Integrated Information System Research Articles

Artificial intelligence in accounting

Artificial Intelligence (AI) technologies open up broad horizons for enhancing business efficiency and advancing various professional domains, boosting their productivity and compe­titiveness. There is an active exploration of approaches to incorporating AI technologies in the accounting sphere, promising a seamless transition from human to machine involvement. The aim of this article is to summarize the acquired experience, identify perspectives, constraints, and risks associated with the use of AI technologies in the professional activities of accountants. The research is based on the hypothesis that widespread use of AI in the professional activity of an accountant with an insufficient level of professional skepticism and caution carries significant threats and risks for both the accountant and the business as a whole. Scientific search methods, comparative and critical analysis, theoretical generalization, and synthesis were used. A prerequisite for imple­menting AI technologies in accounting is expert information systems and ERP systems. The analysis of AI technology implementation experience in various industries demonstrates their relevance in the accounting field for performing routine tasks (automated recognition of primary documents, processing incoming signals, and other standard operations with a simultaneous reduction in the probability of errors), analyzing large datasets, and providing information support for decision-making (pro­ces­sing business data and regulatory docu­ments), training professionals, and organi­zing internal and external communication (parti­cularly between humans and machines). Identi­fied potential risks include breaches of privacy and data security, misinterpretation of output data, and the disregard of activity context, external and internal environments, especially due to the absence of emotional intelligence, which influences the trust level in integrated information systems. The requirement for the application of professional assessments and judgments, mandated by regulatory documents, limits the scope of AI technology utilization in accounting. Future research should focus on exploring the possibilities of widespread integ­ration of AI technologies in information systems for accounting and improving legislation based on the principle of risk assessment.

An Integration of Logistic Regression and Geographic Information System for Development of a Landslide Hazard Index to Land Use: A Case Study in Pingtung County in Southern Taiwan

In Taiwan, mountainous areas account for approximately two-thirds of the total area. The steep terrain and concentrated rainfall during typhoons cause landslides, which pose a considerable threat to mountain settlements. Therefore, models for analyzing rainfall-induced landslide hazards are urgently required to ensure adequate land use in mountainous areas. In this study, focusing on Pingtung County in southern Taiwan, we developed a landslide hazard index (IRL) to land use. Using FORMOSA-2 and SPOT-5 satellite images, data were collected before and after four typhoons (one in 2009 and three in 2013). The ArcGIS random tree classifier was used for interpreting satellite images to explore surface changes and disasters, which were used to analyze slope disturbances. The product of the maximum 3-h rolling rainfall intensity and effective accumulated rainfall was used as a rainfall trigger index (IRT). Considering environmental and slope disturbance factors, an index of slope environmental strength potential (ISESP) was developed through logistic regression (LR). Landslide hazard to land use was estimated using IRT and ISESP. The average coefficient of agreement (Kappa) was approximately 0.71 (medium to high accuracy); the overall accuracy of slope environmental strength potential analysis was approximately 80.4%. At a constant ISESP, IRT increased with the increasing hazard potential of rainfall-induced landslides. Furthermore, IRT and ISESP were positively correlated with landslide occurrence. When large ISESP values occur (e.g., fragile environment and high land development intensity), small IRT values may induce landslides.

Machine learning and IoT based MIMO-UWB Antenna Integrated with Ku-Band for Seamless Wireless Communication

The implementation of machine learning algorithms with advanced antenna technologies, such as Multiple Input Multiple Output Ultra-Wideband -UWB- and Cup-Ban, has emerged as a powerful method for improving wireless communication systems. This research focuses on the effectiveness of applying three machine learning algorithms, which are Deep Q-Networks -DQN- , Label Propagation, and Model-Agnostic Meta-Learning (MAML). Using several experiments, we analyzed the throughput, latency, and packet loss of every algorithm. These were part of the project designed on a wireless signal combined with MIMO-UWB and Ku-Band. Nevertheless, MAML has the highest average throughput of 261.3 Mbps, and the other algorithms follow one by one: DQN with 252.5 Mbps and Label Propagation with 240.6 Mbps. As to latency, the lowest average is 9.0 ms and 11.0 ms for DQN and 14.2 ms for Label Propagation. Finally, MAML proved the least packet losses with the average value of 0.18% and DQN with 0.39% and 0.73% of Label Propagation .The results presented above indicate that by adapting more quickly to changing wireless environments, MAML effectively optimizes communication performance in terms of throughput, latency, and packet loss. Specifically, through its ability to accelerate the allocation of resources and management of networks as a whole, more frequent and rapid learning with MAML results in better system reliability and data integrity.

