A methodology for reliability analysis in health networks.
A reliability model for a health care domain based on requirement analysis at the early stage of design of regional health network (RHN) is introduced. RHNs are considered as systems supporting the services provided by health units, hospitals, and the regional authority. Reliability assessment in health care domain constitutes a field-of-quality assessment for RHN. A novel approach for predicting system reliability in the early stage of designing RHN systems is presented in this paper. The uppermost scope is to identify the critical processes of an RHN system prior to its implementation. In the methodology, Unified Modeling Language activity diagrams are used to identify megaprocesses at regional level and the customer behavior model graph (CBMG) to describe the states transitions of the processes. CBMG is annotated with: 1) the reliability of each component state and 2) the transition probabilities between states within the scope of the life cycle of the process. A stochastic reliability model (Markov model) is applied to predict the reliability of the business process as well as to identify the critical states and compare them with other processes to reveal the most critical ones. The ultimate benefit of the applied methodology is the design of more reliable components in an RHN system. The innovation of the approach of reliability modeling lies with the analysis of severity classes of failures and the application of stochastic modeling using discrete-time Markov chain in RHNs.
Spyrou
Stergiani
S
Laboratory of Medical Informatics, Medical School, Aristotle University of Thessaloniki, 54124 Thessaloniki, Greece. spirou@med.auth.gr
Bamidis
Panagiotis D
PD
Maglaveras
Nicos
N
Pangalos
George
G
Pappas
Costas
C
D000465:Algorithms; D019058:Community Networks / Q000145:classification*; D006115:Greece; D008485:Medical Audit / Q000379:methods*; D015397:Program Evaluation / Q000379:methods*; D011785:Quality Assurance, Health Care / Q000379:methods*; D012680:Sensitivity and Specificity