Katie Allen

dblp:186/5453 · also Katie S. Allen · DBLP profile ↗
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12ranked-venue papers
2as first author
7since 2021 · last 2026
0000-0002-6058-6280ORCID · verified

Domains — the database's venue-derived domains; a paper can count in several

Applied, interdisciplinary, general and emerging computing · 12 · 2 first-author · 7 since 2021Human-computer interaction and ubiquitous computing · 1 · 1 since 2021
YearPublicationVenuePosition
2026 Multi-site analysis of COVID-19 and new-onset diabetes reveals need for improved sensitivity of EHR-based COVID-19 phenotypes - a DiCAYA Network analysis
abstract
OBJECTIVE: We discuss implications of potential ascertainment biases for studies examining diabetes risk following SARS-CoV-2 infection using electronic health records (EHRs). We quantitatively explore sensitivity of results to misclassification of COVID-19 status using data from the U.S.-based Diabetes in Children, Adolescents and Young Adults (DiCAYA) Network on children (≤17 years) and young adults (18-44 years). MATERIALS AND METHODS: In our retrospective case study from the DiCAYA Network, SARS-CoV-2 was identified using labs and diagnoses from June 1, 2020 to December 31, 2021. Patients were followed through December 31, 2022 for new diabetes diagnoses. Sites examined incident diabetes by COVID-19 status using Cox proportional hazards models. Results were pooled in meta-analyses. A bias analysis examined potential impact of COVID-19 misclassification scenarios on results, guided by hypotheses that sensitivity would be <50% and would be higher among those who developed diabetes. RESULTS: Prevalence of documented COVID-19 was low overall and variable across sites (children: 4.4%-7.7%, young adults: 6.2%-22.7%). Individuals with documented COVID-19 were at higher risk of incident diabetes compared to those with no documented infection, but results were heterogeneous across sites. Findings were highly sensitive to COVID-19 misclassification assumptions. Observed results could be biased away from the null under several differential misclassification scenarios. DISCUSSION: Although EHR-based documentation of COVID-19 was associated with incident diabetes, COVID-19 phenotypes likely had low sensitivity, with considerable variation across sites. Misclassification assumptions strongly impacted interpretation of results. CONCLUSION: Given the potential for low phenotype sensitivity and misclassification, caution is warranted when interpreting analyses of COVID-19 and incident diabetes using clinical or administrative databases.
Lorna E. Thorpe, Jasmin Divers, Annemarie Hirsch, Brian S. Schwartz, Jihad S. Obeid, Angela Liese, Tessa L. Crume, Anna Bellatorre, Jiang Bian 0001, Yi Guo 0005, Sarah Bost, Tianchen Lyu, Matthew T. Mefford, Matt Zhou, Eva Lustigova, Levon Utidjian, Mitchell Maltenfort, Patrick Hanley, Meda E. Pavkov, Marc B. Rosenman, Andrea R. Titus, L. Charles Bailey, Christopher B. Forrest, Mitch Maltenfort, Amy Shah, Eneida A. Mendonça, G. Todd Alonso, Sara J. Deakyne Davies, H. Timothy Bunnell, Anne Kazak, Melody Kitzmiller, Manmohan Kamboj, Dimitri A. Christakis, Daksha Ranade, Annemarie G. Hirsch, Joseph J. Dewalle, H. Lester Kirchner, Meredith Lewis, Dione G. Mercer, Cara M. Nordberg, Amy Poissant, Brian E. Dixon, Shaun J. Grannis, Katie Allen, Anna Roberts, Nimish Valvi, Jeff Warvel, Ashley Wiensch, Tamara S. Hannon, Kristi Reynolds, John Chang, Don McCarthy, Rong Wei, Marc Rosenman, George Lales, Anthony Wong, Allison Zelinski, Yuan Luo 0001, Mark Weiner, Pedro Rivera, Thomas Carton, Elizabeth Nauman, Harold P. Lehmann, Meredith Akerman, Rebecca Anthopolos, Stefanie Bendik, Sarah Conderino, Andrew Fair, Jessica Guillaume, Shahidul Islam, Alan Jacobson, David C. Lee, Chinyere Okpara, Anand Rajan, Andrea Titus, Dana Dabelea, Theresa Anderson, Rebecca Conway, Toan Ong, Jack Pattee, Shawna Burgett, Elizabeth Shenkman, William T. Donahoo, William R. Hogan, Piaopiao Li, Mattia Prosperi, Yonghui Wu 0001, Angela D. Liese, Lisa Knight, Caroline Rudisill, Jessica Stucker, Deborah Bowlby, Elaine Apperson, Alex Ewing, Giuseppina Imperatore, Deborah Rolka, Ibrahim Zaganjor
J. Am. Medical Informatics Assoc.46
2023 Strengths, weaknesses, opportunities, and threats for the nation's public health information systems infrastructure: synthesis of discussions from the 2022 ACMI Symposium
abstract