The Perception of Chronic Disease Management of Family Physicians and Family Medicine Residents

Aim: This study aims to evaluate the attitudes and behaviors of family physicians and family medicine residents working in primary care regarding the management of chronic diseases and the use of diagnosis and treatment guidelines. Methods: In the cross-sectional study, data were collected from the participants online, with the help of a questionnaire in the first part consisting of the sociodemographic characteristics of the physicians and their views, behaviors and attitudes about chronic care, and in the second part, a data form prepared by the researchers using the Patient Assessment of Chronic Illness Care Scale. Results: Of the participants, 57.3% use the diagnosis and treatment guidelines prepared for chronic diseases, 37.7% (n=148) do not use guidelines. The remaining 5.1% (n=20) are not aware of such guidelines. Among the physicians who do not use guides, 58.8% stated that they don’t use guides because they don’t have sufficient motivation to use the diagnosis and treatment guides in their daily practice, 49.3% don’t have enough time to use the guides, 29.1% don’t have foreign language proficiency to follow international guides. Conclusion: Family physicians will be able to take a more active role in the management of chronic diseases after the development of diagnostic treatment guides for primary care and the establishment of the necessary infrastructure for integrated information systems that can be accessed electronically. Keywords: chronic disease, family practice, physicians, disease management

The future of accounting in Ukraine: trends and forecasts in the conditions of rapid digital transformation

The paper is devoted to the study of trends and forecasts of the development of accounting in Ukraine in the conditions of rapid digital transformation. Scientific and empirical methods (collection and comparison of information), as well as complex analytical methods (synthesis and generalization) were used during the research. The informational basis of the paper is the scientific works of both domestic and foreign scientists who studied accounting issues in the conditions of rapid digital transformation. The paper noted that accounting in Ukraine today is undergoing major changes in connection with the introduction of digital technologies, which is a necessary and key stage for ensuring the effective functioning of the organization. It was noted that digitization of accounting and its integration into the company's information system is not only a necessity, but also a strategic step in modern business. This process is aimed at optimizing financial processes, ensuring fast and accurate data processing. An integrated information system allows not only to store information, but also to analyze it, providing the company's management with valuable insights for making strategic decisions. This approach turns accounting into a powerful tool that contributes to the growth and success of the enterprise in conditions of constant competition. It is also stated that in the future accounting in Ukraine will continue to evolve rapidly under the influence of digital transformation and the latest technologies. The paper examines key trends and forecasts for the future of accounting in Ukraine in conditions of rapid digital transformation: process automation, "cloud" technologies, data analysis and forecasting, blockchain, cyber security. The conclusion states that the future of accounting in Ukraine is inextricably linked to the digital transformation taking place in modern business. Successful adaptation to the trends considered in the study will require not only technological readiness, but also cultural and organizational changes aimed at continuous improvement of processes and personnel skills.

The Profile of The Public Accountant, a Response to the Organizational Needs of Non-Profit Entities in the Fourth Industrial Revolution?

Objective: Over time, challenging changes have occurred in industries worldwide, which are transforming productive, economic and commercial relationships. These changes have been called revolutions and we are currently experiencing one of them; the so-called fourth industrial revolution, which eliminates the terms between physical and digital activities. This transition will significantly impact education and organizations because they will require people with new technical and methodological skills. This research aims to identify a professional profile for the public accountant, which adapts to the needs of non-profit organizations in the city of Medellín, in response to the demands generated by the arrival of the fourth industrial revolution.
  
