Intelligent audit code generation from free text in the context of neurosurgery

Clinical auditing requires codified data for aggregation and analysis of patterns. However in the medical domain obtaining structured data can be difficult as the most natural, expressive and comprehensive way to record a clinical encounter is through natural language. The task of creating structured data from naturally expressed information is known as information extraction. Specialised areas of medicine use their own language and data structures; the translation process has unique challenges, and often requires a fresh approach. This research is devoted to creating a novel semi-automated method for generating codified auditing data from clinical notes recorded in a neurosurgical department in an Australian teaching hospital. The method encapsulates specialist knowledge in rules that instantaneously make precise decisions for the majority of the matches, followed up by dictionary-based matching of the remaining text

Enhancing rule-based text classification of neurosurgical notes using filtered feature weight vectors

Clinicians need to record clinical encounters in written or spoken language, not only for its work-flow naturalness but also for its expressivity, precision, and capacity to convey all required information, which codified structure data is incapable of. Therefore, the structured data which is required for aggregation and analysis must be obtained from clinical text as a later step. Specialised areas of medicine use their own clinical language and clinical coding systems, resulting in unique challenges for the extraction process. Rule-based information extraction have been used effectively in commercial systems and are favoured because they are easily understood and controlled. However, there is promising research into the use of machine language techniques for extracting information, and this research explores the effectiveness of a hybrid rule-based and machine learning-based audit coding system developed for the neurosurgical department of a major trauma hospital

Development of an intelligent surgical training system for Thoracentesis

Surgical training improves patient care, helps to reduce surgical risks, increases surgeon’s confidence, and thus enhances overall patient safety. Current surgical training systems are more focused on developing technical skills, e.g. dexterity, of the surgeons while lacking the aspects of context-awareness and intra-operative real-time guidance. Context-aware intelligent training systems interpret the current surgical situation and help surgeons to train on surgical tasks. As a prototypical scenario, we chose Thoracentesis procedure in this work. We designed the context-aware software framework using the surgical process model encompassing ontology and production rules, based on the procedure descriptions obtained through textbooks and interviews, and ontology-based and marker-based object recognition, where the system tracked and recognised surgical instruments and materials in surgeon’s hands and recognised surgical instruments on the surgical stand. The ontology was validated using annotated surgical videos, where the system identified “Anaesthesia” and “Aspiration” phase with 100% relative frequency and “Penetration” phase with 65% relative frequency. The system tracked surgical swab and 50 mL syringe with approximately 88.23% and 100% accuracy in surgeon’s hands and recognised surgical instruments with approximately 90% accuracy on the surgical stand. Surgical workflow training with the proposed system showed equivalent results as the traditional mentor-based training regime, thus this work is a step forward a new tool for context awareness and decision-making during surgical training

Predicting the Risk of Falling with Artificial Intelligence

Predicting the Risk of Falling with Artificial Intelligence Abstract Background: Fall prevention is a huge patient safety concern among all healthcare organizations. The high prevalence of patient falls has grave consequences, including the cost of care, longer hospital stays, unintentional injuries, and decreased patient and staff satisfaction. Preventing a patient from falling is critical in maintaining a patient’s quality of life and averting the high cost of healthcare expenses. Local Problem: Two hospitals\u27 healthcare system saw a significant increase in inpatient falls. The fall rate is one of the nursing quality indicators, and fall reduction is a key performance indicator of high-quality patient care. Methods: This quality improvement evidence-based observational project compared the rate of fall (ROF) between the experimental and control unit. Pearson’s chi-square and Fisher’s exact test were used to analyze and compare results. Qualtrics surveys evaluated the nurses’ perception of AI, and results were analyzed using the Mann-Whitney Rank Sum test. Intervention. Implementing an artificial intelligence-assisted fall predictive analytics model that can timely and accurately predict fall risk can mitigate the increase in inpatient falls. Results: The pilot unit (Pearson’s chi-square = p pp\u3c0.001). Conclusions: AI-assisted automatic fall predictive risk assessment produced a significant reduction if the number of falls, the ROF, and the use of fall countermeasures. Further, nurses’ perception of AI improved after the introduction of FPAT and presentation

Department of Computer Science Activity 1998-2004

This report summarizes much of the research and teaching activity of the Department of Computer Science at Dartmouth College between late 1998 and late 2004. The material for this report was collected as part of the final report for NSF Institutional Infrastructure award EIA-9802068, which funded equipment and technical staff during that six-year period. This equipment and staff supported essentially all of the department\u27s research activity during that period

Augmented Reality and Health Informatics: A Study based on Bibliometric and Content Analysis of Scholarly Communication and Social Media