OBJECTIVE: The annual American College of Medical Informatics (ACMI) symposium focused discussion on the national public health information systems (PHIS) infrastructure to support public health goals. The objective of this article is to present the strengths, weaknesses, threats, and opportunities (SWOT) identified by public health and informatics leaders in attendance. MATERIALS AND METHODS: The Symposium provided a venue for experts in biomedical informatics and public health to brainstorm, identify, and discuss top PHIS challenges. Two conceptual frameworks, SWOT and the Informatics Stack, guided discussion and were used to organize factors and themes identified through a qualitative approach. RESULTS: A total of 57 unique factors related to the current PHIS were identified, including 9 strengths, 22 weaknesses, 14 opportunities, and 14 threats, which were consolidated into 22 themes according to the Stack. Most themes (68%) clustered at the top of the Stack. Three overarching opportunities were especially prominent: (1) addressing the needs for sustainable funding, (2) leveraging existing infrastructure and processes for information exchange and system development that meets public health goals, and (3) preparing the public health workforce to benefit from available resources. DISCUSSION: The PHIS is unarguably overdue for a strategically designed, technology-enabled, information infrastructure for delivering day-to-day essential public health services and to respond effectively to public health emergencies. CONCLUSION: Most of the themes identified concerned context, people, and processes rather than technical elements. We recommend that public health leadership consider the possible actions and leverage informatics expertise as we collectively prepare for the future.
Jessica Acharya, Catherine J. Staes, Katie Allen, Joel Hartsell, Theresa A. Cullen, Leslie Lenert, Donald W. Rucker, Harold P. Lehmann, Brian E. Dixon
J. Am. Medical Informatics Assoc.3
2023 Enhancing the nation's public health information infrastructure: a report from the ACMI symposium
abstract
The COVID-19 pandemic exposed multiple weaknesses in the nation's public health system. Therefore, the American College of Medical Informatics selected "Rebuilding the Nation's Public Health Informatics Infrastructure" as the theme for its annual symposium. Experts in biomedical informatics and public health discussed strategies to strengthen the US public health information infrastructure through policy, education, research, and development. This article summarizes policy recommendations for the biomedical informatics community postpandemic. First, the nation must perceive the health data infrastructure to be a matter of national security. The nation must further invest significantly more in its health data infrastructure. Investments should include the education and training of the public health workforce as informaticians in this domain are currently limited. Finally, investments should strengthen and expand health data utilities that increasingly play a critical role in exchanging information across public health and healthcare organizations.
Brian E. Dixon, Catherine J. Staes, Jessica Acharya, Katie Allen, Joel Hartsell, Theresa A. Cullen, Leslie Lenert, Donald W. Rucker, Harold P. Lehmann
J. Am. Medical Informatics Assoc.4
2022 Work-in-Progress - Decolonizing the Digital Divide: Problem Based Spatial Design Through Immersive Technology for STEM Education in Minority Populations
abstract
This work-in-progress paper reports preliminary findings from surveys, participant observation, and co-design discussions with educators and elders of a Native American community about how to modify STEM learning activities for their unique tribal culture in afterschool settings using immersive technology and spatial design.
Tilanka Chandrasekera, Nicole Colston, Tutaleni I. Asino, Cynthia Orona, Katie Allen, Allison Howard, Piper Bott, Oluwafikayo Adewumi
iLRN5
2021 Evaluating the Utility of a Prototype Clinical Decision Support Tool for Chronic Pain Treatment Choices in Primary Care
Katie Allen, Elizabeth C. Danielson, Sarah M. Downs, Olena Mazurenko, Julie DiIulio, Burke W. Mamlin, Christopher A. Harle
AMIA1
2021 Extracting Social Variables from Clinical Documentation to Better Facilitate Response to Patient Need
Katie Allen, Daniel Hood, Jonathan Cummins, Suranga Nath Kasthurirathne, Peter J. Embí, Joshua R. Vest
AMIA1
2021 Race and Ethnicity Agreement Between Medicaid, Health Information Exchange, and Electronic Health Records
Sarah El-Azab, Siu Hui, Heidi Hosler, Katie Allen
AMIA4
2020 Creating a Standardized Approach for Extracting Race and Ethnicity from a Regional Health Information Exchange
Sarah El-Azab, Luke Golichowski, Heidi Hosler, Siu Hui, Katie Allen
AMIA5
2019 An information infrastructure for federated, person-level linkage and query capability across private- and public-sector health data: The Indiana state EMS to HIE ED pilot project
Daniel Hood, Shaun J. Grannis, Peter J. Embí, Josh Martin, Darshan Shah, John Roach, Katie Allen
AMIA7
2018 Improving Mapping Accuracy with a Guide for LOINC Microbiology Terms
Swapna Abhyankar, Mary Zabriskie, Katie Allen, Daniel J. Vreeman
AMIA3
2017 The Capture of Social and Behavioral Determinants of Health in Electronic Health Records
Theresa A. Cullen, Katie Allen, Sara Armson, Anna Roberts, Daniel J. Vreeman
AMIA2
2015 Regenstrief ePRO: A Rule-Based Platform for Capturing Targeted Patient-Reported Outcomes
Jon D. Duke, Daniel J. Vreeman, Jeremy Leventhal, Chen Wen, Katie Allen, David A. Haggstrom
AMIA5