VLDB 2026 Research / reviewers in the wild / expert

Alfred Anzalone

dblp:318/6578 · also Alfred (Jerrod) Anzalone · DBLP profile ↗
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5ranked-venue papers
1as first author
5since 2021 · last 2026
0000-0002-3212-7845ORCID · verified

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

Applied, interdisciplinary, general and emerging computing · 5 · 1 first-author · 5 since 2021
YearPublicationVenuePosition
2026 The digital divide in adoption and advanced use of electronic health records among US hospitals: rural versus urban disparities from 2008 to 2023
abstract
OBJECTIVE: Using 15 years of hospital survey data (2008-2023), we examined rural-urban trends in electronic health records (EHR) adoption and advanced capabilities for data sharing, public health reporting, and care improvements. MATERIALS AND METHODS: We conducted a repeated cross-sectional study using the American Hospital Association Annual Survey Information Technology Supplement (2008-2018, 2020-2023). We constructed hospital-year measures of basic and comprehensive EHR capability, certified EHR technology (CEHRT) adoption, and advanced functions in patient engagement, interoperability, public health reporting, and social determinants of health (SDOH). We compared rural and urban adoption using descriptive analyses, Cochran-Armitage trend tests, and logistic regression with clustered standard errors, adjusted for bed size, ownership, and census division. RESULTS: The cohort included 41 838 hospital-years (45% rural). Comprehensive capability rose from 2% in 2008 to 75% in 2020, and CEHRT was nearly universal by 2023. Rural hospitals lagged urban hospitals in adopting basic and comprehensive EHRs, with the gap peaking at 25% in 2016. By 2021-2023, most hospitals reported patient portals, interoperability, public health reporting, and SDOH capabilities. Rural hospitals remained 4%-15% behind urban peers in advanced functions, particularly interoperability, public health reporting, and SDOH data collection and use. DISCUSSION: Persistent rural-urban gaps in advanced EHR use may limit interoperability, data-driven care, and public health reporting in rural settings. CONCLUSION: EHR adoption is widespread, but substantial rural-urban gaps persist in advanced EHR use. Support for advanced capabilities that enable data sharing, reporting, and data-driven improvement is needed to ensure that rural hospitals and communities benefit from digital transformation beyond adoption.
Alfred Anzalone, Yinting Liu, Elizabeth Reisher, Emily Frankel, Jed R. Hansen, Carol Geary
 J. Am. Medical Informatics Assoc.1
2025 National COVID Cohort Collaborative data enhancements: a path for expanding common data models
abstract
OBJECTIVE: To support long COVID research in National COVID Cohort Collaborative (N3C), the N3C Phenotype and Data Acquisition team created data designs to aid contributing sites in enhancing their data. Enhancements include long COVID specialty clinic indicator; Admission, Discharge, and Transfer transactions; patient-level social determinants of health; and in-hospital use of oxygen supplementation. MATERIALS AND METHODS: For each enhancement, we defined the scope and wrote guidance on how to prepare and populate the data in a standardized way. RESULTS: As of June 2024, 29 sites have added at least one data enhancement to their N3C pipeline. DISCUSSION: The use of common data models is critical to the success of N3C; however, these data models cannot account for all needs. Project-driven data enhancement is required. This should be done in a standardized way in alignment with common data model specifications. Our approach offers a useful pathway for enhancing data to improve fit for purpose. CONCLUSION: In this initiative, we rapidly produced project-specific data modeling guidance and documentation in support of long COVID research while maintaining a commitment to terminology standards and harmonized data.
Kellie M. Walters, Marshall Clark, Sofia Dard, Stephanie S. Hong, Elizabeth Kelly, Kristin Kostka, Adam M. Lee, Robert T. Miller, Michele Morris, Matvey Palchuk, Emily R. Pfaff, Adam B. Wilcox, Alexis Graves, Alfred Anzalone, Amin Manna, Amit Saha, Amy Olex, Andrea Zhou, Andrew E. Williams, Andrew Southerland, Andrew T. Girvin, Anita Walden, Anjali A Sharathkumar, Benjamin R. C. Amor, Benjamin Bates, Brian Hendricks, Caleb Alexander, Carolyn T. Bramante, Cavin Ward-Caviness, Charisse R. Madlock-Brown, Christine Suver, Christopher G. Chute, Christopher Dillon, Chunlei Wu, Clare Schmitt, Cliff Takemoto, Dan Housman, Davera Gabriel, David Eichmann, Diego Mazzotti, Don Brown, Eilis A. Boudreau, Elaine L. Hill, Elizabeth Zampino, Emily Carlson Marti, Evan French, Farrukh M. Koraishy, Federico Mariona, Fred W. Prior, George Sokos, Greg Martin, Harold P. Lehmann, Heidi Spratt, Hemalkumar Mehta, Hythem Sidky, J. W. Awori Hayanga, Jami Pincavitch, Jaylyn Clark, Jeremy Richard Harper, Jessica Islam, Jin Ge, Joel Gagnier, Joel H. Saltz, Johanna