Development of elements of the concept of determining the future demand for medical services based on the results of analysis of data of different nature
DOI:
https://doi.org/10.15587/2706-5448.2020.218286Keywords:
demand for medical services, clustering, structural and parametric identification, healthcare institutionsAbstract
Planning and organizing the functioning of health care institutions is a priority area of activity of their founders. The purpose of such management activities is to ensure the timeliness, quality and completeness of medical services provided to the clients of the institution. At the same time, an important step is to predict the needs for medical services in future periods of time. Forecasting should be carried out taking into account the socio-demographic, medical and behavioral characteristics of persons – potential consumers of medical services and the characteristics of the population structure of the territory in which the medical institution operates. Thus, the object of research is the processes that arise during the analysis of operational and retrospective statistical, medical and social, expert and other data to determine the forecast values of the levels of demand for certain medical services. The results of the analysis should become the basis for making management decisions on planning and organizing the activities of health care institutions in future periods.
In the course of the research, a systematic approach, methods of mathematical modeling and other general scientific methods were used.
The research result is a developed method for forecasting the demand for medical services in future periods of time. The method consists in the implementation of four sequential stages of the analysis of the initial data. In this case, it becomes necessary to solve the problems of clustering, classification, identification and forecasting. The accuracy of the predicted values depends on the choice of methods and algorithms for solving the problems posed and on the completeness of the initial data. As a result of applying the method, it is possible to obtain:
аdivision of persons – potential consumers of medical services into groups in accordance with their socio-demographic portraits, medical data and behavioral characteristics;
– relationship between the number of educated groups and the demand for various medical services;
– predicted values of the number of groups, as well as the demand for medical services.
The results can serve as a basis for making managerial decisions on organizing the activities of medical institutions in future periods of time.
References
- Hrabovskyi, V. A., Klymenko, P. M. (2014). Systemnyi pidkhid do upravlinnia zakladamy okhorony zdorovia. Visnyk Natsionalnoi akademii derzhavnoho upravlinnia pry Prezydentovi Ukrainy, 3, 136–142.
- Danko, V. V. (2017). Upravlinnia zakladamy okhorony zdorovia v suchasnykh umovakh: teoretychnyi aspekt. Visnyk KhNAU. Seriia: Ekonomichni nauky, 4, 225–233.
- Haponova, E. O. (2017). Suchasni tendentsii funktsionuvannia svitovoho rynku medychnykh posluh. Visnyk Kharkivskoho natsionalnoho universytetu imeni VN Karazina. Seriia: Mizhnarodni vidnosyny. Ekonomika. Krainoznavstvo. Turyzm, 6, 20–24.
- Brownlee, S., Chalkidou, K., Doust, J., Elshaug, A. G., Glasziou, P., Heath, I. et. al. (2017). Evidence for overuse of medical services around the world. The Lancet, 390 (10090), 156–168. doi: http://doi.org/10.1016/s0140-6736(16)32585-5
- Glasziou, P., Straus, S., Brownlee, S., Trevena, L., Dans, L., Guyatt, G. et. al. (2017). Evidence for underuse of effective medical services around the world. The Lancet, 390 (10090), 169–177. doi: http://doi.org/10.1016/s0140-6736(16)30946-1
- Grekousis, G., Liu, Y. (2019). Where will the next emergency event occur? Predicting ambulance demand in emergency medical services using artificial intelligence. Computers, Environment and Urban Systems, 76, 110–122. doi: http://doi.org/10.1016/j.compenvurbsys.2019.04.006
- Maltseva, S., Prokofyeva, E., Zaitsev, R. (2017). The Demand for the Healthcare Services: the Opportunities of Big Data in Predicting Patient Flow. International Conference Information Systems 2017 Special Interest Group on Big Data Proceedings, 5.
- Kim, K.-W., Li, G., Park, S.-T., Ko, M.-H. (2016). A Study on Birth Prediction and BCG Vaccine Demand Prediction using ARIMA Analysis. Indian Journal of Science and Technology, 9 (24). doi: http://doi.org/10.17485/ijst/2016/v9i24/96151
- Husein, A. M., Simarmata, A. M. (2019). Drug Demand Prediction Model Using Adaptive Neuro Fuzzy Inference System (ANFIS). SinkrOn, 4 (1), 136–142. doi: http://doi.org/10.33395/sinkron.v4i1.10238
- Husein, A. M., Harahap, M., Aisyah, S., Purba, W., Muhazir, A. (2018). The implementation of two stages clustering (k-means clustering and adaptive neuro fuzzy inference system) for prediction of medicine need based on medical data. Journal of Physics: Conference Series, 978, 012019. doi: http://doi.org/10.1088/1742-6596/978/1/012019
- Baturkin, S. A., Baturkina, E. Iu., Zimenko, V. V., Siginov, I. V. (2012) Statisticheskie algoritmy klasterizatsii dannykh v adaptivnykh obuchaiuschikh sistemakh. Vestnik RGRTUb, 31 (1), 82–85.