 Theoretical framework: For an empirical study, regarding the fact that these entities are always studied from their economic activity, organization and fiscal aspects, but it is the first time that they are taken into account for an analysis in the requirement of their needs with a view to to the corporate purpose of non-profit entities and the fourth industrial revolution.
  
 Method: Under a qualitative approach using the inductive method with a phenomenological design, to collect data we will use the technique of personal interviews with open questions directed to the administrators and/or owners of the Non-Profit Entities (ESAL). With this methodology we can achieve the general objective to the extent that, by applying the design steps and their respective analysis; The levels of association between the different hermeneutical and lexical units can be verified, in order to determine needs and trends between the different participants and the results of the research.
  
 Results and Conclusions: For the processing and analysis of the data, the size of the population of 20 non-profit organizations in the city of Medellín, it was possible to identify the needs of the public accountant profile from the social and technological aspects.
  
 Repercussions of the research: It is a commitment of Public Accountants to maintain the highest levels of professional competence to respond to the needs of organizations generated by the fourth industrial revolution. The accounting professional must have knowledge of integrated data systems, the expansion of cloud computing, the Internet of Things, among others. Therefore, it is necessary to identify the changes that will impact the environment of the public accountant profile and the responses of organizations to these challenges.
  
 Originality/value: It is considered that this research, due to its novelty and originality, will generate new knowledge for Higher Education Institutions - IES, to the extent that, through the needs or requirements of the profile of the public accountant, they can make an adjustment to the curriculum of this discipline, managing to integrate the company with the university, this type of organizations are also taken into account by the HEIs.

AIMS TO DIGITALISE THE CIRCULATION OF PRECIOUS METALS, PRECIOUS STONES AND THEIR PRODUCTS

The need to digitalise economic sectors in today’s world is perceived as an objective reality. In the circulation of precious metals, precious stones and their products, the digitalisation process is at a fairly high level, with continuous improvement. The article analyses the progress made in achieving the goals of the implementation of the State Integrated Information System in the Sphere of Control over the Circulation of Precious Metals and Precious Stones at all stages (GIIS DMDK), which is the main project of digitalisation of the industry.

Using machine learning models to predict falls in hospitalised adults

BackgroundIdentifying patients at high risk of falling is crucial in implementing effective fall prevention programs. While the integration of information systems is becoming more widespread in the healthcare industry, it poses a significant challenge in analysing vast amounts of data to identify factors that could enhance patient safety. ObjectiveTo determine fall-associated factors and develop high-performance prediction tools for at-risk patients in acute and sub-acute care services in Australia. MethodsA retrospective study of 672,400 patients admitted to acute and sub-acute care services within a large metropolitan tertiary health service in Victoria, Australia, between January 1, 2019, and December 31, 2021. Data were obtained from four sources: the Department of Health Victorian Admitted Episodes Dataset, RiskManTM, electronic health records, and the health workforce dataset. Machine learning techniques, including Random Forest and Deep Neural Network models, were used to analyse the data, predict patient falls, and identify the most important risk factors for falls in this population. Model performance was evaluated using accuracy, F1-score, precision, recall, specificity, Matthew's correlation coefficient, and the area under the receiver operating characteristic curve (AUC). ResultsThe deep neural network and random forest models were highly accurate in predicting hospital patient falls. The deep neural network model achieved an accuracy of 0.988 and a specificity of 0.999, while the RF achieved an accuracy of 0.989 and a specificity of 1.000. The top 20 variables impacting falls were compared across both models, and 12 common factors were identified. These factors can be broadly classified into three categories: patient-related factors, staffing-related factors, and admission-related factors. Although not all factors are modifiable, they must be considered when planning fall prevention interventions. ConclusionThe study demonstrated machine learning's potential to predict falls and identify key risk factors. Further validation across diverse populations and settings is essential for broader applicability.

