Analisis Kritis Penerapan CDSS Berbasis Multi-Kriteria dan Data Mining dalam Sistem Pendukung Keputusan Keperawatan: Tinjauan Literatur dari 15 Jurnal

Authors

  • Elsa Puji Rahayu Program Studi S1 Ilmu Keperawatan Sekolah Tinggi Ilmu Kesehatan Ganesha Husada Kediri
  • Fatkhul Mubaroq Program Studi S1 Ilmu Keperawatan Sekolah Tinggi Ilmu Kesehatan Ganesha Husada Kediri
  • Indah Tri Arnadha Program Studi S1 Ilmu Keperawatan Sekolah Tinggi Ilmu Kesehatan Ganesha Husada Kediri
  • Moch. Gandung Satriya UOBK RSUD Simpang Lima Gumul Kediri, Departemen Nursing STIKes Ganesha Husada Kediri

DOI:

https://doi.org/10.61722/jssr.v4i1.7286

Keywords:

decision support system, CDSS, nursing, AHP, data mining, Apriori, evidence-based nursing

Abstract

Background: Nurses play a critical role in clinical decision-making, yet the process often faces challenges such as patient complexity, limited access to information, and high workload. The application of Decision Support System (DSS) or Clinical Decision Support System (CDSS) provides technological assistance to enhance patient safety and elevate the quality of nursing care. Objective: This literature review aims to analyze the implementation of DSS or CDSS in supporting clinical decisions, evaluating nurse performance, applying data mining techniques in nursing practice, and identifying challenges in their application. Methods: This study utilized a literature review approach using 15 relevant journal articles focusing on DSS or CDSS in nursing. Thematic analysis was conducted to categorize evidence into clinical decision support, performance management, data-driven decision making, and implementation barriers. Results: DSS or CDSS improves the accuracy of nursing diagnoses, enhances triage effectiveness, and reduces medication errors. Multi-criteria methods such as AHP, WP, and SAW have been used to provide objective and transparent nurse performance evaluations. Data mining techniques such as Apriori or ARM help explore patterns between interventions and clinical outcomes to support evidence-based nursing. Nevertheless, challenges remain regarding data quality, user adoption, and system integration with nursing workflows. Conclusion: DSS or CDSS has a strong potential to support the digital transformation of nursing services. However, successful implementation requires organizational support, user training, and the development of systems that are user-friendly and aligned with clinical needs.

References

Angga, O. A. N., Tehuayo, H., & El Fazza, F. (2024). Perancangan Sistem Pendukung Keputusan Monitoring Perawat Puskesmas Namrole Menggunakan Metode Weight Product (WP) BERBASIS WEB. Jurnal Ilmu Komputer Dan Teknologi Informasi, 1(2), 131–139. https://doi.org/10.71466/jiktif.v1i2.54

Asy’ari, I. (2025). Penerapan Teknik Association Rule Mining dalam Cloud Nursing System untuk Identifikasi Pola Intervensi Keperawatan Efektif. Journal Of Information Technology, 1(1), 229–236.

Carl, T. (2020). ( Support System in the Making of Nursing Clinical Decisions in the. 8(2), 173–181.

Daeli, N. H. D. (2024). Sistem Pendukung Keputusan Pemilihan Perawat Terbaik Menggunakan Kombinasi Metode AHP-EDAS. JIKTEKS : Jurnal Ilmu Komputer Dan Teknologi Informasi, 2(03), 20–29. https://doi.org/10.70404/jikteks.v2i03.109

Di, P., & Sakit, R. (2012). Sistem pendukung dalam pembuatan keputusan klinis perawat di rumah sakit. https://www.semanticscholar.org/paper/2e786e19badee5738343321621918449cc69ad4f%0Ahttps://osf.io/y4jmt/download

Erawantini, F., & Wibowo, N. S. (2019). Implementasi Rekam Medis Elektronik dengan Sistem Pendukung Keputusan Klinis. Jurnal Teknologi Informasi Dan Terapan, 6(2), 75–78. https://doi.org/10.25047/jtit.v6i2.115

Faedah, N., Nasrullah, N., Sahibu, S., & Burhan, Y. (2021). Sistem Pendukung Keputusan Penilaian Kinerja Perawat Menggunakan Metode Analitycal Hierarcy Process (Ahp) Di Rsud H. Andi Sulthan Daeng Radja Bulukumba Berbasis Web. Jurnal It, 12(1), 45–56. https://doi.org/10.37639/jti.v12i1.197

Kinanti, F. S. (2021). Evaluasi penggunaan sistem informasi manajemen keperawatan di rs jiwa grhasia daerah istimewa yogyakarta. Journal of Information Systems for Public Health, 5(3), 1. https://doi.org/10.22146/jisph.43831

Komariah, M., Pratiwi, Z. S., Budhiyani, H., & Adithia, A. (2020). Jurnal Keperawatan Muhammadiyah Bengkulu. Sereal Untuk, 8(1), 51.

Nababan, F., & Nilasari, P. (2022). Penggunaan Aplikasi CDSS (Clinical Decision Support System) Berbasis Elektronik pada Pasien Anak di Berbagai Setting Layanan Perawatan: Tinjauan Literatur. Jurnal Ilmu Keperawatan Anak, 5(1), 36–48. https://doi.org/10.32584/jika.v5i1.1283

Ngemba, & Rahmawati. (2015). Model Inferensi Sistem Pendukung Keputusan Pathway Klinik Asuhan Keperawatan Bronchopneumonia. Model InferensiSistem Pendukung Keputusan PathwayKlinikAsuhan Keperawatan Bronchopneumonia, 1(3), 1–10.

Putra, C. S. (2019). Peranan Teknologi Informasi dalam Pelayanan Keperawatan di Rumah Sakit. Simtika, 2(3), 28–31.

Riza, R., Hasbi, M., & Prabowo, I. A. (2018). Sistem Pendukung Keputusan Penanganan Pasien Ugd Menggunakan Metode Fuzzy Multiple Attribute Decision Making Dengan Simple Additive Weighting Pada Puskesmas Gajahan Surakarta. Jurnal Teknologi Informasi Dan Komunikasi (TIKomSiN), 6(1), 21–28. https://doi.org/10.30646/tikomsin.v6i1.348

Sihombing, M. H. H., & Saragih, S. (2021). Sistem pendukung keputusan penilaian kinerja perawat menggunakan metode saw (simple additive weighting) (studi kasus: rs.columbia asia). Journal of Information Technology and Accounting, IV(2), 2614–448. http://jurnal.uimedan.ac.id/index.php/JITA/

Suryadi, A., Pasa, I. Y., & Purwanto, M. A. (2025). STUDI LITERATUR : KONTRIBUSI REKAM MEDIS ELEKTRONIK DAN CLINICAL DECISION SUPPORT SYSTEM DALAM MENDUKUNG. 24, 280–293.

Downloads

Published

2025-12-03

Issue

Vol. 4 No. 1 (2026): Februari

Section

Articles

License

Copyright (c) 2025 JOURNAL SAINS STUDENT RESEARCH

Creative Commons License

This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.