Identifying and reducing inappropriate use of medications using Electronic Health Records

Inappropriate use of medications (IUM) is a global problem that can lead to unnecessary harm to the patients and unnecessary costs across the health care system. Identifying and reducing IUM has been a long-lasting challenge and currently, no systematic and automated solution exists to address it. IUM can be manually identified by experts using medication appropriateness criteria (MAC). In this research I first conducted a review of approaches used to identify IUM and reduce IUM. Next, I developed a conceptual model for representing the MAC, and then developed a tool and a workflow for translating the MAC into structured form. Because indications are an important component of the MAC, I conducted a critical appraisal of existing knowledge sources that can be used to that end, namely the medication-indication knowledge-bases. Finally, I demonstrated how these structured MAC can be used to identify patients who are potentially subject to IUM and evaluated the accuracy of this approach. This research identifies the knowledge gaps and technological challenges in identifying and reducing IUM and addresses some of these gaps through the creation of a representation for MAC, a repository of structured MAC, and a set of tools that can assist in evaluating the impact of interventions aimed to reduce IUM or assess its downstream effects. This research also discusses the limitations of existing methods for executing computable decision support rules and proposes solutions needed to enhance these methods so they can support implementation of the MAC

INSIdE NANO : a systems biology framework to contextualize the mechanism-of-action of engineered nanomaterials

Engineered nanomaterials (ENMs) are widely present in our daily lives. Despite the efforts to characterize their mechanism of action in multiple species, their possible implications in human pathologies are still not fully understood. Here we performed an integrated analysis of the effects of ENMs on human health by contextualizing their transcriptional mechanism-of-action with respect to drugs, chemicals and diseases. We built a network of interactions of over 3,000 biological entities and developed a novel computational tool, INSIdE NANO, to infer new knowledge about ENM behavior. We highlight striking association of metal and metal-oxide nanoparticles and major neurodegenerative disorders. Our novel strategy opens possibilities to achieve fast and accurate read-across evaluation of ENMs and other chemicals based on their biosignatures.Peer reviewe

Adverse Drug Event Detection, Causality Inference, Patient Communication and Translational Research

Adverse drug events (ADEs) are injuries resulting from a medical intervention related to a drug. ADEs are responsible for nearly 20% of all the adverse events that occur in hospitalized patients. ADEs have been shown to increase the cost of health care and the length of stays in hospital. Therefore, detecting and preventing ADEs for pharmacovigilance is an important task that can improve the quality of health care and reduce the cost in a hospital setting. In this dissertation, we focus on the development of ADEtector, a system that identifies ADEs and medication information from electronic medical records and the FDA Adverse Event Reporting System reports. The ADEtector system employs novel natural language processing approaches for ADE detection and provides a user interface to display ADE information. The ADEtector employs machine learning techniques to automatically processes the narrative text and identify the adverse event (AE) and medication entities that appear in that narrative text. The system will analyze the entities recognized to infer the causal relation that exists between AEs and medications by automating the elements of Naranjo score using knowledge and rule based approaches. The Naranjo Adverse Drug Reaction Probability Scale is a validated tool for finding the causality of a drug induced adverse event or ADE. The scale calculates the likelihood of an adverse event related to drugs based on a list of weighted questions. The ADEtector also presents the user with evidence for ADEs by extracting figures that contain ADE related information from biomedical literature. A brief summary is generated for each of the figures that are extracted to help users better comprehend the figure. This will further enhance the user experience in understanding the ADE information better. The ADEtector also helps patients better understand the narrative text by recognizing complex medical jargon and abbreviations that appear in the text and providing definitions and explanations for them from external knowledge resources. This system could help clinicians and researchers in discovering novel ADEs and drug relations and also hypothesize new research questions within the ADE domain

Quality of care in incident type 2 diabetes and initial presentation of vascular complications: Prospective cohort study using linked electronic health records from CALIBER research platform

Background. Numbers of new cases of type 2 diabetes (T2D) are increasing rapidly. Early and continuing intervention after T2D presentation is crucial for best possible outcomes, ensuring that the existing high burden of T2D will not be aggravated. Identification of patterns of continuous care and predictors for meeting key targets for T2D management can improve quality of care. Glycaemic control is particularly important for primary prevention of vascular complications but its relationship with contemporary cardiovascular diseases (CVDs) has been less explored. More importantly, long-term glycaemic control can be assessed from routine monitoring, potentially providing new insight into T2D management to prevent vascular complications. Linked electronic health records are invaluable data resources for investigating these issues. Objective. To examine the quality of care in an incident T2D cohort through assessment of temporal trends of care, predictors of glycaemic, blood pressure and lipid control, and associations of short-term and long-term glycaemic control with chronic vascular complications. Methods. The data source for studies in this thesis was CALIBER which links electronic health records from primary care, hospitalisation, myocardial infarction and mortality registries. Patients newly diagnosed with T2D between 1998 and 2010 were followed-up until a censoring administrative date or initial occurrence of vascular complications. Trends in receipt of care and attainment of glycaemic, blood pressure and total cholesterol targets were examined. Predictors for meeting the targets were explored using multinomial logistic regressions. Association of early glycaemic control with a range of specific cardiovascular complications were investigated using Cox regressions. A longitudinal metric for glycaemic control was developed by quantifying time spent at target during follow-up and was tested for its association with cardiovascular and microvascular outcomes using mixed logistic regressions. Results. A total of 52,379 incident T2D patients were identified with a median follow-up of over 4 years. Positive trends were observed for blood pressure and total cholesterol control, but not for glycaemic control, whilst attainment of HbA1c and blood pressure targets over time consistently fell short. Older age at diagnosis was an important predictor for meeting the key targets. In 36,149 patients free from prior CVD, early glycaemic and blood pressure control was associated with lower risk for heart failure and peripheral arterial disease, whereas cholesterol control with myocardial infarction and transient ischaemic attack. Shorter duration at glycaemic target was associated with higher risk of major adverse cardiovascular events, cardiovascular death and diabetic retinopathy. Conclusions. This thesis highlights missed opportunities and inequality in T2D care. Both short-term and long-term glycaemic control are important for reducing risk of vascular complications. Limitations and implications of the findings for clinical practice and research were discussed