Ghina Divani Nasywa
Universitas Sam Ratulangi

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Analisis Distribusi Rekam Medis Berbasis Batch terhadap Dinamika Antrean Poliklinik Psikiatri Menggunakan Hybrid SD-DES Ghina Divani Nasywa; Steven Sentinuwo; Feisy Kambey
Riau Jurnal Teknik Informatika Vol. 5 No. 2 (2026): Juli 2026
Publisher : Prodi Teknik Informatika Universitas Pasir Pengaraian

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30606/rjti.v5i2.4749

Abstract

This study analyzes the effect of batch-based medical record distribution on outpatient queue dynamics at the Psychiatric Polyclinic of RSJ X using a hybrid System Dynamics–Discrete Event Simulation (SD-DES) approach. The simulation model represents the patient service process, including registration, medical record distribution, screening, and psychiatric consultation. The input data consisted of 111 patient entities and 33 batch-based medical record distribution events obtained from six days of field observations, which were then modeled using statistical probability distributions. The model was validated through structural and behavioral validation before being used for sensitivity analysis and scenario experiments. The results showed that the inter-batch interval was the most dominant parameter affecting queue formation compared with batch size, screening duration, and consultation duration. The highest Mean Absolute Deviation (MAD) values were found for the inter-batch interval, reaching 2.44 for the medical record queue, 1.95 for the screening queue, and 0.68 for the consultation queue. An improvement scenario that shortened the distribution interval toward a continuous release pattern successfully reduced the average consultation queue from 6.32 to 0.74 patients. These findings indicate that optimizing medical record distribution timing should become a primary priority for improving outpatient service efficiency. This study is limited to a single hospital setting and structural-behavioral validation; therefore, further research is required to improve the generalizability of the findings.