Predicting treatment process steps from events

Abstract

MotivationThe primary economy-driven documentation of patient-specific information in clinical information systems leads to drawbacks in the use of these systems in daily clinical routine. Missing meta-data regarding underlying clinical workflows within the stored information is crucial for intelligent support systems. Unfortunately, there is still a lack of primary clinical needs-driven electronic patient documentation. Hence, physicians and surgeons must search hundreds of documents to find necessary patient data rather than accessing relevant information directly from the current process step. In this work, a completely new approach has been developed to enrich the existing information in clinical information systems with additional meta-data, such as the actual treatment phase from which the information entity originates. MethodsStochastic models based on Hidden Markov Models (HMMs) are used to create a mathematical representation of the underlying clinical workflow. These models are created from real-world anonymized patient data and are tailored to therapy processes for patients with head and neck cancer. Additionally, two methodologies to extend the models to improve the workflow recognition rates are presented in this work. ResultsA leave-one-out cross validation study was performed and achieved promising recognition rates of up to 90% with a standard deviation of 6.4%. ConclusionsThe method presented in this paper demonstrates the feasibility of predicting clinical workflow steps from patient-specific information as the basis for clinical workflow support, as well as for the analysis and improvement of clinical pathways.