Automating Inpatient Flow With Real-Time Analytics

July 14, 2021
Managing inpatient hospital bed capacity is a complex process, further complicated by crises like the COVID-19 pandemic.
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The Challenge:
Managing inpatient hospital bed capacity is a complex process, further complicated by crises like the COVID-19 pandemic. Hospitals must juggle planned and unplanned demand from a variety of admitting sources. Patient volumes vary by hour, day of the week, month of year, service and level of care. Timing of admissions and discharges is often misaligned, with the majority of discharges happening later in the day when admission requests typically increase, leading to supply and demand challenges. Further complicating the flow is the inefficient process of discharging patients. In both “normal” times and during a crisis, all these inefficiencies in the patient journey lead to extended lengths of stay and excessive unnecessary, non-reimbursable costs. There is no margin for error nor sub-optimal capacity management. Traditional methods of streamlining patient flow consist of lean process events, dashboards without actionable insights, excessive alerts to providers, and/or capital-intensive command centers. These efforts are often not sustainable, nor do they solve the actual challenges with managing inpatient bed capacity. Streamlining patient flow requires 24/7 front-line and system wide visibility into real-time data, and when staff and providers are equipped with this information at their fingertips, they can make impactful decisions in real-time with great confidence. This is where predictive analytics plays an important role in recovery.

The Innovative Solution:
LeanTaaS has successfully provided software solutions that combine lean principles, predictive analytics, and machine learning to transform hospital operating room and infusion center operations. The company’s software is used by over 2,000 operating rooms and 8,000 infusion chairs nationwide to increase patient access, decrease wait times, reduce health care delivery costs, and improve revenue. In response to the growing need to address complex issues with inpatient bed capacity, the company developed and launched iQueue for Inpatient Beds, a cloud-based solution used to drive daily inpatient capacity management decisions at all levels from a single operations focused source of truth. The solution not only provides highly accurate discharge and admissions predictions for each unit by time of day based on real-time data, but also surfaces admission bottlenecks by service and level of care while highlighting high impact transfers, allowing administrators to proactively create space for the right patient at the right time.

The Results:
UCHealth’s Colorado-based network of nationally recognized hospitals adopted iQueue for Inpatient Beds during the height of the pandemic. The tool is used systemwide to run daily bed meetings, perform hourly administrative management, drive capacity protocol standardization, and empower front line staff. For UCHealth, this has resulted in a 4% decrease in time to admit, despite a 18% increase in census due to the pandemic. The tool also guides transfers, and UCHealth experienced a 37% reduction in time to complete their ICU transfers again, even during a pandemic. iQueue for Inpatient beds also makes the very challenging and inherently slow discharge process much quicker and more efficient by surfacing which discharges the staff and providers should focus on - replacing what has been a very tedious process of diving into individual chart reviews and waiting on rounds. Improved discharge efficiency here has resulted in an 8% decrease in opportunity days which represent the difference between the actual length of stay and CMS’ predicted length of stay.

The solution has enabled UCHealth to increase its confidence in their critical capacity decision making. They are now 90% confident in their decisions versus being historically 50% confident. The tool provides them with 24/7 access across the system, empowering the frontline staff’s decision making and reducing the chaos that is inherent in the daily management of inpatient bed capacity.

Jamie Nordhagen, R.N., director of capacity management and patient representatives at UC Health, stated, “The LeanTaaS tool was instrumental in automating patient flow through our Intensive Care Units and opening space for more critical COVID patients. Historically, our bed management system required nurses to manually enter when patients were “ready to move” after physicians wrote downgrade orders for transfer to lower levels of care. LeanTaaS has allowed us to leverage a “pull” versus “push” strategy for lower acuity patients in our ICUs and offloaded some of the administrative burden for our bedside nurses. It has become our single source of truth across departments and clinical disciplines. With iQueue for Inpatient Beds, our team is saving a lot of time during the day and can now do more value-added activities. For example, now that we can accurately predict where we will be in the evening and the following day, we have been able to use our surge spaces more actively only as needed, ultimately creating more capacity within our organization when there is demand. We utilize the LeanTaaS tool and its predictive modeling to drive our patient flow operations. We have established triggers to open and close our surge areas, staff emergency department boarders, and open and close COVID-dedicated units.”

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