Medical Information Processing Research Articles

Performance of ChatGPT on the Taiwan urology board examination: insights into current strengths and shortcomings.

To compare ChatGPT-4 and ChatGPT-3.5's performance on Taiwan urology board examination (TUBE), focusing on answer accuracy, explanation consistency, and uncertainty management tactics to minimize score penalties from incorrect responses across 12 urology domains. 450 multiple-choice questions from TUBE(2020-2022) were presented to two models. Three urologists assessed correctness and consistency of each response. Accuracy quantifies correct answers; consistency assesses logic and coherence in explanations out of total responses, alongside a penalty reduction experiment with prompt variations. Univariate logistic regression was applied for subgroup comparison. ChatGPT-4 showed strengths in urology, achieved an overall accuracy of 57.8%, with annual accuracies of 64.7% (2020), 58.0% (2021), and 50.7% (2022), significantly surpassing ChatGPT-3.5 (33.8%, OR = 2.68, 95% CI [2.05-3.52]). It could have passed the TUBE written exams if solely based on accuracy but failed in the final score due to penalties. ChatGPT-4 displayed a declining accuracy trend over time. Variability in accuracy across 12 urological domains was noted, with more frequently updated knowledge domains showing lower accuracy (53.2% vs. 62.2%, OR = 0.69, p = 0.05). A high consistency rate of 91.6% in explanations across all domains indicates reliable delivery of coherent and logical information. The simple prompt outperformed strategy-based prompts in accuracy (60% vs. 40%, p = 0.016), highlighting ChatGPT's limitations in its inability to accurately self-assess uncertainty and a tendency towards overconfidence, which may hinder medical decision-making. ChatGPT-4's high accuracy and consistent explanations in urology board examination demonstrate its potential in medical information processing. However, its limitations in self-assessment and overconfidence necessitate caution in its application, especially for inexperienced users. These insights call for ongoing advancements of urology-specific AI tools.

ІНФОРМАЦІЙНА ТЕХНОЛОГІЯ ОПРАЦЮВАННЯ МЕДИЧНИХ ПОКАЗНИКІВ

The article presents a new information technology for processing medical indicators, which is based on the method of his-togram analysis. Histogram analysis, known as a key tool in medical statistics, allows visually and quantitatively evaluate the distribution of data, which becomes important for detecting asymmetry, deviations and other characteristics of the indicator base.The analysis of the existing algorithms for presenting and processing medical information shows that they do not fully satisfy the requirements for solving problems that require complex logical conclusions, taking into account the incompleteness and in-consistencies of the input data. To solve this problem, it is necessary to look for new technologies for processing medical data.It is proposed to use a histogram analysis of each indicator for the studied groups: a group of healthy patients (M1), a group of sick patients who received DASH treatment (M2), and a group of sick patients who were treated by the standard method (M3).The purpose of the article is to develop an information technology for the analysis of the probability of changes in the values of medical indicators in the course of the chosen method of treatment.The mathematical justification and algorithm of information technology for calculating medical indicators using the histogram method have been developed, fragmentsof normalized histograms and the obtained results are given. Information technology for processing medical indicators based on the method of histogram analysis is presented.Histogram analysis is an important tool in medical statistics and research. This method allows visually and quantitatively evaluate the distribution of data, which is useful for detecting deviations, asymmetry and other characteristics of distributions.Analysis of the indicators obtained by two different treatment methods showed both positive and negative changes, which require further research. A feature of the proposed information technology is that it allows assessing the state of the indicator as a whole, without reference to a specific patient

Retracted: Artificial Intelligence Technology-Based Medical Information Processing and Emergency First Aid Nursing Management

Retracted: Artificial Intelligence Technology-Based Medical Information Processing and Emergency First Aid Nursing Management

Future Perspectives: A Literature Review of the Urologist’s New Potential Tool-Artificial Intelligence

