Medical Information Technology Research Articles

Understanding IT in Healthcare: Relevance and Training Needs of IT in Private Medical Practice.

The integration of Information Technology (IT) into private medical practice is crucial in modern healthcare. Physicians managing office-related IT without proper knowledge risk operational inefficiencies and security. This study determines the relevance of specific IT topics in medical practice and identifies the training needs of physicians for enhancing IT competencies in healthcare. In March 2023 a cross-sectional online survey was conducted with physicians comprising nine IT-related topics in Tyrol, Austria. The survey results highlighted a strong perceived relevance and high demand for IT education among physicians working in their medical practice, especially in areas of core medical IT and security. The majority of responses indicated high relevance (76.7%) and high demand (69.7%) for IT topics in medical practice. The findings underscore a significant need for targeted IT training and support in medical practices, particularly in areas related to the medical practice and security. Addressing these needs could lead to improved healthcare delivery and better management of technological resources in the healthcare sector.

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.

Artificial intelligence and multimodal data fusion for smart healthcare: topic modeling and bibliometrics

Advancements in artificial intelligence (AI) have driven extensive research into developing diverse multimodal data analysis approaches for smart healthcare. There is a scarcity of large-scale analysis of literature in this field based on quantitative approaches. This study performed a bibliometric and topic modeling examination on 683 articles from 2002 to 2022, focusing on research topics and trends, journals, countries/regions, institutions, authors, and scientific collaborations. Results showed that, firstly, the number of articles has grown from 1 in 2002 to 220 in 2022, with a majority being published in interdisciplinary journals that link healthcare and medical research and information technology and AI. Secondly, the significant rise in the quantity of research articles can be attributed to the increasing contribution of scholars from non-English speaking countries/regions and the noteworthy contributions made by authors in the USA and India. Thirdly, researchers show a high interest in diverse research issues, especially, cross-modality magnetic resonance imaging (MRI) for brain tumor analysis, cancer prognosis through multi-dimensional data analysis, and AI-assisted diagnostics and personalization in healthcare, with each topic experiencing a significant increase in research interest. There is an emerging trend towards issues such as applying generative adversarial networks and contrastive learning for multimodal medical image fusion and synthesis and utilizing the combined spatiotemporal resolution of functional MRI and electroencephalogram in a data-centric manner. This study is valuable in enhancing researchers’ and practitioners’ understanding of the present focal points and upcoming trajectories in AI-powered smart healthcare based on multimodal data analysis.

Temporal-Spatial Correlation Attention Network for Clinical Data Analysis in Intensive Care Unit.

Recent advancements in medical information technology have enabled electronic health records (EHRs) to store comprehensive clinical data which has ushered healthcare into the era of "big data". However, medical data are rather complicated, making problem-solving in healthcare be limited in scope and comprehensiveness. The rapid development of deep learning in recent years has opened up opportunities for leveraging big data in healthcare. In this article we introduce a temporal-spatial correlation attention network (TSCAN) to address various clinical characteristic prediction problems, including mortality prediction, length of stay prediction, physiologic decline detection, and phenotype classification. Leveraging the attention mechanism model's design, our approach efficiently identifies relevant items in clinical data and temporally correlated nodes based on specific tasks, resulting in improved prediction accuracy. Additionally, our method identifies crucial clinical indicators associated with significant outcomes, which can inform and enhance treatment options. Our experiments utilize data from the publicly accessible Medical Information Mart for Intensive Care (MIMIC-IV) database. Finally, our approach demonstrates notable performance improvements of 2.0% (metric) compared to other SOTA prediction methods. Specifically, we achieved an impressive 90.7% mortality rate prediction accuracy and 45.1% accuracy in length of stay prediction.

Electronic Medical Record and Smoking Cessation Activities: Literature Review

Vital information regarding the life and health of a patient is gathered in medical records. The electronic medical record (EMR) is the product of medical record information technology development. Electronic health records and other health information systems have the potential to be useful tools for raising the caliber and efficacy of clinical interventions aimed at reducing tobacco consumption. Electronic medical records are used by doctors, clinics, and health care organizations to track patient smoking cessation outcomes and as a smoking cessation support tool. Thirteen papers from the Google Schoolar database and the reference lists of the included research were used in the review. We looked for research that was written between 2010 and 2020. Overall, these trials discovered that only slight benefits were made by several doctor-recommended therapies related to tobacco use. Following modifications to electronic medical records intended to speed up the collection and management of tobacco use during medical visits, there seems to have been a rise in the documentation of tobacco status and referrals for cessation counseling. To maximize the potential of EMRs for extra therapeutic advantages and smoking cessation in hospital settings, more research is required.

