Healthcare Information Systems Research Articles

Factors Contributing to Successful Information System Implementation and Employee Well-Being in Health Care and Social Welfare Professionals: Comparative Cross-Sectional Study.

The integration of information systems in health care and social welfare organizations has brought significant changes in patient and client care. This integration is expected to offer numerous benefits, but simultaneously the implementation of health information systems and client information systems can also introduce added stress due to the increased time and effort required by professionals. This study aimed to examine whether professional groups and the factors that contribute to successful implementation (participation in information systems development and satisfaction with software providers' development work) are associated with the well-being of health care and social welfare professionals. Data were obtained from 3 national cross-sectional surveys (n=9240), which were carried out among Finnish health care and social welfare professionals (registered nurses, physicians, and social welfare professionals) in 2020-2021. Self-rated stress and stress related to information systems were used as indicators of well-being. Analyses were conducted using linear and logistic regression analysis. Registered nurses were more likely to experience self-rated stress than physicians (odds ratio [OR] -0.47; P>.001) and social welfare professionals (OR -0.68; P<.001). They also had a higher likelihood of stress related to information systems than physicians (b=-.11; P<.001). Stress related to information systems was less prevalent among professionals who did not participate in information systems development work (b=-.14; P<.001). Higher satisfaction with software providers' development work was associated with a lower likelihood of self-rated stress (OR -0.23; P<.001) and stress related to information systems (b=-.36 P<.001). When comparing the professional groups, we found that physicians who were satisfied with software providers' development work had a significantly lower likelihood of stress related to information systems (b=-.12; P<.001) compared with registered nurses and social welfare professionals. Organizations can enhance the well-being of professionals and improve the successful implementation of information systems by actively soliciting and incorporating professional feedback, dedicating time for information systems development, fostering collaboration with software providers, and addressing the unique needs of different professional groups.

An Examination of Factors Influencing Physicians' Acceptance and Use of the e-Nabız System

Aim: This study aims to identify the factors influencing physicians' acceptance and use of the e-Nabız system by comparing two models. Methods: Conducted with 388 physicians from university hospitals across Turkey, the study utilized an online survey based on the Unified Theory of Acceptance and Use of Technology (UTAUT) scale. Descriptive analyses, frequency and percentage distributions, reliability analysis, confirmatory factor analysis, and structural equation modelling were applied to the collected data. Results: In Model 1, the behavioral intention was influenced by performance expectancy and social influence, while usage behavior was shaped by social influence, facilitating conditions, and behavioral intention. Model 1 accounted for 75% of the variance in behavioral intention and 69% in usage behavior. In contrast, Model 2 identified performance expectancy, anxiety, habit, personal technology innovativeness, and workflow as significant predictors of behavioral intention. Usage behavior in Model 2 was influenced by habit, facilitating conditions, anxiety, personal technology innovativeness, workflow, and behavioral intention, explaining 84% of the variance in behavioral intention and 85% in usage behavior. Conclusion: The findings indicate that Model 2 provides a more comprehensive explanation of the factors affecting the acceptance and use of the system. To enhance acceptance and usage, the study suggests focusing on anxiety management, emphasizing performance benefits, aligning the system with workflow, educating users about new technologies, and implementing incentives to foster habitual use. Future research should explore other technology acceptance models in various healthcare information systems to deepen understanding.

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.