Introduction: The overview aims to present the current state of artificial intelligence (AI) tools in the decision-making process, diagnosis, treatment possibilities, or outcome predictions in functional urology. Terminology: Artificial intelligence in healthcare includes computer systems that use reasoning and learning to analyze complex medical data and make predictions. Machine learning focuses on training computers with data to improve performance over time, especially in the case of processing medical information. Deep learning uses complex neural networks, inspired by the functional structure of the human brain, to extract information from medical data, improving the accuracy of diagnoses and speeding up investigations. Current AI Applications in Urology: The integration of artificial intelligence (AI) and artificial neural networks (ANN) in urological practice as a branch of medicine represents a promising potential for improving various aspects of patient care, diagnosis, surgical approaches with subsequent procedures resulting from rational and quick decision-making in the management of urological diseases (urolithiasis, BPH, oncology). AI technologies provide the opportunity to analyze large volumes of complex data, assist in clinical decision-making, optimize treatment strategies, and improve patient outcomes. Conclusion: The future and potential prospects of artificial intelligence as an auxiliary tool are significant, and the complete transition to them is still ahead. The field of AI support in urology is rapidly evolving, with continuous research and development focused on increasing the accuracy of diagnostic tools, refining surgical techniques, and improving treatment optimization. Continued research and clinical trials will be crucial for validating the effectiveness of AI and directing their integration into everyday urological practice.

Efficient Low-Resource Medical Information Processing Based on Semantic Analysis and Granular Computing

The rise of the digital economy and e-commerce has fostered a movement towards efficient low-resource medical information processing, a trend that holds great importance in the healthcare sector. Diabetes, being a widespread chronic condition, has witnessed the introduction of glucometers, which offer patients a convenient method of monitoring their blood sugar levels. However, it is worth noting that a considerable proportion of online comments may be subject to emotional bias or contain inaccurate information. Furthermore, the performance of glucometers can be influenced by several attributes, including price, accuracy and portability, thereby potentially complicating the decision-making process for consumers. Semantic analysis can be employed to acquire valuable information, aiding consumers in reasonably choosing the suitable glucometer. This paper utilizes the benefits of granular computing, an emerging computing paradigm, to effectively handle incomplete and uncertain medical information. It employs generalized fuzzy sets, rough sets and three-way decisions (TWD) techniques to boost the accuracy and reliability of medical information fusion. Subsequently, the MABAC (Multi-Attribute Border Approximation Area Comparison) method is utilized to evaluate the reviews of every glucometer, calculate their aggregated scores, and rank and compare them. Ultimately, in light of consumers’ needs and trade-offs, the glucometer with the highest score can be selected. The proposed approach comprehensively considers the weight and priority of multiple attributes, reduces information overload and mitigates selection difficulties, thereby enhancing the accuracy and reliability of low-resource medical information processing.

Digital Health Information Systems in the Member States of the Commonwealth of Independent States: Status and Prospects

This paper examines the status of the development of national digital health information systems (HIS) in Commonwealth of Independent States (CIS) member states. Data for research were collected using a questionnaire adapted from the questionnaire of the WHO’s Third Global Survey on eHealth. The results showed that the digital transformation of HIS was occurring in all seven CIS member states (participating in the study), which were financed by different resources. Laws and regulations on electronic medical records (EMR) use were present in almost all participating CIS member states. Various international standards and classifications were used to support development and the interoperability of digital health information system (d-HIS), including International Classification of Diseases (ICD), Digital Imaging and Communications in Medicine (DICOM), ISO 18308, Logical Observation Identifiers, Names, and Codes (LOINC), Systematized Nomenclature of Medicine Clinical Terms (SNOMED CT), and ISO TC 215. Several CIS member states had adopted a national information security strategy for the safe processing of both personal data and medical confidential information. The digital transformation of healthcare and the Empowerment through Digital Health initiative are taking place in all CIS member states, which are at different stages of introducing electronic medical and health records.

