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Our Mission
There is a significant unmet medical/economic need to develop new technologies that improve no-show rate predictions within the healthcare field. To reduce patient no-show rates, medical offices use a variety of techniques, including appointment call reminders/texts, policies charging the patient, waitlists for patients, and no-show predictive software applications allowing clinics to schedule other patients within the missed time slot.
Our goal is to outperform current patient no-show algorithms across different medical specialties and EMR software systems. We want to identify modifiable factors about the patient encounter that can increase future compliance, leading to better patient care and increased efficiency of the healthcare system.
Methodology
Our company (Aspirations LLC) has developed a prototype software platform that implements machine learning algorithms to predict patient no-show rates more accurately through integrating Electronic Medical Record (EMR) personal and environmental data. The model interfaces with existing medical record/scheduling databases and generates a probability scoring factor for use by medical offices to predict no-show rates. Initial testing of the Artificial Intelligence-based algorithm resulted in no-show prediction rates of up to 90% as compared to other software applications using least- squares predictive techniques, which realized a 68% prediction rate.
