A major aspect of lean manufacturing is an attempt to flatten production and therefore make things more predictable and therefore efficient.  One might imagine that whilst that is perhaps possible in automobile manufacturing, the emergency room is a much more unpredictable environment, and therefore harder to ‘flatten’.

Alas, a recent paper suggests that this can indeed be done, with predictive analytics used to minimize patient waiting times.  The authors propose a model that translates predictions on the arrival of patients into the emergency room into a better process for dealing with those numbers.

Reducing waiting times

At the moment, hospitals often use congestion data, with ambulances instructed to divert to another facility once an existing facility is too over-loaded.  The authors suggest that this can be improved significantly by using predictive data so that hospital staff can predict when that congestion might occur rather than reacting to it when it does.  By doing this, hospitals can divert patients before load becomes excessive, and thus reduce waiting times for patients.

“These delays can have significant, life-changing ramifications,” the authors say. “These are the kinds of changes that potentially could affect all of us.”

The nice thing about the model is that it appears to work, even if the data that it is fed is noisy, although obviously it works better if the data is cleaner.  This is because it encourages emergency departments to be prepared for congestion, which in itself is beneficial.

“If you’re prepared to handle congestion, you’re never really in a bad spot,” the researchers say. “Any degree of predictive information added to the equation is going to be better than none of it.”

Standard operating

It should perhaps go without saying that the model is designed to deal with business as usual, so doesn’t cope particularly well with extreme events, such as a terrorism incident.  The rarity of such events prompted the team to not consider them at all.

It is of course, early days, and the researchers plan to put the model through its paces to determine just how big an impact this might make, but they are confident that it will help to drive positive changes in emergency rooms throughout the world.

“We’re hoping to educate practitioners, not just hand them a disk of software and say, ‘Use this,’” they say. “Imagine a situation where a hospital gauges its own level of business and puts a notice on the website that says, ‘We’re really busy right now; if you’re not that sick, don’t come in.’ That simple change could improve delays significantly.”

With overcrowding an issue in pretty much every hospital in the world, this approach could be something that yields some real benefits.  We’ve seen such data based approach yield significant results in other fields, so hopefully healthcare will jump on board too.