Advancing Digital Maturity in Healthcare Through Comprehensive Integration of Business Intelligence, K-Means Clustering, and Python Programming

Ani Kartini (), Muharman Lubis, Basuki Rahmad and Muhammad Fakhrul Safitra
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Ani Kartini: Telkom University
Muharman Lubis: Telkom University
Basuki Rahmad: Telkom University
Muhammad Fakhrul Safitra: Telkom University

A chapter in Selected Papers from the 10th International Conference on E-Business and Applications 2024, 2024, pp 83-94 from Springer

Abstract: Abstract This study investigates initiatives aimed at enhancing the digital maturity of the health sector through the integration of Business Intelligence (BI) technologies, with a specific focus on Python programming and K-Means clustering. The research aims to effectively align information technology and business factors within the healthcare context, considering the ongoing digital revolution. Python programming provides a versatile foundation, and the K-Means Clustering algorithm is employed to aggregate health data, enabling the identification of significant patterns. The primary objectives include improving decision- making processes and enhancing patient services. The study also conducts an evaluation of the digital maturity of hospitals in the Province of West Java, emphasizing the utilization of Health Information Systems (HIS). The findings reveal a substantial improvement, while also highlighting areas that require further development, such as data completeness and the adoption of electronic medical records (EMR). This assessment provides a comprehensive understanding of the challenges and opportunities for advancements in the region’s healthcare system. Overall, the study advocates for the implementation of integrated, data-driven solutions to effectively manage the digital transformation of the healthcare industry.

Keywords: CCS CONCEPTS; Information systems; Information systems applications; Enterprise information systems; Enterprise applications (search for similar items in EconPapers)
Date: 2024
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DOI: 10.1007/978-981-97-3409-2_8

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