Abstract
Machine learning (ML) has been widely adopted in the healthcare industry for improving patient outcomes and operational efficiency. Predicting no-show appointments is one of the areas where ML has been applied to optimize appointment scheduling and management. However, there are still challenges to be addressed when applying ML to predict no-show appointments, including data quality and potential biases in the models. In recent years, deep learning (DL) has emerged as a powerful tool for solving complex problems, including those in the healthcare industry. This paper proposes a DL technique to predict no-show appointments in the hospital sector using a dataset of historical patient appointments and their attendance. The study aims to accurately predict which patients are at risk of not attending their appointments and explore ways to improve appointment scheduling and management using these predictions. The paper also discusses the benefits and challenges of using ML and DL methods in healthcare services and provides theoretical and managerial implications for future research and practice.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Javaid, M., Haleem, A., Singh, R.P., Suman, R., Rab, S.: Significance of machine learning in healthcare: features, pillars and applications. Int. J. Intell. Netw. 3, 58–73 (2022)
Lin, Q., Betancourt, B., Goldstein, B.A., Steorts, R.C.: Prediction of appointment no-shows using electronic health records. J. Appl. Stat. 47(7), 1220–1234 (2020)
Dash, S., Shakyawar, S.K., Sharma, M., Kaushik, S.: Big data in healthcare: management, analysis and future prospects. J. Big Data 6(1), 1–25 (2019)
Nguyen, M., Nguyen, T.-T., Dinh, V., Jeon, J.W.: Real-time pedestrian detection using a support vector machine and stixel information. In: 2017 17th International Conference on Control, Automation and Systems (ICCAS), Jeju, Korea (South), pp. 1350–1355 (2017). https://doi.org/10.23919/ICCAS.2017.8204203
Nguyen, V.D., Tran, T.H., Dang, D.T., Debnath, N.C.: Robust Vehicle Detection by Using Deep Learning Feature and Support Vector Machine. In: Hassanien, A.E., et al. (eds.), The 3rd International Conference on Artificial Intelligence and Computer Vision (AICV2023), March 5–7, 2023. AICV 2023. Lecture Notes on Data Engineering and Communications Technologies, vol 164. Springer, Cham (2023). https://doi.org/10.1007/978-3-031-27762-7_14
Chen, M., Decary, M.: Artificial intelligence in healthcare: an essential guide for health leaders. Healthc. Manage. Forum 33(1), 10–18 (2020). SAGE Publications Sage CA: Los Angeles, CA
Jayaraman, P.P., Forkan, A.R.M., Morshed, A., Haghighi, P.D., Kang, Y.-B.: Healthcare 4.0: a review of frontiers in digital health. WIREs Data Min. Knowl. Disc. 10(2), e1350 (2020)
Wen, D., et al.: Characteristics of publicly available skin cancer image datasets: a systematic review. Lancet Digit. Health 4(1), e64–e74 (2021)
Nguyen, N.H., Pham, H.H., Chan, T.T., Nguyen, T.N., Nguyen, H.Q.: VinDr-PCXR: An open, large-scale chest radiograph dataset for interpretation of common thoracic diseases in children. arXiv preprint arXiv:2203.10612 (2022)
Ngiam, K.Y., Khor, I.W.: Big data and machine learning algorithms for health-care delivery. Lancet Oncol. 20(5), e262–e273 (2019)
Stevens, L.M., Mortazavi, B.J., Deo, R.C., Curtis, L., Kao, D.P.: Recommendations for reporting machine learning analyses in clinical research. Circ. Cardiovasc. Qual. Outcomes 13(10), e006556 (2020)
He, L., Madathil, S.C., Oberoi, A., Servis, G., Khasawneh, M.T.: A systematic review of research design and modeling techniques in inpatient bed management. Comput. Ind. Eng. 127, 451–466 (2019)
Elvira, C., Ochoa, A., Gonzalvez, J.C., Mochón, F.: Machine-learning-based no show prediction in outpatient visits. Int. J. Interact. Multimedia Artif. Intell. 4(7), 29 (2018)
Simsek, S., Tiahrt, T., Dag, A.: Stratifying no-show patients into multiple risk groups via a holistic data analytics-based framework. Decis. Support Syst. 132, 113269 (2020)
