Matvey Palchuk

EDBT 2026 Demo / reviewers in the wild / expert

dblp:27/6984 · also Matvey B. Palchuk · DBLP profile ↗

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34ranked-venue papers

5as first author

10since 2021 · last 2026

0000-0002-7737-8752ORCID · verified

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

Applied, interdisciplinary, general and emerging computing · 34 · 5 first-author · 10 since 2021

Year	Publication	Venue	Position
2026	AI-Driven Literature Term Extraction for Optimizing Laboratory Data Capture and Prioritization Aída Muñoz Monjas, David Rubio Ruiz, David Pérez-Rey, Kinwei Arnold Chan, Matvey Palchuk	AIME (2)	5
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.	10
2025	A machine learning approach for automating review of a RxNorm medication mapping pipeline output Matthias Hüser, John E. Doole, Vinicius Pinho, Hossein Rouhizadeh, Douglas Teodoro, Ahson Saiyed, Matvey Palchuk	J. Biomed. Informatics	7
2022	Detecting Patterns of Chemotherapy Administration Using Cluster Analysis in Breast Cancer Patients Julia A. O'Rourke, Jeffrey Warnick, John E. Doole, Jessamine Winer-Jones, Matvey Palchuk	AMIA	5
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.	6
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.	29
2021	National COVID Cohort Collaborative (N3C) Case-control Buddies Marshall Clark, Adam M. Lee, Emily R. Pfaff, Kristin Kostka, Matvey Palchuk, Lora Lingrey, Michele Morris, Robert T. Miller	AMIA	5
2021	Monitoring the Effects of the Pandemic on Cancer-Related Patient Encounters Jack W. London, Elnara Fazio-Eynullayeva, Matvey Palchuk, Peter J. Sankey, Christopher McNair	AMIA	3
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.	38
2021	Assessing real-world medication data completeness Laura A. Evans, Jack W. London, Matvey Palchuk	J. Biomed. Informatics	3
2020	Defining a Cohort of Patients with Multifocal Breast Cancer in Medical Records Julia A. O'Rourke, John E. Doole, Cindy Liu, Matvey Palchuk	AMIA	4
2020	Career Pathways in Industry for Biomedical Informaticians Elisabeth Scheufele, Marion J. Ball, Jason Cooper, Judy Murphy, Matvey Palchuk	AMIA	5
2020	Recommendations for patient similarity classes: results of the AMIA 2019 workshop on defining patient similarity abstract Defining patient-to-patient similarity is essential for the development of precision medicine in clinical care and research. Conceptually, the identification of similar patient cohorts appears straightforward; however, universally accepted definitions remain elusive. Simultaneously, an explosion of vendors and published algorithms have emerged and all provide varied levels of functionality in identifying patient similarity categories. To provide clarity and a common framework for patient similarity, a workshop at the American Medical Informatics Association 2019 Annual Meeting was convened. This workshop included invited discussants from academics, the biotechnology industry, the FDA, and private practice oncology groups. Drawing from a broad range of backgrounds, workshop participants were able to coalesce around 4 major patient similarity classes: (1) feature, (2) outcome, (3) exposure, and (4) mixed-class. This perspective expands into these 4 subtypes more critically and offers the medical informatics community a means of communicating their work on this important topic. Nathan D. Seligson, Jeremy L. Warner, William S. Dalton, Robert S. Miller, Debra Patt, Kenneth L. Kehl, Matvey Palchuk, Gil Alterovitz, Laura K. Wiley, Ming Huang 0006, Feichen Shen, Yanshan Wang, Khoa A. Nguyen, Anthony F. Wong, Funda Meric-Bernstam, Elmer V. Bernstam, James L. Chen	J. Am. Medical Informatics Assoc.	8
