EVALUATION OF ARTIFICIAL INTELLIGENCE (AI) IMPLEMENTATION IN CHEST RADIOLOGY BASED ON SERVICE TURN AROUND TIME AND COST PER CASE AT AWAL BROS GROUP HOSPITALS
DOI:
https://doi.org/10.54973/jham.v7i1.939Keywords:
Artificial_Intelligence, Radiology, Turn_around_time, Cost _per_CaseAbstract
Artificial Intelligence (AI) has been increasingly adopted in radiology services to improve diagnostic and operational efficiency. However, its implementation requires substantial investment, highlighting the need for evaluation from both effectiveness and economic perspectives. This study aimed to evaluate the effectiveness of AI implementation in chest radiology services across Awal Bros Group Hospitals by assessing service turnaround time (TAT) and cost per case. A quantitative analytical study with a retrospective pre–post observational design was conducted using secondary data collected from eight hospitals within the Awal Bros Group network. Service performance before and after AI implementation was compared using turnaround time indicators, including waiting time and report interpretation time. An economic evaluation was also performed to assess operational efficiency and cost-effectiveness. The results demonstrated statistically significant improvements in service performance following AI implementation (p < 0.001). The median waiting time decreased from 12.08 to 9.65 minutes, while the mean report interpretation time decreased from 25.95 to 20.93 minutes. The economic evaluation further indicated improved operational efficiency and favorable cost-effectiveness, reflected by a lower cost per case after AI implementation.In conclusion, the implementation of AI in chest radiology services significantly improved operational efficiency by reducing turnaround time and optimizing service costs. These findings support the value of AI as a strategic digital health technology that enhances radiology workflow and contributes to the sustainability of hospital digital transformation.
Downloads
References
Aggarwal, R., Sounderajah, V., Martin, G., Ting, D. S. W., Karthikesalingam, A., King, D., Ashrafian, H., & Darzi, A. (2021). Diagnostic accuracy of deep learning in medical imaging: A systematic review and meta-analysis. Npj Digital Medicine, 4(1), 65. https://doi.org/10.1038/s41746-021-00438-z
Baltruschat, I., Steinmeister, L., Nickisch, H., Saalbach, A., Grass, M., Adam, G., Knopp, T., & Ittrich, H. (2021). Smart chest X-ray worklist prioritization using artificial intelligence: A clinical workflow simulation. European Radiology, 31(6), 3837–3845. https://doi.org/10.1007/s00330-020-07480-7
Batra, K., Xi, Y., Bhagwat, S., Espino, A., & Peshock, R. M. (2023a). Radiologist Worklist Reprioritization Using Artificial Intelligence: Impact on Report Turnaround Times for CTPA Examinations Positive for Acute Pulmonary Embolism. American Journal of Roentgenology, 221(3), 324–333. https://doi.org/10.2214/AJR.22.28949
Batra, K., Xi, Y., Bhagwat, S., Espino, A., & Peshock, R. M. (2023b). Radiologist Worklist Reprioritization Using Artificial Intelligence: Impact on Report Turnaround Times for CTPA Examinations Positive for Acute Pulmonary Embolism. American Journal of Roentgenology, 221(3), 324–333. https://doi.org/10.2214/AJR.22.28949
Borgers, L. (n.d.). The role of Artificial Intelligence (AI) in radiology: The current status of FDA approved systems.
Boverhof, B.-J., Redekop, W. K., Bos, D., Starmans, M. P. A., Birch, J., Rockall, A., & Visser, J. J. (2024). Radiology AI Deployment and Assessment Rubric (RADAR) to bring value-based AI into radiological practice. Insights into Imaging, 15(1), 34. https://doi.org/10.1186/s13244-023-01599-z
Brin, D., & Tau, N. (2025). Cost-effectiveness of artificial intelligence tools in radiology: A systematic review. European Radiology. https://doi.org/10.1007/s00330-025-12242-4
De Moel-Mandel, C., Lynch, C., Issaka, A., Braver, J., Zisis, G., Carrington, M. J., & Oldenburg, B. (2023). Optimising the implementation of digital-supported interventions for the secondary prevention of heart disease: A systematic review using the RE-AIM planning and evaluation framework. BMC Health Services Research, 23(1), 1347. https://doi.org/10.1186/s12913-023-10361-6
He, C., Liu, W., Xu, J., Huang, Y., Dong, Z., Wu, Y., & Kharrazi, H. (2024). Efficiency, accuracy, and health professional’s perspectives regarding artificial intelligence in radiology practice: A scoping review. iRADIOLOGY, 2(2), 156–172. https://doi.org/10.1002/ird3.63
Hendrix, N., Veenstra, D. L., Cheng, M., Anderson, N. C., & Verguet, S. (2022). Assessing the Economic Value of Clinical Artificial Intelligence: Challenges and Opportunities. Value in Health, 25(3), 331–339. https://doi.org/10.1016/j.jval.2021.08.015
Indonesia, M. (2023). Teknologi Kecerdasan Buatan di Bidang Kesehatan. Mediaindonesia.Com. https://epaper.mediaindonesia.com/detail/teknologi-kecerdasan-buatan-di-bidang-kesehatan
Lukoševičius, S. (n.d.). THE CURRENT STATUS OF ARTIFICIAL INTELLIGENCE (AI) IN RADIOLOGY.
