I am a big advocate for using analytics, statistics, KPIs and automated reports, and sharing that knowledge across an organization’s leadership and team members. Analytics will show you things in ways you may not have seen before. Unfortunately, what it sometimes fails to do is paint the whole picture.
Data is conveniently available with a click of a button but, oftentimes, organizations cannot rely solely on data to solve a problem. You or a trusted member of your team needs to go and “get their hands dirty.” As an executive, I was a firm believer that when an issue seemed to be unsolvable or claimed to be impossible to fix, you should be the one to go get your hands dirty.
A resounding problem across the health industry and emergency services is that emergency departments are increasingly overfilled and, in turn, are thus slow to get patients moved to the floors. This has a direct impact on the amount of emergency response resources available on the street and revenue generated for the hospital.
I was part of a team that had a problem all related to this and, in the end, we were able to find a solution. Allow me to give you some of the backstory before explaining how the solution presented itself.
Investigating a Response Time Challenge
While contracted with a major hospital group to provide inter-facility transport and emergency services in the surrounding area, we were constantly measuring response times both for emergency and non-emergency transport calls. Issues began to present themselves in the form of being consistently late to both of those call types. As a result, the complaints became more and more serious. Emergency response times in the area were increasing to a dangerous level, making community leaders concerned. Social workers were also upset their patients were not being transported at the scheduled times, nursing home administrators were frustrated that their patients were not arriving at the scheduled time, and the list continued with several other grievances.
This now became a daily talking point in our executive meetings: how transports were increasingly delayed and why. The leadership team had different perspectives and proposed solutions. The obvious proposed solution was to add more staff and resources. However, adding more staff and resources would increase overhead and decrease profits. It took a deeper investigation into the issue to reveal that adding resources would not solve the problem.
Diving Deeper Into the Data
It became apparent that we needed to start looking at the available data in a different way. We found that call volume had not significantly increased; at least not enough to justify the amount of resources being proposed. We became more granular in our reporting, however, and found that there was a significant increase in emergency room (ER) drop-off times. The ambulance crews were being held up because they could not get the patients off their stretchers. The beds in the ER were not being cleared because the patients were not being moved up to the floor.
The problem was identified, but solving it was complex because it called for bringing our hospital partners in the mix to devise a solution. We met with hospital leadership and presented our findings. We learned they were trying to solve their own problem of patient turn around. Efficient patient flow in and out of the ER has a direct impact on their revenue generation. The patient back flow in the ER was negatively affecting both businesses.
Now we had to solve the problem. It was time to get our hands dirty.
Getting Our Hands Dirty
We went on site and examined the patient flow process from the beginning to end—every step, measureable stat, button, signature, and phone call involved. We learned when and how the staff determined a patient was to be discharged or admitted and what processes had to be done for each scenario to come together. We learned that the hospital had an internal call center for coordinating patient room transfers and discharges. Analyzing that data, we found there was a considerable time difference between when a patient was scheduled to be transported, when they left (the bed was empty) and when the process started to get that room ready for another patient.
The hospital had an automated system of getting housekeeping to clean the room, reserving the room for the incoming patient, getting internal transport scheduled to get the patient from the ER to the floor, when that process was completed and so forth. All automated processes were very efficient and tied in with use of their internal patient tracking systems. However, what was not automated was how and when the center was notified that the room was ready for another patient. This was a manual process done either by phone or by staff, marked manually in the computer system when the room was empty.
For example, our data showed that a patient was scheduled for pick-up at 1 pm and left the scene at approximately 1:20 pm. Comparing the hospital data, we were able to see that the internal process to get the discharged patient’s room ready for a new patient was not initiated until 3 pm. The data revealed a consistent pattern of delays in staff contacting housekeeping to get a room ready for a new patient.
Implementing a Solution
Staff culture, policies and protocols can be difficult to change as there are people involved and people dislike change. It stood out that there were other ways to notify the hospital’s internal center after a patient was discharged and a room became available: this could be done by the patient transportation provider.
We had to train our staff and the hospital had to make a policy change, but it was a relatively easy fix. The ambulance crews would notify the hospital call center as soon as they arrived to pick up a discharged patient. Over time, this became a more streamlined, automated process utilizing a combination of vehicle tracking, automated emails and status sharing.
In the end, the hospital was able to show measurable increased patient inflow, and the transportation provider was able to improve response times and overall customer satisfaction without the expense of increased resources. However, this wouldn’t have been possible if both parties relied on data alone: we had to investigate, collaborate and get our hands dirty.