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All Journal Jurnal Keperawatan Jupiter: Publikasi Ilmu Keteknikan Industri, Teknik Elektro dan Informatika
Rita Rahmawati
Universitas Muhammadiyah Gombong
Aerospace Engineering Automotive Engineering Chemical Engineering, Chemistry & Bioengineering Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Nursing

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Digitalization of National Health Insurance (JKN) claims and hospital operational efficiency in BPJS Kesehatan partner hospitals: A Systematic Literature Review with Conditional Meta-Analysis Shafitri Nire pangestika; Ernawati Ernawati; Rita Rahmawati; Lazuardi Fatahilah Hamdi; Cahyu Septiwi
Jurnal Keperawatan Vol. 17 No. 2 (2026): May
Publisher : University of Muhammadiyah Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22219/jk.v17i2.45167

Abstract

Introduction: Digitalization of National Health Insurance (JKN) claims is a major administrative health reform in Indonesia; since JKN's 2014 launch, transaction volumes have exceeded 1.37 million claims per day, straining conventional paper-based systems. No prior review has systematically synthesized digitalization's cumulative impact on operational efficiency in BPJS Kesehatan partner hospitals.Objectives: This study aimed to synthesize evidence on the relationship between JKN claim digitalization and hospital operational efficiency, and to estimate the pooled effect on claim cycle time where study homogeneity permits.Methods: A Systematic Literature Review using the PRISMA 2020 protocol searched five databases—PubMed/MEDLINE, Scopus, Web of Science, Google Scholar, and Garuda—for publications from January 2014 to December 2024. Quality was assessed using STROBE, CASP, MMAT, and AMSTAR 2, and a conditional random-effects meta-analysis targeted claim cycle time, the outcome with adequate homogeneity across studies.Results: From 438 records, 25 articles met the inclusion criteria, reporting six efficiency dimensions: claim cycle time, first-pass rejection rate, cash flow/days in accounts receivable, staff utilization, ICD coding accuracy, and administrative cost per claim. Meta-analysis of 11 homogeneous studies showed a pooled mean reduction in claim cycle time of 19.6 days (95% CI: 14.2–25.1) post-implementation, with high heterogeneity (I² = 78.4%).Conclusions: The integrated TAM-UTAUT-RBV framework explains this evidence: systems combining automated bridging with real-time validation outperform partial digitalization, with human resource competency and IT infrastructure as key contextual factors. Findings carry implications for future research and for BPJS Kesehatan, the Ministry of Health, and hospitals in prioritizing capacity-adjusted digitalization strategies.
Penggunaan Large Language Model untuk Dukungan Keputusan Klinis pada Hipertensi dan Diabetes Melitus Tipe 2: Sebuah Scoping Review Rita Rahmawati; Aang Anwarudin; Muhammad As’ad; Aliya Shidqina Salsabila
Jupiter: Publikasi Ilmu Keteknikan Industri, Teknik Elektro dan Informatika Vol. 4 No. 4 (2026): Juli : Jupiter: Publikasi Ilmu Keteknikan Industri, Teknik Elektro dan Informat
Publisher : Asosiasi Riset Ilmu Teknik Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.61132/jupiter.v4i4.1509

Abstract

Hypertension and type 2 diabetes mellitus (T2DM) are two non-communicable diseases with a high burden in primary healthcare and have become important areas of research on clinical decision support using large language models (LLMs) since 2024. Retrieval-Augmented Generation (RAG) is increasingly used to ground LLM outputs in clinical guidelines, yet the specific contribution of embedding models as an independent variable remains underexplored. This scoping review aimed to map the 2024–2026 literature on the use of LLMs to support clinical decision-making for hypertension and T2DM and to identify existing research gaps. The review followed the Arksey and O’Malley framework and PRISMA-ScR reporting guidelines. Searches were conducted in Google Scholar, PubMed/PubMed Central, arXiv, and IEEE Xplore using Boolean combinations of terms related to LLMs, RAG, hypertension, and T2DM. Fifteen studies met the inclusion criteria. The findings indicate that RAG consistently improves answer accuracy and reduces hallucinations compared with prompting-only approaches, although its benefits decrease as the capabilities of the underlying model increase. Only one study directly manipulated the embedding model as an experimental variable and found trade-offs between general and domain-specific embeddings in retrieval sensitivity and specificity. No studies evaluated RAG or embedding models using Indonesian clinical texts. These findings support the potential of RAG for clinical decision support while highlighting the need for further research on embedding models in the Indonesian medical context.
Co-Authors Aang Anwarudin Aliya Shidqina Salsabila Cahyu Septiwi Ernawati Ernawati Lazuardi Fatahilah Hamdi Muhammad As’ad Pangestika, Shafitri Nire