Introducing An Air Traffic Control for Hospitals | Qventus, Inc.

Introducing An Air Traffic Control for Hospitals

Our mission at analyticsMD has been simple: improve healthcare through the power of data. What started as a journey to empower frontline healthcare workers with easier access to data has evolved to an AI delivering decisions in real time.

Providers in the United States are under tremendous pressure to deliver higher outcomes and a better patient experience at a lower cost. Many of us believe in the transformative power of data and analytics to achieve this. The sad reality is that current analytics only aggregate data in dashboards and graphs without clear insights.

The key to solving this problem is to focus on the frontline staff and clinicians. Everyday, these care providers make countless decisions that determine the cost, quality and experience of a patient’s care. Busy front line workers deal with one emergency after another; they are too busy to be looking at graphs and charts.  We empower staff by replacing the need to log in and look at numbers and instead delivering specific recommendations as they need them.

Our Artificial Intelligence (AI) “DecisionOS” monitors, predicts, and prescribes optimal actions at the right time to the right end user. For a clinician or an operator, this is an assistant who’s got your back and will scour through data to suggest actions you should take on the fly. It has been exciting to see the broad based success in applying this methodology. Our partners have seen dramatic improvements in patient safety, patient satisfaction and cost of care by helping the staff take data driven decisions every day in various departments of the hospital.

Today, we are excited to announce the next step. As we add different decision recipes for our AI, the next logical step for us is to “connect the dots” and create a virtual air traffic control system that coordinate actions across siloed departments and teams in the hospital and the broader health system.

With this Air Traffic Control system, hospitals will understand in real time the impact of poor performance in inpatient flow on the OR and the PACU or how to anticipate and improve outpatient access and its effect on the how busy the ER is going to be. For each of the decisions monitored, our AI will learn from not one but a cohort of hospitals. Imagine if we could more precisely anticipate a patient’s surgery length. Or imagine how different the world might look if, by analyzing real-time data from multiple hospitals in California experiencing a viral outbreak, we can recommend to hospitals in Nevada or Washington to up staff and prepare effectively.

If you believe in this vision to simplify healthcare for our frontline staff and are interested in learning more, please reach us directly at

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