“Why don't they talk to our daughter?”: Eye-tracking AAC and medical communication in Rett syndrome

This investigation explores medical advocacy for Rett syndrome—a rare neurological disorder causing loss of speech and hand function—through the lens of eye-tracking augmentative and alternative communication (AAC) technology. We gathered data from five individuals with Rett syndrome and their caregivers, encompassing semi-structured virtual interviews, video analyses of AAC device usage, and examination of AAC pageset screenshots. The findings reveal diverse AAC strategies employed to express discomfort or illness and highlight caregivers' pivotal role in processing medical information. Notwishstanding challenges like cost, time limitations in medical contexts, training needs, and the lack of standardized AAC symptom descriptions, eye-tracking AAC technology has the potential to enhance symptom assessment, foster patient autonomy, and facilitate personalized medical care. This study illuminates the transformative power of this AAC technology in medical communication, showcasing its promise in tackling communication challenges and underscoring its capacity to enhance quality of life for those with Rett syndrome and similar health conditions.

Free-Space Optical Communication with an Optimized Lipschitz Exponent for Biosignal Telemetry

Abstract Healthcare monitoring is a rapidly developing network in the field of advanced medical treatment. The network combines the ideology of wireless communication, signal processing, medical information and real-time processing units to support the medical monitoring system. The proposed work focuses on the development of a Free-Space Optical (FSO) system to transmit the biosignals from a remote distance to the physician. Generally, the data transmitted over the FSO system is affected by various atmospheric conditions such as air medium, O2, and H2O molecules. To tackle these problems, the Biosignals Electrocardiogram (ECG) and Electroencephalogram (EEG) are processed in the Optimized Lipschitz Exponent (OLE) function before transmission over the FSO medium. In this novel technique, the OLE function measures the informative data from the biosignals by calculating the local regularities and singularity. This collects the most informative signals and transmits them in the signal over the FSO medium. This particular hybridization helps to transmit the required data without distortion. The Bit Error Rate (BER) of 10−9 is obtained, which satisfies the healthcare monitoring condition. The result section shows that the proposed model has minimum losses compared to the original signal.

Understanding of Machine Learning with Deep Learning: Architectures, Workflow, Applications and Future Directions

In recent years, deep learning (DL) has been the most popular computational approach in the field of machine learning (ML), achieving exceptional results on a variety of complex cognitive tasks, matching or even surpassing human performance. Deep learning technology, which grew out of artificial neural networks (ANN), has become a big deal in computing because it can learn from data. The ability to learn enormous volumes of data is one of the benefits of deep learning. In the past few years, the field of deep learning has grown quickly, and it has been used successfully in a wide range of traditional fields. In numerous disciplines, including cybersecurity, natural language processing, bioinformatics, robotics and control, and medical information processing, deep learning has outperformed well-known machine learning approaches. In order to provide a more ideal starting point from which to create a comprehensive understanding of deep learning, also, this article aims to provide a more detailed overview of the most significant facets of deep learning, including the most current developments in the field. Moreover, this paper discusses the significance of deep learning and the various deep learning techniques and networks. Additionally, it provides an overview of real-world application areas where deep learning techniques can be utilised. We conclude by identifying possible characteristics for future generations of deep learning modelling and providing research suggestions. On the same hand, this article intends to provide a comprehensive overview of deep learning modelling that can serve as a resource for academics and industry people alike. Lastly, we provide additional issues and recommended solutions to assist researchers in comprehending the existing research gaps. Various approaches, deep learning architectures, strategies, and applications are discussed in this work.

Digitalization in health care: Promising tools

Aim. The presented study aims to consider the possibilities and features of the development of digitalization in health care under modern conditions (through the example of Russia).Tasks. The authors describe the essence and trends of digitalization and the industry-specific features of its manifestation in health care; analyze the available digital technologies from the perspective of their use in health care; identify directions for the development of RWD and RWE systems in Russia and assess the need for a transformation of the institutional environment of health care in the context of their introduction.Methods. This study uses general scientific research methods (analysis, synthesis, monographic method, grouping), as well as methods of institutional and sectoral techno-economic analysis.Results. The process of digital transformation in the health care sector is analyzed, and the introduction of RWD and RWE systems and artificial intelligence systems is examined in detail. In the context of a study of development trends in digital health care, recommendations for improving the collection, processing, and storage of medical information are given. New means of working with medical data and directions for the development of digital medical services and medical care using digital technologies are described.Conclusions. Digitalization in health care is aimed at improving the efficiency and effectiveness of medical care and medical services through the application of innovative approaches and the expanded use of digital tools. Global experience shows that patients and medical organizations can successfully use personalized solutions in the field of digital health care to improve disease prevention, self-care, and access to health care, providing that they are designed with allowance for the needs and preferences of the patient. However, these issues require improved processes not only at the level of organizations, but also at the level of industrial regulatory bodies.