An Enterprise Architecture Planning for Tour and Travel Company Using TOGAF ADM

Purpose: This study aims to address inefficiencies in service delivery processes within PT. Auva Eqtena Tour and Travel, particularly concerning manual operations and administrative document management. By utilizing the TOGAF ADM (The Open Group Architecture Framework Architecture Development Method) methodology, the research seeks to develop a strategic plan for information systems that enhances service efficiency and streamlines operations.
  
 Methods: The research employs the TOGAF ADM methodology to guide the strategic planning process for information systems improvement. Four key aspects are considered: vision architecture, business architecture, information system architecture, and technology architecture. Through a systematic approach, the study identifies areas for improvement and proposes strategic initiatives to optimize the utilization of information systems and technology.
  
 Results and Discussion: Analysis of the current operational processes within PT. Auva Eqtena Tour and Travel reveals inefficiencies in manual operations and document management, leading to delays and increased resource consumption. The application of TOGAF ADM methodology facilitates the development of strategic plans across various architectural domains. The proposed initiatives include enhancements in vision architecture, business processes, information system integration, and technological infrastructure to optimize information system utilization and improve service delivery efficiency.
  
 Implications of the Research: The findings have significant implications for PT. Auva Eqtena Tour and Travel, providing actionable insights for enhancing service efficiency and operational effectiveness through strategic information systems planning. By aligning business objectives with technology capabilities, the proposed initiatives aim to streamline processes, reduce costs, and enhance customer satisfaction.
  
 Originality/Value: This study contributes to the existing literature by applying the TOGAF ADM methodology to address operational challenges within the tour and travel industry. By integrating strategic planning principles with architectural frameworks, the research offers a structured approach to information systems development and optimization. The proposed strategic plan provides a roadmap for PT. Auva Eqtena Tour and Travel to leverage information systems effectively, thereby enhancing its competitive position and service delivery capabilities in the market.

The design of the Inpatient Care Monitoring and Evaluation System for the Executive Report at X Hospital

<div><table cellspacing="0" cellpadding="0" align="left"><tbody><tr><td align="left" valign="top"><p><em>One of the information systems needed in a hospital to support services is the inpatient information system. Several field constraints that often occur and can impede patient services include: limited bed space, hindered coordination between units or rooms, sudden changes in bed space requirements, the lack of integration of information systems, incomplete and inaccurate documentation of patient mobilization, the length of the patient discharge process, unavailable facilities in rooms, and the existence of hospital service indicators that must be achieved. Based on the above, a real-time and integrated information system is required, starting from the registration of inpatients to their discharge, in order to improve the quality of service and patient satisfaction. The design of the Inpatient Monitoring and Evaluation System for the Executive Report is an enhancement of the hospital information system that integrates all inpatient patient service activities, including supporting facilities, to generate useful information for monitoring and developing inpatient care services. This information can be used as a basis for decision-making by the leaders. The design of the Inpatient Monitoring and Evaluation System for the Executive Report utilizes the Systems Development Life Cycle (SDLC) method in the form of a prototype, with  the drafting phase starting from design to development. The final result of this information system design consists of comprehensive, integrated, and real-time information related to patient activities from admission to discharge, presented in a dashboard that is useful for monitoring and evaluating inpatient care services. It can also serve as a basis for decision-making by hospital leaders and achieving hospital indicators.</em></p><p><em> </em></p></td></tr></tbody></table></div><p><strong><em>Keywords: Bed management,  dashboard, information,  inpatient, integration</em></strong></p>

