HOT-FIT-Based Evaluation of the Outpatient Registration System at RSUD Komodo
DOI:
https://doi.org/10.62951/ijhsb.v2i2.387Keywords:
Hospital Information System, Outpatient Registration, HOT-FIT Model, System Performance, Technical BarriersAbstract
process standardization, and system optimization. The study concludes that the HOT-FIT model is This study evaluates the performance of the outpatient registration information system at RSUD Komodo using the Human-Organization-Technology Fit (HOT-FIT) framework. Hospital Information Systems, particularly in the outpatient registration process, are crucial for supporting service efficiency and data accuracy. However, RSUD Komodo has experienced several challenges in the implementation of its SIMRS module, including system slowdowns, sudden monitor failures, and unstable internet connectivity during service hours. These issues hinder operational effectiveness and risk compromising service quality. The objective of this research is to assess system performance comprehensively across human, organizational, and technological dimensions. A qualitative descriptive design was employed, involving in-depth interviews with five key informants: registration staff, IT personnel, coder, head of the medical records unit, and head of the casemix team. The findings show that in the human dimension, users lacked sufficient training and adaptation strategies. In the organizational aspect, weak coordination and the absence of standardized procedures were identified. In the technology dimension, hardware malfunctions and slow system performance significantly disrupted services. These interconnected issues reveal the need for capacity buildingan effective tool for evaluating hospital information systems, offering a structured approach to identifying and resolving performance gaps in outpatient service modules.
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References
F. D. Davis, “Perceived usefulness, perceived ease of use, and user acceptance of information technology,” MIS Q., vol. 13, no. 3, pp. 319–340, 1989, doi: 10.2307/249008.
W. H. DeLone and E. R. McLean, “The DeLone and McLean model of information systems success: A ten-year update,” J. Manag. Inf. Syst., vol. 19, no. 4, pp. 9–30, 2003, doi: 10.1080/07421222.2003.11045748.
N. M. Yusof, J. Kuljis, A. Papazafeiropoulou, and L. Stergioulas, “An evaluation framework for health information systems: human, organization and technology-fit factors (HOT-fit),” Int. J. Med. Inform., vol. 77, no. 6, pp. 386–398, 2008, doi: 10.1016/j.ijmedinf.2007.08.011.
M. Setiawan, H. Nugroho, and A. Hariyati, “Evaluasi sistem dokumentasi keperawatan dengan pendekatan HOT-FIT di RSUD Kota Surakarta,” J. Keperawatan Indonesia, vol. 24, no. 2, pp. 142–150, 2021, doi: 10.7454/jki.v24i2.1284.
R. Ahmad et al., “Application of HOT-FIT Framework in Evaluating eHealth Implementation in Malaysia,” BMC Health Serv. Res., vol. 23, no. 1, p. 501, 2023, doi: 10.1186/s12913-023-09658-2.
E. Çallı et al., “Deep learning for chest X-ray analysis: A survey,” Med. Image Anal., vol. 72, p. 102125, 2021, doi: 10.1016/j.media.2021.102125.
P. Rajpurkar and M. P. Lungren, “The Current and Future State of AI Interpretation of Medical Images,” N. Engl. J. Med., vol. 388, no. 21, pp. 1981–1990, 2023, doi: 10.1056/NEJMra2301725.
P. Korfiatis et al., “Implementing Artificial Intelligence Algorithms in the Radiology Workflow: Challenges and Considerations,” Mayo Clin. Proc. Digit. Heal., vol. 3, no. 1, p. 100188, 2025, doi: 10.1016/j.mcpdig.2024.100188.
M. Ennab and H. Mcheick, “Advancing AI Interpretability in Medical Imaging: A Comparative Analysis of Pixel-Level Interpretability and Grad-CAM Models,” Mach. Learn. Knowl. Extr., vol. 7, no. 1, 2025, doi: 10.3390/make7010012.
D. W. Messinger, “A new method for XRF and RGB image registration,” npj Herit. Sci., vol. 11, no. 1, pp. 1–9, 2025, doi: 10.1038/s40494-025-01603-3.
S. Pariyasto et al., “Lung X-ray Image Similarity Analysis Using RGB Pixel Comparison Method,” J. Infotel, vol. 9, no. 1, pp. 11–20, 2025.
M. A. S. Al Husaini et al., “Thermal-based early breast cancer detection using inception V3, inception V4 and modified inception MV4,” Neural Comput. Appl., 2021, doi: 10.1007/s00521-021-06372-1.
F. F. Rulyan et al., “Literature Analysis on Health Information Systems (HIS): Trends, Challenges, and Benefits in Enhancing Healthcare Services in Indonesia,” Proc. Semin. Nas. UNIMUS, vol. 6, pp. 928–942, 2023.
C. D. Akwaowo et al., “Adoption of Electronic Medical Records in Developing Countries: A Multi-State Study of the Nigerian Healthcare System,” Front. Digit. Health, vol. 4, p. 1017231, 2022, doi: 10.3389/fdgth.2022.1017231.
T. Zhou et al., “Rethinking Semantic Segmentation: A Prototype View,” Proc. IEEE Comput. Soc. Conf. Comput. Vis. Pattern Recognit., vol. 2022, pp. 2572–2583, 2022, doi: 10.1109/CVPR52688.2022.00261.
M. S. Ummah, Informatics in Medical Imaging, vol. 11, no. 1, 2019. [Online]. Available: http://scioteca.caf.com/bitstream/handle/123456789/1091/RED2017
P. Whiting et al., “The development of QUADAS: A tool for the quality assessment of studies of diagnostic accuracy included in systematic reviews,” BMC Med. Res. Methodol., vol. 3, p. 25, 2003, doi: 10.1186/1471-2288-3-25.
A. Maftukhah, A. Fadlil, and S. Sunardi, “Butterfly Image Classification using CNN with AlexNet Architecture,” J. Infotel, vol. 16, no. 1, pp. 82–95, 2024, doi: 10.20895/infotel.v16i1.1004.
D. Yuliawati et al., “IoT for Monitoring Parking System using OCR,” J. Infotel, vol. 15, no. 2, pp. 169–174, 2023, doi: 10.20895/infotel.v15i2.859.
E. Tiu et al., “Expert-level Detection of Pathologies from Unannotated Chest X-ray Images via Self-supervised Learning,” Nat. Biomed. Eng., vol. 6, no. 12, pp. 1399–1406, 2022, doi: 10.1038/s41551-022-00936-9.D. R. I. M. Setiadi, S. Rustad, P. N. Andono, and G. F. Shidik, “Digital image steganography survey and investigation (goal, assessment, method, development, and dataset),” Signal Processing, vol. 206, p. 108908, May 2023, doi: 10.1016/j.sigpro.2022.108908.
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