Hospital leaders must improve efficiency and speed, provide high-quality care at a reasonable cost while improving margins, and meet patient needs. Big data and analytics once held the promise of addressing these issues and transforming hospitals. Instead, hospitals are inundated with data without context or actionability, and clinicians are burning out from the thousands of disparate decisions they need to make in an intense, highly dynamic environment.
Decision Science and Machine Learning Enable Course Corrections
A real-time operations management solution from Qventus can help teams mobilize and proactively streamline patient flow by preventing anticipated workflow bottlenecks. Prioritized, actionable alert notifications are delivered to the right team members at the right time on their device of choice. Using decision science and machine learning, the solution continuously monitors operational data, predicts potential issues, and delivers recommended course corrections. It helps solve patient flow challenges across the hospital and health system, including the ED, perioperative, and inpatient areas.
A Solution that Predicts, Prescribes and Persuades to Streamline Patient Flow
Continuous Monitoring of Operational Data Helps Drive Efficiency
Decision Science and Machine Learning
The solution continuously monitors operational data from the EHR and other systems and predicts potential events that could impact patient flow, such as ED surges, OR scheduling changes, or inpatient discharge delays.
Course Corrections to Streamline Patient Flow
The solution identifies opportunities for action based upon context, taking into consideration cost and benefit, and delivers recommended course corrections to the right team members at the right time to help them prevent anticipated workflow bottlenecks.
Caregivers receive actionable, prioritized alerts via a prescriptive nudge on their device of choice, helping teams to mobilize, collaborate and address issues regardless of each team member’s location.
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