Medical Information Management Research Articles

AI-Driven Cardiomegaly Detection Using Cutting-Edge Chest X–ray imagery

This project aims to develop an innovative approach for detecting cardiomegaly, a condition characterized by an enlarged heart, using machine learning techniques, specifically Support Vector Machines (SVM), and state-of-the-art chest X-ray imagery. Cardiomegaly serves as a significant indicator of various cardiac diseases, and early detection through accurate imaging analysis can lead to timely intervention and improved patient outcomes. The proposed machine learning solution utilizes SVM algorithms trained on a diverse dataset of chest X-ray images to automatically identify signs of cardiomegaly with high precision and reliability. By harnessing the power of SVM, this system offers a non-invasive and cost-effective method for screening large populations for cardiac abnormalities, facilitating early diagnosis and appropriate medical management. Key features of the developed solution include robust feature extraction techniques, real-time analysis, and integration with existing healthcare infrastructure for seamless implementation in clinical settings. Moreover, the system prioritizes patient privacy and data security by adhering to relevant regulations and standards governing medical imaging and information management. Overall, this project represents a significant advancement in the field of cardiac health monitoring, providing healthcare professionals with a valuable tool for early detection and management of cardiomegaly, ultimately improving patient care and reducing the burden of cardio vascular disease

Medical Education and Artificial Intelligence: Web of Science-Based Bibliometric Analysis (2013-2022).

Incremental advancements in artificial intelligence (AI) technology have facilitated its integration into various disciplines. In particular, the infusion of AI into medical education has emerged as a significant trend, with noteworthy research findings. Consequently, a comprehensive review and analysis of the current research landscape of AI in medical education is warranted. This study aims to conduct a bibliometric analysis of pertinent papers, spanning the years 2013-2022, using CiteSpace and VOSviewer. The study visually represents the existing research status and trends of AI in medical education. Articles related to AI and medical education, published between 2013 and 2022, were systematically searched in the Web of Science core database. Two reviewers scrutinized the initially retrieved papers, based on their titles and abstracts, to eliminate papers unrelated to the topic. The selected papers were then analyzed and visualized for country, institution, author, reference, and keywords using CiteSpace and VOSviewer. A total of 195 papers pertaining to AI in medical education were identified from 2013 to 2022. The annual publications demonstrated an increasing trend over time. The United States emerged as the most active country in this research arena, and Harvard Medical School and the University of Toronto were the most active institutions. Prolific authors in this field included Vincent Bissonnette, Charlotte Blacketer, Rolando F Del Maestro, Nicole Ledows, Nykan Mirchi, Alexander Winkler-Schwartz, and Recai Yilamaz. The paper with the highest citation was "Medical Students' Attitude Towards Artificial Intelligence: A Multicentre Survey." Keyword analysis revealed that "radiology," "medical physics," "ehealth," "surgery," and "specialty" were the primary focus, whereas "big data" and "management" emerged as research frontiers. The study underscores the promising potential of AI in medical education research. Current research directions encompass radiology, medical information management, and other aspects. Technological progress is expected to broaden these directions further. There is an urgent need to bolster interregional collaboration and enhance research quality. These findings offer valuable insights for researchers to identify perspectives and guide future research directions.

A secure blockchain framework for healthcare records management systems

AbstractElectronic health records are one of the essential components of health organizations. In recent years, there have been increased concerns about privacy and reputation regarding the storage and use of patient information. In this regard, the information provided as a part of medical and health insurance, for instance, can be viewed as proof of social insurance and governance. Several problems in the past few decades regarding medical information management have threatened patient information privacy. In intelligent healthcare applications, the privacy of patients' data is one of the main concerns. As a result, blockchain is a severe necessity as it can enhance transparency and security in medical applications. Accordingly, this paper uses the design science method to propose a secure blockchain framework for healthcare records management systems. The proposed framework comprises five components: a blockchain network, smart contracts, privacy key management, data encryption, and integration with healthcare information technology. In the proposed framework, healthcare organizations can manage healthcare information securely and privately. Additionally, a secure storage system for electronic records is proposed to meet these organizations' needs. It provides security and privacy for healthcare organizations, especially when managing healthcare information, and also proposes a secure storage system for electronic records to meet the needs of the organizations.