2020	Mapping clinical procedures to the ICD-10-PCS: The German operation and procedure classification system use case A. Millan-Fernandez-Montes, David Pérez-Rey, Gema Hernandez-Ibarburu, Matvey Palchuk, Christina Mueller, Brecht Claerhout	J. Biomed. Informatics	4
2019	Ascertaining Medication Adherence Utilizing Open Claims Data John E. Doole, Pamela B. Landsman-Blumberg, Matvey Palchuk	AMIA	3
2018	Assessing NLP Accuracy: Focus on Anatomic Pathology Laura A. Evans, Jack W. London, Matvey Palchuk	AMIA	3
2016	How informatics are being applied in industry: challenge and opportunity Zhaohui J. Cai, Jorge A. Caballero, Matvey Palchuk, Shahram Ebadollahi, Elizabeth S. Chapman	AMIA	4
2015	Organizing Drugs in RxNorm by Therapeutic Classes Matvey Palchuk, Michael Kamerick	AMIA	1
2014	A General Propensity Matching Algorithm to Control for Potential Confounders in Observational Studies using Outcomes Miner Julianna Kohler, Manigandan Easwaran, Gerardo Soto-Campos, Tanmay Gupta, Santhosh Narayanan, Elisabeth Scheufele, Matvey Palchuk	AMIA	7
2014	Modeling Propensity for Hospital Readmission with Claims Data Gerardo Soto-Campos, Elisabeth Scheufele, Aditya Sane, Santhosh Narayanan, Matvey Palchuk	AMIA	5
2014	Genetic Variant Databases: Current Practices for Development and Curation Keeon Tabrizi, Robert Coopersmith, David Hardison, Elisabeth Scheufele, Matvey Palchuk	AMIA	5
2014	Extending tranSMART for Meta-Analysis of Genomic Data Across Trials Haiguo Wu, Elisabeth Scheufele, Dina Aronzon, Matvey Palchuk	AMIA	4
2013	Successful Algorithm for Mapping AEs from Multiple Sources to MedDRA Elisabeth Scheufele, Keeon Tabrizi, Haiguo Wu, Himanso Sahni, Matvey Palchuk, Dina Aronzon	AMIA	5
2011	Development of a tool within the electronic medical record to facilitate medication reconciliation after hospital discharge abstract Serious medication errors occur commonly in the period after hospital discharge. Medication reconciliation in the postdischarge ambulatory setting may be one way to reduce the frequency of these errors. The authors describe the design and implementation of a novel tool built into an ambulatory electronic medical record (EMR) to facilitate postdischarge medication reconciliation. The tool compares the preadmission medication list within the ambulatory EMR to the hospital discharge medication list, highlights all changes, and allows the EMR medication list to be easily updated. As might be expected for a novel tool intended for use in a minority of visits, use of the tool was low at first: 20% of applicable patient visits within 30 days of discharge. Clinician outreach, education, and a pop-up reminder succeeded in increasing use to 41% of applicable visits. Review of feedback identified several usability issues that will inform subsequent versions of the tool and provide generalizable lessons for how best to design medication reconciliation tools for this setting. Jeffrey L. Schnipper, Catherine L. Liang, Claus Hamann, Andrew S. Karson, Matvey Palchuk, Patricia C. McCarthy, Melanie Sherlock, Alexander Turchin, David W. Bates	J. Am. Medical Informatics Assoc.	5
2010	An unintended consequence of electronic prescriptions: prevalence and impact of internal discrepancies abstract Many e-prescribing systems allow for both structured and free-text fields in prescriptions, making possible internal discrepancies. This study reviewed 2914 electronic prescriptions that contained free-text fields. Internal discrepancies were found in 16.1% of the prescriptions. Most (83.8%) of the discrepancies could potentially lead to adverse events and many (16.8%) to severe adverse events, involving a hospital admission or death. Discrepancies in doses, routes or complex regimens were most likely to have a potential for a severe event (p=0.0001). Discrepancies between structured and free-text fields in electronic prescriptions are common and can cause patient harm. Improvements in electronic medical record design are necessary to minimize the risk of discrepancies and resulting adverse events. Matvey Palchuk, Elizabeth A. Fang, Janet M. Cygielnik, Matthew Labreche, Maria Shubina, Harley Z. Ramelson, Claus Hamann, Carol A. Broverman, Jonathan S. Einbinder, Alexander Turchin	J. Am. Medical Informatics Assoc.	1
