Mergers and Health System Growth: Matching Supply with Demand
Cooper Franklin, a 14 year-old-boy, had been experiencing headaches for three weeks after sustaining a fall from a ladder while placing holiday decorations on the house. His mother notified the on-call nurse at the pediatrics office after he woke up with vomiting and slurred speech. The nurse suggested to the family that they go to the local community hospital. At the emergency department, the family waited an hour to be triaged due to a high volume of influenza. There was an additional two-hour delay after being seen by the initial healthcare provider due to two traumas from a motor vehicle accident. Unfortunately, at hour four, the child was noted to have a subdural hematoma on head CT and was sent by med-flight helicopter to the tertiary care center for an emergency evacuation.
Sadly, this is not a unique story within the U.S. healthcare system. We often hear about long wait times and delays that may affect care to the patient. Much of these challenges could be attributed to the complexity of health systems and intricate workflows. While mergers have been viewed with mixed results, they do offer the possibility of utilizing the existing resources in a more rational manner, thereby improving patient outcomes and creating a more efficient operational system. The opportunity is to place the right patient at the right place at the right time within the healthcare system.
Based on a CMS report in 2018, healthcare spending is at an all-time high in the U.S., with an expenditure of $11,172 per person, accounting for 17.7 percent of GDP. With policy changes leading to shrinkage in reimbursements and dwindling margins, it has become necessary for health systems to deliver the same, or even better care, more efficiently. This can be achieved in the form of mergers or acquisitions.
Moreover, these strategic partnerships can share knowledge and resources, thereby creating new possibilities. Based on study by Drs. Noether and May from Charles Rivers Associates, a global consulting firm, this synergy is possible through- scale related benefits in the form of allocation of fixed costs over larger patient volumes, supply chain savings, access to capital, efficiency of back office function, and standardization of clinical protocols. Financial analysis of non-federal mergers for six-year period (2009 to 2014) noted a 2.5 percent reduction in the operating expense per admission in acquired hospitals, leading to a cost saving of 5.8 million dollars at each hospital.
Evidence shows that excess wait in the ED can lead to poor patient outcomes including poor patient satisfaction. Novel technology firms are utilizing advanced data analytics with simulation modeling to address issues related to patient flow, ED wait times, patients leaving without being seen, and ED operation outcomes. The next era of advancement is to match resources with demand within a highly complex system of dozens of hospital with various capabilities. This disruptive innovation can be achieved by applying queueing theory, predictive analytics and artificial intelligence.
Lines or queues form when there are limited resources (supply) to either a certain service or product, or when the demand is high. However, there are other cases when the supply is adequate whereas the demand occurs in boluses or in an asynchronous manner. Queueing theory can improve the use of service by limiting what we call traffic jams or asynchronous use.
The initial concepts of queuing theory were developed by Dr. Anger Krarup Erlang in 1917, when he applied it to the Danish telephone system in Copenhagen. In 1909, he wrote a seminal paper applying Poisson distribution to “traffic” of telephone calls. His goal was to determine the number of telephone operators needed to match the volume of calls at any one given time. Queuing theory’s basic concept is predicated on intra arrival time, service time or time to complete the specific task, numbers of servers, and the number of potential customers. This theory has been further refined and modified and applied to other industries.
Queuing theory allows administrative leaders to “smooth flow” in a hospital by matching supply with demand and intelligently schedule surgical and medical elective procedures Monday through Sunday. Variability in supply chain like a surge in ED visits or inpatient admissions are known to cause disruption in patient flow and is often considered as the biggest enemy to achieving an optimized process. This can lead to a resource mismatch and cause what is referred to as ‘idle time’ wasting of millions of dollars in revenue. Queuing theory can be applied to the idea of matching the supply to the demand from among multiple hospitals with various capabilities for quaternary, tertiary, and nontertiary care.
It is crucial for healthcare industry to be in a position to intelligently customize the care delivery methods. We need to tailor our practices based on the variable needs of each patient. The archaic “one size fits all model” will only continue to lead to waste within healthcare. Evidence compels that the alliance between technology and humans should only get stronger in making data-driven decisions.
Artificial intelligence could provide clinical decision support in the form of patient specific data driven recommendations such as the likelihood that a neurosurgeon was needed for Cooper Franklin. Machine learning algorithms can predict these changes and can help health systems boost their performance. Let’s focus on the right patient at the right place at the right time for the next Cooper Franklin that comes to the health system.
Chi Huang, M.D., executive medical director of general medicine and hospital medicine shared services at Wake Forest Baptist Health System, co-authored the piece with his colleagues John Blalock, Mike Waid and Padageshwar Sunkara, M.D.