Assessment of Information Technology in Electronic Medical Records Using the Concept Health Technology Assessment at X Jepara Hospital

<div><table cellspacing="0" cellpadding="0" align="left"><tbody><tr><td align="left" valign="top"><p><em>Hospitals as health service providers need to study the equipment used in health services, which is called a Health Technology Assessment. This research aims to determine the assessment of electronic medical record information technology using the Health Technology Assessment concept at regional General Hospital X Jepara. This research uses a qualitative method with a descriptive design. The research was conducted at the regional general hospital X Jepara which was carried out from March to June 2023. There were 13 informants in this research with 9 main informants and 3 triangulation informants. Data collection techniques use in-depth interviews, observation, and document study. Data analysis for this research is data reduction, data presentation, and conclusion. The results of this research on the concept of effectiveness can increase productivity, be more efficient, make it very easy to publish patient medical records in summary and detail, and doctors and nurses can easily access patient data by logging in using their password. Patient privacy is also maintained with limited access to open it. Service is fast and can increase satisfaction and improve hospital services from an economic perspective using health technology assessment methods. The characteristic aspect of using electronic medical records is that it makes hospital services easier. The security aspect of patient data is guaranteed because system access can only be accessed by users with an account, and the rights of each user are guaranteed. The management of the regional general hospital will easily use electronic medical records and improve the service and quality of Regional General Hospital X Jepara.</em></p></td></tr></tbody></table></div> <strong><em>Keywords: Electronic medical record, Effectiveness, Economic, Technical characteristic, Security</em></strong>

Radiophysical Research Methods in the Development of New Medical Equipment and Information Technologies

Radiophysical Research Methods in the Development of New Medical Equipment and Information Technologies

Личный кабинет и расширение полномочий пациентов в цифровых экосистемах медицинской помощи.

With the proliferation of web technologies and social networks, an approach to providing health care based on the active participation of patients in medical processes is gaining more and more support. This implies that the patient is actively involved in managing their health and has the authority and tools to do so. A personal account is considered as the main tool for empowering patients, providing patients with a personal information system with full access to electronic medical records stored in medical organizations. Due to the variety of sources of data on a citizen’s health, a personal account acquires the most complete functional implementation when functioning as part of digital health care ecosystems. The possibilities of personally controlled electronic medical records as part of integrated personal accounts are analyzed. The hypothesis is considered that a patient’s personal account in the medical care ecosystem can significantly change the clinical processes and methods of self-help of patients if it provides the opportunity for systematic communication between patients and doctors, automatic replenishment of data from various information systems, transformation of clinical indicators and observation results into meaningful and useful information. It is concluded that in this context, a patient’s personal account can become a breakthrough medical technology, and the exchange and dissemination of experience in health-saving and treatment will become the main trend in the near future and will help the development of medical information technologies in Russia as a whole.

Overview of the application of knowledge graphs in the medical field

With the booming development of medical information technology and computer science, the medical services industry is gradually transiting from information technology to intelligence. The medical knowledge graph plays an important role in intelligent medical applications such as knowledge questions and answers and intelligent diagnosis, and is a key technology for promoting wise medical care and the basis for intelligent management of medical information. In order to fully exploit the great potential of knowledge graphs in the medical field, this paper focuses on five aspects: inter-drug relationship discovery, assisted diagnosis, personalized recommendation, decision support and intelligent prediction. The latest research progress on medical knowledge graphs is introduced, and relevant suggestions are made in light of the current challenges and problems faced by medical knowledge graphs to provide reference for promoting the wide application of medical knowledge graphs.

Digital literacy among students in medical institutions of higher education in distance learning situations

Digital educational technologies are an innovative way of organizing the educational process, based on the use of electronic and information systems, which ensure visibility of the formation of digital skills among students of higher education institutions. The article analyzes the use of digital technologies by students of medical institutions of higher education when studying natural sciences in the conditions of distance learning, using the example of the educational component «Medical and biological physics; Medical information technologies». The analysis and synthesis of search results in information systems was carried out in order to define research concepts, methods of expert evaluation, observation, modeling, and forecasting. The principles of the «digitalization of Ukraine» policy were concidered, the components of the concept of «digital information literacy» were defined, and the effectiveness of the use of digital programs and devices in the educational process in medical institutions of higher education was investigated. The survey method was used for the research. A structured questionnaire in the form of a Google form was developed to collect information. The role of digital learning technologies in the educational space and their application in the educational process was determined. It was found that the number of e-books in the respondent’s home reduced the probability of weak skills, but had no relationship with strong skills. Frequency of using digital skills at university reduced the likelihood of weak skills, but there was no association with strong skills. This suggests that the use of digital technologies in the university can protect against low productivity, but does not guarantee high qualifications. Students’ general cognitive abilities are positively related to their digital self-efficacy accuracy. Students with more experience in the informational Internet space demonstrate more accurate digital self-efficacy. It is shown that digital technologies in the educational space, due to their visibility, accessibility and orientation to the individual abilities of students, are a necessary condition for increasing the efficiency and effectiveness of the educational process. The use of digital technologies by students of medical institutions of higher education on the example of studying the educational component «Medical and biological physics; Medical information technologies» increase students’ motivation to study, contribute to the development of digital practical skills in future specialists.

