An Overview Of The Metrics Used In Appointment Scheduling Systems And Their Classification
Keywords:
Ambulatory Care Facilities, Queuing Theory, Evaluation MetricsAbstract
In this Study are reviewed that are used to assess how well appointment scheduling systems performing. The English-language papers is searched from the The Google Scholar search engine and the PUBMED databases, WEB OF SCIENCE, SCOPUS, we classified assessment metrics based on queuing theory. Findings: 85 papers, for in-depth examination. We categories appointment scheduling system evaluation measures in addition to their definition and usage frequency. There are 24 measurements in all, with 12 (%50), 7 (%29), and 5 (%21) having to do with the arrivals (patient), clinic line (patient), and server (physician) categories, respectively. The majority of metrics is patient-related, which may emphasize how crucial the patient's viewpoint is when assessing appointment scheduling systems.
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Ala, A., Simic, V., Deveci, M., & Pamucar, D. (2023). Simulation-based analysis of appointment scheduling system in healthcare services: a critical review. Archives of Computational Methods in Engineering, 30(3), 1961-1978.
Alshammari, A., Alotaibi, F., & Alnafrani, S. (2024). Prediction of Outpatient No-Show Appointments Using Machine Learning Algorithms for Pediatric Patients in Saudi Arabia. International Journal of Advanced Computer Science Applications, 15(8).
Apio, A. L., Kissi, J., & Achampong, E. K. (2023). A Systematic Review of Artificial Intelligence-Based Methods in Healthcare. Int. J. Public Health, 12(1259), 10-11591.
Carreras-García, D., Delgado-Gómez, D., Llorente-Fernández, F., & Arribas-Gil, A. (2020). Patient no-show prediction: a systematic literature review. Entropy, 22(6), 675.
Cox III, J. F. (2021). Using the theory of constraints’ processes of ongoing improvement to address the provider appointment scheduling system execution problem. Health Systems, 10(1), 41-72.
Graham TAD, Ali S, Avdagovska M, Ballermann M. (2020) “Effects of a web-based patient portal on patient satisfaction and missed appointment rates: Survey study” J Med Internet Res. 22(5): e17955.
Habibi, M. R. M., Abadi, F. M., Tabesh, H., Arki, H. V., Abu-Hanna, A., & Eslami, S. (2024). Overview and classification of evaluation metrics of appointment scheduling systems. Frontiers in Health Informatics, 13, 192.
Jeffin, J., Senith, S., Kirubaraj, A. A., & Ramson, S. J. (2022). Machine learning for prediction of clinical appointment no-shows. International Journal of Mathematical, Engineering and Management Sciences, 7(4), 558.
Ji, M., Mosaffa, M., Ardestani-Jaafari, A., Li, J., & Peng, C. (2023). Integration of text-mining and telemedicine appointment optimization. Annals of Operations Research, 1-25.
Knight, D., Aakre, C. A., Anstine, C. V., Munipalli, B., Biazar, P., Mitri, G., & Halamka, J. D. (2023). Artificial Intelligence for Patient Scheduling in the Real-World Health Care Setting: A Metanarrative Review. Health Policy and Technology, 100824.
Kasaie, A., & Rajendran, S. (2023). Integrating machine learning algorithms and explainable artificial intelligence approach for predicting patient unpunctuality in psychiatric clinics. Healthcare Analytics, 4, 100242.
Niu, T., Lei, B., Guo, L., Fang, S., Li, Q., Gao, B., ... & Gao, K. (2023). A Review of Optimization Studies for System Appointment Scheduling. Axioms, 13(1), 16.
Qureshi, Z., Maqbool, A., Mirza, A., Iqbal, M. Z., Afzal, F., Kanubala, D. D., ... & Shah, S. K. (2021). Research Article Efficient Prediction of Missed Clinical Appointment Using Machine Learning.14(2),18
Srinivas, S., & Salah, H. (2021). Consultation length and no-show prediction for improving appointment scheduling efficiency at a cardiology clinic: a data analytics approach. International Journal of Medical Informatics, 145, 104290.
Simsek, S., Tiahrt, T., & Dag, A. (2020). Stratifying no-show patients into multiple risk groups via a holistic data analytics-based framework. Decision Support Systems, 132, 113269.
Salazar, L. H. A., Leithardt, V. R., Parreira, W. D., da Rocha Fernandes, A. M., Barbosa, J. L. V., & Correia, S. D. (2021). Application of machine learning techniques to predict a patient’s no-show in the healthcare sector. Future Internet, 14(1), 3.
Volk A.S, Davis M.J, Abu-Ghname A, Warfield RG, Ibrahim R, Karon G, (2020) “Ambulatory access: Improving scheduling increases patient satisfaction and revenue.” Plast Reconstr Surg. 146(4): 913- 9.
Wu, D., Cui, W., Wang, X., Huo, Y., Yu, G., & Chen, J. (2021). Improvement in outpatient services using the WeChat calling system in the Shanghai Children’s Hospital. Pakistan Journal of Medical Sciences, 37(4), 993.
Xie, W., Yang, X., Cao, X., & Liu, P. (2019). Effects of a comprehensive reservation service for non-emergency registration on appointment registration rate, patient waiting time, patient satisfaction and outpatient volume in a tertiary hospital in China. BMC health services research, 19, 1-7.
Xie, H., Prybutok, G., Peng, X., & Prybutok, V. (2020). Determinants of trust in health information technology: An empirical investigation in the context of an online clinic appointment system. International Journal of Human–Computer Interaction, 36(12), 1095-1109.
Yan, J., Yao, J., & Zhao, D. (2021). Patient satisfaction with outpatient care in China: a comparison of public secondary and tertiary hospitals. International Journal for Quality in Health Care, 33(1), mzab003
Youn, S., Geismar, H. N., & Pinedo, M. (2022). Planning and scheduling in healthcare for better care coordination: Current understanding, trending topics, and future opportunities. Production and Operations Management, 31(12), 4407-4423.
Zheng, M., Li, T., Wang, H., Zhong, H., Zhou, H., Fan, Y. & Wang, L. (2024). Impact of digital self-scheduling on operations management and patient experience in hospital outpatient settings: a systematic review and meta-analysis., 24(11) 55-65
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