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Like many health systems, Boston Medical Center had inefficiencies in their care processes that led to increased costs and impacted quality and experience. They decided to first focus discharge planning improvement efforts on the 10% of length of stay outliers—but they were missing the other 90% of patients.
For Multidisciplinary Discharge Rounds (MDRs), Qventus uses AI and machine learning to autopopulate or suggest discharge dates, dispositions, and barriers to discharge to reduce frontline burden and improve discharge planning quality.
The solution triggers automation workflows to make sure ancillaries are prioritizing orders for discharge, and also nudges leadership teams in real time if processes slip. This ensures that by the time the patient is clinically ready for discharge, they are ready to leave the hospital and provides a better experience for their patients and staff.
