Healthcare Information Systems Research Articles

Deploying a user-friendly GIS mapping tool in post-earthquake Turkey and Syria

Background The February 2023 earthquakes in Turkey and Syria exposed significant gaps in healthcare information systems, which severely hampered communication, coordination, and the overall public health response. These disruptions were primarily due to the lack of real-time, accessible data on healthcare facilities. Methods In response, an interactive Geographic Information System (GIS) tool, coupled with an integrated WhatsApp chatbot, was developed under the platform Afetsaglikharitasi.org. This system enabled real-time mapping and updates on the status of healthcare services, even in areas with limited internet connectivity, facilitating more efficient resource coordination during the disaster response. Results By March 30, 2023, the disaster health map included data from 537 health centers and had reached over 800,000 individuals via social media platforms such as Instagram, Facebook, and Twitter. The platform attracted over 3,500 unique users, with most accessing it via mobile devices to locate healthcare services. Additionally, shortcomings in the Turkey National Disaster Response Plan (Türkiye Afet Müdahale Planı – TAMP) were identified, particularly the lack of integration of digital health technologies, which hindered resource allocation during the earthquake response and underscored the need for their inclusion in disaster management strategies. Conclusions This study demonstrates the effectiveness of digital health tools in enhancing disaster response and underscores the importance of integrating user-centered, deployable technologies to improve healthcare delivery in emergency settings.

Data-driven diabetes management: a statistical assessment information system leveraging big data

ABSTRACT Amidst the escalating global burden of diabetes and its multifaceted complications, there arises an urgent need for robust healthcare information systems tailored to address the complexities of diabetic patient management. This paper introduces a novel statistical assessment healthcare information system engineered to meticulously analyze diabetes-related data by harnessing the power of big data technologies. Diabetes, affecting millions worldwide, exerts substantial strain on healthcare infrastructures, necessitating innovative solutions to optimize patient outcomes and treatment efficacy. Leveraging advancements in data processing and analytics alongside vast medical datasets, this study pioneers a statistical assessment model explicitly crafted for diabetes analysis within a big data paradigm. The model’s efficacy is rigorously evaluated using key performance metrics such as accuracy and F-measure, within the Hadoop framework. Remarkably, the results demonstrate a significant enhancement in performance compared to conventional methodologies, affirming the model’s potential to revolutionize the accuracy and efficiency of diabetes analysis within a big data milieu. Moving forward, further research avenues include refining the model’s algorithms to accommodate diverse data sources and expanding its applicability to encompass a broader spectrum of diabetes-related factors.

Security of Magnetic Resonance Medical Images using Region-based Lossless Image Compression in Healthcare Information Systems

Purpose The purpose of region-based medical image compression is to optimize the compression process by focusing on specific regions of interest within medical images. Unlike traditional compression methods that treat the entire image uniformly, region-based compression techniques identify and prioritize certain areas or regions within the image that are deemed more diagnostically significant or relevant. By allocating more resources to compressing these critical regions while reducing compression in less important areas, region-based compression methods aim to achieve higher compression efficiency while preserving diagnostic quality. This approach is particularly valuable in medical imaging, where accurate representation of anatomical structures or pathological findings is paramount for clinical diagnosis and decision-making. Region-based compression can help reduce storage requirements, transmission bandwidth, and processing time without compromising the diagnostic integrity of medical images, thereby facilitating more efficient healthcare delivery and telemedicine applications. Methods In this study, we utilized distortion-limiting compression techniques to optimize the compression process for specific regions within medical images. We employed lossless scalable RBC (Region-Based Compression) using Discrete Wavelet Transform (DWT) for Digital Imaging and Communication in Medicine (DICOM) images. The initial step involved medical image pre-processing, followed by segmentation to separate the image into regions of interest (ROI) and non-ROI. Compression techniques were then applied to reduce network bandwidth and storage requirements. Fractal lossy compression was employed for the non-ROI portion, while context-tree weighting lossless compression was proposed for the ROI portion, effectively compressing the image while rejecting noisy background elements. During decompression, the original medical image can be reconstructed using the reverse process. This approach optimizes storage and transmission efficiency while preserving diagnostic integrity in medical imaging applications. Results The experiment involved testing various medical images, and the proposed method outperformed previous techniques in terms of results. According to the findings, the improvement in Peak Signal-to-Noise Ratio (PSNR) over current techniques reached up to 24.23 dB compared to the Joint Photographic Experts Group (JPEG). Additionally, it achieved up to 12.22 dB improvement compared to other transform approaches. These significant enhancements prompted the development of a web and mobile platform for compressing and sending medical images, particularly microscopic ones, in real time. Conclusion This research focuses on employing wavelet transform techniques to compress the Region of Interest (ROI) within medical images. This ROI-based compression approach is particularly valuable as it retains essential diagnostic information while reducing the overall file size. Such a technique holds significant promise for telemedicine systems, especially in rural regions where network resources may be limited or constrained. By selectively compressing the most diagnostically relevant areas of medical images, this approach ensures that critical information is preserved while optimizing data transmission and storage efficiency. This can ultimately enhance access to medical imaging services and facilitate remote diagnosis and treatment in underserved areas with limited network infrastructure.

