Article Per Year (5 Year)
p-Index From 2021 - 2026
1.879
P-Index
This Author published in this journals
All Journal EMBRIO : Jurnal Kebidanan Jurnal Kreativitas PKM JAIM (Jurnal Abdi Masyarakat) Jurnal Bidan Pintar (JuBiTar) Jurnal Ilmu Keperawatan dan Kebidanan Research and Evidence on Knowledge in Administration and Management — Medical Electronic Data and Information Systems
Fauzia Laili
Universitas Kadiri
Humanities Economics, Econometrics & Finance Education Health Professions Medicine & Pharmacology Nursing Public Health Social Sciences Other

Published : 9 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 1 Documents
Search

 1 
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.
Co-Authors alvika awwatiszahro Anis Nikmatul Nikmah Bram Mustiko Utomo Darmining Darmining Desvina Ariani Dewi Nur Afifi Dhita Kris Prasetyanti Eko Winarti Elis Andaresta Erik Irham Lutfi Fitriasnani, Meirna Eka Galuh Pradian Yanuaringsih Harnida Harnida Huda Rohmawati Ida Tri Wahyuni Istikarina Fikri Khofifah Rafika Kurnia Kurnia Novitasari Kurnia Novita Lindha Kusumawati Lindha Sri Kusumawati Mayasari Putri Ardela Nadia Vivi K Nara Lintan Mega Puspita Nurul Istikvarini Pria Sabila Putri Rahma Kusuma Dewi Raras Sucma Siswi Wulandari siti aminah Siti Hajrianti Siti Khotimah Siti Siti Aminah Sondang Sidabuntar Teguh Pramono weni tri purnani Yeni Puspita Anggraini zulfatus saadah