Improving Usability and Adoption of Tablet-based Electronic Health Record (EHR) Applications

abstract: The technological revolution has caused the entire world to migrate to a digital environment and health care is no exception to this. Electronic Health Records (EHR) or Electronic Medical Records (EMR) are the digital repository for health data of patients. Nation wide efforts have been made by the federal government to promote the usage of EHRs as they have been found to improve quality of health service. Although EHR systems have been implemented almost everywhere, active use of EHR applications have not replaced paper documentation. Rather, they are often used to store transcribed data from paper documentation after each clinical procedure. This process is found to be prone to errors such as data omission, incomplete data documentation and is also time consuming. This research aims to help improve adoption of real-time EHRs usage while documenting data by improving the usability of an iPad based EHR application that is used during resuscitation process in the intensive care unit. Using Cognitive theories and HCI frameworks, this research identified areas of improvement and customizations in the application that were required to exclusively match the work flow of the resuscitation team at the Mayo Clinic. In addition to this, a Handwriting Recognition Engine (HRE) was integrated into the application to support a stylus based information input into EHR, which resembles our target users’ traditional pen and paper based documentation process. The EHR application was updated and then evaluated with end users at the Mayo clinic. The users found the application to be efficient, usable and they showed preference in using this application over the paper-based documentation.Dissertation/ThesisMasters Thesis Computer Science 201

Semantic enrichment for enhancing LAM data and supporting digital humanities. Review article

With the rapid development of the digital humanities (DH) field, demands for historical and cultural heritage data have generated deep interest in the data provided by libraries, archives, and museums (LAMs). In order to enhance LAM data’s quality and discoverability while enabling a self-sustaining ecosystem, “semantic enrichment” becomes a strategy increasingly used by LAMs during recent years. This article introduces a number of semantic enrichment methods and efforts that can be applied to LAM data at various levels, aiming to support deeper and wider exploration and use of LAM data in DH research. The real cases, research projects, experiments, and pilot studies shared in this article demonstrate endless potential for LAM data, whether they are structured, semi-structured, or unstructured, regardless of what types of original artifacts carry the data. Following their roadmaps would encourage more effective initiatives and strengthen this effort to maximize LAM data’s discoverability, use- and reuse-ability, and their value in the mainstream of DH and Semantic Web

The European Landscape of Qualitative Social Research Archives: Methodological and Practical Issues

In this article I set about describing current practices in archiving and reusing qualitative data. I discuss where can you find archived sources of qualitative data, and discuss some of the debates surrounding methodological, ethical and theoretical considerations relating to re-using data. I then address more pragmatic issues involved acquiring, preserving, providing access to and supporting the use of the data. Where best do qualitative data collections sit?in traditional libraries or archives alongside historical documents or as part of more holistic digital collections of contemporary social science research resources? This question relates to accessibility, resource discovery and cataloging methods, data preparation and documentation and promotional and outreach efforts to encourage data use. The ESDS Qualidata unit at the UK Data Archive is used as case study for showcasing archival practices, and is situated within the broader European landscape of social science-oriented data archives. Infrastructure requirements for running an archive are discussed and a look forward future developments

Semantic enrichment for enhancing LAM data and supporting digital humanities. Review article

With the rapid development of the digital humanities (DH) field, demands for historical and cultural heritage data have generated deep interest the data provided by libraries, archives, and museums (LAMs). In order to enhance LAM data’s quality and discoverability while enabling a self-sustaining ecosystem, “semantic enrichment” becomes a strategy increasingly used by LAMs during recent years. This article introduces a number of semantic enrichment methods and efforts that can be applied to LAM data at various levels, aiming to support deeper and wider exploration and use of LAM data in DH research. The real cases, research projects, experiments, and pilot studies shared in this article demonstrate endless potential for LAM data, whether they are structured, semi-structured, or unstructured, regardless of what types of original artifacts carry the data. Following their roadmaps would encourage more effective initiatives and strengthen this effort to maximize LAM data’s discoverability, use- and reuse-ability, and their value in the mainstream of DH and Semantic Web

A Vietnamese Handwritten Text Recognition Pipeline for Tetanus Medical Records

Machine learning techniques are successful for optical character recognition tasks, especially in recognizing handwriting. However, recognizing Vietnamese handwriting is challenging with the presence of extra six distinctive tonal symbols and vowels. Such a challenge is amplified given the handwriting of health workers in an emergency care setting, where staff is under constant pressure to record the well-being of patients. In this study, we aim to digitize the handwriting of Vietnamese health workers. We develop a complete handwritten text recognition pipeline that receives scanned documents, detects, and enhances the handwriting text areas of interest, transcribes the images into computer text, and finally auto-corrects invalid words and terms to achieve high accuracy. From experiments with medical documents written by 30 doctors and nurses from the Tetanus Emergency Care unit at the Hospital for Tropical Diseases, we obtain promising results of 2% and 12% for Character Error Rate and Word Error Rate, respectively

Crowdsourcing and Scholarly Culture: Understanding Expertise in an Age of Popularism

The increasing volume of digital material available to the humanities creates clear potential for crowdsourcing. However, tasks in the digital humanities typically do not satisfy the standard requirement for decomposition into microtasks each of which must require little expertise on behalf of the worker and little context of the broader task. Instead, humanities tasks require scholarly knowledge to perform and even where sub-tasks can be extracted, these often involve broader context of the document or corpus from which they are extracted. That is the tasks are macrotasks, resisting simple decomposition. Building on a case study from musicology, the In Concert project, we will explore both the barriers to crowdsourcing in the creation of digital corpora and also examples where elements of automatic processing or less-expert work are possible in a broader matrix that also includes expert microtasks and macrotasks. Crucially we will see that the macrotask–microtask distinction is nuanced: it is often possible to create a partial decomposition into less-expert microtasks with residual expert macrotasks, and crucially do this in ways that preserve scholarly values