Arrow
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

Problem

Nationally, patients are “no-shows” for 30% of their scheduled appointments, representing a $150 billion financial burden on the healthcare system annually. In addition to financial strain, missing appointments can lead to poor continuity of care, increased acute care utilization, and declines in health that could have been mitigated or prevented with earlier diagnosis and treatment.

Size of the Problem

  • $150 billion is the annual cost directly attributed to missed appointments (1).
  • More than 20% of adults in the U.S. experience nonfinancial barriers that lead to unmet or delayed care (6).
  • 30% as many as 30% of appointments are missed nationwide (1).

Why it matters

Across the nation, patients are “no-shows” for 30% of their scheduled appointments. Such a high rate of missed appointments creates a financial burden for clinics and a potential health burden for individuals. For clinics, each missed appointment costs an average of $200, accumulating to an annual amount that can exceed $250,000 per clinic and $150 billion across the health system nationally (1,2).

For individuals, missed appointments can lead to poor continuity of care, increased acute care utilization, and declines in health that could have been mitigated or prevented with earlier diagnosis and treatment (3,4). For some patients, such as those with chronic conditions, repeatedly missed appointments can even lead to an increased risk of mortality. This is particularly notable for patients with long-term mental health conditions—among these patients, people that miss more than two appointments annually increase their risk of mortality by eight times that of similar patients who do not miss appointments (5).

Solution

Predictive analytics and AI can help healthcare organizations (HCOs) increase appointment attendance rates, reduce the financial burden of no-shows, and improve the health outcomes of their patients. Using AI-based models, HCOs can predict which patients are most likely to no-show, identify the most significant contributing factors (e.g. long lead times, no-show history, lack of private insurance) and integrate these factors with distinct social determinants of health and complex patient data (6). HCOs can use these insights to proactively address patient-specific barriers in ways that promote care continuity and lead to better health outcomes.

Datasources

  • Rx Claims: Data extracted from health insurance pharmacy claims with details about each medication and its type, fill dates, days supply, pharmacy location, and prescribing clinician.
  • Social Needs Assessments: Self-reported data that identify an individual's specific needs and the acute social and economic challenges they are experiencing.Operations & Services: Data from health plans, clinics and service providers that capture details about service interactions, billing and payment, technical issues, and complaints.

Citations

  1. Gier, Jamie. “Missed appointments cost the U.S. healthcare system $150B each year.” Healthcare Innovation, 26 Apr. 2017. Accessed 17 Mar. 2021.
  2. 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.
  3. 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.
  4. 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.
  5. 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.

Book a Free Consultation

Trusted by the world's top healthcare institutions