Managing and evaluating the integration of information technologies in healthcare institutions

• Autorzy: Smolanka Volodymyr , Gavurova Beata , Polishchuk Volodymyr

Identyfikatory

DOI

10.17512/pjms.2024.30.2.18

Warianty tytułu

PL

Zarządzanie i ocena integracji technologii informacyjnych w instytucjach opieki zdrowotnej

• Języki publikacji: EN

Abstrakty

EN

In the paper, a study was conducted on developing an expert model for evaluating the level of information technologies (IT) implementation in primary healthcare institutions, to improve management efficiency on the example of the countries of the Visegrad Group. The research is based on the theory of fuzzy sets, which allows you to effectively reveal the uncertainty of the reasoning of patients and experts, that is, their subjective vision, and increase the degree of validity of decisions. The value of the expert model lies in the fact that it examines the issues of IT implementation in primary healthcare institutions as a whole. That is, from the individual impression of patients to the opinions of experts within the ambulatory health care system. Model tuning was performed on a large sample of real data from four countries. The modeling results are the aggregated level of IT implementation in primary care institutions and a linguistic assessment of the management of the ambulatory health care system within the studied region. An example of assessment based on real data of 3,119 primary care patients in the Visegrad Group countries is illustrated.

PL

W artykule przeprowadzono badanie dotyczące opracowania modelu eksperckiego do oceny poziomu wdrożenia technologii informacyjnych (IT) w instytucjach podstawowej opieki zdrowotnej w celu poprawy efektywności zarządzania na przykładzie krajów Grupy Wyszehradzkiej. Badanie opiera się na teorii zbiorów rozmytych, która pozwala skutecznie ujawnić niepewność rozumowania pacjentów i ekspertów, czyli ich subiektywną wizję, oraz zwiększyć stopień ważności decyzji. Wartość modelu eksperckiego polega na tym, że bada on kwestie wdrażania IT w placówkach podstawowej opieki zdrowotnej jako całości. To znaczy, od indywidualnych odczuć pacjentów do opinii ekspertów w ramach systemu ambulatoryjnej opieki zdrowotnej. Dostrajanie modelu przeprowadzono na dużej próbie rzeczywistych danych z czterech krajów. Wynikiem modelowania jest zagregowany poziom wdrożenia IT w placówkach podstawowej opieki zdrowotnej oraz językowa ocena zarządzania systemem ambulatoryjnej opieki zdrowotnej w badanym regionie. Zilustrowano przykład oceny opartej na rzeczywistych danych 3119 pacjentów podstawowej opieki zdrowotnej w krajach Grupy Wyszehradzkiej.

Słowa kluczowe

EN

primary healthcare institutions; experts; management efficiency; intellectual analysis of knowledge; fuzzy modelling

PL

instytucje podstawowej opieki zdrowotnej; eksperci; efektywność zarządzania; intelektualna analiza wiedzy; modelowanie rozmyte

• Wydawca: Czestochowa University of Technology
• Czasopismo: Polish Journal of Management Studies
• Rocznik: 2024
• Tom: Vol. 30, No. 2
• Strony: 297--313
• Opis fizyczny: Bibliogr. 46 poz., tab.

Twórcy

autor

Smolanka Volodymyr

• Uzhhorod National University, Faculty of Medicine

• https://orcid.org/0009-0003-5583-3341

autor

Gavurova Beata

• MBA, Department of Addictology, First Faculty of Medicine, Charles University, Prague, Czech Republic

• https://orcid.org/0000-0002-0606-879X

autor

Polishchuk Volodymyr

• Technical University of Košice, Faculty of Aeronautics; Uzhhorod National University, Faculty of Information Technology

