Method for the recommendation of medications in the gynecology area of the regional teaching hospital "Ri-obamba"

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Alba Margot Núñez Quispe
Guadalupe Eduvige Cuello Freire
Paola Cristina Núñez Quispe

Abstract

Medication administration is one of the most frequent nursing interventions in the hospital and community area, so it is essential to have standardized standards for its correct performance, thus ensuring the quality of care in patient care. an efficient and safe way. Currently, patient safety is a discipline that has arisen due to the various adverse events that people have endured due to errors and damage in health centers; therefore, the main objective is to prevent the damage that may arise in medical care, minimizing the incidence and impact of these adverse events and increasing the patient's recovery from these risks. The degree of compliance with an indicator of the quality of care in care is expressed through a direct relationship of the performance of neutrality, representing a domain of neutrosophic values ​​to model uncertainty. The implementation of Soft Computing techniques has been used to represent uncertainty in decision-making processes of this nature. This research describes a solution to the problem posed by developing a method for drug recommendation in the area of ​​gynecology. The research favors guidelines, procedures and technical tools that allow guaranteeing patient safety for the correct admiration of medications through the different routes for the nursing staff that works in the gynecology area of ​​the General Teaching Hospital of Riobamba for the improvement of the quality of life of the patient.

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Method for the recommendation of medications in the gynecology area of the regional teaching hospital "Ri-obamba". (2022). Neutrosophic Computing and Machine Learning. ISSN 2574-1101, 21, 11-22. https://fs.unm.edu/NCML2/index.php/112/article/view/197
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How to Cite

Method for the recommendation of medications in the gynecology area of the regional teaching hospital "Ri-obamba". (2022). Neutrosophic Computing and Machine Learning. ISSN 2574-1101, 21, 11-22. https://fs.unm.edu/NCML2/index.php/112/article/view/197