Does the development of strategic readiness represent a response to the relationship of strategic competencies and the healthcare information system? Evidence from private hospitals in Jordan

This study introduces a model designed to improve the strategic readiness of private hospitals in Amman by incorporating strategic competencies as an independent variable and using a healthcare information system as a mediator. Targeting private hospitals with over 140 beds, the research included a population of 3263 employees across various managerial levels. Data collection methods involved interviews and electronic questionnaires, resulting in a sample size of 344. Statistical analyses comprised exploratory and confirmatory factor analysis, structural equation modeling, and hypothesis testing with SMART PLS 3.3.3 software. The results indicated medium levels of both strategic competencies and healthcare information systems, while strategic readiness was found to be low. Nevertheless, the proposed model showed a direct positive effect of strategic competencies on strategic readiness, with the healthcare information system acting as a significant partial mediator. Evaluation metrics included the arithmetic mean, standard deviation, and path analysis. This model surpasses traditional methods by effectively linking strategic competencies and information systems to enhance strategic readiness, providing a strong framework for improving hospital responses to crises and dynamic changes. The study suggests focusing on enhancing and developing strategic competencies and integrating a comprehensive healthcare information system to optimize hospital operations and increase readiness.

Enhancing Healthcare Information Systems in Ethiopian Hospitals: Exploring Challenges and Prospects of a Cloud-based Model for Smart and Sustainable Information Services

Hospitals are the primary hubs for healthcare service providers in Ethiopia; however, hospitals face significant challenges in adopting digital health information systems solutions due to disparate, non-interoperable systems and limited access. Information technology, especially via cloud computing, is crucial in healthcare for efficient data management, secure storage, real-time access to critical information, seamless provider communication, enhanced collaboration, and scalable IT infrastructure. This study investigated the challenges to standardizing smart and green healthcare information services and proposed a cloud-based model for overcoming them. We conducted a mixed-methods study in 11 public hospitals, employing quantitative and qualitative approaches with diverse stakeholders (N = 103). The data was collected through surveys, interviews, and technical observations by purposive quota sampling with the Raosoft platform and analyzed using IBM SPSS. Findings revealed several shortcomings in existing information systems, including limited storage, scalability, and security; impaired data sharing and collaboration; accessibility issues; no interoperability; ownership ambiguity; unreliable data recovery; environmental concerns; affordability challenges; and inadequate policy enforcement. Notably, hospitals lacked a centralized data management system, cloud-enabled systems for remote access, and modern data recovery strategies. Despite these challenges, 90.3% of respondents expressed interest in adopting cloud-enabled data recovery systems. However, infrastructure limitations, inadequate cloud computing/IT knowledge, lack of top management support, digital illiteracy, limited innovation, and data security concerns were identified as challenges to cloud adoption. The study further identified three existing healthcare information systems: paper-based methods, electronic medical catalog systems, and district health information systems2. Limitations of the paper-based method include error-proneness, significant cost, data fragmentation, and restricted remote access. Growing hospital congestion and carbon footprint highlighted the need for sustainable solutions. Based on these findings, we proposed a cloud-based model tailored to the Ethiopian context. This six-layered model, delivered as a Software-as-a-Service within a community cloud deployment, aims to improve healthcare services through instant access, unified data management, and evidence-based medical practices. The model demonstrates high acceptability and potential for improving healthcare delivery, and implementation recommendations are suggested based on the proposed model.

