Abstract

To stop further increase of health care cost, the German government
successively changed the regulatory framework including the reimbursement
policies of hospitals. One of the major innovations is the lump-sum
compensation of in-patient treatments based on Diagnosis Related Groups
(DRGs). Since the implementation of the DRG-system in 2004, clinics are no
longer being paid per day of performed services for each treated patient,
but based on the syndrome/diagnosis of the respective patient. Compared to
the former hospital and nursing charges, that were identical each day of
performed services, DRGs now lead to different economical incentives for
hospitals. From now on, hospitals need to utilize their resources much more
efficiently in order to reduce waiting and treatment time of patients as
well as to minimize cost per case. Mandatory for the accomplishment of
those objectives is the quality of processes, meaning the efficiency and
effectiveness of each process step that needs to be performed during
patient treatment. In order to reduce the length of stay of patients as
well as to reduce the cost for treatment, hospitals need appropriate tools
and methods. The dynamic behavior of complex clinical processes in process
optimization studies can only be modeled, analyzed and optimized with
adequate, well performing simulation systems. Among others, those
simulation systems shall provide a validated process framework for modeling
and simulating clinical processes, comprising re-usable and executable
building blocks. This is basis for comparisons of accomplished results, for
instance in benchmark studies. On the other hand, the length of time for
the development of simulation models can be drastically reduced by using
standardized and re-usable building blocks. In this thesis a validated
process framework was developed with the simulation system MLDesigner. This
process framework comprises standardized building blocks for rapid modeling
and simulation of hospital processes. Those building blocks are
modularized, structured in pre-defined libraries of MLDesigner and named
after the respective clinical process step. Selected clinical care paths
were the basis for the development of the process framework. The developed
building blocks were utilized for modeling, simulating and optimizing the
processes of a cancer treatment center. As part of the process optimization
study a significant reduction of patients’ waiting times was achieved. The
process framework represents the concrete approach for standardization of
modeling and simulation of clinical processes. Due to its modular set-up
the process framework can be added and extended by additional processes at
any time. The existing building blocks can be utilized for further
simulation studies, e.g. as the basis for system design or the optimization
of the integration of different sectors of the healthcare environment, i.e.
integration of processes between doctors in private practices, hospitals
and rehabilitation cent