Sharing and Cooperation of Improved Cross-Entropy Optimization Algorithm in Telemedicine Multimedia Information Processing.

In order to improve the efficiency of medical multimedia information sharing, this paper combines cloud computing technology and SOA (service-oriented architecture) technology to build a medical multimedia information sharing system. Building a medical information sharing platform requires integrating information resources stored in information systems of medical institutions and nonmedical information systems related to medical information and forming a huge resource pool. It is important to mine and analyze the information resources in the resource pool to realize the sharing and interaction of medical information. To this end, this paper proposes a gain-adaptive control algorithm with online adjustable parameters and investigates the extension of the mutual entropy optimization algorithm in the control domain and its integrated processing capability in the process of medical multimedia information processing. In addition, this paper constructs a medical multimedia information sharing and collaboration platform with medical multimedia information sharing and telemedicine as the core and verifies the effectiveness of the platform through experiments. The simulation results and comparison results with other systems prove that the system in this paper can realize fast data processing, retrieve and analyze massive data, and meet the demand of remote intelligent diagnosis under the premise of safety and stability. Meanwhile, the system in this paper can help hospitals achieve fast and accurate diagnosis, which has strong theoretical and practical values.

TeaBERT: An Efficient Knowledge Infused Cross-Lingual Language Model for Mapping Chinese Medical Entities to the Unified Medical Language System.

Medical entity normalization is an important task for medical information processing. The Unified Medical Language System (UMLS), a well-developed medical terminology system, is crucial for medical entity normalization. However, the UMLS primarily consists of English medical terms. For languages other than English, such as Chinese, a significant challenge for normalizing medical entities is the lack of robust terminology systems. To address this issue, we propose a translation-enhancing training strategy that incorporates the translation and synonym knowledge of the UMLS into a language model using the contrastive learning approach. In this work, we proposed a cross-lingual pre-trained language model called TeaBERT, which can align synonymous Chinese and English medical entities across languages at the concept level. As the evaluation results showed, the TeaBERT language model outperformed previous cross-lingual language models with Acc@5 values of 92.54%, 87.14% and 84.77% on the ICD10-CN, CHPO and RealWorld-v2 datasets, respectively. It also achieved a new state-of-the-art cross-lingual entity mapping performance without fine-tuning. The translation-enhancing strategy is applicable to other languages that face the similar challenge due to the absence of well-developed medical terminology systems.

Prediction of causative genes in inherited retinal disorders from spectral‐domain optical coherence tomography utilizing deep learning techniques

AbstractTechnological advancement has markedly increased the medical information required for clinical management. Processing medical information automatically and comprehensively through the application of artificial intelligence (AI) is becoming essential to improve the quality of medical data management and reduce medical expenses.Deep learning techniques have been successfully applied in medical fields, especially in analysing clinical images for the predicted diagnosis of common ocular disorders, such as diabetic retinopathy and age‐related macular degeneration. In contrast, due to limited clinical resources, AI‐oriented bioinformatic engineering has not yet been applied in a clinic for orphan diseases, such as inherited retinal disease (IRD), although IRD is the most prevalent cause of blindness.Therefore, the purpose of the current study was to investigate the utility of deep learning in a nationwide cohort of patients with IRD, which enables to explore the typical phenotypes of IRD based on unique findings from clinical images and predict the genetic diagnosis.Clinical and genetic data with IRD were reviewed. Three categories of genetic diagnosis were selected based on the high prevalence of their causative genes: Stargardt disease (ABCA4), retinitis pigmentosa (EYS), and occult macular dystrophy (RP1L1). Optical coherence tomographic (OCT) images were cropped in a standardized manner with a macro algorithm. Algorithms for pipeline analyses, based on TensorFlow Inception V‐3 were determined with learning parameters through evaluation with a randomized 4‐fold cross‐validation method, given the accuracy of concordance (aimed >80%) between the genetic diagnosis and the machine diagnosis (ABCA4, EYS, RP1L1, and normal).A total of 178 images from 75 subjects were examined. The mean training accuracy was 98.5%, ranging from 90.6 to 100.0. The mean overall test accuracy was 90.9% (82.0–97.6).This study highlighted a novel application of deep neural networks in the prediction of the causative gene in IRD retinopathies from SD‐OCT, with a high prediction accuracy. The obtained study results have been registered in the database to support clinical/genetic diagnosis. These achievements will extensively promote the improvement of medical care quality by providing accurate real‐time diagnoses, reducing cost by avoiding unnecessary referrals and testing, enriching the education of non‐specialists, and realizing an application of personalized medicine.