Applications of Machine Learning in Medicine

As medical data and information technologies advance, an increasing number of practitioners are recognizing or planning to use artificial intelligence. Radically alter medical practice through the use of cutting-edge machine learning techniques. Research is now being done to determine how machine learning and predictive analysis might be used to tailor individual therapies. In order to create a medical model that can rapidly and reliably forecast new data, machine learning must first learn a large quantity of medical data and investigate the dependencies in data concentration. This allows for the early detection of diseases and the support of therapeutic decisions. Clinical medicine must continue to identify and treat severely ill emergency patients quickly while dealing with a relative paucity of medical resources. The age of big data has made clinical demands and thoughtful medical treatment generate demand. The solution to the fore mentioned challenges lies in the assistance supplied by machines.

An enhanced grey wolf optimizer boosted machine learning prediction model for patient-flow prediction

Large and medium-sized general hospitals have adopted artificial intelligence big data systems to optimize the management of medical resources to improve the quality of hospital outpatient services and decrease patient wait times in recent years as a result of the development of medical information technology and the rise of big medical data. However, owing to the impact of several elements, including the physical environment, patient, and physician behaviours, the real optimum treatment effect does not meet expectations. In order to promote orderly patient access, this work provides a patient-flow prediction model that takes into account shifting dynamics and objective rules of patient-flow to handle this issue and forecast patients' medical requirements. First, we propose a high-performance optimization method (SRXGWO) and integrate the Sobol sequence, Cauchy random replacement strategy, and directional mutation mechanism into the grey wolf optimization (GWO) algorithm. The patient-flow prediction model (SRXGWO-SVR) is then proposed using SRXGWO to optimize the parameters of support vector regression (SVR). Twelve high-performance algorithms are examined in the benchmark function experiments' ablation and peer algorithm comparison tests, which are intended to validate SRXGWO's optimization performance. In order to forecast independently in the patient-flow prediction trials, the data set is split into training and test sets. The findings demonstrated that SRXGWO-SVR outperformed the other seven peer models in terms of prediction accuracy and error. As a result, SRXGWO-SVR is anticipated to be a reliable and efficient patient-flow forecast system that may help hospitals manage medical resources as effectively as possible.

Challenge for medical doctor in the future

Background: Indonesia faces many challenges after the COVID-19 outbreaks, particularly the needs of medical doctor, which lead to almost all university provide a doctor faculty. To produce a good quality doctor, not only in skills and knowledge including communication skills with patients and colleagues is still challenging. Objectives: This editorial was written to summarized the challenge of medical doctor in the future. Editorial: A good quality doctor, not only in skills and knowledge including communication skills with patients and colleagues. In addition, doctors need to be able to utilize medical technology or information technology. Communication skill is needed to bridge the relationship of doctor-patients by negotiation-consensus communication is able to improve the patient’s outcome. Inter-collegial relationship is needed as a part of teamwork communication for a better patient’s outcome. Doctor needs to update their skill on information technology and medical technology as a part of medical services on the society. Conclusion: To be a good medical doctor, not only need a medical skill, but also communication skills, empathy and humanity, respectful, and professional. A doctor is not only a profession, but also an effort of saving the patient’s life from diseases.

Application of a new nano-TiO2 composite antibacterial agent in nursing management of operating room: Based on real-time information push assistant system

With the maturity of the hospital real-time information management system at this stage, the main nursing informatics of the hospital has also been fully developed. Based on the analysis of the application status of medical information technology and Internet technology, the real-time information management system is applied to mobile medical and its development status. This paper realizes the auxiliary system of real-time information push. Several 5 nm thick two-dimensional TiO2 ultra-thin nanosheets were prepared by low-cost liquid phase method, and the application of new nano-TiO2 composite antibacterial agent in the nursing management of the operating room was systematically studied. In the nursing management of the operating room, the patients in the control group were dissatisfied with 18%, basically satisfied with 40%, and very satisfied with 42%, while the patients in the experimental group were dissatisfied with 6%, basically satisfied with 42%, and very satisfied with 52%. However, in the control group, the dissatisfaction rate of nurses' nursing care accounted for 14%, accounting for 34% of the basic satisfaction rate, 52% of the very satisfied rate. In the experimental group, the dissatisfaction rate was 4%, the basic satisfaction rate was 28%, and the very satisfied degree was 68%. The real-time information management system provides a wide range of medical potential space, and can help hospitals realize intelligent medical management, so as to have a great understanding of the digitalization of medical information, scientific medical operation process, human communication and services, and can provide more comprehensive nursing services for patients.

