Evaluating clinical variation in traumatic brain injury data

Current methods of clinical guideline development have two large challenges: 1) there is often a long time-lag between the key results and publication into recommended best practice and 2) the measurement of adherence to those guidelines is often qualitative and difficult to standardise into measurable impact. In an age of ever-increasing volumes of accurate data captured at the bedside in specialist intensive care units, this thesis explores the possibility of constructing a technology that can interpret that data and present the results as a quantitative and immediate measure of guideline adherence. Applied to the Traumatic Brain Injury (TBI) domain, and specifically to the management of ICP and CPP, a framework is developed that makes use of process models to measure the adherence of clinicians to three specific TBI guidelines. By combining models constructed from physiological and treatment ICU data, and those constructed from guideline text, a distance is calculated between the two, and patterns of guideline adherence are inferred from this distance. The framework has been developed into an online application capable of producing adherence output on most standardised ICU datasets. This application has been applied to the Brain-IT and MIMIC III repositories and evaluated on the Philips ICCA bedside monitoring system. Patterns of guideline adherence are presented in a variety of ways including minute-by-minute windowing, tables of non-adherence instances, statistical distribution of instances, and a severity chart summarising the impact of non-adherence in a single number

Knowledge-driven Inference of Medical Interventions

Physiological monitoring equipment routinely collects large amounts of time series patient data. In addition to influencing the treatment of a patient, this data is often used in medical research. However, treatment data (e.g. sedation) can be difficult to collect. In this paper we describe the AMITIE (Automated Medical Intervention and Treatment Inference Engine) system which infers a medical intervention from physiological time series data. The system comprises several domain ontologies and an algorithm to detect abnormal physiological readings and infer the subsequent associated medical intervention. To evaluate this approach we have applied AMITIE in the neuro-intensive care unit domain