The number of chronic disease cases is growing at phenomenal rates as our general population ages. End-stage diseases of all the major organs: heart, lung, liver, kidney and pancreas ultimately rely on transplantation as the only means remaining to return a patient to reasonable health.
Because of this growth in demand, the line of patients waiting to receive organ transplants is seemingly endless. Aside from the fact that there are not enough organs being donated to meet this need, the transplant community's next biggest problem is having sufficient resources to meet the increasing demand.
Transplantation care has traditionally involved following every patient for life. Today, however, resources to follow these patients indefinitely are taxed to the limit in an environment where pressures to restrict healthcare spending prevail.
NovaSim, one of our most valued SIMUL8 Certified Solution Providers, worked with Johns Hopkins in developing simulation as an essential tool in their efforts to improve process efficiency. It is critical that these steps be taken today so that the Johns Hopkins Comprehensive Transplant Center will be in a position to keep pace with the increasing demand for transplant care in the future.
In any healthcare setting, the needs of each patient are unique. This is particularly true of transplant care. Each patient progresses through a series of treatment phases, with each phase requiring a different mix of staff resources and treatment times. Many of the staff resources are highly specialized, and have complex availability patterns due to the need to fulfill responsibilities in other departments of the hospital.
Using simulation, Johns Hopkins has emulated the very complex process that is involved in every transplant case. After filling-in the necessary staff resources required, time commitments were established for all of the various tasks they perform.
With this as a starting point they were able to simulate the flow-through of each of the various stages leading to transplant and then chronic follow-up. Ultimately they were able to model the flow of the entire process so that it reasonably replicates what happens in the hospital.