Carreras-García, D., Delgado-Gómez, D., Llorente-Fernández, F., Arribas-Gil, A.: Patient no-show prediction: a systematic literature review. Entropy 22(6), 675 (2020)
Jeffin Joseph, S., Senith, A.A., Kirubaraj, S.R., Ramson, J.: Machine learning for prediction of clinical appointment no-shows. Int. J. Math. Eng. Manage. Sci. 7(4), 558–574 (2022)
Mohammadi, I., Wu, H., Turkcan, A., Toscos, T., Doebbeling, B.N.: Data analytics and modeling for appointment no-show in community health centers. J. Prim. Care Community Health 9, 2150132718811692 (2018)
Luiz, H.A., et al.: Application of machine learning techniques to predict a patient’s no-show in the healthcare sector. Future Internet 14(1), 3 (2021)
Ahmad, M.U., Zhang, A., Mhaskar, R.: A predictive model for decreasing clinical no-show rates in a primary care setting. Int. J. Healthc. Manage. 14(3), 829–836 (2021)
Ahmadi, E., Garcia-Arce, A., Masel, D.T., Reich, E., Puckey, J., Maff, R.: A metaheuristic-based stacking model for predicting the risk of patient no-show and late cancellation for neurology appointments. IISE Trans. Healthc. Syst. Eng. 9(3), 272–291 (2019)
Qureshi, Z., et al.: Efficient prediction of missed clinical appointment using machine learning. Comput. Math. Methods Med. 2021, 1–10 (2021)
Krishnan, U., Sangar, P.: A rebalancing framework for classification of imbalanced medical appointment no-show data. J. Data Inf. Sci. 6(1), 178–192 (2021)
Dantas, L.F., Fleck, J.L., Cyrino, F.L., Oliveira, S.H.: No-shows in appointment scheduling – a systematic literature review. Health Policy 122(4), 412–421 (2018)
Aung, Y.Y.M., Wong, D.C.S., Ting, D.S.W.: The promise of artificial intelligence: a review of the opportunities and challenges of artificial intelligence in healthcare. Br. Med. Bull. 139(1), 4–15 (2021)
Dashtban, M., Li, W.: Predicting non-attendance in hospital outpatient appointments using deep learning approach. Health Syst. 11(3), 189–210 (2021)
Howard, J.: Artificial intelligence: implications for the future of work. Am. J. Ind. Med. 62(11), 917–926 (2019)
Holzinger, A., Jurisica, I.: Knowledge discovery and data mining in biomedical informatics: the future is in integrative, interactive machine learning solutions. In: Holzinger, A., Jurisica, I. (eds.) Interactive Knowledge Discovery and Data Mining in Biomedical Informatics, pp. 1–18. Springer Berlin Heidelberg, Berlin, Heidelberg (2014). https://doi.org/10.1007/978-3-662-43968-5_1
Dang, T.H., Nguyen, T.A., Van, M.H., Santin, O., Tran, O.M.T., Schofield, P.: Patient-centered care: transforming the health care system in Vietnam with support of digital health technology. J. Med. Internet Res. 23(6), e24601 (2021)
Oanh, T.T., Tung, N.T.: Predicting hypertension based on machine learning methods: a case study in Northwest Vietnam. Mob. Netw. Appl. 27(5), 2013–2023 (2022)
Tuan, M.N.D., Thanh, N.N., Le Tuan, L.: Applying a mindfulness-based reliability strategy to the internet of things in healthcare – a business model in the Vietnamese market. Technol. Forecast. Soc. Change 140, 54–68 (2019)
Cottrell, M., et al.: Sustaining allied health telehealth services beyond the rapid response to COVID-19: learning from patient and staff experiences at a large quaternary hospital. J. Telemedicine Telecare 27(10), 615–624 (2021)
Pham, Q.-V., Nguyen, D.C., Huynh-The, T., Hwang, W.-J., Pathirana, P.N.: Artificial intelligence (AI) and big data for coronavirus (COVID-19) pandemic: a survey on the state-of-the-arts. IEEE Access 8, 130820 (2020)
Anthony, B.: Implications of telehealth and digital care solutions during COVID-19 pandemic: a qualitative literature review. Inf. Health Soc. Care 46(1), 68–83 (2021)
Gromisch, E.S., Turner, A.P., Leipertz, S.L., Beauvais, J., Haselkorn, J.K.: Who is not coming to clinic? a predictive model of excessive missed appointments in persons with multiple sclerosis. Multiple Sclerosis Relat. Disord. 38, 101513 (2020)
