VLDB 2026 Research / reviewers in the wild / expert

Justin Starren

dblp:64/5041 · also Justin B. Starren · DBLP profile ↗
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77ranked-venue papers
12as first author
8since 2021 · last 2026
0000-0002-5403-1115ORCID · corroborated

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

Applied, interdisciplinary, general and emerging computing · 76 · 12 first-author · 8 since 2021Human-computer interaction and ubiquitous computing · 1
YearPublicationVenuePosition
2026 PhenoFit: a framework for determining computable phenotyping algorithm fitness for purpose and reuse
abstract
BACKGROUND: Computational phenotyping from electronic health records (EHRs) is essential for clinical research, decision support, and quality/population health assessment, but the proliferation of algorithms for the same conditions makes it difficult to identify which algorithm is most appropriate for reuse. OBJECTIVE: To develop a framework for assessing phenotyping algorithm fitness for purpose and reuse. FITNESS FOR PURPOSE: Phenotyping algorithms are fit for purpose when they identify the intended population with performance characteristics appropriate for the intended application. FITNESS FOR REUSE: Phenotyping algorithms are fit for reuse when the algorithm is implementable and generalizable-that is, it identifies the same intended population with similar performance characteristics when applied to a new setting. CONCLUSIONS: The PhenoFit framework provides a structured approach to evaluate and adapt phenotyping algorithms for new contexts increasing efficiency and consistency of identifying patient populations from EHRs.
Laura K. Wiley, Luke V. Rasmussen, Rebecca T. Levinson, Jennifer Malinowski, Sheila Manemann, Melissa P. Wilson, Martin Chapman, Jennifer A. Pacheco, Theresa Walunas, Justin Starren, Suzette J. Bielinski, Rachel L. Richesson
 J. Am. Medical Informatics Assoc.10
2025 Large language models accurately identify immunosuppression in intensive care unit patients
abstract
OBJECTIVE: Rule-based structured data algorithms and natural language processing (NLP) approaches applied to unstructured clinical notes have limited accuracy and poor generalizability for identifying immunosuppression. Large language models (LLMs) may effectively identify patients with heterogenous types of immunosuppression from unstructured clinical notes. We compared the performance of LLMs applied to unstructured notes for identifying patients with immunosuppressive conditions or immunosuppressive medication use against 2 baselines: (1) structured data algorithms using diagnosis codes and medication orders and (2) NLP approaches applied to unstructured notes. MATERIALS AND METHODS: We used hospital admission notes from a primary cohort of 827 intensive care unit (ICU) patients at Northwestern Memorial Hospital and a validation cohort of 200 ICU patients at Beth Israel Deaconess Medical Center, along with diagnosis codes and medication orders from the primary cohort. We evaluated the performance of structured data algorithms, NLP approaches, and LLMs in identifying 7 immunosuppressive conditions and 6 immunosuppressive medications. RESULTS: In the primary cohort, structured data algorithms achieved peak F1 scores ranging from 0.30 to 0.97 for identifying immunosuppressive conditions and medications. NLP approaches achieved peak F1 scores ranging from 0 to 1. GPT-4o outperformed or matched structured data algorithms and NLP approaches across all conditions and medications, with F1 scores ranging from 0.51 to 1. GPT-4o also performed impressively in our validation cohort (F1 = 1 for 8/13 variables). DISCUSSION: LLMs, particularly GPT-4o, outperformed structured data algorithms and NLP approaches in identifying immunosuppressive conditions and medications with robust external validation. CONCLUSION: LLMs can be applied for improved cohort identification for research purposes.