Demystifying quantum blockchain for healthcare

AbstractThe healthcare industry and the battle against the COVID‐19 pandemic are two areas where blockchain technology might be useful. In this study, blockchain's significance is examined. Blockchain technology and related procedures will be used in future healthcare systems for collecting sensor data, automated patient monitoring, and safe data storage. Because it can store a large amount of data in a dispersed and secure way and provide access whenever and wherever it is needed, this technology greatly simplifies the process of carrying out activities. The advantages of quantum computing, such as the speed with which patients can be found and monitored, may be fully used with the help of quantum blockchain. Quantum blockchain is an additional resource that may be used to safeguard the veracity, integrity, and availability of stored information. Combining quantum computing with blockchain technology may allow faster and more secure medical information processing. In this research, the authors examine the potential uses of blockchain and quantum technology in the healthcare industry. Quantum technologies, blockchain‐based technologies, and other cutting‐edge ICTs (such as ratification intelligence, machine learning, drones, and so on) were investigated and contrasted in this article.

Medical Image Fusion using ECNN- and OMBO-based Adaptive Weighted Fusion Rule

Medical imaging and information processing technologies are constantly evolving, resulting in a wide range of multimodality therapeutic pictures for clinical illness investigation. Physicians often require medical images produced using various modalities such as computed tomography (CT), magnetic resonance (MR), and positron emission computed tomography (PET) for clinical diagnosis. Many deep learning-based fusion methods have recently been proposed. In Convolutional Neural Network (CNN)-based fusion methods, only the last layer results are used as the image features, which result in the loss of useful information at middle layers. The fusion rule, based on the weighted averaging, causes noises in the source images and suppresses salient features of the image. In order to solve these issues, this paper proposes medical image fusion using Enhanced CNN (ECNN)- and Opposition-based Monarch Butterfly Optimization (OMBO)-based adaptive weighted fusion rule (AWFR). The ECNN contains feature extraction and reconstruction components. Both these components are trained in order to minimize the pixel loss and structural similarity loss. A pair of multimodal medical image is passed as input to the ECNN model to extract the low level and high level features. For the extracted features from ECNN, weighted fusion rule is applied in which OMBO algorithm is applied to adaptively optimize the weights of the fusion rule.

Approach to Software Integration of Heterogeneous Sources of Medical Data Based on Microservice Architecture

The task of processing medical information is currently being solved in our country and abroad by means of heterogeneous medical information systems, mainly at the local and regional levels. The ever-increasing volume and complexity of the accumulated information, along with the need to ensure transparency and continuity in the processing of medical data (in particular, for bronchopulmonary diseases) in various organizations, requires the development of a new approach to integrating their heterogeneous sources. At the same time, an important requirement for solving the problem is the possibility of web-oriented implementation, which will make the corresponding applications available to a wide range of users without high requirements for their hardware and software capabilities. The paper considers an approach to the integration of heterogeneous sources of medical information, which is based on the principles of building microservice web architectures. Each data processing module can be used independently of other program modules, providing a universal entry point and the resulting data set in accordance with the accepted data schema. Sequential execution of processing steps implies the transfer of control to the corresponding program modules in the background according to the Cron principle. The schema declares two types of data schemas - local (from medical information systems) and global (for a single storage system), between which the corresponding display parameters are provided according to the principle of constructing XSLT tables. An important distinguishing feature of the proposed approach is the modernization of the medical information storage system, which consists in creating mirror copies of the main server with periodic replication of the relevant information. At the same time, the interaction between clients and data storage servers is carried out according to the type of content delivery systems with the creation of a connection session between end points based on the principle of the nearest distance between them, calculated using the haversine formula. The computational experiments carried out on test data on bronchopulmonary diseases showed the effectiveness of the proposed approach both for loading data and for obtaining them by individual users and software systems. Overall, the reactivity score of the corresponding web-based applications was improved by 40% on a stable connection.