Development and validation of an instrument to evaluate the perspective of using the electronic health record in a hospital setting

BackgroundEvaluating healthcare information systems, such as the Electronic Health Records (EHR), is both challenging and essential, especially in resource-limited countries. This study aims to psychometrically develop and validate an instrument (questionnaire) to assess the factors influencing the successful adoption of the EHR system by healthcare professionals in Moroccan university hospitals.MethodsThe questionnaire validation process occurred in two main stages. Initially, data collected from a pilot sample of 164 participants underwent analysis using exploratory factor analysis (EFA) to evaluate the validity and reliability of the retained factor structure. Subsequently, the validity of the overall measurement model was confirmed using confirmatory factor analysis (CFA) in a sample of 368 healthcare professionals.ResultsThe structure of the modified HOT-fit model, comprising seven constructs (System Quality, Information Quality, Information technology Service Quality, User Satisfaction, Organization, Environment, and Clinical Performance), was confirmed through confirmatory factor analysis. Absolute, incremental, and parsimonious fit indices all indicated an appropriate level of acceptability, affirming the robustness of the measurement model. Additionally, the instrument demonstrated adequate reliability and convergent validity, with composite reliability values ranging from 0.75 to 0.89 and average variance extracted (AVE) values ranging from 0.51 to 0.63. Furthermore, the square roots of AVE values exceeded the correlations between different pairs of constructs, and the heterotrait-monotrait ratio of correlations (HTMT) was below 0.85, confirming suitable discriminant validity.ConclusionsThe resulting instrument, due to its rigorous development and validation process, can serve as a reliable and valid tool for assessing the success of information technologies in similar contexts.

Success Factors in the Implementation of Healthcare Information Systems (HIS) among Developing Countries

Developing countries face significant challenges establishing healthcare information systems (HIS) due to inadequate government regulations and outdated technology infrastructures. For example, the lack of digital patient data collection in Bangladesh exacerbates the accessibility to quality healthcare. Standardizing hospital healthcare systems could streamline patient management, potentially servicing more patients effectively. This study utilizes a systematic literature review (SLR) to consolidate key factors enhancing HIS in such regions. By reviewing 4014 studies from databases like IEEE Xplore and Science Direct, the research identified 12 success criteria organized into four main themes, emphasizing the crucial role of resource allocation in deploying effective HIS. Bangladesh Journal of Medical Science Vol. 23 No. 04 October’24 Page : 957-966

The direct effect of institutional factors on healthcare information systems (HIS) organisational interoperability in Malaysian public hospitals.

Background: Organisational interoperability (OIoP) of the Healthcare Information System (HIS) is crucial for the success of HIS, however little is known about the impact of institutional factors. Objective: This cross-sectional study aimed to investigate the direct effect of institutional factors on OIoP for HIS in public sector hospitals in Malaysia. Methods: A conceptual OIoP framework was developed using the Personal Health Systems Interoperability and Refined eHealth European Interoperability frameworks. A self-administered questionnaire survey was used to solicit data from 300 healthcare professionals. Data were assessed through an Exploratory Factor Analysis followed by a Confirmatory Factor Analysis. Results: Structured equation modelling revealed Security and Privacy Compliance, and Stakeholder Engagement and Awareness to significantly and positively affect OIoP (R2 = 0.380). Conclusion: Healthcare organisations should prioritise clear and effective policies and regulations and enough budget and resources for the suggested framework.

Rancang Bangun Sistem Informasi Pelayanan Kesehatan Berbasis Website Pada Salvio Medical Information

In an era of rapid advances in Information Technology, health is an important aspect of human life. Clinics and hospitals compete to meet patient expectations, but often patients struggle to find suitable services, wasting valuable time. A common problem faced by many visitors to Bali is the confusion of finding a hospital or clinic that suits their needs. Salvio Medical Information, as a healthcare institution in Bali, faces similar challenges with a lack of a comprehensive information system. This can result in errors in data management, difficulties in accessing information, as well as delays in service to guests who need health care. Therefore, this research aims to design a website-based healthcare information system for Salvio Medical Information. The development approach uses the Extreme Programming model to enable rapid software development. Thus, this research is expected to make a significant contribution to efforts to develop health services that are more efficient and able to better meet the needs of patients. The expected result is a web-based health service information system that is built using the PHP programming language with the help of the Laravel framework and the extreme programming method. Where in the system built custemer can place orders through the website and the admin can manage service content and partner content on the website.

