Development of a Machine Learning-Based Web Application for Quality Justification in Dialysis Healthcare

Umi Khoirun  Nisak; Irwan Alnarus  Kautsar; Laili Rahmatyul  Ilmi; Nabila Insyira  Natasya; Cholifah Cholifah; Aditiawardana Aditiawardana

doi:10.21070/pels.v6i0.1962

(1) * Umi Khoirun Nisak Medical Record & Health Information, Universitas Muhammadiyah Sidoarjo, East Java
Indonesia
(2) Irwan Alnarus Kautsar Informatic Universitas Muhammadiyah Sidoarjo East Java
Indonesia
(3) Laili Rahmatyul Ilmi Medical Record & Health Information, Universitas Muhammadiyah Sidoarjo, East Java
Indonesia
(4) Nabila Insyira Natasya Medical Record & Health Information, Universitas Muhammadiyah Sidoarjo, East Java
Indonesia
(5) Cholifah Cholifah Midwifery Universitas Muhammadiyah Sidoarjo, East Java
Indonesia
(6) Aditiawardana Aditiawardana Department of Internal Medicine Soetomo Hospital, East Java
Indonesia
(*) Corresponding Author

DOI: https://doi.org/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.

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