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Inpatient bed staffing, managing hospital capacity, hospital staffing AI

Webinar Write Up: Inpatient bed capacity management – unlock beds and reduce patient length of stay

At the December 2021 Transform event, UCHealth Director of Operational Intelligence Isobel Handler, MBA, MHA, discussed in great detail her flagship hospital’s journey in implementing iQueue for Inpatient Beds to precisely identify and address their roadblocks to effective inpatient bed capacity management. 

Find the whole session here

The Context: 

Made up of 12 hospitals that cover the front range of Colorado, the UCHealth system has been both locally and nationally recognized as a leader in healthcare quality.

UCHealth’s flagship University of Colorado Hospital consists of 703 beds, 8,000 employees, and currently sees 49,000 inpatient admissions annually. In placing a large and diverse patient population among so many beds and units, the three most significant inpatient bed capacity management challenges came down to inconsistent decision-making, a reactionary culture, and insufficient data.

Challenge #1: Making consistent and data-driven decisions about inpatient bed capacity. UCHealth bed staff relied on experience and intuition to make critical decisions, not data. Fluctuations in census were significant in scale for such a large organization, and therefore challenging to predict and manage. 

Challenge #2: Planning proactively for census changes; moving out of “crisis” mode. Staff utilized “internally-developed” inpatient bed capacity management tools relying on metrics that were pulled from the medical record and transferred to spreadsheets. There was a limited set of capacity actions that could be taken once a unit was low on beds or the emergency department (ED) was full. Staff were also skeptical of predictions made using this method.

Challenge #3: Finding the right bed for the right patient among a diverse patient population. With 21 units and 88 services in the Med Surg and intensive care unit (ICU) divisions, it was a challenge to ensure that a bed was available in the correct unit at any given time. 

Isobel Handler explained, “Without sophisticated data points, we had to tell our capacity management leaders what our strategy was going to be for the day … relying on feel, anecdote, and their own experience to make critical decisions. There are a lot of decisions that we make about inpatient bed capacity in a given day. A patient placement nurse makes about 200 placements in a day. So he or she will need to process a lot of information in order to make those decisions as optimized as possible. Without really good tools, that’s impossible.”

Getting out of crisis mode was a motivator as well. Handler continued, 

“Before implementing iQueue, our only real tool was escalation. At some point, when everything’s a priority, nothing is a priority. It was an inefficient way to react to capacity. It created a lot of chaos and it was something that was a big staff dissatisfier.”

UCHealth turned to LeanTaaS to find a more effective and sustainable way to solve these challenges and equip staff to optimally manage patient flow and inpatient bed capacity. 

What UCHealth did:

UCHealth implemented LeanTaaS’ iQueue for Inpatient Beds at University of Colorado Hospital  in February 2020. Systemwide visibility components have been live in all 12 UCHealth hospitals since October 2020. iQueue empowered staff throughout the system to begin making better decisions informed with real-time data. The solution also supplied an expanded inpatient bed capacity management toolkit that allowed staff to: 

Make data-driven decisions: University of Colorado Hospital instituted tangible criteria for bed management, designed with their beds and patient population in mind. Examples include: 

  • If predicted open beds fall below 10, open a surge area; when predicted open beds exceed 40, close a surge area. 
  • When there are fewer than five open ICU beds, prioritize downgrades from ICU
  • Specific unit interventions (i.e. Orthopaedics) to address known increases and decreases in census 
  • Rules around managing COVID units to limit the geographic scope of those patients 

Work with reliable predictions:  

  • iQueue discharge and admission predictions are based on specific unit and service level trends, representing highly trusted numbers among capacity management leadership, unit leadership, and medical directors
  • Predictions allow staff to be proactive with capacity planning, so they are actively managing capacity rather than responding to a crisis, improving staff and provider satisfaction

Establish unit fingerprints:

  • LeanTaaS worked with UCHealth to create “fingerprints” for each unit to better predict supply and demand for beds using different variables 
  • LeanTaaS and UCHealth staff collaborated to find patterns that could be used to make better predictions. This mathematical model creates every unit’s “fingerprint” for every hour, every day of the week
  • Using historical data, patient-specific data, and real-time data, hospital staff were able to predict how many beds will be needed in the next two hours; how many beds will open up in next two hours; and the net shortfall given the current queue of patients.

As Handler described, “the iQueue predictions are based on unit level detail. These are much more reliable, and more trusted across the organization. When we see a bed balance, it’s something that we believe, and we use it to make really important and proactive decisions about capacity. We’re also able to effectively communicate what’s going on in the hospital and why we’re making decisions using this tool. Having good predictions based on data science, rather than a boot-strapped Excel spreadsheet, has transformed the way that we can make decisions about capacity. We can manage it rather than react to it.”

The results:

Handler concluded,Using iQueue, we were able to transform our inpatient bed capacity management strategy to be based on objective triggers rather than anecdotal feel. We plan proactively… and actually started managing rather than reacting. We make much better decisions in patient placement. We’re able to prioritize the patients who need an intervention most. We’ve been really successful being able to finally communicate our capacity status and the reasoning behind some of the decisions.”

UCHealth has seen the following results in the University of Colorado Hospital since putting iQueue tools in place: 

  • 37% reduction in time to complete ICU Transfers
  • 8% decrease in opportunity days (difference between Med Surg length of stay and Center for Medicare & Medicaid Services length of stay)
  • 4% decrease in time to admit (despite 18% increase in COVID census)
  • 90% improved staff confidence in critical capacity decisions (compared to 50%)

Handler added that it is important to note that these results occurred during the pandemic. iQueue contributed to UCHealth’s ability to manage inpatient beds during a particularly challenging time.  

What comes next:

Through the UCHealth and LeanTaaS partnership, improvements are being added to the tool, aimed at making it even more prescriptive. Handler explained, “We are working on enhancements to the discharge toolkit and engaging with our care management teams to see how we can leverage this tool to make their lives more data driven and to prioritize their work in a given day.”

“We’re building out some daily retrospective analytics that will tell us based on what happened yesterday and the actions that we took, what was the effect on our hospital and our capacity management and how can we learn from that.”

UCHealth is also looking at the potential benefit of utilizing the tool for staffing. Their goal is to make sure they are using their nursing resources as efficiently and reliably as possible.

To view the entire webinar and learn more detail about the UCHealth bed management transformation, visit the webinar page here.

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