Voice-Based Patient Registration and Information Retrieval System

Voice-Based Patient Registration and Information Retrieval System

  IJRES-book-cover  International Journal of Recent Engineering Science (IJRES)          
  
© 2021 by IJRES Journal
Volume-8 Issue-5
Year of Publication : 2021
Authors : Olaniyi Ralph Obayemi, Rahmon Ariyo Badru, Azeez Ajani Waheed, Oluseye Ayobami, Akinmoluwa
DOI : 10.14445/23497157/IJRES-V8I5P103

How to Cite?

Olaniyi Ralph Obayemi, Rahmon Ariyo Badru, Azeez Ajani Waheed, Oluseye Ayobami, Akinmoluwa, "Voice-Based Patient Registration and Information Retrieval System," International Journal of Recent Engineering Science, vol. 8, no. 5, pp. 13-20, 2023. Crossref, https://doi.org/10.14445/23497157/IJRES-V8I5P103

Abstract
Hospital patient registration and consultation processes in Nigeria are laden with long queues of sick patients due to misplaced or loss of patients’ folders managed manually or with a partial-automated system. There is a high waiting time which slows down promptness to receiving good health care. To proffer a solution to these problems, the research study developed a voice-based patient information retrieval system as a means of reducing the patients’ waiting time. The system was developed using; MATLAB R2021a 64-bit installed on Windows 10 platform, WAMP server 64-bit software as a background application to manage the patients’ database, MFCC algorithm was used to extract the voice features of captured patient’s voice, and KNearest Neighbour algorithm used as a classifier for voice recognition and matching of returning patients input voice data, at the login platform. A total of 100 questionnaires obtained through ethical approval were randomly distributed among patients to comparatively obtain various times utilized at each section of the hospital using the conventional and the developed systems. During the implementation, the system was integrated into the database of the University of Ilorin Teaching Hospital and utilized for patient registration and voice capture. Consequently, the system allowed the patient to log in after the discovery of a match voice data, and results obtained showed that 57% of the respondents’ patients who commenced consultation with the developed system utilized between 5 - 10 minutes and 43% utilized between 11 - 40 minutes, while 85% that used the conventional system utilized between 1 - 3 hours; as total waiting time. The adoption of this research output will reduce patient waiting time at the hospital.

Keywords
K-Nearest neighbour, MATLAB, Mel Frequency Cepstral Coefficient, Patient, Waiting time.

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