Devy Febrianti
Administrasi Kesehatan, Universitas Muhammadiyah Sidenreng Rappang

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ANALYSIS OF COMPOUNDED MEDICATION WAITING TIME IN OUTPATIENT SERVICES AT NENE MALLOMO REGIONAL GENERAL HOSPITAL, SIDENRENG RAPPANG REGENCY, 2026 Regina Giswa Fitri Ramadhani; Khaeriyah Adri; Devy Febrianti; Sunandar Said
JTH: Journal of Technology and Health Vol. 4 No. 1 (2026): July: JTH: Journal of Technology and Health
Publisher : CV. Fahr Publishing

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.61677/jth.v4i1.861

Abstract

Timeliness of compounded medication dispensing is an important indicator of outpatient pharmacy quality. The 2024 Internal Quality Report of Nene Mallomo Regional General Hospital showed that only 47% of compounded prescriptions met the hospital target, while the national maximum waiting-time standard is 60 minutes. This study examined the association of service hours and prescription volume with compounded medication waiting time in the hospital’s outpatient pharmacy. An analytical observational study with a cross-sectional design used total sampling of 290 compounded-prescription visits recorded from January to March 2026. Secondary data were obtained from pharmacy service reports and the Hospital Management Information System. Univariate analysis, bivariate regression, classical-assumption testing, and multiple linear regression were performed; skewed prescription-volume and waiting-time data were transformed using natural logarithms for the adjusted model. In bivariate analysis, service hours were associated with waiting time (p = 0.040), and high prescription volume was associated with longer waiting time (p = 0.023). In the adjusted model, prescription volume remained significantly associated with waiting time (B = 0.132; p = 0.023), whereas service hours did not (B = -0.110; p = 0.051). The model was significant overall (p = 0.012) but explained only 3.0% of waiting-time variation. Prescription volume was therefore the only significant predictor in the adjusted model. Workload-based staffing, a dedicated compounded-prescription flow, advance preparation of frequently used materials, and HMIS-based monitoring are recommended, while other operational determinants should be examined in future studies.