Adam B. Wilcox

VLDB 2026 Research / reviewers in the wild / expert

dblp:64/1616 · DBLP profile ↗

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57ranked-venue papers

18as first author

12since 2021 · last 2025

—ORCID · none

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

Applied, interdisciplinary, general and emerging computing · 57 · 18 first-author · 12 since 2021

Year	Publication	Venue	Position
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.	12
2024	Real world performance of the 21st Century Cures Act population-level application programming interface abstract OBJECTIVE: To evaluate the real-world performance of the SMART/HL7 Bulk Fast Health Interoperability Resources (FHIR) Access Application Programming Interface (API), developed to enable push button access to electronic health record data on large populations, and required under the 21st Century Cures Act Rule. MATERIALS AND METHODS: We used an open-source Bulk FHIR Testing Suite at 5 healthcare sites from April to September 2023, including 4 hospitals using electronic health records (EHRs) certified for interoperability, and 1 Health Information Exchange (HIE) using a custom, standards-compliant API build. We measured export speeds, data sizes, and completeness across 6 types of FHIR. RESULTS: Among the certified platforms, Oracle Cerner led in speed, managing 5-16 million resources at over 8000 resources/min. Three Epic sites exported a FHIR data subset, achieving 1-12 million resources at 1555-2500 resources/min. Notably, the HIE's custom API outperformed, generating over 141 million resources at 12 000 resources/min. DISCUSSION: The HIE's custom API showcased superior performance, endorsing the effectiveness of SMART/HL7 Bulk FHIR in enabling large-scale data exchange while underlining the need for optimization in existing EHR platforms. Agility and scalability are essential for diverse health, research, and public health use cases. CONCLUSION: To fully realize the interoperability goals of the 21st Century Cures Act, addressing the performance limitations of Bulk FHIR API is critical. It would be beneficial to include performance metrics in both certification and reporting processes. James R. Jones, Daniel Gottlieb 0001, Andrew J. McMurry, Ashish Atreja, Pankaja M. Desai, Brian E. Dixon, Philip R. O. Payne, Anil J. Saldanha, Prabhu R. V. Shankar, Yauheni Solad, Adam B. Wilcox, Momeena S. Ali, Eugene Kang, Andrew M. Martin, Elizabeth Sprouse, David E. Taylor, Michael Terry, Vlad Ignatov, Kenneth D. Mandl	J. Am. Medical Informatics Assoc.	11
2024	Cumulus: a federated electronic health record-based learning system powered by Fast Healthcare Interoperability Resources and artificial intelligence abstract OBJECTIVE: To address challenges in large-scale electronic health record (EHR) data exchange, we sought to develop, deploy, and test an open source, cloud-hosted app "listener" that accesses standardized data across the SMART/HL7 Bulk FHIR Access application programming interface (API). METHODS: We advance a model for scalable, federated, data sharing and learning. Cumulus software is designed to address key technology and policy desiderata including local utility, control, and administrative simplicity as well as privacy preservation during robust data sharing, and artificial intelligence (AI) for processing unstructured text. RESULTS: Cumulus relies on containerized, cloud-hosted software, installed within a healthcare organization's security envelope. Cumulus accesses EHR data via the Bulk FHIR interface and streamlines automated processing and sharing. The modular design enables use of the latest AI and natural language processing tools and supports provider autonomy and administrative simplicity. In an initial test, Cumulus was deployed across 5 healthcare systems each partnered with public health. Cumulus output is patient counts which were aggregated into a table stratifying variables of interest to enable population health studies. All code is available open source. A policy stipulating that only aggregate data leave the institution greatly facilitated data sharing agreements. DISCUSSION AND CONCLUSION: Cumulus addresses barriers to data sharing based on (1) federally required support for standard APIs, (2) increasing use of cloud computing, and (3) advances in AI. There is potential for scalability to support learning across myriad network configurations and use cases. Andrew J. McMurry, Daniel Gottlieb 0001, Timothy A. Miller, James R. Jones, Ashish Atreja, Jennifer Crago, Pankaja M. Desai, Brian E. Dixon, Matthew Garber, Vlad Ignatov, Lyndsey A Kirchner, Philip R. O. Payne, Anil J. Saldanha, Prabhu R. V. Shankar, Yauheni Solad, Elizabeth Sprouse, Michael Terry, Adam B. Wilcox, Kenneth D. Mandl	J. Am. Medical Informatics Assoc.	18