Comparative performance analysis of large language models: ChatGPT-3.5, ChatGPT-4 and Google Gemini in glucocorticoid-induced osteoporosis

BackgroundsThe use of large language models (LLMs) in medicine can help physicians improve the quality and effectiveness of health care by increasing the efficiency of medical information management, patient care, medical research, and clinical decision-making.MethodsWe collected 34 frequently asked questions about glucocorticoid-induced osteoporosis (GIOP), covering topics related to the disease’s clinical manifestations, pathogenesis, diagnosis, treatment, prevention, and risk factors. We also generated 25 questions based on the 2022 American College of Rheumatology Guideline for the Prevention and Treatment of Glucocorticoid-Induced Osteoporosis (2022 ACR-GIOP Guideline). Each question was posed to the LLM (ChatGPT-3.5, ChatGPT-4, and Google Gemini), and three senior orthopedic surgeons independently rated the responses generated by the LLMs. Three senior orthopedic surgeons independently rated the answers based on responses ranging between 1 and 4 points. A total score (TS) > 9 indicated ‘good’ responses, 6 ≤ TS ≤ 9 indicated ‘moderate’ responses, and TS < 6 indicated ‘poor’ responses.ResultsIn response to the general questions related to GIOP and the 2022 ACR-GIOP Guidelines, Google Gemini provided more concise answers than the other LLMs. In terms of pathogenesis, ChatGPT-4 had significantly higher total scores (TSs) than ChatGPT-3.5. The TSs for answering questions related to the 2022 ACR-GIOP Guideline by ChatGPT-4 were significantly higher than those for Google Gemini. ChatGPT-3.5 and ChatGPT-4 had significantly higher self-corrected TSs than pre-corrected TSs, while Google Gemini self-corrected for responses that were not significantly different than before.ConclusionsOur study showed that Google Gemini provides more concise and intuitive responses than ChatGPT-3.5 and ChatGPT-4. ChatGPT-4 performed significantly better than ChatGPT3.5 and Google Gemini in terms of answering general questions about GIOP and the 2022 ACR-GIOP Guidelines. ChatGPT3.5 and ChatGPT-4 self-corrected better than Google Gemini.

Medication management information priorities of people living with dementia and their carers: a scoping review.

People living with dementia and their carers often experience difficulties in effectively managing medications and have indicated they lack necessary support, information and guidance. Recognising the medication management information needs of this population is an important first step in addressing these issues. To identify the priorities for information on medication management expressed by people living with dementia and their carers. A scoping review with systematic search was conducted from inception to 12 May 2023 for any original studies that reported the information needs of people living with dementia and their carers (informal, i.e. unpaid or within an existing relationship) regarding medication management. Two authors independently screened the abstracts, full-texts and extracted data. Study characteristics were described descriptively, and themes of information need were extracted using an iterative approach. Of the 11 367 records screened, 35 full-texts were included. All studies (n = 35) involved carers, with 17 also including people living with dementia. Most studies (n = 30) were conducted in the community and used qualitative methods (n = 32). Five major themes of information need were identified: critical medication information; medication effects; medication indication(s); disease progression and impact on medications; and safe and appropriate administration of medications. People living with dementia and their carers indicated they need more medication management information generally and want it simple, tailored and relevant. This review highlights the key medication information priorities for people living with dementia and their carers and will help guide the provision of medication management guidance and development of new information resources.

Medical information management system based on multi-source heterogeneous big data

ABSTRACT This study aims to provide a medical information management system using multi-source heterogeneous big data to improve medical service quality and efficiency, with a motivation on its potential in medical insurance DRG payment. The system framework uses Back Propagation Neural Network (BPNN) technology to efficiently process and analyze multi-source medical data. Comparative experiments and parameter adjustments evaluated the system’s performance. Results show that the BPNN model achieved excellent accuracy (92.5%), recall (93%), and F1 value (92.8%) on the test data set, outperforming other models such as PSO(88%), CNN(89%), and RNN(90%). The system’s response speed was also significantly improved, with an average response time of 0.38 seconds, compared to 0.89 seconds for traditional systems. A 72-hour stability test confirmed the system’s reliability and ability to meet user needs. The proposed system demonstrates improved performance and user experience, making it a promising solution for medical information management and DRG payment applications.