It's not one size fits all: a case for how equity-based knowledge translation can support rural and remote communities to optimize virtual health care.

People living in rural and remote British Columbia (BC) in Canada experience complex barriers to care, resulting in poorer health outcomes compared to their urban counterparts. Virtual healthcare (VH) can act as a tool to address some of the care barriers, including reducing travel time, cost, and disruptions to people's lives. Conversely, VH can exacerbate inequities through unique difficulties in rural implementation, such as a lack of access to necessary infrastructure (eg internet), social supports, and technological capacity (eg devices and literacy). The impacts of the COVID-19 pandemic induced a rapid shift to VH, providing new opportunities for health care while simultaneously highlighting and exacerbating inequities for people living in rural and remote settings. Equity-informed knowledge translation processes can help address these concerns. This commentary reports on an equity-informed knowledge translation process engaged by a diverse group of health researchers, community members, and practitioners in BC. Informed by equity principles from the Canadian Coalition for Global Health Research, this knowledge exchange and translation process led to the co-creation of two practical tools: a set of VH appointment tip sheets and an open access report. Through stakeholder engagement and literature consultation, VH appointments were found to have many benefits for those in rural and remote communities, including expanding access to basic and specialized health services. However, some hesitation was noted when relying solely on these modes of care, as they can lack relationality, clarity, and time to process medical information. The tip sheets resulting from this process are an interactional-level tool developed to address this concern and optimize VH appointments, for rural patients and care providers. They offer the respective stakeholder group insights on how to actively prepare for and participate in inclusive virtual care. On a systems level, there is a continually echoed need for equity-based processes to ensure that VH is striking the balance of meeting rural health needs without exacerbating inequities. Additionally, incorporating the voices of rural and remote community members is essential. To help address this gap, an open-access report was compiled to serve as a small-scale example of integrating rural voices with existing literature to recommend systems-level adjustments. Overall, VH holds promise as an effective tool for addressing inequities experienced by those living in rural areas. To maximize this potential, rural and remote stakeholders must be proactively engaged and listened to throughout the processes of considering, planning, and implementing shifts in the utilization of VH options.

Mechanism of Antibacterial Enhancement and Drug Resistance Based on Smart Medical Imaging on Antibiotics.

With the development of antibacterial, synergistic, and drug resistance research, the requirements for the specificity of antibiotics are getting higher and higher. In the research based on the specificity of antibiotics, this article uses intelligent medical image processing methods to study the specificity of the antibacterial efficiency of nanocopper oxide and the inhibition of drug resistance. Copper oxide nanorods have the properties of surface effect, quantum size effect, volume effect, and macroscopic quantum tunneling effect. Compared with ordinary copper oxide, the nanoscale gives them special properties of electricity, optics, and catalysis. In this article, in the research based on the specificity of antibiotics, the specificity of antimicrobial efficiency and drug resistance inhibition of nanocopper oxide are studied by using smart medical information processing methods. Drug sensitivity paper tablet method is a drug sensitivity experiment to determine drug sensitivity to make accurate and effective use of drugs for treatment. Colony growth method is used to take the equivalent volume of fermentation liquid at different times to determine the content of bacteria. In this article, Staphylococcus aureus is cultivated by the drug-sensitive disk method and the colony growth method. Then, according to this type of antibiotic and bacterial group combination, Staphylococcus aureus is divided into a penicillin group, nanocopper oxide group, and cephalosporin group. 0.5 g of the corresponding antibiotic was added to each group. TMP (trimethoprim) acts as a synergist, and the ratio of TMP to antibiotic is 1 : 5. Finally, we compared the inhibitory concentration indexes of the above three groups and inferred the synergistic effect of antibiotics and the inhibitory effect of drug resistance through the specificity of the antibiotics that the antibacterial activity was further studied. The results showed that the antibacterial effect of TMP combined with nano-CuO was 38% higher than that of the penicillin group and 41% higher than that of the cephalosporin group. In addition, the combined effect of TMP and antibiotics is greater than the combined effect of TMP and antibiotics alone. From the observation of smart medical system processing, it is speculated that the reason may be that they provide each other with a suitable environment. Because of this combined effect between the TMP and the antibiotic, it can influence each other. From the results, the combined effect is 48% higher than the combined effect. Therefore, according to the results of medical imaging, the combination of antibiotics and antibacterial synergists can improve specificity and antibacterial rate.