Artificial Intelligence in Biology

This article provides an in-depth analysis of the use of Artificial Intelligence (AI) in various aspects of biology, including healthcare, agriculture, and environmental monitoring. It highlights AI's ability to mimic human intelligence and analyze large datasets for predictions and tasks. The article also discusses its integration into Chinese medicine, where AI-guided diagnostic and therapeutic systems optimize clinical treatments and health management. AI is also used in disease management, analyzing data on diseases and pests, predicting their impact on ecosystems, and implementing preventative measures. The article also highlights the role of integrated information systems in environmental monitoring. Artificial intelligence (AI) has significant potential in healthcare research and chemical discoveries. Pharmaceutical companies are using AI to improve drug development by utilizing computational biology and machine learning systems to predict molecular behavior and the likelihood of finding a useful drug. This saves time and money on unnecessary tests. Clinical studies, electronic medical records, high-resolution medical images, and genomic profiles can be used as resources for drug development. Strong AI systems can analyze extensive data sets in pharmaceutical and medical research. This review focuses on integrating knowledge of cancer drugs, drug resistance, next-generation sequencing, genetic variants, and structural biology in cancer precision drug discovery.

Does nature learn? Information integration and rare events in systems of increasing complexity

Does nature learn? Information integration and rare events in systems of increasing complexity

Integrated Decision Support for Disaster Risk Management: Aiding Preparedness and Response Decisions in Wildfire Management

A central challenge in disaster risk management (DRM) is that there are key dependencies and uncertainty between the decisions made at the mitigation, preparedness, response, and recovery stages. Decision support systems for disaster management require information systems that allow timely and reliable integration of data sources from different domains, including information on hazards and vulnerabilities for risk analysis, as well as organizational and logistical information for decision analysis. We propose an analytics-centered framework that integrates predictive and prescriptive models responding to unique characteristics of DRM. The framework relies on probabilistic risk assessment and uses optimization-based simulation of the response phase as a means to inform decisions at the preparedness stage. This paper presents a case study regarding the analysis of preparedness and response decisions for wildfire control in Uruguay. Numerical results illustrate insights from the risk-informed analyses. For instance, slight reductions in the preparedness budget can lead to disproportionate losses during the response stage, whereas slight increases have little effect unless explicitly directed to control high-consequence scenarios. Motivated by a real-world problem, this case study emphasizes the challenges for integrated information systems that enable the potential of analytical decision support frameworks for DRM.

Social Media, Mobile Payment, and Mobile Gaming for Intentional and Behavioral Recommendations

During the corona virus disease 2019 (COVID-19) pandemic, the Internet-based zero-contact economy model has increased people’s online social, entertainment, and business activities. Social media are social network platforms that support users’ social interaction to foster an information society. Mobile gaming refers to the use of mobile terminal devices for online entertainment. The combination of mobile payment and social media provides online users with an integrated information system platform for socializing, mobile gaming entertainment, and payment. A recommendation system filters information to predict a user’s rating or preference for a product/service. With recent advances in computer science, recommendation systems are gradually moving to develop intentional and behavioral recommendations. Thus, this study implements data mining analytics, including clustering analysis and association rules, to investigate mobile gaming, mobile payment, and social media preferences in Taiwanese gaming players. This study first finds several meaningful clusters/patterns/rules to present research findings and practical implications for intentional and behavioral recommendations (N = 2326).

Smart hospital: achieving interoperability and raw data collection from medical devices in clinical routine.

Today, modern technology is used to diagnose and treat cardiovascular disease. These medical devices provide exact measures and raw data such as imaging data or biosignals. So far, the Broad Integration of These Health Data into Hospital Information Technology Structures-Especially in Germany-is Lacking, and if data integration takes place, only non-Evaluable Findings are Usually Integrated into the Hospital Information Technology Structures. A Comprehensive Integration of raw Data and Structured Medical Information has not yet Been Established. The aim of this project was to design and implement an interoperable database (cardio-vascular-information-system, CVIS) for the automated integration of al medical device data (parameters and raw data) in cardio-vascular medicine. The CVIS serves as a data integration and preparation system at the interface between the various devices and the hospital IT infrastructure. In our project, we were able to establish a database with integration of proprietary device interfaces, which could be integrated into the electronic health record (EHR) with various HL7 and web interfaces. In the period between 1.7.2020 and 30.6.2022, the data integrated into this database were evaluated. During this time, 114,858 patients were automatically included in the database and medical data of 50,295 of them were entered. For technical examinations, more than 4.5 million readings (an average of 28.5 per examination) and 684,696 image data and raw signals (28,935 ECG files, 655,761 structured reports, 91,113 x-ray objects, 559,648 ultrasound objects in 54 different examination types, 5,000 endoscopy objects) were integrated into the database. Over 10.2 million bidirectional HL7 messages (approximately 14,000/day) were successfully processed. 98,458 documents were transferred to the central document management system, 55,154 materials (average 7.77 per order) were recorded and stored in the database, 21,196 diagnoses and 50,353 services/OPS were recorded and transferred. On average, 3.3 examinations per patient were recorded; in addition, there are an average of 13 laboratory examinations. Fully automated data integration from medical devices including the raw data is feasible and already creates a comprehensive database for multimodal modern analysis approaches in a short time. This is the basis for national and international projects by extracting research data using FHIR.