ELECTRONIC MEDICAL RECORDS AS DIGITAL TRANSFORMATION IN INDONESIAN HEALTH SERVICES 4.0

The application of Electronic Medical Record in the era of Digital Transformation Indonesia 4.0 is the main pillar in revolutionizing health services. EMR presents a fundamental shift in medical information management by replacing traditional medical records with integrated electronic forms. Thus, this makes quickly access and share patient data, improve coordination between providers, and inform more accurate clinical decision making. This study aims to systematically review the implementation Digital Transformation in Indonesian as a planning to achieve Health Services 4.0. This study implements systematic reviews using Preferred Reporting Systematic Reviews and Meta-analysis (PRISMA) approach. Research articles published from google scholar, PubMed and sagepub. To take appropriate and relevant article research, relevant keywords or terms are constructed as the following words: Digital transformation OR digital revolution OR digital 4.0; Health services OR health care OR Health facilities; Digital medical records. 30 articles were collected from: Google Scholar, PubMed and Sagepub. The quality of these articles was evaluated, resulting in 7 articles being synthesized in the final literature review report.The results show that EMR analyzes in-depth data, supports the development of evidence-based health policies, and personalizes care according to patient characteristics. The adoption of EMR also presents challenges, including data security and privacy protection where cybersecurity and regulatory compliance are critical.

Forecasting rheumatoid arthritis patient arrivals by including meteorological factors and air pollutants

The burden of rheumatoid arthritis (RA) has gradually elevated, increasing the need for medical resource redistribution. Forecasting RA patient arrivals can be helpful in managing medical resources. However, no relevant studies have been conducted yet. This study aims to construct a long short-term memory (LSTM) model, a deep learning model recently developed for novel data processing, to forecast RA patient arrivals considering meteorological factors and air pollutants and compares this model with traditional methods. Data on RA patients, meteorological factors and air pollutants from 2015 to 2022 were collected and normalized to construct moving average (MA)- and autoregressive (AR)-based and LSTM models. After data normalization, the root mean square error (RMSE) was adopted to evaluate models’ forecast ability. A total of 2422 individuals were enrolled. Not using the environmental data, the RMSEs of the MA- and AR-based models’ test sets are 0.131, 0.132, and 0.117 when the training set: test set ratio is 2:1, 3:1, and 7:1, while they are 0.110, 0.130, and 0.112 for the univariate LSTM models. Considering meteorological factors and air pollutants, the RMSEs of the MA- and AR-based model test sets were 0.142, 0.303, and 0.164 when the training set: test set ratio is 2:1, 3:1, and 7:1, while they were 0.108, 0.119, and 0.109 for the multivariable LSTM models. Our study demonstrated that LSTM models can forecast RA patient arrivals more accurately than MA- and AR-based models for datasets of all three sizes. Considering the meteorological factors and air pollutants can further improve the forecasting ability of the LSTM models. This novel method provides valuable information for medical management, the optimization of medical resource redistribution, and the alleviation of resource shortages.

Research on Personal Medical Information Protection Based on Big Data

The rapid development of big data, cloud computing, and artificial intelligence has brought new opportunities to the development of the medical field. The emergence of electronic medical records and medical information systems has provided great convenience for people's medical treatment. However, the arrival of the big data era also poses new challenges to the protection of human medical privacy. The development and application of medical big data have led to explosive growth of data in the medical field, putting pressure on the management of medical institutions. The sharing of medical data between medical institutions and between medical institutions and third parties also poses significant security risks. Moreover, medical data leakage incidents have been continuously exposed in recent years, seriously infringing on the identity and privacy rights of patients. This article aims to explore the research on personal medical information protection based on big data. Firstly, the importance and necessity of protecting personal medical information were analyzed, as well as the current application status and existing problems of big data in medical information management. Secondly, a comparative analysis of domestic and international research on medical privacy protection was elaborated, including the analysis of medical information privacy protection standards in the United States, Europe, and Asia, and the differences and commonalities of international medical information privacy protection standards were compared and analyzed. Then, the theoretical basis of medical information privacy protection was summarized. Then, the application methods and technologies of big data in personal medical information protection were discussed. Finally, relevant countermeasures and suggestions for the protection of personal medical information in big data were discussed, including privacy protection technologies and policy recommendations, as well as ethical issues and norms for the protection of medical information privacy. Through this study, it is hoped that it can provide reference and inspiration for the protection of personal medical information in big data, and provide ideas and suggestions for future development directions.

