Doctor of Philosophy

dissertationFollowing liver transplantation, patients require lifelong immunosuppressive care and monitoring to prevent organ rejection, drug toxicity, and death. Traditionally, transplant centers use paper-based processes that are not scalable and can lead to inefficiencies and deficiencies in information management. Clinical decision support (CDS) tools may help to overcome information management challenges, and a system-agnostic approach may help to disseminate these tools nationwide. We sought to inform the development of new transplant information systems by analyzing existing information systems. To meet this overall objective, we administered a survey and found that all liver transplant programs used manual, paper-based processes and nearly all used electronic health record (EHR) systems. Programs also had immunosuppression guidelines with similar logic patterns. Then we analyzed long-term use of a computerized notification system at one transplant center and found that a system designed specifically for the posttransplant workflow can meet long-term information management needs. Next, we assessed the clinical outcomes associated with computerized notifications for laboratory monitoring of immunosuppressive care and found that a system designed specifically for the posttransplant workflow was associated with improved clinical outcomes. Following this, we described workflow processes at two transplant centers and found that a transplant-specific notification system was associated with changes in workflow process iv measures and the satisfaction of performing laboratory monitoring tasks compared to a general EHR notification system. Finally, we administered a questionnaire to coordinators using a transplant-specific notification system and identified the usage of specific data elements in computerized notifications for posttransplant laboratory monitoring. Our findings show that near universal use of EHRs provides an infrastructure for implementing CDS tools, and logic patterns for posttransplant laboratory monitoring can be generalized to other U.S. transplant centers. Transplant-specific computerized notifications may be part of a system of processes that improve the scalability, quality, and satisfaction of patient management by postliver transplant coordinators. However, these systems must be flexible enough to accommodate new immunosuppressants and changing or additional parameters used in computerized logic as clinical practice or needs of the patient population evolve. Proactive notifications sent directly to patients regarding upcoming due dates via patient portals may also improve patient outcomes

Monitoring and detection of agitation in dementia: towards real-time and big-data solutions

The changing demographic profile of the population has potentially challenging social, geopolitical, and financial consequences for individuals, families, the wider society, and governments globally. The demographic change will result in a rapidly growing elderly population with healthcare implications which importantly include Alzheimer type conditions (a leading cause of dementia). Dementia requires long term care to manage the negative behavioral symptoms which are primarily exhibited in terms of agitation and aggression as the condition develops. This paper considers the nature of dementia along with the issues and challenges implicit in its management. The Behavioral and Psychological Symptoms of Dementia (BPSD) are introduced with factors (precursors) to the onset of agitation and aggression. Independent living is considered, health monitoring and implementation in context-aware decision-support systems is discussed with consideration of data analytics. Implicit in health monitoring are technical and ethical constraints, we briefly consider these constraints with the ability to generalize to a range of medical conditions. We postulate that health monitoring offers exciting potential opportunities however the challenges lie in the effective realization of independent assisted living while meeting the ethical challenges, achieving this remains an open research question remains.Peer ReviewedPostprint (author's final draft

A canonical theory of dynamic decision-making

Decision-making behavior is studied in many very different fields, from medicine and eco- nomics to psychology and neuroscience, with major contributions from mathematics and statistics, computer science, AI, and other technical disciplines. However the conceptual- ization of what decision-making is and methods for studying it vary greatly and this has resulted in fragmentation of the field. A theory that can accommodate various perspectives may facilitate interdisciplinary working. We present such a theory in which decision-making is articulated as a set of canonical functions that are sufficiently general to accommodate diverse viewpoints, yet sufficiently precise that they can be instantiated in different ways for specific theoretical or practical purposes. The canons cover the whole decision cycle, from the framing of a decision based on the goals, beliefs, and background knowledge of the decision-maker to the formulation of decision options, establishing preferences over them, and making commitments. Commitments can lead to the initiation of new decisions and any step in the cycle can incorporate reasoning about previous decisions and the rationales for them, and lead to revising or abandoning existing commitments. The theory situates decision-making with respect to other high-level cognitive capabilities like problem solving, planning, and collaborative decision-making. The canonical approach is assessed in three domains: cognitive and neuropsychology, artificial intelligence, and decision engineering