Youn, S.H., Geismar, N., Pinedo, M.: Planning and scheduling in healthcare for better care coordination: Current understanding, trending topics, and future opportunities. Prod. Oper. Manage. 31(12), 4407–4423 (2022)
Li, Y., Tang, S.Y., Johnson, J., Lubarsky, D.A.: Individualized no-show predictions: effect on clinic overbooking and appointment reminders. Prod. Oper. Manage. 28(8), 2068–2086 (2019)
Topuz, K., Uner, H., Oztekin, A., Yildirim, M.B.: Predicting pediatric clinic no-shows: a decision analytic framework using elastic net and Bayesian belief network. Ann. Oper. Res. 263(1), 479–499 (2018)
Suk, M.Y., Kim, B., Lee, S.G., You, C.H., Kim, T.H.: Evaluation of patient no-shows in a Tertiary Hospital: focusing on modes of appointment-making and type of appointment. Int. J. Environ. Res. Public Health 18(6), 3288 (2021)
Amberger, C, Schreyer, D.: What do we know about no‐show behavior? a systematic, interdisciplinary literature review. J. Econ. Surv. (2022)
Tsai, W.-C., Lee, W.-C., Chiang, S.-C., Chen, Y.-C., Chen, T.-J.: Factors of missed appointments at an academic medical center in Taiwan. J. Chin. Med. Assoc. 82(5), 436–442 (2019)
Guck, T.P., Guck, A.J., Brack, A.B., Frey, D.R.: No-show rates in partially integrated models of behavioral health care in a primary care setting. Families Syst. Health 25(2), 137 (2007)
Cheung, D.L., Sahrmann, J., Nzewuihe, A., Espiritu, J.R.: No-show rates to a sleep clinic: drivers and determinants. J Clin. Sleep Med. 16(9), 1517–1521 (2020)
Elkhider, H., et al.: Predictors of no-show in neurology clinics. Healthcare 10(4), 599 (2022)
Rosenbaum, J.I., Mieloszyk, R.J., Hall, C.S., Hippe, D.S., Gunn, M.L., Bhargava, P.: Understanding why patients no-show: observations of 2.9 million outpatient imaging visits over 16 years. J. Am. Coll. Radiol. 15(7), 944–950 (2018)
Liu, D., et al.: Machine learning approaches to predicting no-shows in pediatric medical appointment. NPJ Digit. Med. 5(1), 1–11 (2022)
Dantas, L.F., Hamacher, S., Cyrino, F.L., Oliveira, S.D.J., Barbosa, F.V.: Predicting patient no-show behavior: a study in a bariatric clinic. Obesity Surg. 29(1), 40–47 (2018)
Christodoulou, E., Ma, J., Collins, G.S., Steyerberg, E.W., Verbakel, J.Y., Van Calster, B.: A systematic review shows no performance benefit of machine learning over logistic regression for clinical prediction models. J. Clin. Epidemiol. 110, 12–22 (2019)
Lekham, L.A., Wang, Y., Hey, E., Lam, S.S., Khasawneh, M.T.: A multi-stage predictive model for missed appointments at outpatient primary care settings serving rural areas. IISE Trans. Healthc. Syst. Eng. 11(2), 79–94 (2021)
Al Rafi, A.S., Rahman, T., Al Abir, A.R., Rajib, T.A., Islam, M., Mukta, M.S.H.: A new classification technique: random weighted LSTM (RWL). In: 2020 IEEE Region 10 Symposium (TENSYMP), pp. 262–265. IEEE (2020)
Periyathambi, N., et al.: Machine learning prediction of non-attendance to postpartum glucose screening and subsequent risk of type 2 diabetes following gestational diabetes. PLoS ONE 17(3), e0264648 (2022)
Acknowledgment
The authors would like to thank Eastern International University (EIU) and Becamex International Hospital (BIH) Vietnam for funding this research.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2023 The Author(s), under exclusive license to Springer Nature Switzerland AG
About this paper
Cite this paper
Nguyen, P.T., Dang, D.T., Nguyen, V.D. (2023). A Robust Deep Learning Techniques for No-Show Prediction in Hospital Appointments. In: Hassanien, A., Rizk, R.Y., Pamucar, D., Darwish, A., Chang, KC. (eds) Proceedings of the 9th International Conference on Advanced Intelligent Systems and Informatics 2023. AISI 2023. Lecture Notes on Data Engineering and Communications Technologies, vol 184. Springer, Cham. https://doi.org/10.1007/978-3-031-43247-7_1
Download citation
DOI: https://doi.org/10.1007/978-3-031-43247-7_1
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-031-43246-0
Online ISBN: 978-3-031-43247-7
eBook Packages: Intelligent Technologies and RoboticsIntelligent Technologies and Robotics (R0)