METHODS: A cross-sectional study was conducted from January 2020 to May 2023 at Rumah Sakit Akademik, Universitas Gadjah Mada, Yogyakarta, Indonesia. Clinical pharmacists reviewed electronic health data to examine DRPs. The Fisher's exact test evaluated the association between DRPs and LoS.
RESULTS: A total of 60.7% (n = 17) of the participants were females, with the majority falling into the age group ≥ 65 years old (n = 11, 29.7%). A significant portion experienced LoS > 7 days (n = 17, 60.7%). Antidiabetic monotherapy was predominant, and the categories of DRPs included adverse drug reaction (n = 15, 40.5%), dosage too high (n = 6, 16.2%), wrong drug (n = 6, 16.2%), non-adherence (n = 4, 10.8%), need for additional therapy (n = 4, 10.8%) and dosage too low (n = 2, 5.4%). A significant association was observed between non-adherence and LoS (P = 0.016). The possibility of experiencing LoS of 1-7 days increased by 3.43 times with improved non-adherence (OR = 3.43; 95% CI: 1.83, 6.39). In this context, non-adherence refers to DRPs associated with the non-compliance of patients with the prescribed treatment plan.
CONCLUSION: This study concludes that non-adherence was significantly associated with hospital LoS.
MATERIALS AND METHODS: This descriptive study utilises a desk review approach and employs the WHO Data Quality Assurance (DQA) Tool to assess data quality of ASDK. The analysis involves measuring eight health indicators from ASDK and Survei Status Gizi Indonesia (SSGI) conducted in 2022. The assessment focuses on various dimensions of data quality, including completeness of variables, consistency over time, consistency between indicators, outliers and external consistency.
RESULTS: Current study shows that routine health data in Indonesia performs high-quality data in terms of completeness and internal consistency. The dimension of data completeness demonstrates high levels of variable completeness with most variables achieving 100% of the completeness.
CONCLUSION: Based on the analysis of eight routine health data variables using five dimensions of data quality namely completeness of variables, consistency over time, consistency between indicators, outliers. and external consistency. It shows that completeness and internal consistency of data in ASDK has demonstrated a high data quality.