Problem |Adherence to treatment for patients in behavioral health and substance use disorder is a substantial issue for providers. Patients who decide to discharge against medical advice endanger themselves by leaving without treatment, and burden medical facilities by increasing bed occupation variance. Increased variance in bed occupation raises costs and is detrimental to revenue for providers that treat BH & SUD patients.
Outcome| Implement a statistical and machine learning approach like FIXXER™ to analyze risk factors for patients leaving AMA. FIXXER™ provides key indicators impacting patient’s AMA risk, defines thresholds and benchmarks for key operational indicators, and guides clinical and operational interventions required to provide the highest quality and most financially sustainable care.
Solution| To minimize the probability of leaving AMA, providers must identify key risk factors associated with a higher AMA probability and implement mitigating interventions. Healthcare facilities cannot control their patient’s behavior, so risk factors linked to operations and treatment protocols are essential to decrease unplanned discharges, limit bed occupancy volatility and reduce lost revenue.
