IJBTT

Implementation of Machine Learning to Mitigate the Deficit of Health Personnel and Optimize Healthcare

Implementation of Machine Learning to Mitigate the Deficit of Health Personnel and Optimize Healthcare
	© 2023 by IJETT Journal
	Volume-71 Issue-1
	Year of Publication : 2023
	Author : Rosa Perez-Siguas, Hernan Matta-Solis, Eduardo Matta-Solis, Hernan Matta-Perez, Luis Perez-Siguas, Zaida Olinda Pumacayo-Sanchez
	DOI : 10.14445/22315381/IJETT-V71I1P224

How to Cite?

Rosa Perez-Siguas, Hernan Matta-Solis, Eduardo Matta-Solis, Hernan Matta-Perez, Luis Perez-Siguas, Zaida Olinda Pumacayo-Sanchez, "Implementation of Machine Learning to Mitigate the Deficit of Health Personnel and Optimize Healthcare," International Journal of Engineering Trends and Technology, vol. 71, no. 1, pp. 271-282, 2023. Crossref, https://doi.org/10.14445/22315381/IJETT-V71I1P224

Abstract
The health system in Peru over the years has presented various deficiencies that intensified during the pandemic; one of these problems continues to be the lack of health personnel. The country has a high deficit of human resources for the current demand for care in the different health centers nationwide. The present investigation proposes developing a web system that predicts the need for care in the different medical specialties using a field of artificial intelligence known as machine learning to analyse data in the format of electronic medical records. The results obtained show that efficient planning allows optimizing the organization of health personnel to cover the demand for the different health care services, in addition to reducing the administrative workload that is often assigned to care personnel, and that through the automation and rapid response offered by a system that uses artificial intelligence means more time is available to improve patient flow and provide prompt and timely care.

Keywords
Artificial intelligence, Electronic medical records, Health center, Machine learning, Medical care.

