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.
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 …
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.
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).
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.
Marcial, Laura Haak; Santini, Silas; Kery, Caroline; Brown, Stephen; Chew, Rob & Blumenfeld, Barry