Efficiency Strategies for Facilitating Computerized Clinical Documentation in Ambulatory Care

Most providers have experienced increased documentation demands with the use of electronic health records (EHRs). We sought to identify efficiency strategies that providers use to complete clinical documentation tasks in ambulatory care. Two observers performed ethnographic observations and interviews with 22 ambulatory care providers in a U.S. Veterans Affairs Medical Center. Observation notes and interview transcripts were coded for recurrent strategies relating to completion of the EHR progress notes. Findings included: the use of paper artifacts for handwritten notations; electronic templates for automation of certain parts of the note; use of shorthand and phrases rather than narrative writing; copying and pasting from previous EHR notes; directly entering information into the EHR note during the patient encounter; reliance on memory; and pre-populating an EHR note prior to seeing the patient. We discuss the findings in the context of distributed cognition to understand how clinical information is propagated and represented toward completion of a progress note. The study findings have important implications for improving and streamlining clinical documentation related to human factors workload management strategies

Relocating, Downsizing, and Merging: Inventory Projects to Manage Change in a Digital Environment

With a new library location and newly created librarian position, the Health Sciences Library (HSL) of the University Libraries at the University of Memphis needed a comprehensive inventory. Having previously completed a small-scale inventory, technical services librarians led the project to assess the HSL collection before the newly hired librarian arrived. Beyond ensuring that all materials were in the collection and reflected properly in the integrated library system (ILS), an up-to- date inventory asserts the value of the physical collections to a variety of campus stakeholders. This chapter offers ideas for working collaboratively with personnel across library departments to conduct and complete a major technical services project

Forensic information management system

The primary objectives of this project were to reduce the paper work, increase data reliability and reduce process turnaround time at the West Virginia State Police Forensic Laboratory (WVSPFL) Charleston, WV. The objective was achieved by reengineering and automating various processes in the seven units of the laboratory. A software tool called Forensic Information Management System (FIMS) was developed. FIMS was implemented in Visual Studio.Net; MS-SQL server was used as the database. The FIMS can be accessed via the internet/intranet, Personal Digital Assistant (PDA), or a stand-alone desktop computer. The key benefits of FIMS are its ability to integrate information flow, information exchange between Originating (ORI) agencies, track the status of cases submitted to WVSPFL, report generation and directly uploading of data to the Laboratory Information Management System (LIMS)

Designing an Adaptive Web Navigation Interface for Users with Variable Pointing Performance

Many online services and products require users to point and interact with user interface elements. For individuals who experience variable pointing ability due to physical impairments, environmental issues or age, using an input device (e.g., a computer mouse) to select elements on a website can be difficult. Adaptive user interfaces dynamically change their functionality in response to user behavior. They can support individuals with variable pointing abilities by 1) adapting dynamically to make element selection easier when a user is experiencing pointing difficulties, and 2) informing users about these pointing errors. While adaptive interfaces are increasingly prevalent on the Web, little is known about the preferences and expectations of users with variable pointing abilities and how to design systems that dynamically support them given these preferences. We conducted an investigation with 27 individuals who intermittently experience pointing problems to inform the design of an adaptive interface for web navigation. We used a functional high-fidelity prototype as a probe to gather information about user preferences and expectations. Our participants expected the system to recognize and integrate their preferences for how pointing tasks were carried out, preferred to receive information about system functionality and wanted to be in control of the interaction. We used findings from the study to inform the design of an adaptive Web navigation interface, PINATA that tracks user pointing performance over time and provides dynamic notifications and assistance tailored to their specifications. Our work contributes to a better understanding of users' preferences and expectations of the design of an adaptive pointing system