2023	Ten simple rules for organizations to support research data sharing abstract Scientific discovery depends on access to data and the knowledge this data makes possible.Research data sharing is increasingly recognized as a priority for organizations to support the successful conduct of research.The National Institutes of Health states, "data sharing enables researchers to rigorously test the validity of research findings, strengthen analyses through combined datasets, reuse hard-to-generate data, and explore new frontiers of discovery" [1].Conversely, in the absence of data sharing, there are increased risks related to the robustness, rigor, and replicability of results, and the potential of valuable data is diminished.For these reasons and more, institutional data sharing capacity is a critical topic for organizations to scrutinize, discuss, and advance.Advocacy and support for data sharing are often discussed with an emphasis on understanding and supporting the practices of individual investigators or scientific communities [2,3].However, a researcher's ability to successfully engage in and benefit from sound data sharing depends on their organizational setting and, specifically, the organization's data sharing capacity.For example, sharing data is easier and more equitable when organizational processes and procedures are established and documented, and research workforce members can access centralized training and infrastructure resources.The effect of how an organization approaches and supports data sharing extends beyond the success of its investigators.Institutions that share data can participate in innovative largescale initiatives and pursue new funding opportunities.Universities that contribute to creating Robin Champieux, Tony Solomonides, Marisa Conte, Svetlana Rojevsky, Jimmy Phuong, David A. Dorr, Elizabeth Zampino, Adam B. Wilcox, Matthew B. Carson, Kristi L. Holmes	PLoS Comput. Biol.	8
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.	6
2021	Extracting Patient-level Social Determinants of Health into the OMOP Common Data Model Jimmy Phuong, Elizabeth Zampino, Nicholas J. Dobbins, Juan Espinoza, Daniella Meeker, Heidi Spratt, Charisse R. Madlock-Brown, Nicole Gray Weiskopf, Adam B. Wilcox	AMIA	9
2021	Demonstrations in Synthetic Data and the National COVID Cohort Collaborative (N3C) Adam B. Wilcox, Randi E. Foraker, Jason A. Thomas, Jon D. Morrow, Noa Zamstein	AMIA	1
2021	Predicting COVID-19 Regional Case Loads with Synthetic Data Adam B. Wilcox, Noa Zamstein, Randi E. Foraker, Jason A. Thomas, Kenneth Wilkins, Jon D. Morrow	AMIA	1
2021	The Spectrum of Engagement in Chronic Kidney Disease Care at a Large Healthcare System Adam B. Wilcox	AMIA	2
2021	Prediction of COVID-19 Case Severity Using Synthetic Data Derived from the National COVID Cohort Collaborative Noa Zamstein, Andrew J. Neumann, Randi E. Foraker, Jason A. Thomas, Adam B. Wilcox, Jon D. Morrow	AMIA	5
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.	14
2021	Use of electronic health records to support a public health response to the COVID-19 pandemic in the United States: a perspective from 15 academic medical centers abstract Our goal is to summarize the collective experience of 15 organizations in dealing with uncoordinated efforts that result in unnecessary delays in understanding, predicting, preparing for, containing, and mitigating the COVID-19 pandemic in the US. Response efforts involve the collection and analysis of data corresponding to healthcare organizations, public health departments, socioeconomic indicators, as well as additional signals collected directly from individuals and communities. We focused on electronic health record (EHR) data, since EHRs can be leveraged and scaled to improve clinical care, research, and to inform public health decision-making. We outline the current challenges in the data ecosystem and the technology infrastructure that are relevant to COVID-19, as witnessed in our 15 institutions. The infrastructure includes registries and clinical data networks to support population-level analyses. We propose a specific set of strategic next steps to increase interoperability, overall organization, and efficiencies. Subha Madhavan, Lisa Bastarache, Jeffrey S. Brown, Atul J. Butte, David A. Dorr, Peter J. Embí, Charles P. Friedman, Kevin B. Johnson, Jason H. Moore, Isaac S. Kohane, Philip R. O. Payne, Jessica D. Tenenbaum, Mark G. Weiner, Adam B. Wilcox, Lucila Ohno-Machado	J. Am. Medical Informatics Assoc.	14
