Predicting
Re-admission Moves from Fiction to Fact
Over the last 45 years I have watched ‘technology’ gain an
increasingly stronger foothold on the data and information we contend with on a
daily basis. Focusing technology on making ever better decisions is of course
critical. Perhaps in no sector are the benefits of computer-aided decision
making becoming more demonstrable than in healthcare. Lives depend on such
tools.
The historic technology landscape has seen us traverse the
disciplines of knowledge management, in the creation of the experienced-based
organization and the emergence of the knowledge-driven organization all
inspired by computer assisted tools. Most recently we have been drenched in the
world of ‘big data’. First came the attention to wealth management, now comes
healthcare.
The rising importance of reducing hospital re-admissions has
many ‘motivators’. Government penalties against hospitals for poor re-admission
rates are paying a huge role. Reputational damage to hospitals for poor re-admission
rates are debilitating. The difference that I now see, is that ‘knowing’ is one
thing but the ability to now also do something about has become the real
difference. Like the weather, which remains outside of our control, knowing
about it is important but what can you do about it? Well, re-admissions
probability management has reached a state where you can not only know about it
you can do ‘something’ about it. Actually, you can now do many things about it.
What has happened is that specific use case computer models
can exert a proactive discipline on care giver decisions and staffing time
allocation. The conventional model is that a designated number of nurses arrive
to address the residents on the assigned patient floor. This may not always be
the best approach. What if floor five has more critical patients at risk for
re-admission than floor two? What tools are available to instantly reallocate
nursing resources to the most critical re-admission probabilities? With today’s
predictive analytics such actions can be taken in real time. And the cost of
adopting this technology has plummeted. No longer is ‘big data’ the exclusive
domain of a few data scientists. Health care professionals, nursing floor staff
can do ‘big data’.
The
transformation has been in creating a usable tool that links the ‘data’ to the
‘intervention’ choices at the care giver level and in real time. This is a very
dramatic transformation.
Extending this on-going ‘knowledge link’ to the hone care or
skilled nursing service provider only increases the likelihood that a
re-admission can be avoided. With probability based successful identification
rates exceeding 85% health care can now know exactly where to look and take
action. Commercial businesses have been applying this model for years, out of
necessity, and with years of success. If I operate a fleet of trucks or
airplanes I need to have a pretty good idea of where the potential problems are
and which vehicles or planes are likely to surface with a problem next. Taking
this exact model into the health care sector means I can now know which
patients are most likely headed back to the hospital if I don’t not take action
becomes a manageable agenda. Now, I don’t mean to equate matters that pertain
to people with machines but the case is hard one to argue. Both need
maintenance. and I can now know which ones are going to
break down.
With the oncoming onslaught of ‘boomer’s’ needing care,
unless these new systems are put into place, emergency rooms will be over-run
with avoidable re-admissions. Predictive analytics in healthcare has now made
what was previously deemed fiction, fact.
Contact me at mike@chartacloud.com if you would like to learn more. I'd be happy to share what I have learned about this technology with you or your team.
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