Analisis Segmentasi Pasien Berdasarkan Persepsi Kualitas Pelayanan  dengan Algoritma Clustering

Frangky Frangky; Rudolf Sinaga; M. Raihansyah

doi:10.47065/explorer.v5i1.1818

Frangky Frangky Universitas Dinamika Bangsa, Jambi, Indonesia
Rudolf Sinaga * Universitas Dinamika Bangsa, Jambi, Indonesia
M. Raihansyah Sekolah Tinggi Ilmu Kesehatan Garuda Putih, Jambi, Indonesia

DOI: https://doi.org/10.47065/explorer.v5i1.1818

Keywords: Patient Segmentation; Service Quality Perception; K-Means; Clustering

Abstract

Patient segmentation based on perceptions of service quality is a crucial step in improving patient experiences, optimizing resources, and enhancing healthcare service quality. However, understanding patients' needs and priorities in depth poses a challenge, particularly for hospitals serving populations with diverse demographic backgrounds. This study aims to cluster patients in a private hospital in Jambi City based on their perceptions of service quality using the K-Means algorithm. Data were collected from a 2022-2023 survey, covering patient demographics and perceptions of service quality. The data were processed through preprocessing steps, including missing value imputation, normalization, and encoding. The optimal number of clusters was determined using the Elbow and Silhouette Score methods. The results revealed three main clusters with distinct characteristics. The first cluster (34.29%) includes patients prioritizing service speed and procedural ease. The second cluster (46.12%) consists of patients who emphasize staff competence and cost fairness as their main priorities. The third cluster (19.59%) comprises patients with higher educational backgrounds who are more critical of facility quality and complaint handling. Evaluation using the Davies-Bouldin index demonstrated good cluster separation (score -0.645). This study concludes that patient segmentation based on perceptions of service quality can serve as a foundation for strategic decision-making to improve hospital service quality. Recommendations for future research include applying other algorithms such as DBSCAN, integrating sentiment analysis, and employing a hybrid approach to predict patient needs. These approaches are expected to provide a deeper understanding and more effective personalization of patient care.

References

Ginting R, Novalinda Ginting C, Septianto T. Patient Segmentation: An Analysis Based on Demographic Characteristics, Geography, and Patient Behavior. Jurnal Aisyah: Jurnal Ilmu Kesehatan 2023;8:1011–6. https://doi.org/10.30604/jika.v8i2.2010.

Bertolini M, Luraghi G, Belicchi I, Migliavacca F, Colombo G. Evaluation of segmentation accuracy and its impact on patient-specific CFD analysis. International Journal on Interactive Design and Manufacturing 2022;16:545–56. https://doi.org/10.1007/s12008-021-00802-z.

Hess H, Ruckli AC, Bürki F, Gerber N, Menzemer J, Burger J, et al. Deep-Learning-Based Segmentation of the Shoulder from MRI with Inference Accuracy Prediction. Diagnostics 2023;13. https://doi.org/10.3390/diagnostics13101668.

Finnegan RN, Chin V, Chlap P, Haidar A, Otton J, Dowling J, et al. Open-source, fully-automated hybrid cardiac substructure segmentation: development and optimisation. Phys Eng Sci Med 2023;46:377–93. https://doi.org/10.1007/s13246-023-01231-w.

Bokhorst JM, Nagtegaal ID, Fraggetta F, Vatrano S, Mesker W, Vieth M, et al. Deep learning for multi-class semantic segmentation enables colorectal cancer detection and classification in digital pathology images. Sci Rep 2023;13. https://doi.org/10.1038/s41598-023-35491-z.

Zamzam AH, A-AAKI, HK, & WAKA. A Strategic Corrective Maintenance Prioritization Assessment for Medical Equipment. Springer Science and Business Media Deutschland GmbH 2022;86:133–41.

Singh V, Garg A. SERVICE QUALITY AND SERVICE SATISFACTION IN THE INPATIENT SETTING: MODERATING ROLE OF INSURANCE STATUS. Asia Pacific Journal of Health Management 2022;17. https://doi.org/10.24083/apjhm.v17i2.1399.

Sari PI, Harsinah V, Tinggi S, Kesehatan I, Aceh M, Aceh B, et al. Hubungan Persepsi Pasien Tentang Kualitas Pelayanan dengan Minat Kunjungan Ulang di RSIA Cempaka Az-Zahra Kota Banda Aceh Relationship between patient perceptions regarding service quality and interest in repeat visits at RSIA Cempaka Az-Zahra Banda Aceh City. vol. 9. n.d.

Yusefi AR, Sarvestani SR, Kavosi Z, Bahmaei J, Mehrizi MM, Mehralian G. Patients’ perceptions of the quality of nursing services. BMC Nurs 2022;21. https://doi.org/10.1186/s12912-022-00906-1.

Halaweh M, Salameh FF. Using Social Media Data for Exploring Healthcare Service Quality. International Journal of Healthcare Information Systems and Informatics 2023;18. https://doi.org/10.4018/IJHISI.325064.

Fathillah MI. The Effect of Service Quality and Price Perception on Patient Loyalty Mediated by Patient Satisfaction. International Journal of Social Service and Research 2023;3:669–79. https://doi.org/10.46799/ijssr.v3i3.303.

Chinchmalatpure M, & DMP. Quality Healthcare Prediction using K Means And Clara Partition Based Clustering Algorithm For Big Data Analytics. Int J Eng Adv Technol 2020;9:1140–4.

Chai R, Liu C, Chen Q. Energy efficiency optimization-based joint resource allocation and clustering algorithm for M2M communication systems. IEEE Access 2019;7:168507–19. https://doi.org/10.1109/ACCESS.2019.2954713.

L V R, Soundar K R. An advancement in energy efficient clustering algorithm using cluster coordinator-based CH election mechanism (CCCH). Measurement: Sensors 2023;25. https://doi.org/10.1016/j.measen.2022.100623.

Kumar S, Solanki VK, Choudhary SK, Selamat A, Crespo RG. Comparative study on ant colony optimization (ACO) and k-means clustering approaches for jobs scheduling and energy optimization model in internet of things (IoT). International Journal of Interactive Multimedia and Artificial Intelligence 2020;6:107–16. https://doi.org/10.9781/ijimai.2020.01.003.

Akinlusi FM, Olayiwola AA, Adeniran A, Rabiu KA, Oshodi YA, Ottun TA. Patients’ Perception of the Quality of Gynecological Services in a Tertiary Public Health Facility in Lagos, Nigeria. J Patient Exp 2022;9. https://doi.org/10.1177/23743735221077550.

Gu L, Tian B, Xin Y, Zhang S, Li J, Sun Z. Patient perception of doctor communication skills and patient trust in rural primary health care: the mediating role of health service quality. BMC Primary Care 2022;23. https://doi.org/10.1186/s12875-022-01826-4.

Mehedi Hasan M, Karim Z. PATIENT EXPECTATIONS AND SERVICE QUALITY PERCEPTIONS IN HEALTHCARE: A STUDY OF HOSPITALS IN BANGLADESH. JOURNAL OF HEALTHCARE IN DEVELOPING COUNTRIES 2023;3:19–21. https://doi.org/10.26480/jhcdc.01.2023.19.21.

Deepa D, Jothi KR. Classification of Request-Based Mobility Load Balancing in Fog Computing. Computer Systems Science and Engineering 2023;46:137–51. https://doi.org/10.32604/csse.2023.032485.