Factors Affecting Late Medication Administration in the Hospital Setting

This pilot study extends nursing’s historical efforts to prevent medication errors by using a database research approach to better understand why medication errors persist in acute care settings. The pilot study was conducted 3 units and float pool nurses. Data were analyzed using descriptive statistics and multilevel regression modeling.
Date: December 2020
Creator: Estes, Carey & McCoy, Thomas
System: The UNT Digital Library

Global Sentiment Towards COVID-19 on Twitter

Twitter is one of the major social media platforms highlighting public opinion. With over 330 million users across the globe, Twitter provides insights into global sentiments on many topics. One can estimate global sentiments towards certain events relating to COVID-19 by analyzing the most common phrases and their related sentiment scores from Twitter API data. This project has compiled the most used trigrams in tweets relating to COVID-19 to calculate sentiment scores for the period from March 22 to August 7, 2020. Another goal of the project is to optimize data collection from Twitter API. Twitter limits access to tweet contents to 900 requests per 15 minutes for unpaid API users. For student data scientists, paying for increased API usage is financially infeasible. So, to deal with the rate limit, the project has written functions using the Tweepy python library to collect Twitter API data. The Pandas library has also been used to sample 139000 tweets from over 300 million. The IEEE Dataset provided sentiment scores for the full population. So, to check the integrity of my sample, I performed a Pearson correlation test between the full dataset and sample data, and got 0.84, showing the sample is representative of …
Date: December 2020
Creator: Auroni, Neil
System: The UNT Digital Library

Knowledge Management in the Technical Information Center/Library of a Navy Lab and as a Whole, as well as Metrics to Measure the Scientific Health of a R&D Center

In order to perform research data triangulation, there were three main sources of data: 1. External/Internal Survey of 15 Library Directors (5 in the Navy; 10 from Government/Universities), 2. Literature Review/Industry Best Practices, and 3. Navy Lab Interviews (Ten) . The results include "Harvest” the personal collections of classified and other materials (reach out to the end users to put documents in library repository); Need to modernize our workflow; Having research material that can be easily accessed for desktops; Need to share information and knowledge; Focus on the needs of your community and evolve with those needs.
Date: December 2020
Creator: Liebowitz, Jay
System: The UNT Digital Library

Mitigating Usability Issues in Legacy EHR Systems to Improve Patient Safety: A Learning Health System Approach

This paper describes an on-going research project examining currently used processes in both hospitals and EHR vendor companies for identifying, prioritizing, and mitigating electronic health record (EHR) software issues, and usability issues, that may compromise patient safety as they arise in the implemented or legacy EHR system. This on-going project includes 3 interviews with CMIOs at hospitals and 3 interviews with EHR vendors. Next steps include surveying both groups. The project can be considered as part of a learning health system (LHS) approach in that it seeks to identify best practices in terms of the process for addressing these EHR issues. The LHS approach seeks to improve long term outcomes in health care by identifying optimal delivery processes and to do so in a systematic, rather than a haphazard way. (Friedman & Rigby, 2013).
Date: December 2020
Creator: Meehan, Rebecca
System: The UNT Digital Library

Using Query Expansion to Improve Findability of Resources Addressing Multiple Chronic Conditions

With advances in natural language processing (NLP), machine learning (ML) and artificial intelligence (AI), there are new opportunities for improving findability among existing public-facing resources. This project seeks to inform findability, especially for multiple chronic condition (MCC) resources, by describing current search capabilities and limitations across several of AHRQ’s publicly available domains and by identifying and piloting a novel NLP/ML approach to make suggested improvements. This work intentionally engages with the overlap of numerous disciplines including information extraction, information retrieval, data and text mining, knowledge management, and best practices in health care. We are looking to apply this work across all domains but will start by focusing on specific AHRQ domains. Given limited API access, we scraped the content of digital.ahrq.gov and the patient centered medical home (PCMH) resources and performed automated search using a set of related terms that align with an MCC scenario: hypertension, osteoarthritis, and chronic kidney disease. We obtained results confirming the limitations of existing search.
Date: December 2020
Creator: Marcial, Laura Haak; Santini, Silas; Kery, Caroline; Brown, Stephen; Chew, Rob & Blumenfeld, Barry
System: The UNT Digital Library

University Archives and their relationship with Campus records management

This poster highlights a project undertaken at the University of Houston by the Gerald D. Hines College of Architecture and Design to select and transition records into the University's archives. The relationship between colleges or department and the University archives is examined, and the workflows necessary to ensure consistency through the transfer. The poster was created as part of coursework for INFO 5375 Archival Appraisal in Spring 2020 at the University of North Texas.
Date: Spring 2020
Creator: Tutt, Courtney
System: The UNT Digital Library

Strategies for Increasing Findabilitiy of Language Data

Poster discussing practical methods for ensuring the quality of descriptive metadata associated with linguistic datasets in language archive deposits. Digital language archives are valuable tools for facilitating language revitalization, providing data on lesser-known languages, and supporting reproducibility of research and development of linguistic theory, though their potential remains unrealized as the data available in language archives are rarely accessed by linguists or language communities. Reasons for this under-utilization are the issues with data standardization and metadata quality. Including basic grammatical and typological information would allow wider audiences to reach the material.
Date: January 4, 2020
Creator: Burke, Mary
System: The UNT Digital Library