Ironically the prevailing attitude among clinicians remains; “healthcare does not consider itself a process or system industry” therefore, it is not one which would significantly benefit from leveraging technology to improve its processes. As a data science community within the healthcare industry, we must all push the envelope to demonstrate that Healthcare has a lot to gain by becoming more efficient and effective via process improvement technologies as it clearly has done by embracing clinical improvement technologies.
Dale Schroyer, a certified data scientist, and ProModel’s leading healthcare simulation expert overheard these comments while attending an immersion workshop on RCA, or root cause analysis, at the NPSF Patient Safety Congress earlier this year.
This program looked at what hospitals do when an adverse event occurs. According to the workshop instructors, Dr. James P. Bagian and Mr. Joseph M. DeRosier, “Usually, such events occur because of system faults or failures, not necessarily human error. The challenge is determining what the faults in the system are, how they can be fixed and instituting actions to fix them and measure those fixes.”
Schroyer found it a fascinating topic because of the similarities to what is done in the aerospace industry in which he started his career. One of the instructors was also from the aerospace industry. Both instructors teach at the University of Michigan which is also Schroyer’s alma mater.
From listening and interacting with conference attendees, most of whom were nurses and doctors, Schroyer observed that healthcare does not consider itself a process industry. However, the mere fact that doctors and nurses were having the conversation is a considerable step in the right direction.
Many in attendance wanted to know what techniques would best serve them in convincing their coworkers back home that the system approach is a good and necessary one for the healthcare industry that can benefit patients, hospitals, nurses and physicians. Using a predictive/prescriptive analytic tool such as discrete event simulation (DES) is one possible approach.
Schroyer spoke with the instructors, as well as other attendees, about simulation as a tool to improve patient flow and other hospital system shortfalls. They mentioned that the barriers to simulation are many such as a long, cumbersome learning curve.
Guest post by Dan Hickman, chief technology officer, ProModel.
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:
For many hospitals, the length of stay and cost per case metrics exceed CMS value-based care efficiency measures affecting reimbursements and the bottom line.
The daily reality of hospital staff revolves around logistics — the timely and accurate flow of patients coupled with staff, equipment, and facilities needed to accommodate and provide care within the hospital. Yet most hospitals lack the tools to define, visualize, predict and optimize the logistical flow of real-time needs into the near-term future.
Compounding the problem, many patients cite time spent ‘waiting’ as an issue affecting their experience, and ultimately patient satisfaction and the hospital’s HCAHPS scores.
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.