Reliability analysis of intelligent manufacturing systems based on improved FMEA combined with machine learning

Along with the booming of intelligent manufacturing, the reliability management of intelligent manufacturing systems appears to be becoming more significant. Failure mode and effects analysis (FMEA) is a prospective reliability management instrument extensively utilized to manage failure modes of systems, products, processes, and services in various industries. However, the conventional FMEA method has been criticized for its inherent limitations. Machine learning can handle large amounts of data and has merits in reliability analysis and prediction, which can help in failure mode classification and risk management under limited resources. Therefore, this paper devises a method for complex systems based on an improved FMEA model combined with machine learning and applies it to the reliability management of intelligent manufacturing systems. First, the structured network of failure modes is constructed based on the knowledge graph for intelligent manufacturing systems. Then, the grey relation analysis (GRA) is applied to determine the risk prioritization of failure modes. Hereafter, the k-means algorithm in unsupervised machine learning is employed to cluster failure modes into priority classes. Finally, a case study and further comparative analysis are implemented. The results demonstrate that failure modes in system security, production quality, and information integration are high-risk and require more resources for prevention. In addition, recommendations for risk prevention and monitoring of intelligent manufacturing systems were given based on the clustering results. In comparison to the conventional FMEA method, the proposed method can more precisely capture the coupling relationship between the failure modes compared with. This research provides significant support for the reliability and risk management of complex systems such as intelligent manufacturing systems.

Data Communication as a key to integrated river basin management

This article discusses data communication as a key to implement integrated water resource management (IWRM) in river basin management. The performance of a river basin is determined by many aspects/sectors which are managed by different level of governments and various agencies within them. Implementing IWRM requires integrated data information system provided by those various government agencies. Although it has been regulated in the Law No 17 Year 2019, integrated information system reliable to support decision-making process has not existed yet. This article reviews what is the condition of water related data in Bengawan Solo River Basin Management – the type of water related data, the data frequencies, and the data sharing mechanism. It aims to examine data communication among actors and supports the decision-making process in Bengawan Solo River Basin Management. There are three stages were undertaken to achieve this overall aim, that are 1) identifying the type of water-related data need be shared in sharing water resource from the literature; 2) identifying the availability of water-related data identified in the stage 1 in the existing data provided by government agencies; 3) conducting a series of focused group discussion (FGD) to elaborate the use of the available data to understand its limitations to support decisionmaking process. The research found that the implementation of IWRM in Bengawan Solo River Basin is difficult to be achieved because there is a lack of data communication among data providers. It points that the absence of data communication occurred because there is no definite data sharing framework specify what data need to be shared, the frequency of data collection and updating, and the mechanism in sharing data legitimated by all agencies involved in Bengawan Solo River Basin Management. Thus, it reveals that data cannot be used maximally to support effective river basin management not because of technology drawbacks or a lack of data, but its uncertainty on how data needs to be communicated. In addition, there no certainty of who is the responsible agency or authority to fulfill five aspects of data communication. This is the final homework of IWRM implementation in managing river basin in Indonesia.

[From comprehensive geriatric assessment to an integrated multidimensional assessment system: interRAI tools and their applications].