Artificial Intelligence Technology-Based Medical Information Processing and Emergency First Aid Nursing Management.

This study was aimed at exploring the new management mode of medical information processing and emergency first aid nursing management under the new artificial intelligence technology. This study will use the artificial intelligence algorithm to optimize medical information processing and emergency first aid nursing management process, in order to improve the efficiency of emergency department and first aid efficiency. The successful rescue rates of hemorrhagic shock, coma, dyspnea, and more than three organs injury were 96.7%, 92.5%, 93.7%, and 87.2%, respectively, after the emergency first aid nursing mode was used in the hospital emergency center. The success rates of first aid within three years were compared, which were 91.8%, 93.4%, and 94.2%, respectively, showing an increasing trend year by year. 255 emergency patients in five batches in June and five batches in July were selected as the research objects by convenience sampling method. Among them, 116 cases in June were taken as the experimental group, and 139 cases in July were taken as the control group, which was used to verify the efficiency of the design model in this study. The results showed that the triage time of the two groups was 8.16 ± 2.07 min and 19.21 ± 6.36 min, respectively, and the difference was statistically significant (P < 0.01). The triage coincidence rates were 96.35% and 90.04%, respectively, and the difference was statistically significant (P < 0.05). The research proved that the design of intelligent medical information processing and emergency first aid nursing management research model can effectively improve the triage efficiency of the wounded, assist the efficiency of emergency nursing of medical staff, and improve the survival rate of emergency patients, which is worthy of clinical promotion.

ИСТОРИЧЕСКИЕ АСПЕКТЫ МЕТОДОЛОГИИ СБОРА И ОБРАБОТКИ МЕДИКО-СТАТИСТИЧЕСКОЙ ИНФОРМАЦИИ В РОССИЙСКОЙ ФЕДЕРАЦИИ

Significance. The article briefly presents the history of the development of the provision of medical and statistical data, the emergence of unified forms of accounting and responsible accounting bodies, analysis and processing of the information received. The main historical aspects of the methodology of collecting and processing medical and statistical information in the Russian Federation, the transition from "manual" labor to reporting in electronic form are demonstrated. Purpose: to study the methodology of collection and processing of medical and statistical information in the Russian Federation in the historical aspect and at the present. Material and methods. Content analysis, historical-genetic, diagnostic, conceptual and methodological analysis. CyberLeninka, RSCI, Scopus, Web of Science databases were used to conduct information search. Results. The formation of the methodology for collecting and processing medical statistics in Russia in the twentieth century, the system of collecting and processing medical statistical information at the federal level in the XXI century, as well as methods for ensuring the reliability of statistical data are studied. The analysis of various aspects of the methodology for collecting and processing medical statistical information is presented. Modernization of the process of collecting and processing medical and statistical information based on the «MedStatWEB» system for coordination, organization and operational monitoring of the process of providing information at the federal level is shown. A multilevel system of control and verification of the reliability of medical and statistical information (format, mathematical and methodological groups) is presented. Conclusion. Statistical reporting of medical organizations is developing in the changing conditions of the functioning of the domestic healthcare industry. The historical development of the system of collecting and processing statistical reports is associated with structural regulatory changes in the healthcare of the Russian Federation. The presented model of the federal level control system can be used both at the level of the subject of the Russian Federation and at the level of a medical organization.