The following is a guest article by Mohan Giridharadas, CEO & Founder of LeanTaaS.
Recently I heard a story from a leading cancer center. An elderly man in the final stages of his losing battle with cancer waited for an hour past the scheduled time for his infusion appointment. Finally, in frustration, his wife went to the front desk and said, “We’re going to skip his infusion today. He doesn’t have much time left to live and he would rather spend it with his grandchildren than sit in this waiting room any longer.” And so, they left.
This kind of scenario happens far too often. It is difficult to find a time slot for a medical procedure or test, and yet the hospital assets needed for these – such as infusion chairs and operating rooms – often sit idle for hours each day. Not using these assets optimally, nor those like inpatient beds that are essential to hospital function, leads to not only extended waits for patients but depleted operational performance overall.
COVID complicated the utilization of healthcare capacity
Most health systems operate at the edge of their capacity – just like freeways during rush hour. Just like freeways, a shock to the system on either the demand or supply side will push the health system into a state of chaos and gridlock.
The COVID pandemic was a shock to both sides. On the healthcare demand side, tens of thousands of people in a small geographic area suddenly needed intensive medical care. On the supply side, there were abrupt shortages, starting with PPE, then ventilators, then ICU and subsequently inpatient beds, and now nurses and staff. With new variants like Omicron arriving and prolonging the pandemic, the shocks are continuing to come.
The crisis also put enormous financial pressure on many health systems, so the expensive old approach of building more facilities and buying more assets is no longer feasible. The focus is beginning to shift, as it must, to the substantial opportunity of getting more out of the existing assets.
Asset utilization in healthcare is broken — but other industries have it solved
In current circumstances, assets often sit idle in hospitals during the day, only to be put to use later in the evening with staff working overtime hours. A given patient is forced to make an appointment weeks in advance, then wait hours beyond the scheduled time when they arrive for it. This leads naturally to a delay in needed care, or in the case of the cancer patient above, a waste of the precious time they have left.
To truly address these problems, hospitals must apply learnings from other industries, like airlines, package delivery, or ride sharing companies, to their own day-to-day operations. Like healthcare, these industries operate on the challenge of matching a fixed supply of assets with a constantly shifting, unpredictable demand for them. But unlike healthcare, they have learned to match this supply and demand successfully with sophisticated math. By bringing a similar level of analytic rigor to operational workflows, healthcare organizations can achieve this as well.
Addressing operational challenges with analytics, or not
Other industries address the complexity of supply and demand problems directly. Embedded in their operational workflows are sophisticated algorithms that predict demand patterns and the availability of required supply as accurately as possible. These predictions are augmented with real-time alerts to become even more accurate. Company systems run simulations thousands of times to make their operations resilient to unexpected swings in either the demand or the supply. Plus, these continuously intervene in near real-time to keep the demand and supply in balance. This is what we need to get healthcare asset utilization to an acceptable level of performance.
Health systems, on the other hand, tend to ignore the complexity of balancing supply and demand. Instead, they try to power their way through it by sheer determination. Front line staff make decisions under pressure, using intuition and spreadsheets or standard reports from the EHR. Surprisingly often they do manage to treat all the patients who were supposed to have been treated that day. But muscling their way through the process like this creates enormous stress on both staff and patients, and results in the very same extended wait times experienced by the elderly cancer patient. Then the entire scenario will repeat all over again tomorrow, the day after, and the day after that.
Healthcare deserves a solution that adequately matches its problem
For a very long time, health systems have assumed their EHR, which they have spent tens of millions of dollars to deploy, must solve their operational problems. In fact, EHRs are excellent at putting all the clinical and financial information from each patient encounter into a single database. However, they simply do not have the optimization algorithms required to solve the supply/demand match problem, nor do they have the AI/ML algorithms to continuously learn and improve as situations change. The spend on a tool like EHR is not relevant if the tool is not built to perform this function. Furthermore, ignoring the mathematical complexity of balancing supply and demand will not make the problem go away.
Instead, health systems need sophisticated demand prediction models – for each unit of service, for each hour of each day of the week – that learn continuously. They need a sophisticated understanding of the elements of supply capacity needed for each service to extract the interconnectedness and the constraints under which they must operate. Then, it takes active intervention on a continuous basis to nudge the demand or the supply in a direction to constantly stay as close to equilibrium as possible.
By unlocking this capacity now – not next year or in two years, but today – hospitals can reduce stress on staff, improve financial performance, and finally serve more patients while enhancing their experience. Untenable wait times, such as those experienced by the patient who decided their precious time was better spent elsewhere, can become a thing of the past.
