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All Journal INDONESIAN JOURNAL OF PHARMACY CURRENT INTERNAL MEDICINE RESEARCH AND PRACTICE SURABAYA JOURNAL PELS (Procedia of Engineering and Life Science)
Aditiawardana Aditiawardana, Aditiawardana
Department Of Nephrology, Faculty Of Medicine, Dr. Soetomo Hospital, Jl. Mayjen Prof. Dr. Moestopo No.6-8, Airlangga, 60286, Surabaya, Indonesia
Biochemistry, Genetics & Molecular Biology Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering Engineering Health Professions Immunology & microbiology Medicine & Pharmacology Public Health

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Journal : PELS (Procedia of Engineering and Life Science)

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Development of a Machine Learning-Based Web Application for Quality Justification in Dialysis Healthcare Nisak, Umi Khoirun; Kautsar, Irwan Alnarus; Ilmi, Laili Rahmatyul; Natasya, Nabila Insyira; Cholifah, Cholifah; Aditiawardana, Aditiawardana
Procedia of Engineering and Life Science Vol 6 (2024): The 3rd International Scientific Meeting on Health Information Management (3rd ISMoHIM
Publisher : Universitas Muhammadiyah Sidoarjo

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21070/pels.v6i0.1962

Abstract

The quality of healthcare services, especially for chronic conditions like kidney failure requiring dialysis, is critical. This study aims to develop a machine learning-based web application to evaluate and justify dialysis healthcare quality. Conducted at Siti Khodijah Hospital from January to June 2024, the research employed a developmental and experimental design involving 123 medical professionals. The methodology included needs assessment, system design, algorithm selection, data collection, model training, system integration, and validation. The web application, named Renal Data Processor, features user-friendly navigation, robust data visualization, and machine learning algorithms. It provides real-time analysis and predictive insights, allowing healthcare providers to make data-driven decisions. Results showed significant improvements, including a 20% reduction in data entry time and a 15% enhancement in nurse certification tracking efficiency. User feedback indicated high satisfaction with the application's functionality and its impact on workflow efficiency. In conclusion, the Renal Data Processor has enhanced dialysis healthcare quality by streamlining data management and providing actionable insights. This study demonstrates the potential of machine learning to transform healthcare delivery and outcomes, suggesting further research to expand its capabilities and applicability in other healthcare settings.
Co-Authors Ardityo R Ardhany, Ardityo R Artaria Tjempakasari, Artaria atika Budi Darma Siahaan Budi Suprapti Chandra Irwanadi Chandra Irwanadi Chandra Irwanadi, Chandra Cholifah, Cholifah Dana Pramudya Dicky Pendi Alaudin Sangadji Djoko Santoso Fauzia N Liani Hani, Farahdhila Hartono, Frenky Ilmi, Laili Rahmatyul Irwan Alnarus Kautsar Lindawati Alimsardjono M Thaha, M Natasya, Nabila Insyira Nunuk Mardiana, Nunuk P Pranawa Pranawa Pranawa pranawa pranawa, pranawa Tri P Asmarawati, Tri P Umi Khoirun Nisak Widodo Widodo Widodo Widodo Widodo Widodo Zuhri, Muhammad Isnaini