Qventus for Command Centers
Drive systemness & operational efficiency across the enterprise
Combine new innovations in artificial intelligence with industry-leading operating models to maximize existing enterprise resources, improve patient flow, and reduce frontline burden.
Optimize asset utilization
Enhance patient experience
Decrease cognitive burden
One hospital overwhelmed with patients while another sends home on-call nurses. Constant competition between hospitals — even within the same health system — for limited SNF beds. Hours spent combing through outdated dashboards to find bottlenecks holding up boarders and discharges across the enterprise.
Even as individual facilities optimize operations within their four walls, health systems still lack effective mechanisms to gain situational awareness, distribute demand, and prioritize resource needs. With the pandemic accentuating the need to achieve systemness, health systems continue to struggle to accommodate outside transfers, balance uneven patient loads, and manage post-acute care availability.
It’s time for a new approach.
Qventus combines unique expertise in AI, behavioral science, and operations management to help health systems drive greater systemness for patient flow operations. With the Qventus Inpatient Solution focused on hardwiring discharge planning best practices to reduce length of stay within individual facilities, the Command Center Solution extends the benefits of these practices to the system level, allowing health systems to intelligently distribute demand, effectively optimize care progression, and strategically manage post-acute placements.
Create situational awareness & predict bottlenecks across the enterprise
Gain system-wide visibility across multiple altitudes
Qventus provides real time situational awareness at each level of the enterprise. Using sophisticated machine learning models, Qventus empowers command centers with key information to predict demand and prioritize resource needs across the system.
Pinpoint prescriptive, actionable opportunities to resolve issues
Qventus automatically detects predicted bottlenecks and “nudges” teams across sites to resolve them. This reduces noise and cognitive burden for staff by removing the manual work of combing through dashboards, triaging alerts, and coordinating across sites.
Intelligent Demand Distribution
Load-balance demand across facilities
Optimize enterprise throughput with effective balancing of patient demand
Using sophisticated algorithms and machine learning models, Qventus uncovers opportunities to load-balance patients across facilities. Putting the right patient in the right place at the right time, health systems can reduce boarding time, decrease diversions, and expand patient access.
Optimize discharge and barrier management
Extend support to frontline teams for on-track discharge
Qventus hardwires escalations to Command Centers when units need support to meet discharge targets. With visibility into available resources across the enterprise, Command Centers can effectively deploy needed help to resolve barriers before they extend length of stay or impact morning discharge goals.
Strategically manage capacity through centralized post-acute care placement
Effectively prioritize post-acute placement needs through a centralized approach
With early discharge planning hardwired through the Inpatient Solution, Command Centers can effectively prioritize patients across facilities for post-acute placement to reduce length of stay and strategically open capacity within individual facilities. This coordinated approach not only reduces competition within the system for limited post-acute beds, but also decreases burden for frontline care teams.
Services for Enterprise-Wide Transformation
Current State Assessment
Standard Work Design
We need to truly systematize health systems. With an enterprise-wide platform that integrates into processes and removes the burden from providers, we’re able to increase efficiency and throughput, reduce cost, and ultimately better meet the needs of our patients.