Implementing intelligent predictive models for patient disease risk in cloud data warehousing

A data warehouse, which stores data after it has been extracted, processed, and organized into files and folders, is a cloud data warehousing solution for storing structured data from one or more sources. When data is stored in an organized format within files and folders, it becomes easily accessible and facilitates strategic, data-driven decision-making. This paper introduces the importance of cloud data warehousing in medical information management and discusses its application in a disease prediction model. By analyzing medical data and constructing predictive models, it can assist medical decision-makers in taking timely actions to enhance the quality and efficiency of medical services, providing patients with more personalized treatment plans.

Integrating blockchain and ZK-ROLLUP for efficient healthcare data privacy protection system via IPFS

With the rapid development of modern medical technology and the dramatic increase in the amount of medical data, traditional centralized medical information management is facing many challenges. In recent years blockchain, which is a peer-to-peer distributed database, has been increasingly accepted and adopted by different industries and use cases. Key areas of healthcare blockchain applications include electronic medical record (EMR) management, medical device supply chain management, remote condition monitoring, insurance claims and personal health data (PHD) management, among others. Even so, there are a number of challenges in applying blockchain concepts to healthcare and its data, including interoperability, data security privacy, scalability, TPS and so on. While these challenges may hinder the development of blockchain in healthcare scenarios, they can be improved with existing technologies In this paper, we propose a blockchain-based healthcare operations management framework that is combined with the Interplanetary File System (IPFS) for managing EMRs, protects data privacy through a distributed approach while ensuring that this medical ledger is tamper-proof. Doctors act as full nodes, patients can participate in network maintenance either as light nodes or as full nodes, and the hospital acts as the endpoint database of data, i.e., the IPFS node, which saves the arithmetic power of nodes and allows the data stored in the hospitals and departments to be shared with the other organizations that have uploaded the data. Therefore, the integration of blockchain and zero-knowledge proof proposed in this paper helps to protect data privacy and is efficient, better scalable, and more throughput.

Knowledge, Attitudes, and Practices about Electronic Personal Health Records: A Cross-Sectional Study in a Region of Northern Italy

The Electronic Personal Health Record (EPHR) provides an innovative service for citizens and professionals to manage health data, promoting patient-centred care. It enhances communication between patients and physicians and improves accessibility to documents for remote medical information management. The study aims to assess the prevalence of awareness and acceptance of the EPHR in northern Italy and define determinants and barriers to its implementation. In 2022, a region-wide cross-sectional study was carried out through a paper-based and online survey shared among adult citizens. Univariable and multivariable regression models analysed the association between the outcome variables (knowledge and attitudes toward the EPHR) and selected independent variables. Overall, 1634 people were surveyed, and two-thirds were aware of the EPHR. Among those unaware of the EPHR, a high prevalence of specific socio-demographic groups, such as foreign-born individuals and those with lower educational levels, was highlighted. Multivariable regression models showed a positive association between being aware of the EPHR and educational level, health literacy, and perceived poor health status, whereas age was negatively associated. A higher knowledge of the EPHR was associated with a higher attitude towards the EPHR. The current analysis confirms a lack of awareness regarding the existence of the EPHR, especially among certain disadvantaged demographic groups. This should serve as a driving force for a powerful campaign tailored to specific categories of citizens for enhancing knowledge and usage of the EPHR. Involving professionals in promoting this tool is crucial for helping patients and managing health data.

A medical big data access control model based on smart contracts and risk in the blockchain environment.

