Systemic analysis and modelling of diagnostic errors in medicine

Diagnostic accuracy is an important index of the quality of health care service. Missed, wrong or delayed diagnosis has a direct effect on patient safety. Diagnostic errors have been discussed at length; however it still lacks a systemic research approach. This thesis takes the diagnostic process as a system and develops a systemic model of diagnostic errors by implementing system dynamics modelling combined with regression analysis. It aims to propose a better way of studying diagnostic errors as well as a deeper understanding of how factors affect the number of possible errors at each step of the diagnostic process and how factors contribute to patient outcomes in the end. It is executed following two parts: In the first part, a qualitative model is developed to demonstrate how errors can happen during the diagnostic process; in other words, the model illustrates the connections among key factors and dependent variables. It starts from discovering key factors of diagnostic errors, producing a hierarchical list of factors, and then illustrates interrelation loops that show how relevant factors are linked with errors. The qualitative model is based on the findings of a systematic literature review and further refined by experts’ reviews. In the second part, a quantitative model is developed to provide system behaviour simulations, which demonstrates the quantitative relations among factors and errors during the diagnostic process. Regression modelling analysis is used to estimate the quantitative relationships among multi factors and their dependent variables during the diagnostic phase of history taking and physical examinations. The regression models are further applied into quantitative system dynamics modelling ‘stock and flow diagrams’. The quantitative model traces error flows during the diagnostic process, and simulates how the change of one or more variables affects the diagnostic errors and patient outcomes over time. The change of the variables may reflect a change in demand from policy or a proposed external intervention. The results suggest the systemic model has the potential to help understand diagnostic errors, observe model behaviours, and provide risk-free simulation experiments for possible strategies.

Name dc:type.qualificationname

phd

Level dc:type.qualificationlevel

doctoral

Grantor dc:publisher.institution

City University London

Year dc:date.issued

2016

Author dc:creator

• Guo, Shijing

• QA75 Electronic computers. Computer science