Loomba, John Buse, Jomol P. Mathew, Joni L. Rutter, Julie A. McMurry, Justin Guinney, Justin Starren, Karen Crowley, Katie Rebecca Bradwell, Ken Wilkins, Kenneth R. Gersing, Kenrick Dwain Cato, Kimberly Murray, Lavance Northington, Lee Allan Pyles, Leonie Misquitta, Lesley Cottrell, Lili M. Portilla, Mariam Deacy, Mark M. Bissell, Mary Emmett, Mary Morrison Saltz, Melissa A. Haendel, Meredith C. B. Adams, Meredith Temple-O'Connor, Michael G. Kurilla, Nabeel Qureshi, Nasia Safdar, Nicole Garbarini, Noha Sharafeldin, Ofer Sadan, Patricia A. Francis, Penny Wung Burgoon, Peter N. Robinson, Philip R. O. Payne, Rafael Fuentes, Randeep Jawa, Rebecca Erwin-Cohen, Rena Patel, Richard A. Moffitt, Richard L. Zhu, Rishi Kamaleswaran, Robert Hurley, Saiju Pyarajan, Samuel G. Michael, Samuel Bozzette, Sandeep Mallipattu, Satyanarayana Vedula, Scott Chapman, Shawn T. O'Neil, Soko Setoguchi, Tellen D. Bennett, Tiffany Callahan, Umit Topaloglu, Usman Sheikh, Valery Gordon, Vignesh Subbian, Warren A. Kibbe, Wenndy Hernandez, Will Beasley, Will Cooper, William Hillegass, Xiaohan Tanner Zhang
 J. Am. Medical Informatics Assoc.14
2022 Developing support for interoperation of research in healthcare equity
James R. Campbell 0001, James Svoboda, Alfred Anzalone, Mary Campbell, Johnelle Sparks, Carol Geary
 AMIA3
2022 Synergies between centralized and federated approaches to data quality: a report from the national COVID cohort collaborative
abstract
OBJECTIVE: In response to COVID-19, the informatics community united to aggregate as much clinical data as possible to characterize this new disease and reduce its impact through collaborative analytics. The National COVID Cohort Collaborative (N3C) is now the largest publicly available HIPAA limited dataset in US history with over 6.4 million patients and is a testament to a partnership of over 100 organizations. MATERIALS AND METHODS: We developed a pipeline for ingesting, harmonizing, and centralizing data from 56 contributing data partners using 4 federated Common Data Models. N3C data quality (DQ) review involves both automated and manual procedures. In the process, several DQ heuristics were discovered in our centralized context, both within the pipeline and during downstream project-based analysis. Feedback to the sites led to many local and centralized DQ improvements. RESULTS: Beyond well-recognized DQ findings, we discovered 15 heuristics relating to source Common Data Model conformance, demographics, COVID tests, conditions, encounters, measurements, observations, coding completeness, and fitness for use. Of 56 sites, 37 sites (66%) demonstrated issues through these heuristics. These 37 sites demonstrated improvement after receiving feedback. DISCUSSION: We encountered site-to-site differences in DQ which would have been challenging to discover using federated checks alone. We have demonstrated that centralized DQ benchmarking reveals unique opportunities for DQ improvement that will support improved research analytics locally and in aggregate. CONCLUSION: By combining rapid, continual assessment of DQ with a large volume of multisite data, it is possible to support more nuanced scientific questions with the scale and rigor that they require.
Emily R. Pfaff, Andrew T. Girvin, Davera Gabriel, Kristin Kostka, Michele Morris, Matvey Palchuk, Harold P. Lehmann, Benjamin R. C. Amor, Mark Bissell, Katie R. Bradwell, Sigfried Gold, Stephanie S. Hong, Johanna Loomba, Amin Manna, Julie A. McMurry, Emily Niehaus, Nabeel Qureshi, Anita Walden, Xiaohan Tanner Zhang, Richard L. Zhu, Richard A. Moffitt, Christopher G. Chute, William G. Adams, Shaymaa Al-Shukri, Alfred Anzalone, Ahmad Baghal, Tellen D. Bennett, Elmer V. Bernstam, Mark M. Bissell, Brian Bush, Thomas R. Campion Jr., Victor Castro, Jack Chang, Deepa D. Chaudhari, Wenjin Chen, San Chu, James J. Cimino, Keith A. Crandall, Mark Crooks, Sara J. Deakyne Davies, John Dipalazzo, David A. Dorr, Daniel Eckrich, Sarah E. Eltinge, Daniel G. Fort, Georgiy Golovko, Snehil Gupta, Melissa A. Haendel, Janos G. Hajagos, David A. Hanauer, Brett M. Harnett, Ronald Horswell, Nancy Huang, Steven G. Johnson, Michael Kahn, Kamil Khanipov, Curtis Kieler, Katherine Ruiz De Luzuriaga, Sarah E. Maidlow, Ashley Martinez, Jomol Mathew, James C. McClay, Gabriel McMahan, Brian Melancon, Stéphane M. Meystre, Lucio Miele, Hiroki Morizono, Ray Pablo, Lav P. Patel, Jimmy Phuong, Daniel J. Popham, Claudia P. Pulgarin, Indra Neil Sarkar, Nancy Sazo, Soko Setoguchi, Selvin Soby, Sirisha Surampalli, Christine Suver, Uma Maheswara Reddy Vangala, Shyam Visweswaran, James von Oehsen, Kellie M. Walters, Laura K. Wiley, David A. Williams, Adrian H. Zai
 J. Am. Medical Informatics Assoc.25
2021 COVID-19 Mortality Prediction among Patients with Cancer Using a Large National Cohort
Noha Sharafeldin, Vithal Madhira, Katie R. Bradwell, Qianqian Song 0002, Benjamin Bates, Yu R. Shao, Jing Su 0003, Alfred Anzalone, Timothym Bergquist, Sarah Cutrona, Ben S. Gerber, Peter N. Robinson, Justin Guinney, Umit Topaloglu
 AMIA9