Implementation of the Observational Medical Outcomes Partnership Model in Electronic Medical Record Systems: Evaluation Study Using Factor Analysis and Decision-Making Trial and Evaluation Laboratory-Best-Worst Methods.

Electronic medical record (EMR) systems are essential in health care for collecting and storing patient medical data. They provide critical information to doctors and caregivers, facilitating improved decision-making and patient care. Despite their significance, optimizing EMR systems is crucial for enhancing health care quality. Implementing the Observational Medical Outcomes Partnership (OMOP) shared data model represents a promising approach to improve EMR performance and overall health care outcomes. This study aims to evaluate the effects of implementing the OMOP shared data model in EMR systems and to assess its impact on enhancing health care quality. In this study, 3 distinct methodologies are used to explore various aspects of health care information systems. First, factor analysis is utilized to investigate the correlations between EMR systems and attitudes toward OMOP. Second, the best-worst method (BWM) is applied to determine the weights of criteria and subcriteria. Lastly, the decision-making trial and evaluation laboratory technique is used to illustrate the interactions and interdependencies among the identified criteria. In this research, we evaluated the AliHealth EMR system by surveying 98 users and practitioners to assess its effectiveness and user satisfaction. The study reveals that among all components, "EMR resolution" holds the highest importance with a weight of 0.31007783, highlighting its significant role in the evaluation. Conversely, "EMR ease of use" has the lowest weight of 0.1860467, indicating that stakeholders prioritize the resolution aspect over ease of use in their assessment of EMR systems. The findings highlight that stakeholders prioritize certain aspects of EMR systems, with "EMR resolution" being the most valued component.

User involvement in the design and development of medical devices in epilepsy: A systematic review.

This systematic review aims to describe the involvement of persons with epilepsy (PWE), healthcare professionals (HP) and caregivers (CG) in the design and development of medical devices is epilepsy. A systematic review was conducted, adhering to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. Eligibility criteria included peer-reviewed research focusing on medical devices for epilepsy management, involving users (PWE, CG, and HP) during the MDD process. Searches were performed on PubMed, Web of Science, and Scopus, and a total of 55 relevant articles were identified and reviewed. From 1999 to 2023, there was a gradual increase in the number of publications related to user involvement in epilepsy medical device development (MDD), highlighting the growing interest in this field. The medical devices involved in these studies encompassed a range of seizure detection tools, healthcare information systems, vagus nerve stimulation (VNS) and electroencephalogram (EEG) technologies reflecting the emphasis on seizure detection, prediction, and prevention. PWE and CG were the primary users involved, underscoring the importance of their perspectives. Surveys, usability testing, interviews, and focus groups were the methods used for capturing user perspectives. User involvement occurs in four out of the five stages of MDD, with production being the exception. User involvement in the MDD process for epilepsy management is an emerging area of interest holding a significant promise for improving device quality and patient outcomes. This review highlights the need for broader and more effective user involvement, as it currently lags in the development of commercially available medical devices for epilepsy management. Future research should explore the benefits and barriers of user involvement to enhance medical device technologies for epilepsy. This review covers studies that have involved users in the development process of medical devices for epilepsy. The studies reported here have focused on getting input from people with epilepsy, their caregivers, and healthcare providers. These devices include tools for detecting seizures, stimulating nerves, and tracking brain activity. Most user feedback was gathered through surveys, usability tests, interviews, and focus groups. Users were involved in nearly every stage of device development except production. The review highlights that involving users can improve device quality and patient outcomes, but more effective involvement is needed in commercial device development. Future research should focus on the benefits and challenges of user involvement.

Сравнительный анализ развития Eдиных Информационных Систем Здравоохранения (ЕИСЗ) в странах Африки, Северной Америки и Европы: проблемы и перспективы.

This study presents a comparative analysis of the development of unified healthcare information systems (UHIS) in North America, Europe, and Africa. The research examines key aspects of UHIS implementation, including population coverage by electronic health records, telemedicine development, system interoperability, and regulatory frameworks. The analysis revealed significant regional differences in approaches to healthcare digitalization and UHIS implementation levels. North America is characterized by high digital technology adoption but faces challenges in integrating disparate systems. The European region shows heterogeneity, with Northern European countries leading, while Southern and Eastern Europe lag in UHIS development. The African continent presents the most contrasting picture, with a small group of leading countries and most states in the early stages of digitalization. The study identified common challenges across all regions, including ensuring interoperability, protecting personal data, and overcoming healthcare professionals' resistance to change. Special attention is given to innovative approaches, such as the use of mobile technologies in Africa to overcome infrastructure limitations. The research results emphasize the need for a comprehensive approach to UHIS implementation, considering not only technological but also socio-economic factors. The study concludes that successful UHIS development requires a clear national strategy, adequate funding, and active involvement of all stakeholders.

