Case studies

Case list:

  • Case 1 - Using Lean Management
  • Case 2 - Using Discrete Event Simulation in the ED/ER
  • Case 3 - Using Discrete Event Simulation predicting hemodialysis capacity
  • Curriculum mapping - Using  TheBrain to coordinate MD curriculum development

Reduce redundency in ER

Using a Lean Management approach it was possible to reduce redundant functions in the way a healthcare provider handled 'acute' patient.
For more details see my publication 'Knowledge Management as a tool in Health Care Systems optimization; PDF-Link

ER before lean

Figure 1 shows the 'acute flow' before reorganized

ER after lean

Figure 2 shows the 'acute flow' after it reorganized


Reduce bottlenecks in ER

Using Discrete Event Simulation to identify bottlenecks in an ER and subsequence make a simple rearrangement of staff duties.

For more details see my publication A. Lassen Nielsen, H. Hilwig, N. Kissoon, S. Teelucksingh. Discrete event simulation as a tool in optimization of a professional complex adaptive system. Stud Health Technol Inform. 2008;136:247-52 

and the presentation from MIE 2008


Figure 1 shows the patient flow in the ER

Initially 174 patients were seen on an average 24 hour shift. The average waiting time was 9.2 hours. The simulation showed that the 1st Triage nurse had a 99.5% utilization, the clerk 73.9%, the emergency physician 44.3% and the ECG-nurse only 6.9%.
It is clear that the 1 triage nurse was the bottleneck. By relocation the ECG-nurse to help the 1 triage nurse when there was no ECG's required the waiting time was predicted to be reduced to 5.0 hours, and a more even workload achieved The simulation showed that the 1st Triage nurse now would have a 95.8% utilization, the clerk 88.7%, the emergency physician 87.4% and the ECG-nurse 89.7%.

Predicting the need for hemodialysis capacity.

Discrete Event Simulation is a logical (or mathematical) model of a system where changes occur at separate points in time. Even though most changes related to RRT happen at separate points some changes occur as a continues process as well, hence a more appropriate term describing our model would be as a mixed continuous-discrete model1. The core of our simulation model is the universal RRT model as originally presented by Davies & Roderick. It basically deals with the flow of patients within what we could call the ‘chronic RRT-core’. It admits patients with ESRD onto RRT based on known gross incidences of patients in need of RRT. The model then simulates interaction between the three available modalities (PD, HD, TX) based on known frequencies on transfer between the modalities. There is by nature only one-way out of the model, namely death. Hence mortality within each of the modalities determines a patient’s exit. The strengths of this model is its simplicity and universality. For example, an intervention resulting in improved survival can be easily entered into the model thereby permitting a mathematical assessment of this intervention. 

See poster presented at EDTA meeting 2010

See poster presented at 'EVIDENCE 2011' conference London, UK

Curriculum mapping

Take a tour of the brain: Starting with the kidney curriculum/syllabus                              Opens in a new window

If it has caught your interest please contact me for more information >Contact