Predict Appointment No-Shows

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).

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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).

1. Early Risk Identification with Predictive Analytics: Using machine learning algorithms, AI systems can analyze previous appointment histories and behavioral patterns to identify patients at higher risk of missing their appointments. This allows healthcare centers to intervene early, whether through personalized reminders or consultations to address barriers to attendance.‍
2. Optimization of Appointment Scheduling: AI can be used to optimize appointment schedules based on the likelihood of attendance, thus minimizing gaps in the medical agenda. By predicting times with a higher risk of no-shows, centers can adjust their scheduling to maximize the use of available resources.‍
3. Enhancement in Patient Communication and Engagement: Implement automated communication systems that use AI to send appointment reminders via multiple channels such as SMS, email, and phone calls. These systems can adapt the tone and content of the message based on each patient's preferences and history, thereby improving the effectiveness of the reminders.‍
4. Predictive Model to Prevent Medical Appointment No-Shows: We have trained an artificial intelligence model using a balanced database designed to predict whether a patient will miss their medical appointment. This model is trained with variables such as patient age, gender, lead time before the appointment, previous no-shows, insurance status, distance to the healthcare center, and the day of the week of the appointment. The goal is to proactively identify patients who are likely to miss their appointments, allowing healthcare providers to take measures to increase attendance rates and improve both patient health outcomes and the operational efficiency of the center.

• 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.

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