Application of the Bayesian belief network model to evaluate variances in a clinical caremap: Radical prostatectomy case study

A clinical caremap is a cost-effective tool for clinical process improvement that has been accepted in hospitals and various healthcare organizations in many countries. However, compared to the literature describing the initial development of the clinical caremaps, the evaluation of the impact of the variances in the caremap pathway on the patient's expected outcomes and the patient's length of stay (LOS) remains relatively less analyzed. In this research, we deal with the issue of variances in the clinical caremap by building a Bayesian belief network named BBN_RPC to model the radical prostatectomy caremap. The BBN_RPC model provides insight into probabilistic dependencies that exist among the activities (variables) in the caremap. We then use the BBN_RPC model to analyze possible variances and to make inferences. The results show that most of the activities in the caremap are related with each other and to some extent linked with the patient's length of stay (LOS), whereas different activities have different weights on the LOS. Using radical prostatectomy patients' data from a retrospective chart study conducted at the Ottawa Civic Hospital, we have applied the BBN_RPC model to predict a patient's future conditions and the LOS, based on the current observations. Predictive accuracy of the BBN_RPC model was evaluated by cross validation tests, which showed the accuracy for predicting the patient's LOS, given the patient's observations during the first two post-op days, is at approximately 94% level.

Grantor dc:publisher

University of Ottawa (Canada)

Year dc:date

2013

Author dc:creator

• Li, Mingmei

• Health Sciences, Health Care Management.
• Engineering, System Science.

Language dc:language

en

Identifier

Source: Masters Abstracts International, Volume: 43-06, page: 2407.
http://dx.doi.org/10.20381/ruor-18322

OAI identifier oai:identifier

oai:ruor.uottawa.ca:10393/26693