The CHIME graduate programme in health informatics

In 1999 University College London inaugurated a programme of graduate part-time Health Informatics courses to support the UK National Health Service?s Information for Health strategy. The programme has attracted students from across the UK and abroad, with a diverse range of backgrounds and skills and has proved a challenging and rewarding experience for students and tutors alike. The modular programme aims to provide a thorough grounding in the theory and practice of Health Informatics and addresses important application areas. The guiding principle is that Health Informatics graduates need to understand computers and programming but that, since the majority are not going to become programmers, programming methods should not dominate the curriculum.In the taught phase of the programme students attend college for 3 days a month and complete an assignment each month, based on home study. Students may graduate with a certificate or diploma, or go on to tackle a dissertation leading to an MSc. Research projects have included a patient record system based on speech input, a mathematical model for illustrating to patients the risks associated with smoking, an analysis of Trust staff's preparedness for Information for Health and a patient information leaflet giving advice about drug related information on the Web. As we move towards our fifth intake of students, we are in the process of evaluating our programme and carrying out a follow up study of our graduates? subsequent career pathways

Student Nurse Perceptions of Effective Medication Administration Education

Nursing faculty strive to educate students in a manner that prevents errors, promoting quality, patient-centered care. This endeavor is dependent upon meaningful and effective education that incorporates educational experiences reflective of the service sector. Anecdotal reports from clinical faculty and student nurses suggest that academic medication administration education may not optimally prepare students for safe entry into clinical practice. The aim of this phenomenologic qualitative research is to understand student nurse perceptions regarding teaching strategies and learning activities that prepared them for safe medication administration in acute care clinical settings. Focus group interviews resulted in two broad themes that are identified as Effective Education and Gaps in Education. Within these broad themes, findings revealed that students value faculty demonstrations, peer-learning opportunities, and repetitive practice with timely feedback. Study findings also pointed to educational gaps. Students reported needing to learn communication and conflict resolution strategies that would help them manage real-world interruptions, distractions, and computer generated alerts. Study findings recommend implementing relevant decision-support technology within academic lab learning activities

Advances in Teaching & Learning Day Abstracts 2005

Proceedings of the Advances in Teaching & Learning Day Regional Conference held at The University of Texas Health Science Center at Houston in 2005

Beyond Volume: The Impact of Complex Healthcare Data on the Machine Learning Pipeline

From medical charts to national census, healthcare has traditionally operated under a paper-based paradigm. However, the past decade has marked a long and arduous transformation bringing healthcare into the digital age. Ranging from electronic health records, to digitized imaging and laboratory reports, to public health datasets, today, healthcare now generates an incredible amount of digital information. Such a wealth of data presents an exciting opportunity for integrated machine learning solutions to address problems across multiple facets of healthcare practice and administration. Unfortunately, the ability to derive accurate and informative insights requires more than the ability to execute machine learning models. Rather, a deeper understanding of the data on which the models are run is imperative for their success. While a significant effort has been undertaken to develop models able to process the volume of data obtained during the analysis of millions of digitalized patient records, it is important to remember that volume represents only one aspect of the data. In fact, drawing on data from an increasingly diverse set of sources, healthcare data presents an incredibly complex set of attributes that must be accounted for throughout the machine learning pipeline. This chapter focuses on highlighting such challenges, and is broken down into three distinct components, each representing a phase of the pipeline. We begin with attributes of the data accounted for during preprocessing, then move to considerations during model building, and end with challenges to the interpretation of model output. For each component, we present a discussion around data as it relates to the healthcare domain and offer insight into the challenges each may impose on the efficiency of machine learning techniques.Comment: Healthcare Informatics, Machine Learning, Knowledge Discovery: 20 Pages, 1 Figur

User-centered visual analysis using a hybrid reasoning architecture for intensive care units

One problem pertaining to Intensive Care Unit information systems is that, in some cases, a very dense display of data can result. To ensure the overview and readability of the increasing volumes of data, some special features are required (e.g., data prioritization, clustering, and selection mechanisms) with the application of analytical methods (e.g., temporal data abstraction, principal component analysis, and detection of events). This paper addresses the problem of improving the integration of the visual and analytical methods applied to medical monitoring systems. We present a knowledge- and machine learning-based approach to support the knowledge discovery process with appropriate analytical and visual methods. Its potential benefit to the development of user interfaces for intelligent monitors that can assist with the detection and explanation of new, potentially threatening medical events. The proposed hybrid reasoning architecture provides an interactive graphical user interface to adjust the parameters of the analytical methods based on the users' task at hand. The action sequences performed on the graphical user interface by the user are consolidated in a dynamic knowledge base with specific hybrid reasoning that integrates symbolic and connectionist approaches. These sequences of expert knowledge acquisition can be very efficient for making easier knowledge emergence during a similar experience and positively impact the monitoring of critical situations. The provided graphical user interface incorporating a user-centered visual analysis is exploited to facilitate the natural and effective representation of clinical information for patient care