It’s 6 a.m.: Do You know Where Your Patients Are? And Where They’ll Be Next?

Guest post by Dan Hickman, chief technology officer, ProModel.

Dan Hickman
Dan Hickman

With six in 10 U.S. hospitals functioning at operational capacity, patient flow optimization provides one of the most cost-effective ways to increase a hospital’s bottom line.

Around 6 a.m. every day, hospital-wide “huddles” occur to discuss and determine a collective understanding of the state of operations. Most of these huddles take less than an hour and provide hospital and departmental leaders a snapshot of census status and expected discharges.

But hospitals are complex, dynamic systems. By 7 a.m. a flood of patients could hit the ED, affecting everything from staffing to the census, and carefully crafted plans disintegrate.

Consider the current state of patient flow at most hospitals.

Most health systems today have a reactionary approach to admit, transfer and discharge (ADT), patient flow, census, and staffing. Moreover, there is no way of accurately predicting future patient flows to right-size staffing and optimize workflows.

Discharge processes are open loop, resulting in costly delays. Most hospital staff use spreadsheets stating the number of discharges planned for the next 48 hours. However, there is no way to look at patient census with diagnosis codes tied to the typical length of stay.

The current state of patient flow results in multiple problems:

Hospitals are really good at examining what’s happened to a patient in the past. The staff knows where they’ve been, but they haven’t taken the next leap, which other industries have, at projecting out where they think patients will “flow” during their stay and how the next 24 to 48 hours could affect the status and the census. There are parallels with other highly complex industries where accuracy and logistical management are critical to safety and success. One example — air traffic control.

The simulation technologies used in air traffic control are superb at answering what-if predictive questions—questions that depend on multiple variables, such as, “What are the risks of conflict between trajectories?” or “What affects airport congestion and fuel usage?” Simulation and machine learning technologies address the issues arising from increased traffic, seasonal fluctuations, the variety of aircraft in service, and weather.

In hospital and health system management, the shortcoming is not a lack of relevant data to predict patient flow. What’s been missing are empirical models of complex processes that influence the behavior and impact of the data elements, such as the ones air traffic control effectively employs today.

Leading hospitals can apply machine learning combined with modeling, simulation, analytics and existing EHR and RTLS data to improve the bottom line efficiency of the hospital and the patient experience by:

Predictive analytics solutions can estimate a hospital’s future capacity and bed utilization, some using all the historical and real-time data hospitals have invested so much to acquire and archive, and applying machine learning with proven simulation and modeling analysis, to predict the near-term future and prescribe interventions; offering insight on staffing, equipment and facility adjustments.

The trend toward adoption of patient flow technology will likely broaden into ambulatory surgical centers and large outpatient clinics as hospital and health systems acquire and integrate them into the larger system.

The key to success involves connecting all the pieces of the hospital with all the data available bi-directionally. True patient flow optimization opens up the great untapped potential to help hospital staff and administrators utilize existing resources more fully, more efficiently. Systems providing both predictive and prescriptive technologies provide operational insights and deliver specific action plans to enhance patient outcomes, lower costs and drive additional revenues.

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