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Muhammad Yunus
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m.yunus@polije.ac.id
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+628123413933
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j-remi@polije.ac.id
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Program Studi Rekam Medik Politeknik Negeri Jember Jl. Mastrip PO Box 164, Jember, Jawa Timur
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J-REMI : Jurnal Rekam Medik dan Informasi Kesehatan
Published by Politeknik Negeri Jember
ISSN : -     EISSN : 2721866X     DOI : https://doi.org/10.25047/jremi
Core Subject : Health,
Health Professions Public Health
J-REMI : Jurnal Rekam Medik dan Informasi Kesehatan is a scientific journal that is managed and published by the Program Studi Rekam Medik, Jurusan Kesehatan, Politeknik Negeri Jember. J-REMI contains the publication of research results from students, lecturers and or other practitioners in the field of medical records and health information with coverage and focus on the fields of Health Information Management, Health Information Systems, Health Information Technology, Health Quality Information Management and Classification, Coding of Diseases and Problems. Health and Action.
Arjuna Subject : Profesi Kesehatan - Menejmen Informasi Kesehatan
Articles 285 Documents
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Instrumen Audit Kualitas Rekam Medis Elektronik Menggunakan Content Validity Index (CVI) Yuniana Eka Pratiwi; Hosizah Hosizah; Sri Jumiati Agustina
J-REMI : Jurnal Rekam Medik dan Informasi Kesehatan Vol 7 No 4 (2026): September (Issue in Progress)
Publisher : Politeknik Negeri Jember

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25047/j-remi.v7i4.6748

Abstract

Although the National Brain Center Hospital Prof. Dr. Mahar Mardjono Jakarta has implemented an Electronic Medical Record (EMR) system, the quality of its data has not yet met the completeness standards established by the Indonesian Ministry of Health. As the hospital's most prevalent condition, ischemic stroke requires accurate and complete medical documentation to support effective clinical decision-making. Therefore, a comprehensive EMR audit instrument is needed to systematically evaluate data quality. This study aimed to develop and validate an EMR data quality audit instrument for ischemic stroke patients. A Research and Development (R&D) design was employed using the Data Quality Management (DQM) framework based on the data quality characteristics defined by the American Health Information Management Association. The instrument was validated by six experts using the Content Validity Index (CVI), including the Item-Level Content Validity Index (I-CVI) and the Scale-Level Content Validity Index (S-CVI), with indicators derived from the ischemic stroke clinical pathway. The validation results showed I-CVI values ranging from 0.83 to 1.00 and an S-CVI value of 0.99, indicating excellent content validity. The instrument is therefore considered highly valid and suitable for systematic EMR data quality audits to improve the quality of medical documentation.
Pendekatan Machine Learning untuk Menganalisis Faktor-Faktor yang Berkontribusi Terhadap Kesalahan Pengkodean ICD-10 di Semen Padang Hospital Rury Moryanda; Nurul Abdillah; Denos Imam Fratama; Dicky Fatrias
J-REMI : Jurnal Rekam Medik dan Informasi Kesehatan Vol 7 No 3 (2026): June
Publisher : Politeknik Negeri Jember

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25047/j-remi.v7i3.6775

Abstract

ICD-10 coding errors remain a major challenge in healthcare facilities, affecting the validity of morbidity data, the accuracy of BPJS Kesehatan claims processing, and the quality of health information management. This study aimed to identify the main factors contributing to ICD-10 coding errors and evaluate the effectiveness of machine learning algorithms in detecting such errors. Electronic medical record data were analyzed through data cleaning, preprocessing, descriptive analysis, and machine learning modeling. Three algorithms were applied: Random Forest, Support Vector Machine (SVM), and Neural Network. Model performance was evaluated using accuracy, precision, and sensitivity metrics. The findings revealed an ICD-10 coding error rate of 33.8%, primarily caused by nonspecific diagnoses and insufficient clinical information. Among the tested models, the Neural Network achieved the highest accuracy (72%), followed by SVM (68%) and Random Forest (60%). These results suggest that machine learning techniques can effectively support the early detection of ICD-10 coding errors and enhance the quality of health data management. The adoption of machine learning–based predictive models may improve coding accuracy and facilitate evidence-based decision-making in health information management.
Pengembangan Aplikasi Pencatatan dan Pengingat Tablet Tambah Darah Berbasis WhatsApp untuk Remaja Putri Arifin Ilham; Maula Ismail Mohammad; Lina Khasanah; Bambang Karmanto; Suratmi Suratmi
J-REMI : Jurnal Rekam Medik dan Informasi Kesehatan Vol 7 No 3 (2026): June
Publisher : Politeknik Negeri Jember