• https://orcid.org/0000-0003-4586-1333

Bibliografia

• 1.Alamoodi, A. H., Zughoul, O., David, D., Garfan, S., Pamucar, D., Albahri, O. S., ... and Sharaf, I. M., (2024). A Novel Evaluation Framework for Medical LLMs: Combining Fuzzy Logic and MCDM for Medical Relation and Clinical Concept Extraction. Journal of Medical Systems, 48, 81.
• 2.AlHazme, R. H., Haque, S. S., Wiggin, H. and Rana, A. M. (2016). The impact of health information technologies on quality improvement methodologies’ efficiency, throughput and financial outcomes: a retrospective observational study. BMC Medical Informatics and Decision Making, 16, 154.
• 3.Al-Dmour, J. A., Sagahyroon, A., Al-Ali, A. and Abusnana, S., (2019). A fuzzy logic-based warning system for patients classification. Health Informatics Journal, 25(3), 1004-1024.
• 4.Alhajri, N., Simsekler, M. C. E., Alfalasi, B., Alhashmi, M., Memon, H., Housser, E., ... and Alhashemi, H., (2022). Exploring quality differences in telemedicine between hospital outpatient departments and community clinics: cross-sectional study. JMIR Medical Informatics, 10(2), e32373.
• 5.Amirkhani, A., Papageorgiou, E. I., Mohseni, A. and Mosavi, M. R., (2017). A review of fuzzy cognitive maps in medicine: Taxonomy, methods, and applications. Computer methods and programs in biomedicine, 142, 129-145.
• 6.Baillieu, R., Hoang, H., Sripipatana, A., Nair, S. and Lin, S. C., (2020). Impact of health information technology optimization on clinical quality performance in health centers: A national cross-sectional study. PloS one, 15(7), e0236019.
• 7.Brenner, S. K., Kaushal, R., Grinspan, Z., Joyce, C., Kim, I., Allard, R. J., ... and Abramson, E. L., (2016). Effects of health information technology on patient outcomes: a systematic review. Journal of the American Medical Informatics Association, 23(5), 1016-1036.
• 8.Daniel, O. U., (2018). Effects of health information technology and health information exchanges on readmissions and length of stay. Health Policy and Technology, 7(3), 281-286.
• 9.Data from 3,119 patients of Visegrad Group for assessment of the level of IT implementation in primary care institutions.
• 10.Deimazar, G., Kahouei, M., Zamani, A. and Ganji, Z., (2018). Health information technology in ambulatory care in a developing country. Electron Physician, 10(2), 6319-6326.
• 11.Dorosti, S., Fathi, M., Ghoushchi, S. J., Khakifirooz, M. and Khazaeili, M., (2020). Patient waiting time management through fuzzy based failure mode and effect analysis. Journal of Intelligent & Fuzzy Systems, 38(2), 2069-2080.
• 12.Erakhtina, A. A., (2022). Investments in HealthCare, Life Expectancy, and Economic Growth. Problems of Economic Transition, 63(1-3), 20-33.
• 13.Moore, E. C., Tolley, C. L., Bates, D. W. and Slight, S. P., (2020). A systematic review of the impact of health information technology on nurses’ time. Journal of the American Medical Informatics Association, 27(5), 798-807.
• 14.Fink, O., Wang, Q., Svensen, M., Dersin, P., Lee, W. J. and Ducoffe, M., (2020). Potential, challenges and future directions for deep learning in prognostics and health management applications. Engineering Applications of Artificial Intelligence, 92, 103678.
• 15.Huang, Y.-H., Gramopadhye, A. K., (2016). Recommendations for health information technology implementation in rural hospitals. International Journal of Health Care Quality Assurance, 29(4), 454-474.
• 16.Hysong, S. J., Arredondo, K., Hughes, A. M., Lester, H. F., Oswald, F. L., Petersen, L. A., ... and Haidet, P., (2022). An evidence-based, structured, expert approach to selecting essential indicators of primary care quality. PLoS One, 17(1), e0261263.
• 17.Ramesh, B., Lakshmanna, K., (2024). A Novel Early Detection and Prevention of Coronary Heart Disease Framework Using Hybrid Deep Learning Model and Neural Fuzzy Inference System. IEEE Access, 12, 26683-26695.
• 18.Kringos, D., Nuti, S., Anastasy, C., Barry, M., Murauskiene, L., Siciliani, L. and De Maeseneer, J., (2019). Re-thinking performance assessment for primary care: Opinion of the expert panel on effective ways of investing in health. European Journal of General Practice, 25(1), 55-61.
• 19.Mardani, A., Hooker, R. E., Ozkul, S., Yifan, S., Nilashi, M., Sabzi, H. Z. and Fei, G. C., (2019). Application of decision making and fuzzy sets theory to evaluate the healthcare and medical problems: a review of three decades of research with recent developments. Expert Systems with Applications, 137, 202-231.
• 20.Menachemi, N., Chukmaitov, A., Saunders, Ch. and Brooks, R. G., (2008). Hospital quality of care: Does information technology matter? The relationship between information technology adoption and quality of care. Health Care Management Review, 33(1), 51-59.
• 21.Narwal, M., (2023). Digital Distractions In The Workplace: Exploring Cyberloafing Impact on Employee Behaviour and Innovation. Virtual Economics, 6(4), 7-24.
• 22.Nazarian-Jashnabadi, J., Bonab, S. R., Haseli, G., Tomaskova, H. and Hajiaghaei-Keshteli, M., (2023). A dynamic expert system to increase patient satisfaction with an integrated approach of system dynamics, ISM, and ANP methods. Expert Systems with Applications, 234, 121010.