Comparative analysis of weka-based classification algorithms on medical diagnosis datasets.

With the advent of 5G and the era of Big Data, the rapid development of medical information technology around the world, the massive application of electronic medical records and cases, and the digitization of medical equipment and instruments, a large amount of data has accumulated in the database system of hospitals, which includes clinical diagnosis data and hospital management data. This study aimed to examine the classification effects of different machine learning algorithms on medical datasets so as to better explore the value of machine learning methods in aiding medical diagnosis. The classification datasets of four different medical fields in the University of California Irvine machine learning database were used as the research object. Also, six categories of classification models based on the Bayesian theorem idea, integrated learning idea, and rule-based and tree-based idea were constructed using the Weka platform. The between-group experiments showed that the Random Forest algorithm achieved the best results on the Indian liver disease patient dataset (ILPD), delivery cardiotocography (CADG), and lymphatic tractography (LYMP) datasets, followed by Bagging and partition and regression tree. In the within-group algorithm comparison experiments, the Bagging algorithm achieved better results than other algorithms based on the integration idea for 11 metrics on all datasets, mainly focusing on 2 binary datasets. Logit Boost had only 7 metrics with significant performance, and the best algorithm was Rotation Forest, with 28 metrics achieving optimal values. Among the algorithms based on tree ideas, the logistic model tree algorithm achieved optimal results on all metrics on the mammographic dataset (MAGR). The classification performance of BFTree, J48, and Random Tree was poor on each dataset. The best algorithm was Random Forest on the ILPD, CADG, and LYMP datasets with 27 metrics reaching the optimum. Machine learning algorithms have good application value in disease prediction and can provide a reference basis for disease diagnosis.

Research on Medical Security System Based on Zero Trust.

With the rapid development of Internet of Things technology, cloud computing, and big data, the combination of medical systems and information technology has become increasingly close. However, the emergence of intelligent medical systems has brought a series of network security threats and hidden dangers, including data leakage and remote attacks, which can directly threaten patients' lives. To ensure the security of medical information systems and expand the application of zero trust in the medical field, we combined the medical system with the zero-trust security system to propose a zero-trust medical security system. In addition, in its dynamic access control module, based on the RBAC model and the calculation of user behavior risk value and trust, an access control model based on subject behavior evaluation under zero-trust conditions (ABEAC) was designed to improve the security of medical equipment and data. Finally, the feasibility of the system is verified through a simulation experiment.

Analysis of the Number of Tests, the Positivity Rate and Their Dependency Structure During COVID-19 Pandemic

Abstract Recent advances in medical instruments, information technology, and unprecedented data sharing allowed scientists to investigate, trace, and monitor the COVID-19 pandemic faster than any previous outbreak. This extraordinary speed makes COVID-19 a medical revolution that causes some unprecedented analyses, discussions, and models. Modeling the dependence between the number of tests and the positivity rate is one of these new issues. Using four classes of copulas (Clayton, Frank, Gumbel, and FGM), this study is the first attempt tom model the dependency. The estimation of the parameters of the copulas is obtained using the maximum likelihood method. To evaluate the goodness of fit of the copulas, we calculate AIC. The computations are conducted on Matlab R2015b, R 4.0.3, Maple 2018a, and EasyFit 5.6. Findings indicate that at the beginning of a typical epidemic, the number of tests is relatively low and the proportion of positivity is high. As time passes, the number of tests increases, and the positivity rate decreases. The epidemic peaks are occasions that violate the stated general rule –due to the early growth of the number of tests. Also, during both peak and non-peak times, the rising number of tests is accompanied by decreasing the positivity rate. We find that the proportion of positivity is more proportional than the number of tests to the number of infected cases. Therefore, the changes in the positivity rate can be considered a representative of the level of the spreading. Approaching zero positivity rate is a good criterion to scale the success of a healthcare system in fighting against an epidemic. Accordingly, the number and accuracy of tests can play a vital role in the quality level of epidemic data.

Mobile Health Applications in the COVID-19 Pandemic: A Scoping Review of the Reviews.