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25047/j-remi.v7i3.6805

Abstract

Iron deficiency anemia among adolescent girls remains a major public health issue in Indonesia, partly due to low adherence to Iron and Folic Acid Supplementation (IFAS) tablet consumption and the lack of an individual-based monitoring system. This study aimed to develop and evaluate a WhatsApp-based application for reminding and monitoring IFAS intake using a Research and Development (R&D) approach with the Waterfall model. The development process included requirements analysis, system design, implementation, and testing, with data collected through interviews and observations at Sumber Primary Healthcare Center, Cirebon Regency, Indonesia. The application integrates automated WhatsApp reminders, daily IFAS consumption tracking, hemoglobin (Hb) data entry, and interactive graphical reporting features. Usability was assessed using the System Usability Scale (SUS) involving 30 adolescent girls. The application achieved an average SUS score of 66.3, indicating a Marginal level of acceptability with a Grade C rating. The results demonstrate that the system is functionally feasible as a tool to support the implementation and monitoring of IFAS supplementation programs. Nevertheless, further refinement is needed to improve user experience, interaction quality, and overall usability, thereby enhancing user acceptance and the effectiveness of digital health interventions among adolescent girls.
Blockchain untuk Keamanan Data Kesehatan dalam Rekam Medis Elektronik Dina Sonia; Daniel Happy Putra; Mieke Nurmalasari
J-REMI : Jurnal Rekam Medik dan Informasi Kesehatan Vol 7 No 3 (2026): June
Publisher : Politeknik Negeri Jember

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25047/j-remi.v7i3.6830

Abstract

The digital transformation of healthcare through Electronic Medical Records (EMRs) has improved the efficiency and continuity of clinical information management but has also raised significant concerns regarding data security and privacy. The increasing frequency of cyberattacks and the limitations of conventional data protection mechanisms, particularly in developing countries such as Indonesia, highlight the need for more reliable security solutions. This study explores the potential of blockchain technology to enhance EMR security through a Systematic Literature Review (SLR) following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) framework. A total of 13 peer-reviewed articles published within the last five years were systematically reviewed. The findings indicate that blockchain can serve as an additional security layer by improving data integrity and auditability through immutable ledgers, cryptographic hashing, and distributed consensus mechanisms. Data confidentiality is strengthened through patient-centered decentralized access control and advanced cryptographic techniques, while data availability is supported by hybrid architectures using off-chain storage. However, blockchain implementation still faces challenges, including scalability, cryptographic key management, system integration, and regulatory constraints. Overall, blockchain demonstrates strong potential to enhance security and trust within the EMR ecosystem, although it is better positioned as a complementary technology rather than a standalone solution.
Penilaian Indeks Kematangan Digital Rekam Medis Elektronik di Klinik X Ari Sukawan; Bhakti Aryani; Dony Setiawan Hendyca Putra; Diana Barsasella; Yuliani Yuliani
J-REMI : Jurnal Rekam Medik dan Informasi Kesehatan Vol 7 No 3 (2026): June
Publisher : Politeknik Negeri Jember

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25047/j-remi.v7i3.6837

Abstract

Digital transformation in the healthcare sector has become an urgent priority to improve service quality and operational efficiency. A key component of this transformation is the implementation of Electronic Medical Records (EMRs), as mandated by the Indonesian Minister of Health Regulation No. 24 of 2022 and supported by the 2024 Health Digital Transformation Blueprint. Clinic X began implementing EMRs in late 2023 but continues to face challenges, including limited system features, synchronization issues with the Satu Sehat platform, and server disruptions. This study evaluated the clinic’s digital maturity using the Digital Maturity Index (DMI) to formulate strategic recommendations. A mixed-methods approach with a concurrent triangulation strategy was employed through observations, in-depth interviews, and questionnaires based on the official DMI instrument developed by the Indonesian Ministry of Health. The results showed that Clinic X achieved an average maturity level of 3 in Primary Care Readiness, Information System Capability and Infrastructure, and Data Security. The clinic demonstrated strengths in staff digital literacy and infrastructure but weaknesses in governance guidelines and EMR feature development. Overall, the clinic is at the developing stage, indicating the need to strengthen system integration, infrastructure, and information governance to accelerate digital transformation.

Page 29 of 29 | Total Record : 285

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