2020	Housing Stability Phenotype Extracted from Clinical Text Notes and Diagnosis Codes Andrew K. Teng, Adam B. Wilcox	AMIA	2
2020	Leaf: an open-source, model-agnostic, data-driven web application for cohort discovery and translational biomedical research abstract OBJECTIVE: Academic medical centers and health systems are increasingly challenged with supporting appropriate secondary use of clinical data. Enterprise data warehouses have emerged as central resources for these data, but often require an informatician to extract meaningful information, limiting direct access by end users. To overcome this challenge, we have developed Leaf, a lightweight self-service web application for querying clinical data from heterogeneous data models and sources. MATERIALS AND METHODS: Leaf utilizes a flexible biomedical concept system to define hierarchical concepts and ontologies. Each Leaf concept contains both textual representations and SQL query building blocks, exposed by a simple drag-and-drop user interface. Leaf generates abstract syntax trees which are compiled into dynamic SQL queries. RESULTS: Leaf is a successful production-supported tool at the University of Washington, which hosts a central Leaf instance querying an enterprise data warehouse with over 300 active users. Through the support of UW Medicine (https://uwmedicine.org), the Institute of Translational Health Sciences (https://www.iths.org), and the National Center for Data to Health (https://ctsa.ncats.nih.gov/cd2h/), Leaf source code has been released into the public domain at https://github.com/uwrit/leaf. DISCUSSION: Leaf allows the querying of single or multiple clinical databases simultaneously, even those of different data models. This enables fast installation without costly extraction or duplication. CONCLUSIONS: Leaf differs from existing cohort discovery tools because it does not specify a required data model and is designed to seamlessly leverage existing user authentication systems and clinical databases in situ. We believe Leaf to be useful for health system analytics, clinical research data warehouses, precision medicine biobanks, and clinical studies involving large patient cohorts. Nicholas J. Dobbins, Clifford H. Spital, Robert A. Black, Jason M. Morrison, Bas de Veer, Elizabeth Zampino, Robert D. Harrington, Bethene D. Britt, Kari A. Stephens, Adam B. Wilcox, Peter Tarczy-Hornoch, Sean D. Mooney	J. Am. Medical Informatics Assoc.	10
2020	SCOR: A secure international informatics infrastructure to investigate COVID-19 abstract Global pandemics call for large and diverse healthcare data to study various risk factors, treatment options, and disease progression patterns. Despite the enormous efforts of many large data consortium initiatives, scientific community still lacks a secure and privacy-preserving infrastructure to support auditable data sharing and facilitate automated and legally compliant federated analysis on an international scale. Existing health informatics systems do not incorporate the latest progress in modern security and federated machine learning algorithms, which are poised to offer solutions. An international group of passionate researchers came together with a joint mission to solve the problem with our finest models and tools. The SCOR Consortium has developed a ready-to-deploy secure infrastructure using world-class privacy and security technologies to reconcile the privacy/utility conflicts. We hope our effort will make a change and accelerate research in future pandemics with broad and diverse samples on an international scale. Jean Louis Raisaro, Juan Ramón Troncoso-Pastoriza, Raphaelle Beau-Lejdstrom, Riccardo Bellazzi, Robert Murphy, Elmer V. Bernstam, Henry Wang, Mauro Bucalo, Yong Chen 0016, Assaf Gottlieb, Arif Ozgun Harmanci, Miran Kim, Yejin Kim 0001, Jeffrey G. Klann, Catherine Klersy, Bradley A. Malin, Marie Méan, Fabian Prasser, Luigia Scudeller, Ali Torkamani, Julien Vaucher, Mamta Puppala, Stephen T. C. Wong, Milana Frenkel-Morgenstern, Hua Xu 0001, Baba Maiyaki Musa, Abdulrazaq G. Habib, Trevor Cohen, Adam B. Wilcox, Hamisu M. Salihu, Heidi Sofia, Xiaoqian Jiang, Jean-Pierre Hubaux	J. Am. Medical Informatics Assoc.	30
