use cases

Predict Appointment No-Shows

Increase appointment attendance rates, reduce the financial burden of no-shows, and improve the health outcomes with Artificial Intelligence.


Nationally, patients miss 30% of their scheduled appointments, resulting in a $150 billion annual financial burden on the healthcare system (1). This high rate of missed appointments not only creates significant financial strain for clinics, costing an average of $200 per missed appointment and accumulating to over $250,000 annually per clinic, but also poses serious health risks for individuals (1)(2). Missed appointments disrupt continuity of care, increase the use of acute care services, and lead to declines in health that could have been mitigated or prevented with timely diagnosis and treatment (3). For patients with chronic conditions, such as long-term mental health issues, missing more than two appointments per year can increase their risk of mortality by eight times compared to those who do not miss appointments (4).

Why it matters

  • Patients miss 30% of scheduled appointments, resulting in a $150 billion annual financial burden on the healthcare system.
  • Each missed appointment costs clinics an average of $200, leading to over $250,000 in losses annually per clinic.
  • Missed appointments disrupt continuity of care and can increase mortality risk eightfold for patients with chronic conditions, particularly mental health issues.


"NoShowPredict AI" is a predictive AI model specifically designed to calculate the probability of patient no-shows for medical appointments. By evaluating patient-related factors and appointment details, NoShowPredict AI aims to reduce the rate of missed appointments and alleviate the financial and care-related repercussions associated with them.

User person: Clinical Operations Manager, Patient Care Coordinator, Health Financial Director, Health Information Management Specialist, Hospital Manager.

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The synthetic data set used to train the model was based on patterns and insights derived from studies on appointment keeping. The research by Hwang et al. (1), Marbouh et al. (2), McQueenie et al. (3), and Kullgren et al. (4) provides an analysis of the causes and consequences of missed appointments, which were used to recognize potential non-attendance factors and train the model.


  1. Hwang, Andrew S., et al. “Appointment No-Shows' Are an Independent Predictor of Subsequent Quality of Care and Resource Utilization Outcomes.” Journal of General Internal Medicine, vol. 30, no. 10, 17 Mar. 2015, pp. 1426-1433, doi:10.1007/511606-015-3252-3. Accessed 17 Mar. 2021.
  2. Marbouh, Dounia, et al. “Evaluating the Impact of Patient No-Shows on Service Quali” Risk Management and Healthcare Policy, vol. 13, 4 Jun. 2020, pp. 509-517, doi:10.2147/rmhp.s232114. Accessed 17 Mar. 2021.
  3. McQueenie, Ross, et al. “Morbidity, Mortality and Missed Appointments in Healthcare: A National Retrospective Data Linkage Study.” BMC Medicine, vol. 17, no. 1, 11 Jan. 2019, doi:10.1186/s12916-018-1234-0. Accessed 17 Mar. 2021.
  4. Kullgren, Jeffrey T., et al. “Nonfinancial Barriers and Access to Care for U.S. Adults.” Health Services Research, vol. 47, no. 1, 22 Aug. 2011, pp. 462-485, doi:,1111/51475-6773.2011.01308.x. Accessed 18 Mar. 2021.

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