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The Impact of Electronic Health Record Unintended Consequences on Quality of Care Within a High Complexity Healthcare Organization
Get PDF“Unintended consequences are unexpected, and unwanted outcomes that can limit the value of EHR implementation and adversely affect quality of care and patient safety” (Lee & Kang, 2021, p. 898). Few organizations have redesigned the EHR to improve usability to mitigate potential patient safety concerns. This study aims to identify unintended consequences in patient care workflows and determine educational needs related to EHR usability. A mixed method approach was used to investigate unintended consequences in deidentified patient safety reports submitted Jan 1, 2020, through December 31, 2023. The data was analyzed to identify error types and educational deficits. The Acute Care campus accounted for the majority (89%), n = 57 of total Joint Patient Safety Reporting (JPSR) incidents with a minority (11%), n= 7 of total JPSR reports on the Long-Term Care campus. Human error was attributed to 100% of all reported JPSRs. The quantitative data revealed a significant increase in reports between 2021 and 2022, with most reports originating in the Nursing Acute and Nursing Critical Care service lines. These service lines care for complex patients, which may have led to more JPSRs and suggest a correlation between patient acuity and the frequency of reports. Additionally, it is important to consider that an increase in errors could have also contributed to the higher frequency of reports. This implies that additional staff is needed to support these areas to increase patient safety. An analysis of submissions and outcomes of Joint Patient Safety Reporting (JPSRs) over four years, 2020-2023, revealed three high-frequency error types: Manual Release, Misinformation, and Patient Movement. These findings highlight the importance of addressing human factors through education and training
Professional Learning Communities During The Pandemic
Get PDFIn 2019 the world experienced a medical emergency that impacted education across the world. Several states in the United States had professional learning communities introduced into their schools as a means to enhance academic achievement prior to COVID 19. In order to show support to the schools and increase student achievement, a state introduced a specific professional learning community (PLC) model to be followed as a guideline to improvement and sustainability during a crisis. This study compares the state specific PLC model schools to schools who did not follow the state specific PLC model to examine the statistically significant difference between the schools. The findings revealed there was not a significant difference between the schools except for the variable of ethnic composition in schools with a high non-white population. In 2019, this difference was observed in the domains of ELA, reading, and English. Conversely, in 2021, the distinction manifested across all evaluated content areas
PyroScan: Wildfire Behavior Prediction and Tracking System
No full textThis project included creating code for a drone wildfire behavior prediction and tracking system
Call of the Wild
No full textThis is a design of the book cover for Call of the Wild by Jack London.
Aaron Campbell won 1st place for this design in the 2024 Book Cover Art Contest in the Ross Pendergraft Library at Arkansas Tech University.https://orc.library.atu.edu/bookart_2024/1000/thumbnail.jp
Dune, Children of Ibad
No full textThis is a design of a book cover of Dune by Frank Herbert.https://orc.library.atu.edu/bookart_2024/1024/thumbnail.jp
The Book Thief
No full textThis is a design for a book cover of The Book Thief by Markus Zusak.https://orc.library.atu.edu/bookart_2024/1019/thumbnail.jp
To Kill a Mockingbird Cover Illustration
No full textThis is a design of a book cover of To Kill a Mockingbird by Harper Lee.https://orc.library.atu.edu/bookart_2024/1022/thumbnail.jp
Cask of Amontillado
No full textThis is a design of a book cover of The Cask of Amontillado by Edgar Allen Poe.https://orc.library.atu.edu/bookart_2024/1031/thumbnail.jp
Improving Throughput in a Pediatric ED by Designating a Fast-Track Area
Get PDFIn the United States, emergency department overcrowding is a growing national health issue that negatively affects patient experiences in health care. This Quality Improvement (QI) project aimed to improve patient throughput in a pediatric Emergency Department (ED) by implementing a designated fast-track area. A fast-track area provides medical treatment to low acuity patients, which can be evaluated and treated quickly to reduce overcrowding in the emergency room. The setting for this QI project was a 21-bed acute care pediatric hospital in Texas with an annual census of approximately 46,000 patients. The PDCA served as the framework to develop and measure any improvement in patient throughput in the ER; data collection included walk-out rates, patient experience ratings, discharge length of stay for low acuity, and discharge length of stay for all. The 60-day post-implementation results showed improvement in all areas, particularly the walk-out rate decreased by 90%. In conclusion, implementing a designated fast-track area in a pediatric ED has decreased overcrowding in the ED and improved patient throughput
Analyzing the Impact of Socioeconomic Factors on Cancer Clinical Trials Accessibility in the U.S. Using Machine Learning
Get PDFWhile cancer impacts all segments of the United States population, specific groups experience a disproportionate burden of the disease due to social, environmental, and economic disadvantages. This research examines the correlation between socioeconomic factors and the accessibility of cancer clinical trials across U.S. counties, employing a comprehensive dataset, County-Level Socioeconomic and Cancer Clinical Trial Data from Noah Ripper, and advanced machine-learning techniques. Our findings, derived from regression analysis and machine learning models like gradient boosting, highlight significant disparities in trial availability linked to socioeconomic indicators, including poverty rates, population estimates, median income, incidence rates, and mortality rates. Many regression models such as gradient boosting, random forest, linear regression, and K-neighbors, were implemented to find the best fit for the data. The models, particularly gradient boosting, showed about 75% prediction accuracy. The other three showed initial accuracy of 72.79%, 67.16%, and 62.25% respectively. These insights provide a foundation for targeted interventions and resource allocation to improve healthcare equity and cancer treatment outcomes. In the future, we plan to refine the best-performing model to enhance predictive accuracy to at least 90%, aiming for more precise and actionable insights to guide interventions and improve equitable access to cancer care