VLDB 2026 Research / reviewers in the wild / expert

Kellie M. Walters

dblp:288/9090 · DBLP profile ↗
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6ranked-venue papers
2as first author
6since 2021 · last 2025
0000-0002-7473-1709ORCID · reported

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

Applied, interdisciplinary, general and emerging computing · 6 · 2 first-author · 6 since 2021
YearPublicationVenuePosition
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.1
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.83
2022 Demonstrating an approach for evaluating synthetic geospatial and temporal epidemiologic data utility: results from analyzing >1.8 million SARS-CoV-2 tests in the United States National COVID Cohort Collaborative (N3C)
abstract
OBJECTIVE: This study sought to evaluate whether synthetic data derived from a national coronavirus disease 2019 (COVID-19) dataset could be used for geospatial and temporal epidemic analyses. MATERIALS AND METHODS: Using an original dataset (n = 1 854 968 severe acute respiratory syndrome coronavirus 2 tests) and its synthetic derivative, we compared key indicators of COVID-19 community spread through analysis of aggregate and zip code-level epidemic curves, patient characteristics and outcomes, distribution of tests by zip code, and indicator counts stratified by month and zip code. Similarity between the data was statistically and qualitatively evaluated. RESULTS: In general, synthetic data closely matched original data for epidemic curves, patient characteristics, and outcomes. Synthetic data suppressed labels of zip codes with few total tests (mean = 2.9 ± 2.4; max = 16 tests; 66% reduction of unique zip codes). Epidemic curves and monthly indicator counts were similar between synthetic and original data in a random sample of the most tested (top 1%; n = 171) and for all unsuppressed zip codes (n = 5819), respectively. In small sample sizes, synthetic data utility was notably decreased. DISCUSSION: Analyses on the population-level and of densely tested zip codes (which contained most of the data) were similar between original and synthetically derived datasets. Analyses of sparsely tested populations were less similar and had more data suppression. CONCLUSION: In general, synthetic data were successfully used to analyze geospatial and temporal trends. Analyses using small sample sizes or populations were limited, in part due to purposeful data label suppression-an attribute disclosure countermeasure. Users should consider data fitness for use in these cases.
Jason A. Thomas, Randi E. Foraker, Noa Zamstein, Jon D. Morrow, Philip R. O. Payne, Adam B. Wilcox, Melissa A. Haendel, Christopher G. Chute, Kenneth R. Gersing, Anita Walden, Tellen D. Bennett, David Eichmann, Justin Guinney, Warren A. Kibbe, Emily R. Pfaff, Peter N. Robinson, Joel H. Saltz, Heidi Spratt, Justin Starren, Christine Suver, Chunlei Wu, Davera Gabriel, Stephanie S. Hong, Kristin Kostka, Harold P. Lehmann, Richard A. Moffitt, Michele Morris, Matvey Palchuk, Xiaohan Tanner Zhang, Richard L. Zhu, Benjamin R. C. Amor, Mark M. Bissell, Marshall Clark, Andrew T. Girvin, Adam M. Lee, Robert T. Miller, Kellie M. Walters, Yooree Chae, Connor Cook, Alexandra Dest, Racquel R. Dietz, Thomas Dillon, Patricia A. Francis, Rafael Fuentes, Alexis Graves, Andrew J. Neumann, Shawn T. O'Neil, Usman Sheikh, Andréa M. Volz, Elizabeth Zampino, Christopher P. Austin, Samuel Bozzette, Mariam Deacy, Nicole Garbarini, Michael G. Kurilla, Samuel G. Michael, Joni L. Rutter, Meredith Temple-O'Connor, Katie Rebecca Bradwell, Amin Manna, Nabeel Qureshi, Mary Morrison Saltz, Julie A. McMurry, Carolyn T. Bramante, Jeremy Richard Harper, Wenndy Hernandez, Farrukh M. Koraishy, Federico Mariona, Saidulu Mattapally, Amit Saha, Satyanarayana Vedula, Yujuan Fu, Nisha Mathews, Ofer Mendelevitch
 J. Am. Medical Informatics Assoc.38
2022 Supporting research, protecting data: one institution's approach to clinical data warehouse governance
abstract
