Using Markov Models to Find Interesting Patient Pathways
CBMS '07 Proceedings of the Twentieth IEEE International Symposium on Computer-Based Medical Systems
Emergency Readmission Criterion: A Technique for Determining the Emergency Readmission Time Window
IEEE Transactions on Information Technology in Biomedicine
A stochastic model for a visit to the doctor's office
Mathematical and Computer Modelling: An International Journal
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The concept of patient flow modelling has attracted managers, commissioners and clinicians to better understand the operational and clinical functions of the healthcare system. In this context, the current study has two objectives: First, to introduce a random effects continuation-ratio logit model, suitable for detecting stage wise transitions, to patient pathways modelling. Second, we aim at advancing our knowledge with regard to the application of modelling techniques to patient pathways. We study individual clinical pathways of chronic obstructive pulmonary disease (COPD) patients, a source of concern for major stakeholders. Data on COPD patients were extracted from the national English Hospital Episodes Statistics dataset. Individual patient pathways from initial admission through to more than four readmissions are captured. We notice that as patients are frequently readmitted, males are more likely to be in the higher risk group than females. Furthermore, the number of previous readmissions has a direct impact on the propensity of experiencing a further readmission. This model is very useful in detecting the most critical threshold at which multiple readmissions are more probable. Clinicians should note that a first readmission signifies a problem in the process of care and if care is not taken this may be the beginning of many subsequent readmissions. Our method could easily be implemented as a decision support tool to determine disease specific probabilities of multiple readmissions. Therefore, this could be a valuable tool for clinicians, health care managers, and policy makers for informed decision making in the management of diseases, which ultimately contributes to improved measures for hospital performance management.