2018	Counting Readmissions: Surprisingly difficult Ahmad Aljadaan, Peter Tarczy-Hornoch, Adam B. Wilcox, Todd F. Dardas, John H. Gennari	AMIA	3
2016	Designing a Clinical Data Warehouse Architecture to Support Quality Improvement Initiatives John D. Chelico, Adam B. Wilcox, David K. Vawdrey, Gilad J. Kuperman	AMIA	2
2015	Migration of a Computerized Anticoagulation Clinic to a Commercially-Developed EHR Mitchell Hedrick, Aubrey Ramsey, Christopher Radek, Keith Huffman, Adam Miklius, Adam B. Wilcox	AMIA	7
2015	Routine Collection of Patient-Reported Data in Electronic Form in Clinical Settings: An Analysis of Available Technologies Adam B. Wilcox, Susan D. Tew, Justin Poll	AMIA	1
2014	Could Patient Self-reported Health Data Complement EHR for Phenotyping? Daniel Fort, Adam B. Wilcox, Chunhua Weng	AMIA	2
2014	Computerization of Mental Health Integration Complexity Scores at Intermountain Healthcare Thomas A. Oniki, Drayton Rodrigues, Noman Rahman, Saritha Patur, Pascal Briot, David P. Taylor, Adam B. Wilcox, Brenda Reiss-Brennan, Wayne Cannon	AMIA	7
2014	Health data use, stewardship, and governance: ongoing gaps and challenges: a report from AMIA's 2012 Health Policy Meeting abstract Large amounts of personal health data are being collected and made available through existing and emerging technological media and tools. While use of these data has significant potential to facilitate research, improve quality of care for individuals and populations, and reduce healthcare costs, many policy-related issues must be addressed before their full value can be realized. These include the need for widely agreed-on data stewardship principles and effective approaches to reduce or eliminate data silos and protect patient privacy. AMIA's 2012 Health Policy Meeting brought together healthcare academics, policy makers, and system stakeholders (including representatives of patient groups) to consider these topics and formulate recommendations. A review of a set of Proposed Principles of Health Data Use led to a set of findings and recommendations, including the assertions that the use of health data should be viewed as a public good and that achieving the broad benefits of this use will require understanding and support from patients. George Hripcsak, Meryl Bloomrosen, Patricia Flatley Brennan, Christopher G. Chute, James J. Cimino, Don E. Detmer, Margo Edmunds, Peter J. Embí, Melissa M. Goldstein, William Edward Hammond, Gail M. Keenan, Steven E. Labkoff, Shawn P. Murphy, Charles Safran, Stuart M. Speedie, Howard R. Strasberg, Freda Temple, Adam B. Wilcox	J. Am. Medical Informatics Assoc.	18
2013	Analyzing Requests for Clinical Data for Self-Service Penetration Karthik Natarajan, Adam B. Wilcox, Niloo Sobhani, Aurelia Boyer	AMIA	2
2013	Matching Subjects Between a Research and a Clinical Cohort Adam B. Wilcox, Daniel Fort, Suzanne Bakken	AMIA	1
2012	Establishing Data Governance for Comparative Effectiveness Research: Experience of the Washington Heights Inwood Informatics Infrastructure for Comparative Effectiveness Research (WICER) Suzanne Bakken, J. Thomas Bigger, Bernadette Boden-Albala, Penny Feldman, Kathleen Gallagher, Peter D. Stetson, Chunhua Weng, Adam B. Wilcox	AMIA	8
2012	The Comparison of Survey Items in a Community-based Survey with the Patient-Reported Outcomes Measurement Information System (PROMIS) Manuel Co Jr., Adam B. Wilcox, Suzanne Bakken	AMIA	2
2012	Analysis of Query Negotiation between a Researcher and a Query Expert Gregory William Hruby, Adam B. Wilcox, Chunhua Weng	AMIA	2
2012	The Path to Meaningfully Use at a Large HIT-Enabled Academic Medical Center Victoria Tiase, Aurelia Boyer, Virginia Lorenzi, Adam B. Wilcox, I-Ping Shue	AMIA	4
2012	Application of Data Mining Techniques to a Behavioral Risk Factor Data Set to Predict Long-Term Disability after Stroke Sunmoo Yoon, Jose Gutierrez, Adam B. Wilcox, Suzanne Bakken	AMIA	3
