Patient nonadherence occurs when a patient stops engaging with the care they have signed up for or their health plan has authorized. Unfortunately, from physical therapists to dentists, patient nonadherence is a common challenge faced by all types of provider settings. According to Strive Labs, only 30% of patients in outpatient physical therapy settings complete the entire course of care authorized by their health plan. This not only leads to worse health outcomes for patients but also results in lost revenue and patient leakage for providers.
AI allows you to have foresight on which patients are likely to churn before completing their course of care. AI learns from historical cases of churn and their associated characteristics to predict which patients are at risk. With this information, your practice management staff can invest more time into reaching out to these patients to learn their unique circumstances and develop constructive solutions. As AI also gives you the top reasons behind each patient’s probability of churn, you will be able to take preventative measures that are unique to every patient and personalized to their specific issues or needs.
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Healthcare companies are using machine learning and AI to increase top and bottom line through gaining competitive advantages, reducing expenses, and improving efficiencies. They are optimizing all areas of their business from readmission risk and occupancy rates to marketing, in order to make data-driven decisions that lead to increased profitability.