• 23.Nwosu, N. T., (2024). Reducing operational costs in healthcare through advanced BI tools and data integration. World Journal of Advanced Research and Reviews, 22(3), 1144-1156.
• 24.Seyyedi, N., Moghaddasi, H., Asadi, F., Hamidpour, M. and Shoaie, K., (2020). The Effect of Information Technology on the Information Exchange between Laboratories and Ambulatory Care Centers: A Systematic Review. Laboratory Medicine, 51(4), 430-440.
• 25.Okolo, C. A., Ijeh, S., Arowoogun, J. O., Adeniyi, A. O. and Omotayo, O., (2024). Reviewing the impact of health information technology on healthcare management efficiency. International Medical Science Research Journal, 4(4), 420-440.
• 26.Owusu, P. A., Sarkodie, S. A. and Pedersen, P. A., (2021). Relationship between mortality and health care expenditure: Sustainable assessment of health care system. Plos one, 16(2), e0247413.
• 27.Patil, S., Shankar, H., (2023). Transforming healthcare: harnessing the power of AI in the modern era. International Journal of Multidisciplinary Sciences and Arts, 2(1), 60-70.
• 28.Pendergrass, J., Ranganathan, C., (2021). Institutional factors affecting the electronic health information exchange by ambulatory providers. Health Policy and Technology, 10(4), 100569.
• 29.Pereno, A., Eriksson, D., (2020). A multi-stakeholder perspective on sustainable healthcare: From 2030 onwards. Futures, 122, 102605.
• 30.Pinsonneault, A., Addas, S., Qian, C., Dakshinamoorthy, V. and Tamblyn, R., (2017). Integrated health information technology and the quality of patient care: A natural experiment. Journal of Management Information Systems, 34(2), 457-486.
• 31.Rajiani, I., Bačík, R., Fedorko, R., Rigelský, M. and Szczepańska-Woszczyna, K., (2018). The alternative model for quality evaluation of health care facilities based on outputs of management processes. Polish Journal of Management Studies, 17(1), 194-208.
• 32.Reddy, G. T., Reddy, M. P. K., Lakshmanna, K., Rajput, D. S., Kaluri, R. and Srivastava, G., (2020). Hybrid genetic algorithm and a fuzzy logic classifier for heart disease diagnosis. Evolutionary Intelligence, 13, 185-196.
• 33.Sadoughi, F., Nasiri, S. and Ahmadi, H., (2018). The impact of health information exchange on healthcare quality and cost-effectiveness: A systematic literature review. Computer methods and programs in biomedicine, 161, 209-232.
• 34.Shahid, N., Rappon, T. and Berta, W., (2019). Applications of artificial neural networks in health care organizational decision-making: A scoping review. PloS one, 14(2), e0212356.
• 35.Shi, Y., Amill-Rosario, A., Rudin, R. S., Fischer, S. H., Shekelle, P., Scanlon, D. P. and Damberg, C. L., (2021). Barriers to using clinical decision support in ambulatory care: Do clinics in health systems fare better? Journal of the American Medical Informatics Association, 28(8), 1667-1675.
• 36.Shie, A. J., Lee, C. H., Yu, S. Y. and Wang, C., (2021). A fuzzy design decision model for new healthcare service conceptualization. International Journal of Fuzzy Systems, 23, 58-80.
• 37.Song, L., Liu, X., Chen, S., Liu, S., Liu, X., Muhammad, K. and Bhattacharyya, S., (2022). A deep fuzzy model for diagnosis of COVID-19 from CT images. Applied Soft Computing, 122, 108883.
• 38.Suresh, M., Vaishnavi, V. and Pai, R. D., (2020). Leanness evaluation in health-care organizations using fuzzy logic approach. International Journal of Organizational Analysis, 28(6), 1201-1225.
• 39.Tian, S., Yang, W., Le Grange, J. M., Wang, P., Huang, W. and Ye, Z., (2019). Smart healthcare: making medical care more intelligent. Global Health Journal, 3(3), 62-65.
• 40.Tortorella, G. L., Saurin, T. A., Fogliatto, F. S., Rosa, V. M., Tonetto, L. M. and Magrabi, F., (2021). Impacts of Healthcare 4.0 digital technologies on the resilience of hospitals. Technological Forecasting and Social Change, 166, 120666.
• 41.Tóth, Z. E., Jónás, T. and Dénes, R. V., (2019). Applying flexible fuzzy numbers for evaluating service features in healthcare-patients and employees in the focus. Total Quality Management & Business Excellence, 30(sup1), S240-S254.
• 42.Wu, Z., Trigo, V., (2020). Impact of information system integration on the healthcare management and medical services. International Journal of Healthcare Management, 14(4), 1348-1356.
• 43.Xie, H., Prybutok, G., Peng, X. and Prybutok, V., (2020). Determinants of trust in health information technology: An empirical investigation in the context of an online clinic appointment system. International Journal of Human-Computer Interaction, 36(12), 1095-1109.
• 44.Xiong, X., (2021). Bring technology home and stay healthy: The role of fourth industrial revolution and technology in improving the efficacy of health care spending. Technological Forecasting and Social Change, 165, 120556.
• 45.Yucesan, M., Gul, M., (2020). Hospital service quality evaluation: an integrated model based on Pythagorean fuzzy AHP and fuzzy TOPSIS. Soft Computing, 24(5), 3237-3255.
• 46.Zielińska, K., Bačík, R., (2020). Services delivery in budget hotels for customer satisfaction and loyalty. Global Journal of Entrepreneurship and Management, 1(2), 1-15.

Uwagi

Opracowanie rekordu ze środków MNiSW, umowa nr POPUL/SP/0154/2024/02 w ramach programu "Społeczna odpowiedzialność nauki II" - moduł: Popularyzacja nauki (2025).