Advanced document data extraction techniques to improve supply chain performance

In this thesis, a novel machine learning technique to extract text-based information from scanned images has been developed. This information extraction is performed in the context of scanned invoices and bills used in financial transactions. These financial transactions contain a considerable amount of data that must be extracted, refined, and stored digitally before it can be used for analysis. Converting this data into a digital format is often a time-consuming process. Automation and data optimisation show promise as methods for reducing the time required and the cost of Supply Chain Management (SCM) processes, especially Supplier Invoice Management (SIM), Financial Supply Chain Management (FSCM) and Supply Chain procurement processes. This thesis uses a cross-disciplinary approach involving Computer Science and Operational Management to explore the benefit of automated invoice data extraction in business and its impact on SCM. The study adopts a multimethod approach based on empirical research, surveys, and interviews performed on selected companies.The expert system developed in this thesis focuses on two distinct areas of research: Text/Object Detection and Text Extraction. For Text/Object Detection, the Faster R-CNN model was analysed. While this model yields outstanding results in terms of object detection, it is limited by poor performance when image quality is low. The Generative Adversarial Network (GAN) model is proposed in response to this limitation. The GAN model is a generator network that is implemented with the help of the Faster R-CNN model and a discriminator that relies on PatchGAN. The output of the GAN model is text data with bonding boxes. For text extraction from the bounding box, a novel data extraction framework consisting of various processes including XML processing in case of existing OCR engine, bounding box pre-processing, text clean up, OCR error correction, spell check, type check, pattern-based matching, and finally, a learning mechanism for automatizing future data extraction was designed. Whichever fields the system can extract successfully are provided in key-value format.The efficiency of the proposed system was validated using existing datasets such as SROIE and VATI. Real-time data was validated using invoices that were collected by two companies that provide invoice automation services in various countries. Currently, these scanned invoices are sent to an OCR system such as OmniPage, Tesseract, or ABBYY FRE to extract text blocks and later, a rule-based engine is used to extract relevant data. While the system’s methodology is robust, the companies surveyed were not satisfied with its accuracy. Thus, they sought out new, optimized solutions. To confirm the results, the engines were used to return XML-based files with text and metadata identified. The output XML data was then fed into this new system for information extraction. This system uses the existing OCR engine and a novel, self-adaptive, learning-based OCR engine. This new engine is based on the GAN model for better text identification. Experiments were conducted on various invoice formats to further test and refine its extraction capabilities. For cost optimisation and the analysis of spend classification, additional data were provided by another company in London that holds expertise in reducing their clients' procurement costs. This data was fed into our system to get a deeper level of spend classification and categorisation. This helped the company to reduce its reliance on human effort and allowed for greater efficiency in comparison with the process of performing similar tasks manually using excel sheets and Business Intelligence (BI) tools.The intention behind the development of this novel methodology was twofold. First, to test and develop a novel solution that does not depend on any specific OCR technology. Second, to increase the information extraction accuracy factor over that of existing methodologies. Finally, it evaluates the real-world need for the system and the impact it would have on SCM. This newly developed method is generic and can extract text from any given invoice, making it a valuable tool for optimizing SCM. In addition, the system uses a template-matching approach to ensure the quality of the extracted information

Drawing, Handwriting Processing Analysis: New Advances and Challenges

International audienceDrawing and handwriting are communicational skills that are fundamental in geopolitical, ideological and technological evolutions of all time. drawingand handwriting are still useful in defining innovative applications in numerous fields. In this regard, researchers have to solve new problems like those related to the manner in which drawing and handwriting become an efficient way to command various connected objects; or to validate graphomotor skills as evident and objective sources of data useful in the study of human beings, their capabilities and their limits from birth to decline

Analyzing Red Tape: The Performative vs Informative Roles of Bureaucratic Documents

The preparation and transfer of documents bureaucratic procedures are generally viewed solely as a means of transferring information within the organization. When taken as the basis for analyzing and improving bureaucratic systems, this view is too narrow. Another, performative aspect of these documents also needs to be considered in the analysis. This paper elaborates on this additional function of organizational documents and points out the need for a broader framework for analyzing bureaucratic systems

Can diagnostic problem-solving competences of car mechatronics be validly assessed using a paper-pencil test?

In this study, an authentic paper-based key-feature test for electrotechnical diagnostic problem-solving competence was developed, administered to car mechatronic apprentices (N = 206) and validated using diagnostic problem-solving scores. It was hypothesized that the paper-based test scores represent the same construct as the problem-solving scores. The written test turned out to have a relatively high reliability (EAP/PV = .75). Additionally, it was found that the paper-based scores and problem-solving scores were highly correlated (r = .76) but represented empirically distinct dimensions. Presumably, the written test especially covered diagnostic knowledge and failed to cover other relevant subcomponents of diagnostic problem-solving competence. It is argued that this unexpected finding might be caused by construct underrepresentation and construct-irrelevant variance of the paper-based key-feature test.Keywords: Professional competence, domain-specific problem-solving, key-feature items, construct representation, construct-irrelevant varianc