EMBASE, CENTRAL, and SCOPUS Web of Science Core collections were searched for studies comparing ML models to traditional risk scores for CVD risk prediction between the years 2000 and 2021. We included studies that assessed both ML and traditional risk scores in adult (≥18 year old) primary prevention populations. We assessed the risk of bias using the Prediction Model Risk of Bias Assessment Tool (PROBAST) tool. Only studies that provided a measure of discrimination [i.e. C-statistics with 95% confidence intervals (CIs)] were included in the meta-analysis. A total of 16 studies were included in the review and meta-analysis (3302515 individuals). All study designs were retrospective cohort studies. Out of 16 studies, 3 externally validated their models, and 11 reported calibration metrics. A total of 11 studies demonstrated a high risk of bias. The summary C-statistics (95% CI) of the top-performing ML models and traditional risk scores were 0.773 (95% CI: 0.740-0.806) and 0.759 (95% CI: 0.726-0.792), respectively. The difference in C-statistic was 0.0139 (95% CI: 0.0139-0.140), P<0.0001. ML models outperformed traditional risk scores in the discrimination of CVD risk prognostication. Integration of ML algorithms into electronic healthcare systems in primary care could improve identification of patients at high risk of subsequent CVD events and hence increase opportunities for CVD prevention. It is uncertain whether they can be implemented in clinical settings. Future implementation research is needed to examine how ML models may be utilized for primary prevention.This review was registered with PROSPERO (CRD42020220811).

Privacy-Preserving Fast Three-Factor Authentication and Key Agreement for IoT-Based E-Health Systems

Electronic healthcare (e-health) systems have received renewed interest, particularly in the current COVID-19 pandemic (e.g., lockdowns and changes in hospital policies due to the pandemic). However, ensuring security of both data-at-rest and data-in-transit remains challenging to achieve, particularly since data is collected and sent from less insecure devices (e.g., patients’ wearable or home devices). While there have been a number of authentication schemes, such as those based on three-factor authentication, to provide authentication and privacy protection, a number of limitations associated with these schemes remain (e.g., (in)security or computationally expensive). In this study, we present a privacy-preserving three-factor authenticated key agreement scheme that is sufficiently lightweight for resource-constrained e-health systems. The proposed scheme enables both mutual authentication and session key negotiation in addition to privacy protection, with minimal computational cost. The security of the proposed scheme is demonstrated in the Real-or-Random model. Experiments using Raspberry Pi show that the proposed scheme achieves reduced computational cost (of up to 89.9% in comparison to three other related schemes).

Legal aspects of electronic document management in telemedicine

In the article from the standpoint of the theory of administrative and information law, based on the current legislation and regulatory requirements of the European Union, the theoretical and practical aspects of electronic document flow in telemedicine are considered. The topicality of the topic determined by the need to improve the legislation with the aim of comprehensive theoretical justification of increasing the effectiveness of telemedicine activities in the conditions of digital transformation of Ukraine. In the course of the study, the methodology of a systematic complex analysis of legal phenomena was applied using factorial and evolutionary methods of research. It is indicated that in the countries of the European Union, three main models of health care information systems have been formed, which differ in the ways of storing medical information and management: decentralized, centralized and patient-oriented. It was noted that the main legal issues of the renewal of the medical system of Ukraine and the directions of activity in the conditions of reform include telemedicine, and its component - electronic document flow. The essence and features of electronic document management in telemedicine in the countries of the European Union have been clarified. The electronic health care systems of individual countries of the European Union considered, attention is paid to the experience of use. The state of legal provision of information security in telemedicine with regard to electronic document flow studied, taking into account the experience of the countries of the European Union. The analysis of ensuring information security in the context of personal data in the medical systems of European countries and Ukraine was carried out. The further vector and direction of the development of the national health care system in terms of the Concept of the development of electronic health care, which is important for medical care and rehabilitation of citizens who suffered during the war, was determined. Important aspects and measures to optimize activities in the field of telemedicine highlighted, which need to be paid attention to during further reform and creation of new digital resources for a more comfortable transition and use of the latest digital technologies in the field of health care.

