In an ideal scenario, a hospital’s patient flow would be fully optimized.
Patient admission patterns would be almost as predictable as the guest arrival patterns in hotels, with their scheduled check-in and check-out times. Patients would be discharged as soon as they are cleared to do so, staff allocated in advance to the units where they were needed most, and units opened and closed according to actual admissions and discharges. All staff could access the same up-to-date data to coordinate care, get ahead of possible barriers, and make strategic decisions. Patients would receive timely care, while staff would be empowered to focus on delivering it.
Unfortunately, as many health systems experience daily, this scenario rarely matches reality. Patient demand varies and fluctuates. Unlike hotels, hospitals cannot dictate patient arrival and departure times. New patients are admitted before existing patients are discharged, creating throughput issues. Discharges are delayed by missing lab and test orders or lack of care team alignment, and units reach full capacity, forcing patients to board in the ED and PACU. Nursing leaders work tirelessly each shift to distribute strained staffing resources to the locations where they think they’re needed most, with a limited view of patient care needs. As a result, some patients stay longer than necessary while others wait for care, and nursing staff experience burnout due to high patient loads and excessive work hours, impacting care delivery.
Achieving the ideal scenario is possible. Health systems have the expertise to optimize patient flow, but they need the right technology capabilities to effectively support their efforts. Unlike EHRs, purpose-built, AI-powered solutions can provide staff the predictive insights and workflow automation essential to drive proactive coordination across the care continuum.
Unblocking patient flow begins with empowering staff through AI
Inpatient gridlock stems from limited foresight into potential issues, a lack of proactive data, and inefficient communications that prevent leaders and frontline teams from optimizing capacity despite their expertise. EHRs offer dashboards that show past and present capacity and discharge status, but offer no insights into upcoming patient care needs or suggestions to mitigate issues. This is the equivalent of a navigation app that tells the driver they are currently stuck in heavy traffic, without offering an alternate route to avoid it.
With only past and present data, staff can do no more than “admire” the problems of full units or boarding patients while attempting to manually piece together the full picture via spreadsheets, calls and texts. This reactive and time-consuming cycle perpetuates capacity issues while diverting focus from patient care.
To help leaders and frontline teams execute their capacity optimization plans, health systems need technology that provides predictive recommendations, automated workflows, and streamlined coordination. Only technology built on AI can enable proactive guidance at scale, and health systems who have deployed it have already seen noticeable progress toward optimal inpatient flow.
By implementing the AI-driven iQueue for Inpatient Flow solution, the Colorado-based UCHealth reduced avoidable or “opportunity” days by 8% and increased admission volume by 6.4%, meaning over 1,700 additional patients received hospital-based care without increasing bed capacity or staffing needs. A leading Florida health system reduced average length of stay by 13 hours in one facility during the highest census levels in that facility’s history. Staff at these health systems were empowered to achieve these results by the capabilities iQueue offers.
Staff need predictions, prescriptions, and automation to enable better inpatient flow
AI solutions like iQueue empower staff by providing three essential capabilities that are missing from traditional dashboards: predictions, prescriptions, and automation.
Predictive analytics use existing patient flow data to forecast admission and discharge patterns, alerting staff to bottlenecks days in advance. With proactive visibility, staff can get ahead of capacity issues instead of reacting as multiple problems compound. Predictions alone, however, are not enough to promote effective action. A prediction that the ICU will be at capacity in three hours, and that four more patients are likely to arrive who need that level of care, does not help staff reallocate staff or beds to absorb those patients.
The technology’s prescriptive guidance takes prediction to action by prescribing optimal next steps per staffing and bed availability. Rather than piecing together disparate data sources, staff leverage dynamic recommendations to inform nuanced decisions from a single source of truth, and take action to unlock capacity where it is needed most.
Finally, automating repetitive administrative tasks gives staff time and energy back for high-value work. Front-line staff in inpatient areas can spend 20 or 30% of their time on below-license work like pulling data or trading emails with colleagues. A solution that automates many of these tasks, provides up-to-date analytics, and aligns all users on the same information, eliminates these manual workflows and gives staff time back to focus on patient care. Staff at Health First, when using iQueue for Inpatient Flow, were able to reduce their weekly hours spent on manual data collection and phone calls by 200%. Freeing this time staff can focus on strategic decision making as well as patient care.
Giving these three capabilities to staff leads to health systems discharging more patients each day and decreasing avoidable days, driving higher efficiency outcomes for organizations as well as a better experience for patients.
Key areas where AI technology promotes optimized patient flow
Solutions like iQueue for Inpatient Flow leverage AI to equip staff with predictive analytics, prescriptive recommendations, and automated workflows targeted to critical pressure points for patient flow optimization.
By forecasting each patient’s discharge timing and discharge destination at admission, iQueue actively reshapes the discharge curve for maximum efficiency. iQueue’s insights allow staff to get ahead of barriers, minimize delays and focus on priority discharges first. To reduce bottlenecks, iQueue delivers proactive capacity alerts, enabling intervention before issues escalate and cause challenges downstream. With advance notice, staff reassess plans to open beds proactively.
Given these AI-driven insights, staff drive optimized patient flow and outcomes across the health system. The technology enables them to transform their organization for better capacity and better care.
Learn more about iQueue for Inpatient Flow and our health system customers’ success here.