Developing Secure and Interoperable Health Information Systems Using Blockchain Technology to Enhance Data Privacy, Security, and Accessibility in Healthcare

Healthcare information systems are going through a huge change that will make them more efficient, safe, and easy to use. As more persistent records are digitized and private therapeutic information is shared, it is more critical than ever to create beyond any doubt that security, security, and availability are well secured. Blockchain innovation can be a great way to bargain with these issues since it makes a decentralized, unchangeable record that can be utilized to securely store and send healthcare information whereas securing its security and security. This exposition looks at how blockchain innovation might offer assistance make wellbeing data frameworks more secure and more congruous with each other. Healthcare companies can set up a trusted and unchangeable way to oversee understanding information over different frameworks and parties by utilizing blockchain independent plan, cryptography, and voting instruments. One of the leading things approximately blockchain innovation is that it can make data more private. Cryptographic instruments, like encryption and computerized marks, make it conceivable to store and share private wellbeing information securely with individuals who are permitted to see it, without compromising persistent security. Moreover, since blockchain is unchangeable, once information is recorded, it can't be changed or eradicated. This makes the history of who gotten to and changed information clear and simple to check. In conjunction with ensuring protection, blockchain innovation moreover makes things more secure by bringing down the chances of information spills and unlawful get to. Blockchain-based wellbeing data frameworks make information keeping less centralized and use solid encryption strategies to lower the chances of single focuses of failure and unlawful changes. This makes information more secure and dependable. Keen contracts on blockchain stages can too mechanize and execute get to control rules, making beyond any doubt that as it were individuals who are permitted to can see certain restorative data or do certain activities that have as of now been set up. Interoperability is another important part of modern healthcare information systems that lets different healthcare companies and systems work together and share data easily.

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

Network Analysis in Global Emergency Medicine: Mapping Collaborative Structures and Enhancing Connectivity

Introduction Network analysis, which draws from fields including mathematics, sociology, and public health, is essential for interpreting complex data relationships within healthcare information systems. This approach not only highlights intricate care patterns but also enhances the navigability of systems, making complex healthcare information more accessible [1]. Our study builds on previous applications of network analysis in healthcare, specifically examining its use in structuring academic health center websites in the U.S. to improve user-oriented spaces. Our research explores the digital ecosystem of emergency medicine organizations globally, focusing on the International Federation for Emergency Medicine (IFEM). By employing a systematic, automated approach to network mapping, we aim to uncover collaborative patterns and enhance the online presence of emergency resources. This effort supports greater global collaboration and information sharing among emergency medicine organizations, leveraging the strong networks and insights provided by IFEM to ensure universal access to high-quality emergency services. Methodology We utilized a four-step process for mapping and analyzing the global network of emergency medicine organizations: Web Crawling: We collected data from the IFEM website using the Screaming Frog SEO Spider tool. The crawl depth was set to three to ensure comprehensive data capture from IFEM and its connected links. Content Filtering: BeautifulSoup was used to parse HTML content. We filtered the extracted data for relevance to emergency medicine organizations. The data was further refined using the Gemini 1.0 Pro model to isolate pertinent information about the organizations' names and locations. Geolocation Resolution: Location data extracted from the web pages was converted into geographical coordinates using the Geopy library’s Nominatim tool to accurately map of each organization's position. Network Visualization: We constructed a directed network graph using the NetworkX library, with nodes representing organizations and edges representing their interconnections. This graph was visualized with Matplotlib to illustrate the relationships and distribution patterns among the organizations. Results We identified 4,775 external links on the IFEM website and refined them to 156 unique base URLs for in-depth content analysis. Out of 55 countries listed, there were 41 functional links, 10 non-functional, and 4 missing. We effectively isolated 41 relevant multilingual URLs from the functional links, achieving a 100% accuracy rate in identifying pertinent content. Additionally, our analysis uncovered 30 URLs linked to emergency medicine organizations not listed on the IFEM member page, including the American College of Osteopathic Emergency Physicians and the Swiss Society for Emergency and Rescue Medicine. Discussion Our study highlights the utility of network analysis in understanding the digital networks of emergency medicine organizations globally, revealing essential connectivity patterns and their implications for healthcare systems. The methodology used is adaptable for other healthcare areas, enhancing collaboration and addressing language barriers. Findings indicate dynamic changes in digital connectivity, pointing to evolving collaboration and resource sharing. This research provides a foundation for exploring digital connectivity's broader impacts across various sectors.

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.