Lyth, J., Gialias, P., Husberg, M., Bernfort, L., Bjerner, T., Wiberg, M. K., Levin, L.-Å., & Gustafsson, H. (2025). Results from a Swedish model-based analysis of the cost-effectiveness of AI-assisted digital mammography. European Radiology, 36(1), 754–764. https://doi.org/10.1007/s00330-025-11821-9
Molwitz, I., Ristow, I., Erley, J., Akinci D’Antonoli, T., Tejani, A. S., Klontzas, M. E., Huisman, M., Adam, G., Nüesch, S., & Adams, L. (2026). Economic Value of AI in Radiology: A Systematic Review. Radiology: Artificial Intelligence, 8(1), e250090. https://doi.org/10.1148/ryai.250090
Morgan, M. B., Branstetter, B. F., Lionetti, D. M., Richardson, J. S., & Chang, P. J. (2008). The Radiology Digital Dashboard: Effects on Report Turnaround Time. Journal of Digital Imaging, 21(1), 50–58. https://doi.org/10.1007/s10278-007-9008-9
Peraturan Menteri Kesehatan Nomer 24 Tahun 2020 Tentang Pelayanan Radiologi Klinik, No 24 tahun 2020 (2020). https://jdih.kemkes.go.id/documents/peraturan-menteri-kesehatan-nomor-24-tahun-2020
Ranschaert, E., Topff, L., & Pianykh, O. (2021). Optimization of Radiology Workflow with Artificial Intelligence. Radiologic Clinics of North America, 59(6), 955–966. https://doi.org/10.1016/j.rcl.2021.06.006
Ritchie, B., Summerville, L., Sheng, M., Choi, M., Tirumani, S., & Ramaiya, N. (2026). Impact of turnaround time in radiology: The good, the bad, and the ugly. Current Problems in Diagnostic Radiology, 55(2), 268–272. https://doi.org/10.1067/j.cpradiol.2025.04.018
Shin, H. D., Hamovitch, E., Gatov, E., MacKinnon, M., Samawi, L., Boateng, R., Thorpe, K. E., & Barwick, M. (2025). The NASSS (Non-Adoption, Abandonment, Scale-Up, Spread and Sustainability) framework use over time: A scoping review. PLOS Digital Health, 4(3), e0000418. https://doi.org/10.1371/journal.pdig.0000418
Strohm, L., Hehakaya, C., Ranschaert, E. R., Boon, W. P. C., & Moors, E. H. M. (2020). Implementation of artificial intelligence (AI) applications in radiology: Hindering and facilitating factors. European Radiology, 30(10), 5525–5532. https://doi.org/10.1007/s00330-020-06946-y
Thompson, Y. L. E., Fergus, J., Chung, J., Delfino, J. G., Chen, W., Levine, G. M., & Samuelson, F. W. (2026a). Impact of Artificial Intelligence Triage on Radiologist Report Turnaround Time: Real-World Time Savings and Insights From Model Predictions. Journal of the American College of Radiology, 23(1), 63–73. https://doi.org/10.1016/j.jacr.2025.07.033
Thompson, Y. L. E., Fergus, J., Chung, J., Delfino, J. G., Chen, W., Levine, G. M., & Samuelson, F. W. (2026b). Impact of Artificial Intelligence Triage on Radiologist Report Turnaround Time: Real-World Time Savings and Insights From Model Predictions. Journal of the American College of Radiology, 23(1), 63–73. https://doi.org/10.1016/j.jacr.2025.07.033
