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Direct inpatient flow to make the best use of empty beds

Helping bed staff achieve more: five goals to optimize capacity and direct inpatient flow

Pallabi Sanyal-Dey, MD, FHM

Director Client Services, iQueue for Inpatient Beds, Associate Clinical Professor, UCSF School of Medicine

This article was originally published on Becker’s Hospital Review in February 2022.  

Severe staff shortages, new COVID variants, and unpredictable surges are making it critical for hospitals to optimize inpatient capacity.

The short-handed staff who are responsible for capacity decisions must be fully equipped to direct patient flow safely, quickly, and to the best possible use of available bed units.

Too often floor and unit managers are forced to rely on manual data, intuition, and past experience to make capacity decisions and direct patient flow on a daily basis. While they are often correct, these methods are not scalable and cause undue pressure on personnel who are already at great risk of burnout. Inpatient bed staff need easy, actionable information to guide their day.

The following goals, identified during a recent discussion with healthcare leaders across the country, are focus areas where adoption of technology can help support staff, especially in addressing common issues that impede efficient patient flow.

Goal 1: Create better, more productive daily huddles
Staff must grapple with issues of inpatient bed capacity and patient flow, including anticipated gaps in available beds or past safety events, each day to address them on time.

Hospital staff have only rudimentary and insufficient tools for daily morning huddles at their disposal. The rest of the day thereafter is mostly spent on reactive decision making and numerous ad hoc meetings to troubleshoot crises. At some organizations, Excel or color-coded paper spreadsheets are reviewed to predict how many beds will open up and when, and to estimate demand for those from both planned and unplanned sources.

To mitigate this, healthcare organizations are moving towards AI-based technology to better predict inpatient bed demand. These solutions create a system-wide source of truth, predicting admissions, discharges, and end-of-day “balance” by unit. This support helps keep daily huddle meetings targeted and brief, and ensures they only occur at times throughout the day when they are truly needed. One hospital’s daily meetings now last no more than fifteen minutes after adopting the capacity management solution iQueue for Inpatient Beds.

Goal 2: Open and close surge units at the right time
Accurately anticipating the need for surge capacity directly impacts patient care and operational expenses. Overlooking upcoming demand signals can lead to being overwhelmed, while reactively opening an unneeded surge unit wastes time, energy, and resources.

With the right technology, supply and demand side models can be used to predict bed shortages. Factoring in trends of which beds are often occupied, where new demand is coming from, and which departments are likely to have higher utilization empowers staff to be agile and proactive in triaging patient flow and specialized care. This enables staff to match demand with supply correctly and open surge units only when needed.

Goal 3: Manage internal transfers
Internal transfers are often thought of as costly, reactive last resorts, which should only be deployed when bed space is at a premium and specialized staff must be moved outside their usual domain. However, transfers can be used as a proactive tool to support supply-demand matching, if they are deployed wisely and selectively.

For example, by thinking a few moves ahead and moving the right patients to appropriate open beds, placement teams can open up the right slots to meet expected demand for high-value beds. This is made possible through the use of predictive analytics solutions like iQueue.

Goal 4: Achieve safer, more effective discharge practices
Managing safe, timely discharges, especially as patient conditions and post-discharge needs become more complex, feels like “running on a treadmill”.

While freeing a bed through a discharge inevitably takes time, the amount of time it takes is within administrators’ control. Strategically planning and addressing patient discharge needs starting at the time of admission is the most effective way to safely reduce discharge times. Staff’s focus should be freed to direct patient flow overall.

While hospital leaders and staff cannot accelerate a patient’s healing, they can ensure they are safely and efficiently delivered to each next stage in their care journey. Providing a transparent software solution to coordinate the entire care management team helps anticipate potential delays before they occur and better directs patient flow overall.

Predictive modeling, which is already used in other industries like the airline industry, can also be used in discharge planning. These solutions alert case management and social services teams to potential discharge barriers sooner rather than later, allowing teams to address the issues earlier and avoid delays.

Goal 5: Promote system wide visibility
Achieving visibility and distributing patients across a health system has become particularly crucial in the wake of the COVID-19 pandemic. Even in very specialized facilities, all patients need a system-wide approach to sufficiently support their care journeys. Still, this is often a challenge in health systems, where individual facilities may feel they function as islands and struggle to collaborate with others in the network.

As with discharge planning, this issue must be approached strategically. Technology solutions show an accurate overview of the entire system’s capacity, as well as up-to-the-minute information on which hospitals are best equipped to take which patients and which are struggling with capacity at a given time. This visibility allows viable transfers between facilities and allows health systems to direct patient flow and optimize their entire capacity of inpatient beds.

The Ultimate Goal: Predict the daily chess game
In determining patient placement, healthcare leaders play a daily “chess game”. How do you plan as many moves ahead as possible for the ED, OR, ICU or inpatient beds?

The game gets easier with specifically tailored predictive models tapping each element of demand. These include incoming volumes from the OR and ED, as well as external and internal transfers. Predictive models should be updated using real time feeds that capture any delays or unanticipated surges.

Patient placement leaders can master this “chess game” by anticipating the next several moves well in advance, leading to dramatically better outcomes than a purely reactive response with no appreciation for how unit capacities are likely to unfold.

A Final Note: Where is hospital bed capacity headed?
Similar to how apps like Waze take baseline predictions from the speed of traffic for each section of the road for each minute of each day of the week, solutions are now available that model current and future bed availability in each unit.

iQueue for Inpatient Beds uses historical data to mathematically create a model for each unit that predicts the likely number of patients that will be discharged for each hour of each day. These predictions are fueled by real time data feeds and can be used to address incoming demand signals and enable hospital staff to make data-driven placement decisions about individual patients, on a rapid basis, to impact the larger inpatient flow overall and ensure efficient use of beds.

For more details on how health systems have transformed their inpatient bed management, please visit the iQueue for Inpatient Beds Resources page.

Author Bios

Pallabi Sanyal-Dey, MD, FHM

Director Client Services, iQueue for Inpatient Beds, Associate Clinical Professor, UCSF School of Medicine
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