The rapid development of the Hospital Information System has significantly enhanced the convenience of medical research and the management of medical information. However, the internal misuse and privacy leakage of medical big data are critical issues that need to be addressed in the process of medical research and information management. Access control serves as a method to prevent data misuse and privacy leakage. Nevertheless, traditional access control methods, limited by their single usage scenario and susceptibility to single point failures, fail to adapt to the polymorphic, real-time, and sensitive characteristics of medical big data scenarios. This paper proposes a smart contracts and risk-based access control model (SCR-BAC). This model integrates smart contracts with traditional risk-based access control and deploys risk-based access control policies in the form of smart contracts into the blockchain, thereby ensuring the protection of medical data. The model categorizes risk into historical and current risk, quantifies the historical risk based on the time decay factor and the doctor's historical behavior, and updates the doctor's composite risk value in real time. The access control policy, based on the comprehensive risk, is deployed into the blockchain in the form of a smart contract. The distributed nature of the blockchain is utilized to automatically enforce access control, thereby resolving the issue of single point failures. Simulation experiments demonstrate that the access control model proposed in this paper effectively curbs the access behavior of malicious doctors to a certain extent and imposes a limiting effect on the internal abuse and privacy leakage of medical big data.

Application of artificial intelligence technology in hospital management systems

This article explores the application of artificial intelligence technology in hospital management systems, specifically using the innovative work order project as an example. The system can interact with users through intelligent AI interaction through text and voice, enabling intelligent work order repair and query processing. The project includes multiple functions such as work order form generation, attachment uploading, voice file uploading, work order query, and interface docking. In addition, this article also introduces the requirements for integrating hospital logistics business with Turing CHATGPT application scenarios, including equipment failure repair, patient satisfaction surveys, patient follow-ups, and medical staff training and learning. Through detailed descriptions of interface docking, data flow, and functions, this article highlights the potential of AI technology to enhance work efficiency and user experience in hospital management systems. Finally, this article discusses the current achievements and future development directions and calls for further innovation and progress in medical information management systems.

Application of Mobile Technology and Information Management in Emergency Pre- Examination and Triage

The primary objective of emergency pre-examination and triage is to provide the most appropriate clinical service to patients with acute and critical illnesses while ensuring the optimal utilization of limited medical resources. With the advancement of medical information technology, mobile technology, and information management, these features have been gradually incorporated into emergency pre-examination and triage and have shown promising outcomes. In this article, the benefits of implementing mobile technology and the current status of integrated information management to provide a reference for the future development and enhancement of emergency pre-examination and triage are reviewed.

Clinical notes classification system for automated identification of diabetic patients: Hybrid approach integrating rules, information extraction and machine learning

Efficient medical information management is essential in today’s healthcare, significantly to automate diagnoses of chronic diseases. This study focuses on the automated identification of diabetic patients through a clinical note classification system. This innovative approach combines rules, information extraction, and machine learning algorithms to promise greater accuracy and adaptability. Initially, the four algorithms evaluated showed similar performance, with Gradient Boosting standing out with an accuracy of 0.999. They were tested on our clinical and oncology notes, where SVM excelled in correctly labeling non-oncology notes with a 0.99. Gradient Boosting had the best average with 0.966. The combination of rules, information extraction, and Random Forest provided the best average performance, significantly improving the classification of clinical notes and reducing the margin of error in identifying diabetic patients. The principal contribution of this research lies in the pioneering integration of rule-based methods, information extraction techniques, and machine learning algorithms for enhanced accuracy in diabetic patient identification. For future work, we consider implementing these algorithms in natural clinical settings to evaluate their practical performance. Additionally, additional approaches will be explored to improve the accuracy and applicability of clinical note-grading systems in healthcare.

