QUERY Modeling downstream effects (SD6661)

[SDMAIL Dave Baker]

Posted by  "Dave Baker" <davefellspt at gmail.com>

I work in a large academic medical center and am examining the issue of
patient flow in a busy surgical intensive care unit. Much of the inflow to
the unit is from elective/scheduled surgical cases.  My initial analysis has
shown a fair amount of variability in the number of scheduled case requiring
an ICU bed on a given day.  In particular, patient inflow seems to vary with
the day of the week (based on when individual surgeons prefer to operate).  The
downstream effects of a day with high inflow are felt a day or two later, as
unit bed occupancy approaches 100%, and attempts to find beds for patients
become increasingly difficult. This effect cascades further to floor beds,
where patients are transferred to after their ICU stay. Surgeons and nursing
unit staff have different mental models about the problem and feel the
effects differently.

On the surface, SD modeling seems like an appropriate way to tackle a
complex system problem with causes and effects that are distant in time and
space.  However, I'm challenged with how to incorporate the day-of-week
inflow variation into a model. Ideally, I would like to use a model to help
identify the ideal number of cases to schedule on various days of the week
to keep inflow smooth and relatively predictable for the unit staff. Any
advice or examples that have dealt with similar issues?

Also, any comments on the relative merits and applicability of SD versus
Theory of Constraints methodology for complex problem solving such as this?

Thanks very much,

Dave
Posted by  "Dave Baker" <davefellspt at gmail.com>
posting date  Tue, 9 Oct 2007 19:44:50 -0400