Simplifying how healthcare operates

Mudit Garg

Problem Tech Solves

In health systems and hospitals today, managing discharge planning is a manual, complex and chaotic process. This dysfunctional process results in significant variability in the care process and costly excess days that lock up critical bed capacity and resources for hospitals — while simultaneously delivering a poor patient experience. Frontline team members need to analyze hundreds of data points included in each patient's EHR record to determine key elements of the discharge plan including estimated date of discharge, barriers to discharge, and likely disposition. Doing this manually is incredibly challenging -- particularly because teams often only have 30 seconds per patient during multidisciplinary rounds (MDRs). Estimated dates of discharge (EDDs) are manually entered, often not until late in a patient’s stay. Disposition needs are identified days into the stay, losing days if post-acute logistics have not been started early enough. With too many orders to track, discharge barriers are often not identified until the last day, requiring teams to dig through charts, go through open orders, and manually check off barriers. The Qventus Inpatient Solution was designed to solve these challenges and help teams apply best practices proven to reduce excess days by 91%: early discharge planning, and proactive barrier management for complex discharges. Integrating with EHRs, the solution uses AI, machine learning, and behavioral science to standardize and streamline the discharge process. For patients, this means less unnecessary time in the hospital; clearer expectations around their care progression; and better preparation for their transition of care.

Tech Brief

Integrating with EHRs, the Qventus Inpatient Solution combines AI, behavioral science, and process redesign to hardwire discharge planning best practices. The platform automates key steps for discharge planning, predicts barriers to discharge and orchestrates their resolution, and enables leaders to effectively manage accountability. The solution helps teams: -Predict patients’ discharge date 2x more accurately than staff can: Care Progression Manager uses process automation and sophisticated AI models to enable simple, frictionless workflows that reinforce discharge planning best practices. Using machine learning, Qventus can estimate the date a patient will be discharged and any barriers to getting them there. The feature, Discharge Autopilot, takes away the guesswork for overburdened staff and gives patients a significantly more accurate estimation of their stay. -Prioritize orders for ancillary services using machine learning: The Qventus Priority Queues reduce the manual coordination steps to resolve barriers and helps ancillaries prioritize orders based on patients’ discharge readiness. Ancillary teams can better staff priority orders and complete them in a timely manner. -Improve frontline performance to achieve operational efficiency: Performance Improvement Automation enables hospital leaders to track operational efficiency across their system in real-time using the new Discharge QScore -- a single, proprietary process metric highly correlated with excess day reductions and reflects when and how accurately care teams are planning for patient discharge. Leadership teams can measure best practice adherence, automatically detect when processes slip, and reinforce behaviors over time to drive sustained length of stay reductions.

Tech Differentiators

The Qventus Inpatient Solution is built on top of Qventus’ real-time automation platform that integrates with EHRs and enables management of operations as a closed loop system. First, it uses AI & ML to automatically identify operational issues, like bottlenecks in patient flow — in real-time, as they’re occuring, as well as in the future, predicting them before they occur. Then, it orchestrates actions using behavioral science-based techniques for forming habits — as well as by using process automation to complete tasks whenever possible. Finally, it provides statistical process analytics tools to manage accountability for leaders. As opposed to using rudimentary “out-of-the-box” EHR models or building in-house ML expertise & infrastructure (e.g. Epic’s Cognitive Computing Platform), Qventus trains and manages ML models that have been specifically crafted for real-time prediction based on patient and hospital-specific data. This includes: -Large-scale data normalization and automated data quality system that runs thousands of automated checks to accelerate data validation and manage concept drift. -"Time travel" that replays historical data streams and accelerates validation of ML models for real-time performance by months. -Continuous, automatic model retraining that dynamically tunes and adjusts features for the hospital's current operating dynamics. In addition, Qventus uses probabilistic inference models, which go far beyond traditional ML predictions by determining the probabilities of different outcomes and taking different actions — automation or human in the loop suggestions — based on probability and risk profile. What’s more, 100% of Qventus partners achieve statistically significant excess day reductions well within the first year.


One example of Qventus’ power to transform operations is their work with health system partner M Health Fairview, an 11-hospital, integrated academic health system based in Minnesota. Before Qventus, leaders set a goal to increase morning discharges but despaired when they could not reach their goals. With the Qventus Inpatient Solution, M Health Fairview achieved significant improvements in patient flow that unlocked valuable capacity, including: 0.5-1 day reduction in length of stay (LOS), 5-7x increase in discharges before 11am, and over 11,000 excess days removed per year. As a testament to the power of Qventus’ AI, a key physician leader said that “the machine learning that Qventus gives us is amazing. The estimated date of discharge prediction is something we never thought we’d be able to have at our fingertips.” As another example of impact, Qventus health system partner NewYork-Presbyterian (NYP), a nationally ranked academic medical center with more than 2 million annual visits, operated at over 95% inpatient census and had a massive need to create capacity. With Qventus, NYP Columbia-Cornell observed a half day decrease in length of stay, which equates to the capacity of 35 beds -- or one entire unit. What’s more, 30% of patients are now discharged before noon. As one Care Coordination Manager put it: “It’s impossible in a short amount of time to go into every patient’s EHR record and understand discharge priorities. With Qventus, it’s beautifully laid out so that we know what to do to move the patient along.”

Why Us

With over $1 trillion of waste in healthcare, the opportunity to improve operational reliability is massive. Qventus alone is on track to reduce $1 billion of waste per year by 2025 and is uniquely positioned to save billions more over the next decade.

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