의료기관 종사자들의 전자의무기록 인증제 시행 목적에 대한 인식

This study was conducted to examine the perceptions of employees at tertiary and general hospitals, which have passed the certification of Electronic Medical Record (EMR) systems, regarding the objectives of the EMR certification program. The study was conducted from the perspectives of the public, medical institutions, and the industry. Data collection spanned from August 1 to October 31, 2022, involving responses from 1,187 employees across 11 or more accredited hospitals. The analysis included descriptive statistics, mean analysis, regression analysis, and Scheffé's analysis to understand the perceptions from the three different perspectives. The results revealed that from the public's perspective, the highest recognition was for “enhanced information security in medical information leading to the prevention of personal data breaches and security incidents” (t=-3.827, p&lt;.01). From the perspective of medical institutions, the highest but not statistically significant recognition was for “improved clinical decision support such as medication warnings and effective history management of medical information, enabling safer and higher quality medical services” (f=-1.612). From the industrial perspective, there was high recognition for “the capability to meet market quality demands due to national recognition of EMR system quality standards” (f=-3.154, p&lt;.01). Linear regression analysis was conducted to identify factors influencing the recognition of the objectives of the EMR certification system. The results showed that the model explained 27.4% of the variance for the public perspective, 26.9% for the medical institution perspective, and 28.6% for the industrial perspective, with all models being significant (P&lt;.05). Following the implementation of the EMR certification system, medical institution employees positively perceived it from the perspectives of the public, medical institutions, and the industry. Future efforts should focus on enhancing system functionality awareness among employees, improving quality, and government and medical institutions should work together to enhance user interoperability of the system.

Effects of Implementing a Barcode Information Management System on Operating Room Staff: Comparative Study.

Barcode information management systems (BIMS) have been implemented in operating rooms to improve the quality of medical care and administrative efficiency. Previous research has demonstrated that the Agile development model is extensively used in the development and management of information systems. However, the effect of information systems on staff acceptance has not been examined within the context of clinical medical information management systems. This study aimed to explore the effects and acceptance of implementing a BIMS in comparison to the original information system (OIS) among operating and supply room staff. This study was a comparative cohort design. A total of 80 staff members from the operating and supply rooms of a Northern Taiwan medical center were recruited. Data collection, conducted from January 2020 to August 2020 using a mobile-based structured questionnaire, included participant characteristics and the Information Management System Scale. SPSS (version 20.0, IBM Corp) for Windows (Microsoft Corporation) was used for data analysis. Descriptive statistics included mean, SD, frequency, and percentage. Differences between groups were analyzed using the Mann-Whitney U test and Kruskal-Wallis test, with a P value <.05 considered statistically significant. The results indicated that the BIMS generally achieved higher scores in key elements of system success, system quality, information quality, perceived system use, perceived ease of use, perceived usefulness, and overall quality score; none of these differences were statistically significant (P>.05), with the system quality subscale being closest to significance (P=.06). Nurses showed significantly better perceived system use than technicians (1.58, SD 4.78 vs -1.19, SD 6.24; P=.02). Significant differences in perceived usefulness were found based on educational level (P=.04) and experience with OIS (P=.03), with junior college-educated nurses and those with over 6 years of OIS experience reporting the highest perceived usefulness. The study demonstrates that using the Agile development model for BIMS is advantageous for clinical environments. The high acceptance among operating room staff underscores its practicality and broader adoption potential. It advocates for continued exploration of technology-driven solutions to enhance health care delivery and optimize clinical workflows.

Ten years of Hospital Information Systems: A taxonomy attempt

Nowadays, hospital organizations are affected by digital advances. This phenomenon has led to a radical change in the way of working from a purely manual process to an automatic process, giving rise to the hospital information system (HIS). This latter becomes one of the essential tools of hospital management. It is conceived to manage administrative and clinical data with the intention of improving the quality of health care provided by the hospital as well as increasing its efficiency.Lately, several countries launched ambitious projects to reform their health sectors. Digitalization processes prove to be a crucial axis of their reforms. Hence, a reflection on the management of medical information in hospitals is urgently needed. Acting on the HIS seems to be the relevant track because it is a valuable tool for better management and realizing real benefits from administrative and medical data. Hence, this communication aims to presents a taxonomy of the HIS during the last decade.To do so, authors used Scopus data basis. They focus on the last decade (2013-2023), which witnessed significant technological advancements in the healthcare sector, along with the emergence of the COVID-19 pandemic.The results of this communication identify categories of the HIS during the last decades, enable the proposal of a universal taxonomy, and analyze its distribution and evolution over time.

Application of integrated medical information management system in the risk assessment and prevention of perioperative venous thromboembolism in gynecological patients

Application of integrated medical information management system in the risk assessment and prevention of perioperative venous thromboembolism in gynecological patients