Reductionism doesn’t really work with complexity—it tends to lead to what we call the “amoeba effect”. We push on one area (area A) for improvement yet find another area (area B) deteriorating in response. We don’t make the connection, so the next year we push on that other area B and, low and behold, we lose the improvement in area A. So, the next year we double our efforts at doing the same thing we did last time for area A—in fact we create an Area A team to push on it for 5 years with funding, which they do.
That creates a lot of anger in Area B, who demand equal investment and resources, which you eventually feel forced to do, so you now have created two “teams” fighting against each other, taking more and more energy to achieve what seemed much easier 2 years ago. If you are lucky, someone within Team A or Team B (they are really groups who work in Area A and B) comes to tell you what is actually happening, that the two areas are inter-connected and it’s much more complex than you think. Traditional leaders will ostracize this person as a “complainer” or “skeptic” and eventually push them out of the company—while the fluid, adaptive, collaborative leader will put this person in charge of both area A and area B and say “we want both area A and area B to work optimally, reducing waste and improving outcomes…take both pots of $$$ and people and let me know how I can help.”
Managing Polarities: Case Study I [Length-of-Stay AND Re-admissions]
Length-of-Stay become a key factor in hospital management in the late 1980’s and 1990’s as the science of Utilization Management emerged and cost-containment efforts spread across hospital systems. Prior to that era, hospitals were generally paid some portion of what they billed in a class fee-for-service fashion. However, as Managed Care grew into the 1990’s and Medicare began bundling payments based on primary diagnosis at time of admission—AKA DRG [Diagnosis Related Group]—to stem the rising tide of hospital cost inflation, hospitals began to manage Length-of-Stay as a means to limit losses or risk refusal of payment for going beyond the limits placed by Managed Care and Medicare DRGs. Looking into the distant past, birth hospitals were once called “lying inns”—as women typically staid in the hospital for about a month after giving birth to regain their strength, make sure their newborn survived the treacherous first month of life, and could return home safely.
So, the average length-of-stay for the birthing process was about 30 days in the 1930’s and began to drift down naturally as birth became safer and outcomes more predictable. However, these stays dropped rapidly in the 1990’s to point where state legislatures began passing bills to stop hospitals from discharging women the same day as the birth so they could at least sleep a while before being shuffled home with their newborn. Each admission diagnosis was given a general target for length-of-stay based on the average revenue generated, barring complications.
In some cases, hospitals might discharge a patient (thus making a short length-of-stay), then re-admit them the next day, starting a whole new DRG and length-of-stay. In a terrible irony, this was rewarding hospitals for early discharges and re-admissions—indicating that we had a polarity and when the payers rewarded reductions in one pole—length-of-stay—they found themselves with a new pole—rising re-admission rates.