Prediction of Outpatient Visits Using Trend Analysis Method at RSUD Titi Kabila Bone Bolango for 2024-2028

  • Ayudhita Cahyani Daud Department of Public Health, Faculty of Sports and Health, Universitas Negeri Gorontalo, Indonesia
  • Basri Umar Department of Information Technology, Faculty of Engineering, Universitas Gorontalo, Indonesia
  • Tri Amelia Ali Department of Medical Records and Health Information, STIKES Bakti Nusantara Gorontalo, Indonesia
Keywords: Patient Visits, Prediction, Trend Analysis

Abstract

Outpatient prediction using the trend method. A trend is an analysis that explains and measures various changes or developments in data during one period. From 2019 to 2023, outpatient visits totaled 17 polyclinics, with the highest number of patient visits in 2023, with a total of 40,630 patient visits and an increase in 2023, so it is necessary to predict outpatient visits, given the importance of outpatient visit. This study aims to determine the prediction of outpatient visits using the trend analysis method. The type of research used in this study is descriptive, using a prospective approach. The population in this study is a recapitulation of the number of outpatient visits at Toto Kabila Hospital in 2019–2023. The sample in this study used total sampling, where all members were 152,290, The research instrument used work tables, calculating tools, and stationery. The results of the calculation show that the prediction of outpatient visits in 2024–2028 at Toto Kabila Hospital has increased every year, with 52,977 visits in 2028, and the prediction of outpatient at internal polyclinics eyes, and nerves has increased by 109,923 visits, while the skin polyclinic has decreased by 4,330 visits.

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Published
2026-07-27
How to Cite
Ayudhita Cahyani Daud, Umar, B., & Ali, T. A. (2026). Prediction of Outpatient Visits Using Trend Analysis Method at RSUD Titi Kabila Bone Bolango for 2024-2028. International Journal of Health, Economics, and Social Sciences (IJHESS), 8(3), 918~922. https://doi.org/10.56338/ijhess.v8i3.11717