Appointment no-shows are a common problem in the health care industry, especially when it comes to behavioral health.
According to a study by the National Center for Biotechnology Information, the no-show rate for behavioral health appointments reaches a staggering 37.4% on average. That’s higher than nearly all physical health treatments, with pediatrics hovering around 30%, primary care at around 19% and dentistry at 15%. No-shows cost clinics $150 billion annually, and they often lead to unsatisfactory care outcomes.
While there’s no single method for ensuring patients attend their behavioral health appointments, artificial intelligence (AI) is posing a solution. Through machine learning, clinics are now able to look at patient variable data to predict the probability of patient absenteeism. Combining historical data with demographic data and population health metrics provides a more accurate assessment of the probability of a no-show. With this information at hand, behavioral health clinics can take appropriate action before the patient’s appointment date to help increase the likelihood of attendance. For example, if a patient is discovered to have a 33% no-show rate, the clinic can use text message reminders, suggest a more convenient location or provide transportation options to help the patient get to his or her appointment.
To predict a patient’s no-show probability, Social Determinants of Health (SDOH) data is collected, such as conditions in the places where the patient lives, learns, works and plays. Based on this historical, geographic and demographic information, algorithms are used to determine the likelihood of the patient missing an appointment. With this type of readily available data, clinicians gain a better understanding of what’s preventing patients from attending appointments. The AI is then able to add new tools to help clinicians adapt their services to meet individual patient needs. With more personalized care, patients are more likely to feel satisfied with their appointment, which in turn increases their attendance probability.
Another key benefit of this technology is its use for creating data-supported recommendations for overbooking appointments. Because AI can sift through thousands of data entries in real-time, the algorithm can identify which patients are at the highest risk of absence and make recommendations on whether a time slot should be staggered. With this technology in-place, clinicians can make sure their time is consistently maximized to provide the greatest value to their patients.
Adopting the right technology is key for patient success, especially when it comes to behavioral health outcomes. By engaging AI, clinics can start to reduce no-show costs while increasing overall patient satisfaction. If your clinic is ready to reduce patient no-shows, contact us here to learn how HiMS’ Axiom EHR software for mental health can help.