Data quality in hospital information systems: Lessons learned from analyzing 30 years of patient data in a regional German hospital

Background: The integration of Hospital Information Systems (HIS) into healthcare delivery has significantly enhanced patient care and operational efficiency. Nonetheless, the rapid acceleration of digital transformation has led to a substantial increase in the volume of data managed by these systems. This emphasizes the need for robust mechanisms for data management and quality assurance.Objective: This study addresses data quality issues related to patient identifiers within the Hospital Information System (HIS) of a regional German hospital, focusing on improving the accuracy and consistency of these administrative data entries.Methods: Employing a combination of data analysis and expert interviews, this study reviews and programmatically cleanses a dataset with over 2,000,000 patient data entries extracted from the HIS. The areas of investigation are patient admissions, discharges, and geographical data.Results: The analysis revealed that roughly 25% of the dataset was rendered unusable by errors and inconsistencies. By implementing a thorough data cleansing process, we significantly enhanced the utility of the dataset. In doing so, we identified the primary issues affecting data quality, including ambiguities among similar variables and a gap between the intended and actual use of the system.Conclusion: The findings highlight the critical importance of enhancing data quality in healthcare information systems. This study shows the necessity of a careful review of data extracted from the HIS before it can be reliably utilized for machine learning tasks, thereby rendering the data more usable for both clinical and analytical purposes.

Towards Efficient Value-Based Healthcare Measurement Across Hospital Care Pathways - A Scoping Review.

Hospitals widely employ value-based healthcare (VBHC) to effectively manage healthcare quality. VBHC aims to maximize patient outcomes while minimizing costs by using quality measurements. The Dutch Erasmus Medical Centre experiences challenges with the time-consuming efforts to collect, evaluate, and present value-based quality measurements. Using similar VBHC measurement indicators across multiple care pathways could reduce these efforts. This study aims to identify such generic indicators for evaluating and monitoring VBHC across care pathways. A scoping review resulted in 33 articles from which indicators for VBHC measurement were extracted, aggregated and categorized using Donabedian's Structure-Process-Outcome model. The results of this study can inform researchers and VBHC practitioners on generic quality measurement indicators for VBHC management and guide future system development to facilitate the inclusion of standardized quality indicators in healthcare information systems.

Optimizing Cancer Patient Classification Forecasting With Bayesian Pattern Recognition

This research examines patterns in cancer treatment by analyzing electronic medical record (EMR) data, with the goal of optimizing healthcare provision and improving patient outcomes. The study aims to apply Bayesian prediction models and regression analysis to determine the posterior probability of comorbidities and forecast patient arrivals. The implemented algorithms allow for the customization of treatment techniques, resulting in enhanced effectiveness of therapy and improved decision-making in healthcare delivery. Utilizing Bayesian approaches to analyze EMR data provides insights into the intricacies of cancer treatment and related expenses. The application of this study could be useful to enhance healthcare information systems and informatics by using data-driven insights to improve cancer care practices and operational efficiency in hospital settings.

Data Protection in Healthcare Information Systems Using Cryptographic Algorithm with Base64 512 bits

One of the major roles of government is to curb crime. Despite the measures the government has taken to counteract criminal activity, the security situation in many urban centers has gotten worse. The goal of this study was to create and assess a machine learning model with the core function of forecasting crime categories and utilizing contextual features found in the datasets to visualize the locations in which they occur. This was achieved by combining time, space, and contextual information with machine learning to improve crime prediction and mapping. The datasets were collected from various sources were subjected to a number of machine learning algorithms to evaluate how well they performed. The random forest algorithm emerged as the best algorithm with a classification accuracy of 97% or 0.973301 using the confusion matrix. The longitude and latitude features were used to tag the specific locations of crime occurrences on a map.

