In healthcare, a no-show is a patient that misses a scheduled appointment without proper cancellation. Reducing no-shows can have a tremendous, positive impact on a practice resulting in improved efficiency, reduced costs, and improved patient outcomes. Machine learning and artificial intelligence (AI) strategies offer reliable solutions to manage this unwelcomed issue.
Missed appointments disrupt the continuity of an organization’s workflow and lead to inefficient resource allocation. When a patient misses a scheduled appointment, two patients miss the opportunity for care; the patient that did not attend their original appointment and the other patient who could have been scheduled at that time.
For the patients, they risk a delay in preventive services, acute issues shifting to chronic conditions, complications in recovery, and increased health consequences. For the practice, revenue deficits can increase in parallel to no-show rates. Understanding the underlying causes of no-shows is the best means of correction; prevention is the treatment.
Understanding the Problem
Research studies have shown that no-shows result in a total loss of revenue of over $150B a year in the United States alone. At most healthcare facilities and practices, the typical no-show rate ranges between 23% to 34%. In New York, this rate extends closer to 40%, averaging one in every three patients.
Each represents a non-reimbursable visit for that day, reflecting a loss of approximately $200 per patient. The consequence of these missed opportunities become unnecessarily pricey as they accumulate, endangering an organization’s profit margin, and ultimately, even their bottom line.
Unfortunately, patients may not understand the financial impact of a missed, scheduled appointment. Many believe that this is standard practice, and the administrative staff takes no-shows into consideration when reserving the time slot for an appointment, as airline carriers often overbook to compensate for missed travelers. Unlike airline carriers, however, if clinics overbook, the wait times will be increased for patients, leading to patient dissatisfaction and physician burnout.
An automated and effective way of confirmation – reminders
Studying recurring patients’ appointments, Vee Technologies can use artificial intelligence (AI) to provide a solution through machine learning. We look at patient variable data to predict the probability of patient absenteeism.
Combining historical data, demographic data, and population health metrics provides a more accurate assessment of the risk of a no-show. With this information at hand, we can suggest appropriate action before a patient’s appointment date to help increase the likelihood of attendance.
For example, if a patient has 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.
Social Determinants of Health
Social Determinants of Health (SDOH) data is an influential variable in the prediction of a patient’s no-show probability. Based on this historical, geographic, and demographic information, algorithms are used to determine the likelihood of the patient missing an appointment.
With this readily available data, we can ensure that patients are satisfied with their appointment, increasing their attendance probability as we take into consideration the roadblocks preventing patients from attending appointments. Our tools are then able to support customers to adjust their services to meet individual patient needs.