NEOREG : design and implementation of an online neonatal registration system to access, follow and analyse data of newborns with congenital cytomegalovirus infection

Today's registration of newborns with congenital cytomegalovirus (cCMV) infection is still performed on paper-based forms in Flanders, Belgium. This process has a large administrative impact. It is imortant that all screening tests are registered to have a complete idea of the impact of cCMV. Although these registrations are usable in computerised data analysis, these data are not available in a format to perform electronic processing. An online Neonatal Registry (NEOREG) System was designed and developed to access, follow and analyse the data of newborns remotely. It allows patients' diagnostic registration and treatment follow-up through a web interface and uses document forms in Portable Document Format (PDF), which incorporate all the elements from the existing forms. Forms are automatically processed to structured EHRs. Modules are included to perform statistical analysis. The design was driven by extendibility, security and usability requirements. The website load time, throughput and execution time of data analysis were evaluated in detail. The NEOREG system is able to replace the existing paper-based CMV records

Information and communication technology solutions for outdoor navigation in dementia

INTRODUCTION: Information and communication technology (ICT) is potentially mature enough to empower outdoor and social activities in dementia. However, actual ICT-based devices have limited functionality and impact, mainly limited to safety. What is an ideal operational framework to enhance this field to support outdoor and social activities? METHODS: Review of literature and cross-disciplinary expert discussion. RESULTS: A situation-aware ICT requires a flexible fine-tuning by stakeholders of system usability and complexity of function, and of user safety and autonomy. It should operate by artificial intelligence/machine learning and should reflect harmonized stakeholder values, social context, and user residual cognitive functions. ICT services should be proposed at the prodromal stage of dementia and should be carefully validated within the life space of users in terms of quality of life, social activities, and costs. DISCUSSION: The operational framework has the potential to produce ICT and services with high clinical impact but requires substantial investment

Semantic-based decision support for remote care of dementia patients

This paper investigates the challenges in developing a semantic-based Dementia Care Decision Support System based on the non-intrusive monitoring of the patient's behaviour. Semantic-based approaches are well suited for modelling context-aware scenarios similar to Dementia care systems, where the patient's dynamic behaviour observations (occupants movement, equipment use) need to be analysed against the semantic knowledge about the patient's condition (illness history, medical advice, known symptoms) in an integrated knowledgebase. However, our research findings establish that the ability of semantic technologies to reason upon the complex interrelated events emanating from the behaviour monitoring sensors to infer knowledge assisting medical advice represents a major challenge. We attempt to address this problem by introducing a new approach that relies on propositional calculus modelling to segregate complex events that are amenable for semantic reasoning from events that require pre-processing outside the semantic engine before they can be reasoned upon. The event pre-processing activity also controls the timing of triggering the reasoning process in order to further improve the efficiency of the inference process. Using regression analysis, we evaluate the response-time as the number of monitored patients increases and conclude that the incurred overhead on the response time of the prototype decision support systems remains tolerable

Fuzzy rule-based system applied to risk estimation of cardiovascular patients

Cardiovascular decision support is one area of increasing research interest. On-going collaborations between clinicians and computer scientists are looking at the application of knowledge discovery in databases to the area of patient diagnosis, based on clinical records. A fuzzy rule-based system for risk estimation of cardiovascular patients is proposed. It uses a group of fuzzy rules as a knowledge representation about data pertaining to cardiovascular patients. Several algorithms for the discovery of an easily readable and understandable group of fuzzy rules are formalized and analysed. The accuracy of risk estimation and the interpretability of fuzzy rules are discussed. Our study shows, in comparison to other algorithms used in knowledge discovery, that classifcation with a group of fuzzy rules is a useful technique for risk estimation of cardiovascular patients. © 2013 Old City Publishing, Inc