The multidimensional assessment carried out with interRAI tools constitutes an operationalization of the International Classification of Functioning, Disability and Health (ICF) and is adapted to the specificities of each place of care. From a single assessment, the interRAI approach makes it possible to conduct a multidimensional assessment of functional autonomy and to produce a series of indicators (health, areas of intervention, quality of care and consumption of resources). It helps to identify clinical needs to be the subject of a personalized care plan and the strengths and weaknesses of health organizations to modify the professional practices. Compared to standardized geriatric assessment, interRAI tools consider the person's expectations and resources, offer a universal common language, produce a multidimensional synthesis and facilitate the construction of an integrated information system. The basis for their development is scientificity based on evidence.

Implementation of corporate social responsibility in the context of integration with the enterprise management information system

Purpose. To develop recommendations for the corporate digital responsibility (CDR) strategy aimed at increasing the level of integration of corporate social responsibility (CSR) and information systems (IS) of the company. To develop a mathematical model of coordination decisions regarding CSR with production and business activities of companies. Methodology. The introduction of the methods of: logical generalization made it possible to define CDR adapted to Ukrainian realities; analysis and synthesis – to point out the need to integrate CSR with IS; comparative analysis – to determine directions, tasks, requirements for the implementation of CDR; logical abstraction – to substantiate the need to implement strategy for CDR implementation, develop its stages, tasks and goals of each stages; mathematical analysis – to develop mathematical model of coordination of management decisions. Findings. A definition of CDR, adapted to Ukrainian reality, which became the conceptual basis of the presented research, is proposed. The need for integration of CSR and IS and the formation of a new mechanism – CDR, as a result of the evolution of CSR, is substantiated. The directions, tasks, and requirements for the implementation of CDR are defined. The need to implement the strategy for the implementation of CDR are substantiated, its stages, tasks and goals of each stage are developed. It is indicated that the integration of CSR and IS can take place iteratively, with management gaining experience and finding optimal methods and tools. Originality. A mathematical model that allows one not only to coordinate various management decisions, but also to forecast their short-term and long-term results, is developed. Practical value. The proposed approach to CDR strategy formation will increase the effectiveness of CDR and improve the integration of CSR and IS. The developed mathematical model will allow one not only to solve the problem of coordination of management decisions, but will also help to economically spend IT resources and speed up the delivery of results.

THE ELECTRONIC SYSTEM FOR THE MANAGEMENT OF UNIVERSITY EDUCATIONAL PROGRAMS

The article presents the research results of the electronic system for managing university educational programs (EP). The analysis of scientific works showed that higher educational institutions need the introduction of integrated information systems. Such systems are the technical basis of the electronic university and have functionality for managing education and providing communications to university management. An analysis of the main processes of EP management was performed, and interested parties, their roles, and participation in the methods were determined. The proposed mathematical model is the basis of management for introducing the EP, its development, and implementation control based on quality parameters. The software module is a part of the university's management process support system and the JetIQ of Vinnytsia National Technical University's (VNTU's) comprehensive information system. For the analysis of the subject area and the formation of optimal management processes, the methodological recommendations of the National Agency for Higher Education Quality Assurance (NAQA), data export and import system for the Unified State Electronic Database on Education Issues, the experience, and recommendations of experts from the department of quality assurance of education of VNTU, experts and guarantors of educational programs were used. Precedent models are formed about such role scenarios – applicant, student, teacher, guarantor, management, expert, stakeholder (employer). Such models made it possible to create not only the software module for the management of educational programs (QEd – Quality Educational) but also to determine the conditions for publication on the main website of VNTU of educational programs and documentation of their development, syllabi on the foremost showcase and jet-sites of the department, to create scenarios for automatic quality control of the EP. The result of the creation of the software module for the management of educational programs is a system of structured information regarding the development and implementation of the educational program, which allows the guarantor and specialists of the department to ensure the quality of education by use special containers for documentation and interested parties to use public up-to-date information on the university's websites.