Improved Method for Enhanced Quality of Service in IoHT Task Dependency Optimization

Keeping a proper level of task dependency throughout the scheduling process is critical to achieving the aim of decreasing the make-span rate in Internet of Health Things (IoHT) projects. We provide a smart model strategy for effective task scheduling in the IoHT environment for e-healthcare systems by merging hybrid moth flame optimisation (HMFO) with cloud computing. The HMFO algorithm guarantees that all available resources are distributed evenly, resulting in improved quality of service (QoS). We study the Google cluster dataset to learn about the scheduling behaviours of cloud-based jobs in order to train our model. After training, an HMFO model may be used to plan activities in real time. To assess the success of our strategy, we run simulations in the CloudSim environment, taking into account crucial parameters such as resource utilisation, reaction time, and energy consumption. According to a comparative analysis, our hybrid HMFO system surpasses the alternatives in terms of reaction time, average run duration, and cost savings. Our method has proven to be effective due to the favourable effects it has had on response rates, prices, and run times. Combining IoT and cloud computing has the potential to improve healthcare delivery in a variety of ways. One unique strategy we offer for scheduling IOHT jobs is to combine a deep neural network (DNN) algorithm with the MFO technique. Job scheduling in electronic healthcare systems can be optimised with the help of our hybrid MFO-DNN algorithm by taking into account a variety of different objectives, the most important of which are lowering response times while improving resource utilisation and maintaining consistent load balances. The MFO approach searches the search space and provides early solutions, while the DNN algorithm refines and improves those first findings. In comprehensive simulations conducted in a real-world hospital setting, the hybrid MFO-DNN technique outperformed existing scheduling algorithms in terms of reaction time, resource utilisation, and load balancing. The simulated healthcare environments were as true to life as was feasible. The suggested technique has been demonstrated to be both dependable and scalable, making it appropriate for use in large-scale IOHT deployments. This study considerably enhances the state of the art in IOHT task scheduling in E healthcare systems by developing a hybrid optimisation technique that takes advantage of the strengths of both MFO and DNN. The findings indicate that this strategy has the potential to improve the quality and efficiency of healthcare delivery, which helps patients receive care that is both effective and timely.

Recommending Relevant Services in Electronic and Mobile Health Platforms

Electronic and mobile health (EMH) is becoming an integrated part of healthcare as we move in the future. The opportunity in bringing closer healthcare services with the advent of the internet is growing larger. This is why it is important to adequately provide those services to the people that need them and to also further improve them. Regarding electronic and mobile healthcare systems, it is fairly easy for users to get lost while searching for some information due to the vast amount of data that is present for different illnesses, healthcare institutions and healthcare services. In this paper we present a platform that provides various healthcare services to people, namely the Insieme platform (ISE-EMH). Knowing the difficulty of finding relevant information on platforms and that user preferences vary to a great extent, we additionally give an overview of an implementation of the recommendation system that is part of the Insieme platform which helps users pick services that might be relevant to them.

A robust framework for handling health care information based on machine learning and big data engineering techniques

ABSTRACT Confidentiality and security breaches remain significant concerns for electronic healthcare systems even though many relevant rules, principles, and compliance standards are in place to protect health information. It is now essential to use strategies revolving around big data to enhance the dependability of healthcare delivery due to the ever-increasing number of data generated within the healthcare sector. Even though big data processing methods and platforms have been incorporated into the data management designs for medical systems, these designs have trouble addressing urgent situations. Today, it is difficult to predict how big data and ML will affect the healthcare sectors. As a result, a clinical healthcare data warehouse environment utilizing big data analytics and ML is provided in this analysis. Quick digital access to all types of vital data, including patient histories, scan records, insurance claims, and payment history, is guaranteed by healthcare data warehouse systems. This clinical healthcare data warehouse environment can improve an individual's quality of care and monitor the patient's health status in real-time by using ML algorithms and Big data analytics. The effectiveness of the ML technique's performance is measured in terms of accuracy, specificity, sensitivity, precision, recall and Receiver Operating Characteristics (RoC).