Healthcare outcomes have been shown to improve when technology is used as part of patient care. Health Informatics (HI) is a multidisciplinary study of the design, development, adoption, and application of IT-based innovations in healthcare services delivery, management, and planning. Augmented Reality (AR) is an emerging technology that enhances the user’s perception and interaction with the real world. This study aims to illuminate the intersection of the field of AR and HI. The domains of AR and HI by themselves are areas of significant research. However, there is a scarcity of research on augmented reality as it applies to health informatics. Given both scholarly research and social media communication having contributed to the domains of AR and HI, research methodologies of bibliometric and content analysis on scholarly research and social media communication were employed to investigate the salient features and research fronts of the field. The study used Scopus data (7360 scholarly publications) to identify the bibliometric features and to perform content analysis of the identified research. The Altmetric database (an aggregator of data sources) was used to determine the social media communication for this field. The findings from this study included Publication Volumes, Top Authors, Affiliations, Subject Areas and Geographical Locations from scholarly publications as well as from a social media perspective. The highest cited 200 documents were used to determine the research fronts in scholarly publications. Content Analysis techniques were employed on the publication abstracts as a secondary technique to determine the research themes of the field. The study found the research frontiers in the scholarly communication included emerging AR technologies such as tracking and computer vision along with Surgical and Learning applications. There was a commonality between social media and scholarly communication themes from an applications perspective. In addition, social media themes included applications of AR in Healthcare Delivery, Clinical Studies and Mental Disorders. Europe as a geographic region dominates the research field with 50% of the articles and North America and Asia tie for second with 20% each. Publication volumes show a steep upward slope indicating continued research. Social Media communication is still in its infancy in terms of data extraction, however aggregators like Altmetric are helping to enhance the outcomes. The findings from the study revealed that the frontier research in AR has made an impact in the surgical and learning applications of HI and has the potential for other applications as new technologies are adopted

Faculty Publications and Creative Works 1997

One of the ways we recognize our faculty at the University of New Mexico is through this annual publication which highlights our faculty\u27s scholarly and creative activities and achievements and serves as a compendium of UNM faculty efforts during the 1997 calendar year. Faculty Publications and Creative Works strives to illustrate the depth and breadth of research activities performed throughout our University\u27s laboratories, studios and classrooms. We believe that the communication of individual research is a significant method of sharing concepts and thoughts and ultimately inspiring the birth of new of ideas. In support of this, UNM faculty during 1997 produced over 2,770 works, including 2,398 scholarly papers and articles, 72 books, 63 book chapters, 82 reviews, 151 creative works and 4 patents. We are proud of the accomplishments of our faculty which are in part reflected in this book, which illustrates the diversity of intellectual pursuits in support of research and education at the University of New Mexico. Nasir Ahmed Interim Associate Provost for Research and Dean of Graduate Studie

ConnEDCt, a mobile-first framework for clinical Electronic Data Capture

Paper-based data capture has long served as the primary means of collecting research data and continues to be the dominant means of data capture through the present day. Despite inertia with adopting information technology in clinical research, electronic methods of information capture have important benefits over traditional, paper-based methods. Electronic Data Capture (EDC) systems can provide integrated error checking, protocol enforcement, decision support, automated randomization, and quicker access to data and results. As EDC systems become more accessible and resourceful, EDC has begun to replace paper-based data capture. Meanwhile, mobile computing, utilizing smartphones and tablets, has become commonplace in business and our everyday lives. Many EDC solutions support mobile devices, yet few were conceived with a “mobile- first” design philosophy and fewer support extensive study protocol-support features. A significant amount of clinical research is conducted in geographic regions with limited or no Internet access such as impoverished and remote communities. Current EDC solutions remain challenging to use in these contexts. While EDC is an increasingly important tool for clinical research, when EDC solutions are built on web-centric architectures, the lack of Internet coverage means that researchers often need to fall back on paper-based data capture methods or build expensive, custom EDC tools. A customizable Mobile Electronic Data Capture (mEDC) framework with an asynchronous data transport layer will better meet the needs of distributed studies in resource- limited, geographical areas. I developed ConnEDCt, a full-featured mEDC application that is customizable for longitudinal study protocols, with regulatory-compliant security, auditability and an asynchronous data transport model. ConnEDCt is adaptable to different study protocols, has extensive study protocol-support built-in, and supports on- or off-line data synchronization to a central data repository. ConnEDCt focuses on mobility and is designed to serve the needs of complex clinical research studies in regions where other EDC platforms cannot be utilized

Intra-Professional Specialist Differentiation In The UK Surgical Profession

This thesis studies specialist differentiation in the surgical profession of the United Kingdom, seeking to clarify what the combinations of factors favour or hinder it. It draws on interviews with two hundred surgeons, general practitioners and hospital CEO’s, as well as on sociological literature, especially Bucher and Strauss’s (1961) ‘Process Model’, the concept of ‘profession’ developed by Freidson (1970), Larson’s (1977) model of the ‘Professional Project’ and Bourdieu’s (1981, 1984, 1986, 1988, 1990, 1991, 2005) seminal work on ‘Capital’ and ‘Power’ in ‘Social fields’. The thesis aims to interpret specialisation as a dynamic political process influenced by different groups’ deployment of accumulated specific types of ‘capital’, challenging the view, widespread in the profession itself and still present in functionalist sociological literature, that specialist differentiation is an inevitable consequence of advances in knowledge and technology. In addition to the introduction, conclusion and appendices, the thesis includes chapters on: (1) theoretical framework; (2) methodology; (3) the role of knowledge and technology in specialist differentiation; (4) the role of institutions before the establishment of the NHS; (5) the role of the relationship between the profession and the state in the period 1948-1990; (6) ditto in the period 1991-1997; (7) the current situation and how it might develop