2011	Minimizing electronic health record patient-note mismatches abstract We measured the prevalence (or rate) of patient-note mismatches (clinical notes judged to pertain to another patient) in the electronic medical record. The rate ranged from 0.5% (95% CI 0.2% to 1.7%) before a pop-up window intervention to 0.3% (95% CI 0.1% to 1.1%) after the intervention. Clinicians discovered patient-note mismatches in 0.05-0.03% of notes, or about 10% of actual mismatches. The reduction in rates after the intervention was statistically significant. Therefore, while the patient-note mismatch rate is low compared to published rates of other documentation errors, it can be further reduced by the design of the user interface. Adam B. Wilcox, Yueh-Hsia Chen, George Hripcsak	J. Am. Medical Informatics Assoc.	1
2009	The Evolving Use of a Clinical Data Repository: Facilitating Data Access Within an Electronic Medical Record Adam B. Wilcox, David K. Vawdrey, Yueh-Hsia Chen, Bruce Forman, George Hripcsak	AMIA	1
2008	Content and Structure of Clinical Problem Lists: A Corpus Analysis Tielman Van Vleck, Adam B. Wilcox, Peter D. Stetson, Stephen B. Johnson, Noémie Elhadad	AMIA	2
2008	Physician Use of Outpatient Electronic Health Records to Improve Care Adam B. Wilcox, Watson A. Bowes III, Sidney N. Thornton, Scott P. Narus	AMIA	1
2007	Neonatal bilirubin management as an implementation example of interdisciplinary continuum of care tools Sidney N. Thornton, Bryce S. Thompson, Jean A. Millar, Larry D. Eggert, Adam B. Wilcox	AMIA	5
2007	Emergency Department Access to a Longitudinal Medical Record abstract Our goal is to assess how clinical information from previous visits is used in the emergency department. We used detailed user audit logs to measure access to different data types. We found that clinician-authored notes and laboratory and radiology data were used most often (common data types were used up to 5% to 20% of the time). Data were accessed less than half the time (up to 20% to 50%) even when the user was alerted to the presence of data. Our access rate indicates that health information exchange projects should be conservative in estimating how often shared data will be used and the wide breadth of data accessed indicates that although a clinical summary is likely to be useful, an ideal solution will supply a broad variety of data. George Hripcsak, Soumitra Sengupta, Adam B. Wilcox, Robert A. Green	J. Am. Medical Informatics Assoc.	3
2007	A framework for information system usage in collaborative care David A. Dorr, Spencer S. Jones, Adam B. Wilcox	J. Biomed. Informatics	3
2007	The United Hospital Fund meeting on evaluating health information exchange George Hripcsak, Rainu Kaushal, Kevin B. Johnson, Joan S. Ash, David W. Bates, Rachel Block, Mark E. Frisse, Lisa M. Kern, Janet Marchibroda, J. Marc Overhage, Adam B. Wilcox	J. Biomed. Informatics	11
2007	Automated identification of adverse events related to central venous catheters Janet F. E. Penz, Adam B. Wilcox, John F. Hurdle	J. Biomed. Informatics	2
2006	Information Needs of Nurse Care Managers David A. Dorr, Hanh Tran, Paul N. Gorman, Adam B. Wilcox	AMIA	4
2006	Architectural Strategies and Issues with Health Information Exchange Adam B. Wilcox, Gilad J. Kuperman, David A. Dorr, George Hripcsak, Scott P. Narus, Sidney N. Thornton, R. Scott Evans	AMIA	1
2005	Drug-Age Alerting for Outpatient Geriatric Prescriptions: A Joint Study using Interoperable Drug Standards Ashish Atreja, Michael Buck, Anil K. Jain 0004, Cherie Brunker, Theodore T. Suh, C. Martin Harris, Robert Palmer, Adam B. Wilcox	AMIA	8
2005	Physician use of electronic medical records: Issues and successes with direct data entry and physician productivity Paul D. Clayton, Scott P. Narus, Watson A. Bowes III, Tammy S. Madsen, Adam B. Wilcox, Garth Orsmond, Beatriz H. S. C. Rocha, Sidney N. Thornton, Spencer S. Jones, Craig A. Jacobsen, Mark Udall, Michael L. Rhodes, Brent E. Wallace, Wayne Cannon, Jerry Gardner, Stanley M. Huff, Linda Leckman	AMIA	5
2005	The Effect of Longitudinal EMR Access on Laboratory Ordering Spencer S. Jones, Adam B. Wilcox	AMIA	2
2005	Use and Impact of a Computer-Generated Patient Summary Worksheet for Primary Care Adam B. Wilcox, Spencer S. Jones, David A. Dorr, Wayne Cannon, Laurie Burns, Kelli Radican, Kent Christensen, Cherie Brunker, Ann Larsen, Scott P. Narus, Sidney N. Thornton, Paul D. Clayton	AMIA	1