References
[1] Alejandro Izquierdo, Carola Pessino, and Guillermo Vuletin, Better Spending for Better Lives: How Latin America and the Caribbean can do more with Less," Inter-American Development Bank, 2018. Crossref, http://dx.doi.org/10.18235/0001217-es
[2] C. Maguiña Vargas, and V. del Colegio Médico del Perú, “COVID-19 Pandemic and its Impact on the Peruvian Health System,” Revista Peruana de Medicina Integrativa, Crossref, http://dx.doi.org/10.26722/rpmi.2020.53.180.
[3] Jose Machicao, “Artificial Intelligence as Support for an Evolutionary Health Management: Focus in Peru,” Tools for Complex Project Management, 2021. Crossref, http://dx.doi.org/10.13140/RG.2.2.18953.98409
[4] Amy Nelson et al., “Predicting Scheduled Hospital Attendance with Artificial Intelligence,” NPJ Digit Medicine, vol. 2, no. 26, 2019. Crossref, https://doi.org/10.1038/s41746-019-0103-3
[5] Sharan Srinivas, “A Machine Learning-Based Approach for Predicting Patient Punctuality in Ambulatory Care Centers,” International Journal of Environmental Research and Public Health, vol. 17, no. 10, p. 3703, 2020. Crossref, https://doi.org/10.3390/ijerph17103703
[6] Luiz Henrique Salazar et al., “Using Different Models of Machine Learning to Predict Attendance at Medical Appointments,” Journal of Information Systems Engineering and Management, vol. 5, no. 4, p. em0122, 2020. Crossref, https://doi.org/10.29333/jisem/8430
[7] Azeez Ajani Waheed et al., “An Integrated and Secured Web Based Electronic Health Record,” International Journal of Recent Engineering Science, vol. 8, no. 4, pp. 19-26, 2021. Crossref, https://doi.org/10.14445/23497157/IJRES-V8I4P104
[8] S. V. Ulyanov, Advances in Intelligent Systems and Computing, in Advances in Intelligent Systems and Computing, vol. 1095 AISC, 2020, pp. 78–85. doi: 10.1007/978-3-030-35249-3_9.
[9] Lucas Glass, Gary Shorter, and Rajneesh Patil, “AI in Clinical Development Improving Safety and Accelerating Results,” 2022. [Online]. Available: https://www.accenture.com/us-en/insight-artificial-intelligence-healthcare
[10] Alegre M, Alata P, Oshige A, and Quispe S, “Urban Report of Citizen Perception in Lima and Callao,” Lima, 2021. [Online]. Available: http://www.limacomovamos.org/wp-content/uploads/2021/12/EncuestaLCV2021.pdf
[11] Alonso Soto, “Barriers to Effective Care in the Referral Hospitals of Peru’s Ministry of Health: Serving Patients in the 21st Century with 20th Century Resources,” Peruvian Journal of Experimental Medicine Public Health, vol. 36, no. 2, pp. 304-311, 2019. Crossref, https://doi.org/10.17843/rpmesp.2019.362.4425
[12] Stenly Ibrahim Adam, and Stevani Andolo, “A New PHP Web Application Development Framework Based on MVC Architectural Pattern and Ajax Technology,” in 2019 1st International Conference on Cybernetics and Intelligent System (ICORIS), pp. 45-50, 2019. Crossref, https://doi.org/10.1109/ICORIS.2019.8874912
[13] Daniel Guaman, Soledad Delgado, and Jennifewr Perez, “Classifying Model-View-Controller Software Applications Using SelfOrganizing Maps,” IEEE Access, vol. 9, pp. 45201–45229, 2021. Crossref, https://doi.org/10.1109/ACCESS.2021.3066348
[14] Syed Saqib Bukhari et al., “An Investigative Analysis of Different LSTM Libraries for Supervised and Unsupervised Architectures of OCR Training,” in 2018 16th International Conference on Frontiers in Handwriting Recognition (ICFHR), pp. 447-452, 2018. Crossref, https://doi.org/10.1109/ICFHR-2018.2018.00084
[15] Jaymer M. Jayoma, Elbert S. Moyon, and Edsel Matt O. Morales, “OCR Based Document Archiving and Indexing Using PyTesseract: A Record Management System for DSWD Caraga, Philippines,” in 2020 IEEE 12th International Conference on Humanoid, Nanotechnology, Information Technology, Communication and Control, Environment, and Management (HNICEM), pp. 1-6, 2020. Crossref, https://doi.org/10.1109/HNICEM51456.2020.9400000
[16] Huan-Hsin Tseng et al., “The Role of Machine Learning in Knowledge-Based Response-Adapted Radiotherapy,” Frontiers in Oncology, vol. 8, p. 266, 2018. Crossref, https://doi.org/10.3389%2Ffonc.2018.00266
[17] Rui Guo et al., “Degradation State Recognition of Piston Pump Based on ICEEMDAN and XGBoost,” Applied Sciences, vol. 10, no. 18, p. 6593, Sep. 2020. Crossref, https://doi.org/10.3390/app10186593
[18] Seonjae Been, Younsung Choi, and Haewon Byeon, “Can the User Authentication System for the Electronic Medical Record System Improve the Power to Secure in Medical Field? A Security Analysis,” International Journal of Engineering Trends and Technology, vol. 70, no. 8, pp. 387-393, 2022. Crossref, https://doi.org/10.14445/22315381/IJETT-V70I8P239
[19] S. N. de S. (SUSALUD), National Health Satisfaction Survey 2016: Final Results, Minsa, p. 63, 2016. [Online]. Available: http://portal.susalud.gob.pe/wp-content/uploads/archivo/encuesta-sat-nac/2016/PRESENTACION-SUSALUD-2016.pdf
[20] W. Pérez, J. Moreno, and J. Loayza, “Information on Human Resources in the Health Sector in the Framework of the COVID-19 Pandemic,” Minsa, 2020. [Online]. Available: https://cdn.www.gob.pe/uploads/document/file/2040124/Documento.pdf?v=1627330182
[21] T. O. for E. C. and D. (OECD), “Health at a Glance: Latin America and the Caribbean,” OECD, 2020. Crossref, https://doi.org/10.1787/6089164f-en
[22] Javier Abugattás, Bruno Barletti, and Jordy Vilchez Astucuri, “CEPLAN - Spatial analysis of the situation and impacts on health and education services in the context of the COVID-19 pandemic,” National Center for Strategic Planning, 2020. [Online]. Available: www.ceplan.gob.pe
[23] Zoila Ponce de León, “Health System in Peru and COVID-19,” Area of incidence and investigation of the EGPP-PUCP, 2021. [Online]. Available: https://repositorio.pucp.edu.pe/index/handle/123456789/176598
[24] Faisal Hayat et al., “The Influence of Agile Methodology (Scrum) on Software Project Management,” in 2019 20th IEEE/ACIS International Conference on Software Engineering, Artificial Intelligence, Networking and Parallel/Distributed Computing (SNPD), pp. 145-149, 2019. Crossref, https://doi.org/10.1109/SNPD.2019.8935813
[25] Woo Suk Hong, Adrian Daniel Haimovich, and R. Andrew Taylor, “Predicting Hospital Admission at Emergency Department Triage Using Machine Learning,” PLOS One, vol. 13, no. 7, 2018. Crossref, https://doi.org/10.1371/journal.pone.0201016