Background: Mobile health (m-Health) is a combination of electronic communications and medical information technology that has helped patients and health care workers during the COVID-19 outbreak. This study aimed to aggregate and highlight findings from existing review studies about applications of m-health to prevent COVID-19. Methods: This scoping review was conducted based on the Arksey and O'Malley framework after searching the PubMed, Web of Science, and Scopus databases from March 2020 to February 2022. Keywords for the search included the English words "Mobile health";" mobile apps"; "corona disease"; "COVID-19"; and "review." Screening of articles was done in 4 stages. Results: Out of 37,569 papers found in the search, after the screening and review process, 22 articles were finally selected. From the analysis of the studies, 2 main categories emerged with the titles of "primary preventive applications" and "secondary preventive applications." Conclusion: M-health is used in both primary and secondary prevention. The m-health tools can be effective in controlling the spread of COVID-19 and improving the treatment process of this disease by providing various pieces of training related to COVID-19 as well as installing various programs to monitor the condition of patients. Also, m-Health can provide services through the exchange of treatment data between health care providers or between patients and health care providers, as well as provide appropriate training for the remote care needs of patients with COVID-19.

Introducing a Service Model in IoT -based Smart Hospital

Introduction: Nowadays, the world of technology is facing a growing phenomenon called the Internet of Things (IoT). The purpose of this study is to provide the infrastructure requirements, service model, and conceptual model for a smart hospital based on IoT, which is a safe and reliable method due to the integration of exchanges between objects. Method: In this strategic applied study, the infrastructure requirements of a smart hospital based on IoT are explained explains. First, basic requirements were prepared through library studies and, by setting up a questionnaire for medical staff and specialists in medical informatics, information technology, and health information technology, they were evaluated and then finalized using the Delphi technique. Then, based on extracted requirements, the service model of a smart hospital was presented. Finally, the conceptual model of IoT -based smart hospital infrastructure was suggested. Results: Thirty functional and non -functional requirements for IoT -based smart hospital infrastructure were determined. After that, the proposed model of services that can be provided in the hospital, including infrastructure, core and value -added services, was presented. Then, a conceptual model that includes the health ecosystem of the country, consisting of the levels of communication and information infrastructure, digital service systems, and the health system was provided. Conclusion: Agreement on the requirements, service and conceptual model , will help to design and implement the IoT -based smart hospital and m ake it more accurate and simple, according to factors affecting such as precision, speed and reliability in healthcare .

Improving Implementation of an Electronic Prescription System for COVID-19 Vaccination in the Czech Republic: Process Modeling Approach.

It is very difficult to find a consensus that will be accepted by most players when creating health care legislation. The Czech electronic prescription system was launched in 2011 and new functions were introduced in 2018. To ensure that these functions will not conflict with any other existing law, a process modeling tool based on the patent "Method and system for automated requirements modeling" was used successfully in the Czech Republic for the first time. The aim of this project was to develop another successful application of process modeling to add COVID-19 vaccination records to the existing electronic prescription system. The method employed was based on the mathematical theory of hierarchical state diagrams and process models. In the first step, sketches that record the results of informal discussions, interviews, meetings, and workshops were prepared. Subsequently, the architecture containing the main participants and their high-level interactions was drafted. Finally, detailed process diagrams were drawn. Each semiresult was discussed with all involved team members and stakeholders to incorporate all comments. By repeating this procedure, individual topics were gradually resolved and the areas of discussion were narrowed down until reaching complete agreement. This method proved to be faster, clearer, and significantly simpler than other methods. Owing to the use of graphic tools and symbols, the risk of errors, inaccuracies, and misunderstandings was significantly reduced. The outcome was used as an annex to the bill in the legislative process. One of the main benefits of this approach is gaining a higher level of understanding for all parties involved (ie, legislators, the medical community, patient organizations, and information technology professionals). The process architecture model in a form of a graphic scheme has proven to be a valuable communication platform and facilitated negotiation between stakeholders. Moreover, this model helped to avoid several inconsistencies that appeared during workshops and discussions. Our method worked successfully even when participants were from different knowledge areas. The vaccination record process model was drafted in 3 weeks and it took a total of 2 months to pass the bill. In comparison, the initial introduction of the electronic prescription system using conventional legislative methods took over 1 year, involving immediate creation of a text with legislative intent, followed by paragraph-by-section wording of the legislation that was commented on directly. These steps are repeated over and over, as any change in any part of the text has to be checked and rechecked within the entire document. Compared with conventional methods, we have shown that using our method for the process of modification of legislation related to such a complex issue as the integration of COVID-19 vaccination into an electronic prescription model significantly simplifies the preparation of a legislative standard.