- Rokach, L., Maimon, O. (2005). Clustering methods. Data mining and knowledge discovery handbook. Boston: Springer, 321–352. doi: http://doi.org/10.1007/0-387-25465-x_15
- Amanuma, S., Kurematsu, M., Fujita, H. (2012). An Idea of Improvement Decision Tree Learning Using Cluster Analysis. SoMeT, 351–358. doi: http://doi.org/10.3233/978-1-61499-125-0-351
- Kohonen, T. (1988). Self-organization and associative memory. New-York: Springer Verlag, 312. doi: http://doi.org/10.1007/978-3-662-00784-6
- Škrjanc, I., Iglesias, J. A., Sanchis, A., Leite, D., Lughofer, E., Gomide, F. (2019). Evolving fuzzy and neuro-fuzzy approaches in clustering, regression, identification, and classification: A Survey. Information Sciences, 490, 344–368. doi: http://doi.org/10.1016/j.ins.2019.03.060
- Mulesa, O., Snytyuk, V., Myronyuk, I. (2016). Forming the clusters of labour migrants by the degree of risk of hiv infection. Eastern-European Journal of Enterprise Technologies, 3 (4 (81)), 50–55. doi: http://doi.org/10.15587/1729-4061.2016.71203
- De Koninck, P., Nelissen, K., Baesens, B., vanden Broucke, S., Snoeck, M., De Weerdt, J. (2017). An approach for incorporating expert knowledge in trace clustering. International Conference on Advanced Information Systems Engineering. Springer: Cham, 561–576. doi: http://doi.org/10.1007/978-3-319-59536-8_35
- Mulesa, O. Yu. (2015). Adaptation of fuzzy c-means method for determination the structure of social groups. Technology Audit and Production Reserves, 2 (2 (22)), 73–76. doi: http://doi.org/10.15587/2312-8372.2015.41014
- Mulesa, O. Yu., Snytiuk, V. Ye., Herzanych, S. O. (2019). Metod nechitkoi klasyfikatsii na osnovi poslidovnoho analizu valda. Automation of technological and business processes, 11 (4), 35–42. doi: http://doi.org/10.15673/atbp.v11i4.1597
- Kornoushenko, E. K. (2017). Algoritm klassifikatsii putem parnogo sravneniia priznakov. Avtomatika i telemekhanika, 11, 151–166.
- Ruff, L., Vandermeulen, R., Goernitz, N., Deecke, L., Siddiqui, S. A., Binder, A. et. al. (2018). Deep one-class classification. International conference on machine learning, 4393–4402.
- Geifman, Y., El-Yaniv, R. (2017). Selective classification for deep neural networks. Advances in neural information processing systems, 30, 4878–4887.
- Shtovba, S. D. (2003). Identifikatsiia nelineinykh zavisimostei s pomoschiu nechetkogo logicheskogo vyvoda v sisteme MATLAB. Exponenta Pro: Matematika v prilozheniiakh, 2, 9–15.
- Zaichenko, Yu. P. (2007). Nechetkyi metod hrupovoho ucheta arhumentov pry neopredelennikh vkhodnikh dannikh. Systemni doslidzhennia ta informatsiini tekhnolohii, 3, 100–112.
- Mulesa, O. Yu. (2016). Development of evolutionary methods of the structural and parametric identification for tabular dependencies. Technology audit and production reserves, 4 (2 (30)), 13–19. doi: http://doi.org/10.15587/2312-8372.2016.74482
- Haber, R., Unbehauen, H. (1990). Structure identification of nonlinear dynamic systems – A survey on input/output approaches. Automatica, 26 (4), 651–677. doi: http://doi.org/10.1016/0005-1098(90)90044-i
- Aizenberg, I., Sheremetov, L., Villa-Vargas, L., Martinez-Muñoz, J. (2016). Multilayer Neural Network with Multi-Valued Neurons in time series forecasting of oil production. Neurocomputing, 175, 980–989. doi: http://doi.org/10.1016/j.neucom.2015.06.092
- Deb, C., Zhang, F., Yang, J., Lee, S. E., Shah, K. W. (2017). A review on time series forecasting techniques for building energy consumption. Renewable and Sustainable Energy Reviews, 74, 902–924. doi: http://doi.org/10.1016/j.rser.2017.02.085
- Geche, F., Batyuk, A., Mulesa, O., Vashkeba, M. (2015). Development of effective time series forecasting model. International Journal of Advanced Research in Computer Engineering &Technology, 4 (12), 4377–4386.
- Mulesa, O., Geche, F. (2016). Designing fuzzy expert methods of numeric evaluation of an object for the problems of forecasting. Eastern-European Journal of Enterprise Technologies, 3 (4 (81)), 37–43. doi: http://doi.org/10.15587/1729-4061.2016.70515
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