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All Journal Jurnal Sehat Indonesia (JUSINDO) JPNM : Jurnal Pustaka Nusantara Multidisiplin
Rahmadyo Yudhi Prabowo
Politeknik Kesehatan Kemenkes Malang
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Design of a Web-Based Daily Inpatient Census Information System Interface at the Kanjuruhan Regional General Hospital Rahmadyo Yudhi Prabowo; Riyan Pramudiya
Jurnal Sehat Indonesia (JUSINDO) Vol. 8 No. 1 (2026): Jurnal Sehat Indonesia (JUSINDO)
Publisher : CV. Publikasi Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59141/-.v8i1.468

Abstract

The Daily Census of Inpatients (Sensus Harian Rawat Inap [SHRI]) at Kanjuruhan Hospital is still carried out manually using spreadsheets, which has the potential to cause reporting delays, data input errors, and low accuracy of information needed for hospital management decision-making. This study aims to design a web-based SHRI information system interface that suits the needs of users and improves the efficiency, accuracy, and quality of recording and reporting of inpatient data. The research uses a descriptive method with a qualitative and quantitative approach. Data were collected through interviews, observations, and questionnaires distributed to inpatient and medical record officers. The evaluation of the interface design was carried out using a usability questionnaire with a Likert scale. The results of the study show that the interface design developed is able to simplify the process of recording and reporting SHRI, improve data accuracy, and speed up information access. The usability evaluation yielded an average score of 84.4%, categorized as satisfied, indicating that the system is well received by users. The web-based SHRI information system interface design is considered effective, easy to use, and in accordance with user needs, thus having the potential to improve the quality of inpatient data management and support hospital management decision-making more optimally.
Pengembangan Model Artificial Intelligence untuk Mendeteksi Anomali dalam Rekam Medis Pasien sebagai Upaya Pencegahan Kesalahan Klinis Rahmadyo Yudhi Prabowo; Gunawan; Sandri Kesuma
JPNM Jurnal Pustaka Nusantara Multidisiplin Vol. 3 No. 4 (2025): December : Jurnal Pustaka Nusantara Multidisiplin (ACCEPTED)
Publisher : SM Institute

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59945/jpnm.v3i4.868

Abstract

Kesalahan klinis yang bersumber dari ketidakwajaran data rekam medis masih menjadi tantangan signifikan dalam sistem pelayanan kesehatan, karena berpotensi memengaruhi akurasi pengambilan keputusan medis dan keselamatan pasien. Ketidakwajaran tersebut dapat berupa kesalahan pencatatan nilai klinis, ketidaksesuaian dosis obat, maupun inkonsistensi informasi pasien. Penelitian ini bertujuan untuk mengembangkan dan menguji model Artificial Intelligence (AI) berbasis unsupervised anomaly detection dalam mendeteksi anomali pada rekam medis pasien sebagai mekanisme pencegahan dini terhadap kesalahan klinis. Metode Isolation Forest digunakan untuk mempelajari pola mayoritas data dan mengidentifikasi observasi yang menyimpang tanpa memerlukan pelabelan kesalahan sebelumnya. Dataset penelitian berasal dari data rekam medis pasien rawat jalan yang mencakup variabel demografis, diagnosis, nilai laboratorium, dosis obat, serta kelengkapan resume medis. Hasil analisis menunjukkan bahwa model mampu mengidentifikasi sekitar 10% data sebagai anomali dengan skor penyimpangan yang relatif tinggi. Visualisasi hasil memperlihatkan pemisahan yang jelas antara data normal dan data anomali, sekaligus mengindikasikan adanya kombinasi nilai klinis yang tidak lazim dan berpotensi menimbulkan risiko klinis. Temuan ini menegaskan bahwa model AI berbasis Isolation Forest memiliki potensi kuat untuk dikembangkan sebagai sistem peringatan dini guna mendukung keselamatan pasien dan meningkatkan kualitas pengelolaan rekam medis dalam layanan kesehatan.
Co-Authors Gunawan Riyan Pramudiya Sandri Kesuma