2008	Application of Information Technology: "Smart Forms" in an Electronic Medical Record: Documentation-based Clinical Decision Support to Improve Disease Management abstract Clinical decision support systems (CDSS) integrated within Electronic Medical Records (EMR) hold the promise of improving healthcare quality. To date the effectiveness of CDSS has been less than expected, especially concerning the ambulatory management of chronic diseases. This is due, in part, to the fact that clinicians do not use CDSS fully. Barriers to clinicians' use of CDSS have included lack of integration into workflow, software usability issues, and relevance of the content to the patient at hand. At Partners HealthCare, we are developing "Smart Forms" to facilitate documentation-based clinical decision support. Rather than being interruptive in nature, the Smart Form enables writing a multi-problem visit note while capturing coded information and providing sophisticated decision support in the form of tailored recommendations for care. The current version of the Smart Form is designed around two chronic diseases: coronary artery disease and diabetes mellitus. The Smart Form has potential to improve the care of patients with both acute and chronic conditions. Jeffrey L. Schnipper, Jeffrey A. Linder, Matvey Palchuk, Jonathan S. Einbinder, Qi Li 0019, Anatoly Postilnik, Blackford Middleton	J. Am. Medical Informatics Assoc.	3
2007	Clinical Decision Support to Improve Antibiotic Prescribing for Acute Respiratory Infections: Results of a Pilot Study Jeffrey A. Linder, Jeffrey L. Schnipper, Lynn A. Volk, Ruslana Tsurikova, Matvey Palchuk, Maya Olsha-Yehiav, Andrea J. Melnikas, Blackford Middleton	AMIA	5
2006	Improving Care for Acute and Chronic Problems with Smart Forms and Quality Dashboards Jeffrey A. Linder, Jeffrey L. Schnipper, Matvey Palchuk, Jonathan S. Einbinder, Qi Li 0019, Blackford Middleton	AMIA	3
2006	Weight-based Pediatric Prescribing in Ambulatory Setting Matvey Palchuk, Diane L. Seger, Elaine G. Recklet, Carol Hanson, Alexander Alexeyev, Qi Li 0019	AMIA	1
2006	Smart Form Framework as a Foundation for Clinical Documentation Platform Anatoly Postilnik, Matvey Palchuk, Michael Vashevko, Boris Rudelson, Nina Plaks, Jeffrey L. Schnipper, Jeffrey A. Linder, Qi Li 0019, Blackford Middleton	AMIA	2
2006	Decision support for acute problems: The role of the standardized patient in usability testing Jeffrey A. Linder, Alan F. Rose, Matvey Palchuk, Frank Y. Chang, Jeffrey L. Schnipper, Joseph C. Chan, Blackford Middleton	J. Biomed. Informatics	3
2005	Smart Forms: Building Condition-Specific Documentation and Decision Support Tools for Ambulatory EHR Maya Olsha-Yehiav, Matvey Palchuk, Frank Y. Chang, David P. Taylor, Jeffrey L. Schnipper, Jeffrey A. Linder, Qi Li 0019, Blackford Middleton	AMIA	2
2005	Implementing Renal Impairment and Geriatric Decision Support in Ambulatory e-Prescribing Matvey Palchuk, Diane L. Seger, Alexander Alexeyev, Robert Macauley, Andrew C. Seger, Elaine G. Recklet, Tejal K. Gandhi	AMIA	1
2002	Maximizing Data Portability in Patient-controlled Longitudinal Medical Records Matvey Palchuk, Eric C. Pan, Isaac S. Kohane	AMIA	1