2003	Research Paper: The Role of Domain Knowledge in Automating Medical Text Report Classification abstract OBJECTIVE: To analyze the effect of expert knowledge on the inductive learning process in creating classifiers for medical text reports. DESIGN: The authors converted medical text reports to a structured form through natural language processing. They then inductively created classifiers for medical text reports using varying degrees and types of expert knowledge and different inductive learning algorithms. The authors measured performance of the different classifiers as well as the costs to induce classifiers and acquire expert knowledge. MEASUREMENTS: The measurements used were classifier performance, training-set size efficiency, and classifier creation cost. RESULTS: Expert knowledge was shown to be the most significant factor affecting inductive learning performance, outweighing differences in learning algorithms. The use of expert knowledge can affect comparisons between learning algorithms. This expert knowledge may be obtained and represented separately as knowledge about the clinical task or about the data representation used. The benefit of the expert knowledge is more than that of inductive learning itself, with less cost to obtain. CONCLUSION: For medical text report classification, expert knowledge acquisition is more significant to performance and more cost-effective to obtain than knowledge discovery. Building classifiers should therefore focus more on acquiring knowledge from experts than trying to learn this knowledge inductively. Adam B. Wilcox, George Hripcsak	J. Am. Medical Informatics Assoc.	1
2002	The Effect of Text Templates on Physician Data Entry Watson A. Bowes III, Adam B. Wilcox, Scott P. Narus	AMIA	2
2002	The effect of sample size and disease prevalence on supervised machine learning of narrative data Lawrence K. McKnight, Adam B. Wilcox, George Hripcsak	AMIA	2
2002	Disease-specific Data Sheets in the Management of Chronic Conditions - Case Example: Diabetes Scott P. Narus, Adam B. Wilcox, Paul D. Clayton, T. Allan Pryor, Steven Towner, Stephen Barlow	AMIA	2
2002	Knowledge Discovery Using the Electronic Medical Record Adam B. Wilcox, George Hripcsak, Charles Knirsch	AMIA	1
2002	Using natural language processing to analyze physician modifications to data entry templates Adam B. Wilcox, Scott P. Narus, Watson A. Bowes III	AMIA	1
2002	Review Paper: Reference Standards, Judges, and Comparison Subjects: Roles for Experts in Evaluating System Performance abstract Medical informatics systems are often designed to perform at the level of human experts. Evaluation of the performance of these systems is often constrained by lack of reference standards, either because the appropriate response is not known or because no simple appropriate response exists. Even when performance can be assessed, it is not always clear whether the performance is sufficient or reasonable. These challenges can be addressed if an evaluator enlists the help of clinical domain experts. 1) The experts can carry out the same tasks as the system, and then their responses can be combined to generate a reference standard. 2)The experts can judge the appropriateness of system output directly. 3) The experts can serve as comparison subjects with which the system can be compared. These are separate roles that have different implications for study design, metrics, and issues of reliability and validity. Diagrams help delineate the roles of experts in complex study designs. George Hripcsak, Adam B. Wilcox	J. Am. Medical Informatics Assoc.	2
2000	Medical text representations for inductive learning Adam B. Wilcox, George Hripcsak	AMIA	1
1999	Classification algorithms applied to narrative reports Adam B. Wilcox, George Hripcsak	AMIA	1
1998	Natural Language as a Tool in the Development of a Controlled Vocabulary Adam B. Wilcox, Carol Friedman, George Hripcsak	AMIA	1
1998	Knowledge discovery and data mining to assist natural language understanding Adam B. Wilcox, George Hripcsak	AMIA	1
1997	Creating an environment for linking knowledge-based systems to a clinical database: a suite of tools Adam B. Wilcox, George Hripcsak	AMIA	1
1997	Using Palmtop Computers to Retrieve Clinical Information Adam B. Wilcox, George Hripcsak, Charles Knirsch	AMIA	1