Institutions must decide how to manage the use of clinical data to support research while ensuring appropriate protections are in place. Questions about data use and sharing often go beyond what the Health Insurance Portability and Accountability Act of 1996 (HIPAA) considers. In this article, we describe our institution's governance model and approach. Common questions we consider include (1) Is a request limited to the minimum data necessary to carry the research forward? (2) What plans are there for sharing data externally?, and (3) What impact will the proposed use of data have on patients and the institution? In 2020, 302 of the 319 requests reviewed were approved. The majority of requests were approved in less than 2 weeks, with few or no stipulations. For the remaining requests, the governance committee works with researchers to find solutions to meet their needs while also addressing our collective goal of protecting patients.
Kellie M. Walters, Anna Jojic, Emily R. Pfaff, Marie Rape, Donald C. Spencer, Nicholas J. Shaheen, Brent Lamm, Timothy S. Carey
 J. Am. Medical Informatics Assoc.1
2021 Evolving the PCORnet data pipeline for National COVID Cohort Collaborative (N3C)
Adam M. Lee, Marshall Clark, Sofia Z. Dard, Kellie M. Walters
 AMIA4
2021 The National COVID Cohort Collaborative (N3C): Rationale, design, infrastructure, and deployment
abstract
OBJECTIVE: Coronavirus disease 2019 (COVID-19) poses societal challenges that require expeditious data and knowledge sharing. Though organizational clinical data are abundant, these are largely inaccessible to outside researchers. Statistical, machine learning, and causal analyses are most successful with large-scale data beyond what is available in any given organization. Here, we introduce the National COVID Cohort Collaborative (N3C), an open science community focused on analyzing patient-level data from many centers. MATERIALS AND METHODS: The Clinical and Translational Science Award Program and scientific community created N3C to overcome technical, regulatory, policy, and governance barriers to sharing and harmonizing individual-level clinical data. We developed solutions to extract, aggregate, and harmonize data across organizations and data models, and created a secure data enclave to enable efficient, transparent, and reproducible collaborative analytics. RESULTS: Organized in inclusive workstreams, we created legal agreements and governance for organizations and researchers; data extraction scripts to identify and ingest positive, negative, and possible COVID-19 cases; a data quality assurance and harmonization pipeline to create a single harmonized dataset; population of the secure data enclave with data, machine learning, and statistical analytics tools; dissemination mechanisms; and a synthetic data pilot to democratize data access. CONCLUSIONS: The N3C has demonstrated that a multisite collaborative learning health network can overcome barriers to rapidly build a scalable infrastructure incorporating multiorganizational clinical data for COVID-19 analytics. We expect this effort to save lives by enabling rapid collaboration among clinicians, researchers, and data scientists to identify treatments and specialized care and thereby reduce the immediate and long-term impacts of COVID-19.
Melissa A. Haendel, Christopher G. Chute, Tellen D. Bennett, David Eichmann, Justin Guinney, Warren A. Kibbe, Philip R. O. Payne, Emily R. Pfaff, Peter N. Robinson, Joel H. Saltz, Heidi Spratt, Christine Suver, John Wilbanks, Adam B. Wilcox, Andrew E. Williams, Chunlei Wu, Clair Blacketer, Robert L. Bradford, James J. Cimino, Marshall Clark, Evan W. Colmenares, Patricia A. Francis, Davera Gabriel, Alexis Graves, Raju Hemadri, Stephanie S. Hong, George Hripcsak, Dazhi Jiao, Jeffrey G. Klann, Kristin Kostka, Adam M. Lee, Harold P. Lehmann, Lora Lingrey, Robert T. Miller, Michele Morris, Shawn N. Murphy, Karthik Natarajan, Matvey Palchuk, Usman Sheikh, Harold R. Solbrig, Shyam Visweswaran, Anita Walden, Kellie M. Walters, Griffin M. Weber, Xiaohan Tanner Zhang, Richard L. Zhu, Benjamin R. C. Amor, Andrew T. Girvin, Amin Manna, Nabeel Qureshi, Michael G. Kurilla, Samuel G. Michael, Lili M. Portilla, Joni L. Rutter, Christopher P. Austin, Kenneth R. Gersing
 J. Am. Medical Informatics Assoc.43