Current medical education programs present hospitals with unique problems surrounding the scheduling and rotation of medical interns and residents. Numerous studies have looked at the correlation of fatigued workers, errors, and safety. Within the last few years the long hours and rotations of hospital interns and residents has seen ever increasing scrutiny and the health care community is moving in the direction of work hour reduction and restrictions in an effort to increase safety.
Different medical facilities often opt to have unique and separate admitting algorithms based on total medical staff available, work hours, patient volume, diagnosis and number of medical teams operating on any given day. The variance in workload across these teams can be significant depending on how this algorithm is set up.
Heal CG aimed at analyzing the issue in effort to come up with a more equal distribution method for workload across different admitting teams. To do this, the team conducted time studies and collected survey data and input these metrics into a basic objective function to obtain workload variables. With these workload variables, it was possible for us to develop a workload admitting model which would recommend patients be placed on a certain admitting team based on that team's workload.
As opposed to rapid implementation, our group developed a simulation model with the new parameters. It was shocking to see that the simulation model illustrated several unforeseen scenarios, which both our engineering and medical teams did not predict, which would heavily complicate implementation.
However, using the variables obtained from our objective function and some basic statistics, the group was able to recommend several new admitting algorithms aimed at evening workload and reducing work hour violations.
