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The Relationship Between the Completeness of Medical Records and the Quality of Patient Care in Health Centers Siswi Wulandari; Bram Mustiko Utomo; Ayu Tiska Arlik; Mar'atus Sholihah; Pipit Andriyani
Research and Evidence on Knowledge in Administration and Management — Medical Electronic Data and Information Systems Vol. 1 No. 1 (2025): March, 2025
Publisher : CV. Get Press Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.69855/rekammedis.v1i1.295

Abstract

The quality of healthcare services in Primary Health Centers (Puskesmas) is strongly influenced by the completeness of medical records, which are essential for ensuring continuity, safety, and accountability of patient care. Complete documentation supports accurate clinical decision-making and service evaluation. Purpose: This study aimed to analyze the relationship between medical record completeness and the quality of patient care in Puskesmas in the Yogyakarta Special Region. Methods: A quantitative cross-sectional study was conducted involving 100 patient respondents. Data were collected through medical record audits to assess documentation completeness and validated service quality questionnaires to measure patients’ perceptions of care quality. The association between variables was analyzed using the Spearman Rank correlation test with a significance level of 0.05. Results: Only 38% of medical records were classified as complete. However, the analysis showed a significant positive relationship between medical record completeness and patient care quality (r_s = 0.624; p = 0.001). Conclusion: Medical record completeness plays a critical role in improving healthcare quality in Puskesmas and should be strengthened through systematic documentation management and continuous staff training.
Assessing the Quality Gap in Medical Record Data between Underdeveloped and Non-Underdeveloped Regions based on National Health Reporting Quality Indicators Bram Mustiko Utomo; Siswi Wulandari; Arum Sukma; Maria Yasinta Dobhe; Clara Anabel Galla
Research and Evidence on Knowledge in Administration and Management — Medical Electronic Data and Information Systems Vol. 1 No. 2 (2025): September, 2025
Publisher : CV. Get Press Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.69855/rekammedis.v1i2.305

Abstract

Accurate medical record data is crucial for effective healthcare and evidence-based policy in Indonesia. This study aimed to quantify the significant quality discrepancies—in completeness, accuracy, and timeliness—persisting between the underdeveloped 3T regions (Tertinggal, Terdepan, Terluar) and non-3T counterparts. We utilized a comparative design and analyzed multisource secondary data from the Ministry of Health, BPS, and BPJS Kesehatan (2023–2024), employing ANOVA and regression analysis on validated national reporting quality indicators. Results unequivocally demonstrate that 3T regions significantly lag non-3T areas across all metrics ($p < 0.001$). Regional classification was a powerful predictor, independently accounting for $38\%$ of the variance in overall data quality (Adjusted $R^2 = 0.57$). These findings underscore the urgent need for targeted resource allocation toward digital infrastructure and capacity building in 3T regions to foster equity in health information systems, which is paramount for advancing Indonesia’s commitments to Universal Health Coverage (UHC) and the SDGs.
The Efficacy of Utilizing BPJS Health Claim Big Data on the Accuracy of Diagnosis Coding in Type B Hospital Medical Records Fauzia Laili; Siti Aminah; Siswi Wulandari; Khofifah Rafika; Nadia Vivi K
Research and Evidence on Knowledge in Administration and Management — Medical Electronic Data and Information Systems Vol. 1 No. 2 (2025): September, 2025
Publisher : CV. Get Press Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.69855/rekammedis.v1i2.308

Abstract

The pervasive problem of diagnostic coding inaccuracies significantly impacts the financial integrity and efficiency of Indonesia's National Health Insurance (JKN) system in Type B hospitals. This study aims to assess the efficacy of utilizing large-scale BPJS Health claims data to improve coding accuracy and identify its key determinants. A quantitative, retrospective secondary data analysis was conducted on 150,000 claim records spanning 2020–2024. Big Data analytics employing Random Forest (RF) and Classification and Regression Tree (CART) models successfully detected coding discrepancies, achieving an overall accuracy of 87.2% for primary diagnoses. Statistical analysis indicated that the maturity of the Electronic Medical Record (EMR) system (p<0.01) and staff ICD-10 training (p<0.05) are highly significant determinants. Crucially, the application of this predictive analysis resulted in a 12% reduction in coding errors compared to historical methods. In conclusion, the utilization of BPJS claim Big Data substantially enhances coding accuracy and reliability, confirming the necessity of integrating data-driven technology with simultaneous investments in digital infrastructure and continuous human capacity building for the sustainable quality improvement of the Indonesian health system.