Data Protection in Healthcare Information Systems Using Cryptographic Algorithm with Base64 512 bitsAgnes

Recent advancement in technology in Healthcare Information Systems has led to a rise in cyber-attacks and data leakages at the data level. Existing data-level protection techniques that are developed to ensure data-level protection in Healthcare Information Systems lack integration of key security models such as Mandatory Access Controls, Role Based Access Controls and database security approaches in the design and development of data protection methods. In this study, science research methodology was used to design and develop a cryptographic algorithm with Base64 512 bits to enhance data protection at the data level. The algorithm was tested in a healthcare information system through experiments and simulations. The performance of the algorithm was tested to evaluate encryption and decryption process, strength on brute force attack and plain text vulnerability. The results of the observations showed that the developed algorithm with Base64, AES with fixed length of 512 bits, achieved optimal performance.

Characteristics of U.S. Baccalaureate Health Informatics Programs.

Health informatics (HI) is a growing field of study, yet sparse data are available on the characteristics of undergraduate HI programs in the United States. The lack of a central location for U.S. HI undergraduate program data has led to a gap in information to support current efforts to promote academic standards in the field and attract potential students. The objective of this study was to perform an environment scan of colleges and universities within the United States and Puerto Rico to identify undergraduate programs in HI including majors, minors, and undergraduate-level certificates. Potential institutions offering HI programs were identified from the National Center for Education Statistics (NCES), the Commission for Accreditation of Health Informatics and Health Information Management Education (CAHIIM), the Association of University Programs in Health Administration (AUPHA), the Healthcare Information and Management Systems Society (HIMSS), and the American Health Information Management Association (AHIMA). Publicly available materials for these institutions were reviewed, and data were captured for identified HI programs including geographic location; college or school in which the program was offered; type of degree (if applicable); program title; total credits to complete the program; delivery format; and source of data. A total of 83 HI programs were identified from 60 institutions and 26 states. The primary finding of this environment scan was that inconsistencies exist among U.S. HI undergraduate programs including variations in offering college/school, title of program, number of credits required, delivery formats, CIP codes, and the type of information published on university/college websites. This environment scan was a first step in exploring the characteristics of undergraduate HI programs within the United States. Further research is needed on the curricular similarities and differences among the programs, the administrative methods of these interdisciplinary programs, and the information most needed by current and potential undergraduate students.

Evaluation of technical approaches for real-time data transfer from electronic health record systems

Background and ObjectiveReal-time data (RTD) are data that are delivered immediately after creation. The key feature of RTD is low delivery latency. Information systems in health care are extremely time-sensitive and their building block is the electronic health record (EHR). Real-time data from EHRs play an important role to support decision-making, analytics and coordination of care. This is well mentioned in the literature, but the process has not yet been described, providing reference implementations and testing. Real-time data delivery can technically be achieved using several methods. The objective of this work is to evaluate the performance of different transfer methods of RTD from EHRs by measuring delivery latency. MethodsIn our work we used four approaches to transfer RTD from EHRs: REST hooks, WebSocket notifications, reverse proxy and database triggers. We deployed a Fast Health Interoperability Resources (FHIR) server as it is one of the most widely used EHR standard. For the reference implementations we used Python and Golang. Delivery latency was selected as performance metric, derived by subtracting the timestamp of the EHR resource creation from the timestamp of the EHR resource receipt in millisecond. The data was analyzed using descriptive statistics, cumulative distribution function (CDF), Kruskal-Wallis and post-hoc tests. ResultsThe database trigger approach had the best mean delivery latency 13.52±5.56 ms, followed by the reverse proxy 14.43±4.58 ms, REST hooks 19.26±5.76 ms and WebSocket 27.32±9.44 ms. The reverse proxy showed a tighter range of the values and lower variability. There were significant differences in the latencies between all pairs of approaches, except for reverse proxy and database trigger. ConclusionReal-time data transfer is vital for the development of robust and innovative healthcare applications. Properties of current EHR systems as a data source predefine the approaches for transfer. In our work for the first time the performance of RTD transfer from the EHRs with reference implementations is measured and evaluated. We found that database triggers achieve lowest delivery latency. Reverse proxy performed slightly slower, but offered more stability, followed by REST hooks and WebSocket notifications.

Data protection in Healthcare Information Systems Using Cryptographic Algorithm with Base64 512 bits

Data protection in Healthcare Information Systems Using Cryptographic Algorithm with Base64 512 bits