Rural Health Unit Decision Support System with Mapping
Joan Hazel V. Tiongson1, Marifel Grace C. Kummer2
1Joan Hazel V. Tiongson*, Assistant Professor III, Nueva Vizcaya State University, Bayombong, Nueva Vizcaya, Philippines.
2Marifel Grace C. Kummer, Dean, School of Information Technology and Engineering, St. Paul University Philippines – Tuguegarao City, Phulippines.
Manuscript received on August 01, 2020. | Revised Manuscript received on August 05, 2020. | Manuscript published on September 30, 2020. | PP: 611-616 | Volume-9 Issue-3, September 2020. | Retrieval Number: 100.1/ijrte.C4681099320 | DOI: 10.35940/ijrte.C4681.099320
Open Access | Ethics and Policies | Cite | Mendeley
© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: In this highly challenging and demanding world, presence of data and technology are overwhelming. But at present, some institutions still engage in manual-type of operations like the Rural Health Unit of the Municipality of Solano, Nueva Vizcaya. Problems, issues and challenges encountered by the unitin the delivery of its medical servicesand the extent of compliance in ISO/IEC 25010 Software Quality Standards were identified. And with the uncontrollable availability of data, these can be handled and treated using data mining techniques to predict disease occurrences. In this study, the clustering and classification data mining techniques were utilized in order to predict disease occurrences of every barangay of the municipality at a given time. An efficient record management system along with a decision support system was developed to meet the challenges of the unit. It mainly features the disease-occurrence mapping to assist physicians and other health professionals in the unit in their decision-making tasks particularly in diagnosis, treatment and recommendations. In terms of ISO/IEC 25010 Software Quality Standards, the system gained a “very great extent” qualitative rating.
Keywords: Challenges, Clinical Decision Support System (CDSS), Data Mining, Electronic Medical Record (EMR), Health Information Technology (HIT).