Vijeeth Guggilla, Mengjia Kang, Melissa J. Bak, Steven D. Tran, Anna Pawlowski, Prasanth Nannapaneni, Luke V. Rasmussen, Helen K. Donnelly, Ankit Agrawal 0001, David M. Liebovitz, Alexander V. Misharin, G. R. Scott Budinger, Richard G. Wunderink, Theresa Walunas, Catherine A. Gao, Alan R. Hauser, Alec Peltekian, Alexis Rose Wolfe, Alison L. Szabo, Alok N. Choudhary, Amy Ludwig, Anahid Amani Moghadam, Anjana V. Yeldandi, Ankit Bharat, Anna E. Pawlowski, Anthony M. Joudi, Arjun Prakash Tambe, Ashley J. Smith-Nunez, Benjamin D. Singer, Benjamin J. Ulrich, Betty Tran, Cara J. Gottardi, Chiagozie O. Pickens, Clara J. Schroedl, Daniel Meza, Dulce Sarai Garcia, Egon A. Ozer, Elen Gusman, Elisheva D. Shanes, Emily Mower Provost, Emily M. Olson, Erica Marie Hartmann, Erin A. Korth, Estefani Diaz, Estefany R. Guzman, Francisco J. Martinez, Gabrielle Matias, Hiam Abdala-Valencia, Jack T. Sumner, Jacob I Sznajder, Jacqueline M. Kruser, Jakub Glowala, James M. Walter, Jamie H. Rowell, Jason M. Arnold, John Coleman, Jon W. Lomasney, Joseph Isaac Bailey, Judd F. Hultquist, Justin A. Fiala, Justin Starren, Karen M. Ridge, Karolina Senkow, Kathryn A. Helmin, Khalilah L. Gates, Lacy Simmons, Lesley Pinzon, Lindsey D. Gradone, Lisa F. Wolfe, Lucy Luo, Luisa Morales-Nebreda, Manu Jain, Marc Sala, Maxwell Schleck, Melissa H. Ross, Melissa Querrey, Michael J. Cuttica, Michelle Hinsch Prickett, Nandita R. Nadig, Nathaniel Rhodes, Navdeep S. Chandel, Nikolay S. Markov, Peter H. S. Sporn, Qianli Liu, Rachel B. Kadar, Rachel L. Medernach, Ramon Lorenzo-Redondo, Ravi Kalhan, Rebecca K. Clepp, Richard I. Morimoto, Rogan A. Grant, Ruben J. Mylvaganam, Samuel Fenske, Scott A. Laurenzo, Seung Hye Han, Sophia Nozick, Srinivas Panchamukhi, Stephanie C. Eisenbarth, Suchitra Swaminathan, Susan R. Russell, Taylor A. Poor, Thaddeus Cybulski, Theresa A. Lombardo, Thomas Bolig, Thomas Stoeger, Tien Doan, Timothy Rowe, Wan-Ting Liao, Yuan Luo 0001, Yuliana Sokolenko, Ziyan Lu
 J. Am. Medical Informatics Assoc.63
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.72
2023 Characterizing variability of electronic health record-driven phenotype definitions
abstract
OBJECTIVE: The aim of this study was to analyze a publicly available sample of rule-based phenotype definitions to characterize and evaluate the variability of logical constructs used. MATERIALS AND METHODS: A sample of 33 preexisting phenotype definitions used in research that are represented using Fast Healthcare Interoperability Resources and Clinical Quality Language (CQL) was analyzed using automated analysis of the computable representation of the CQL libraries. RESULTS: Most of the phenotype definitions include narrative descriptions and flowcharts, while few provide pseudocode or executable artifacts. Most use 4 or fewer medical terminologies. The number of codes used ranges from 5 to 6865, and value sets from 1 to 19. We found that the most common expressions used were literal, data, and logical expressions. Aggregate and arithmetic expressions are the least common. Expression depth ranges from 4 to 27. DISCUSSION: Despite the range of conditions, we found that all of the phenotype definitions consisted of logical criteria, representing both clinical and operational logic, and tabular data, consisting of codes from standard terminologies and keywords for natural language processing. The total number and variety of expressions are low, which may be to simplify implementation, or authors may limit complexity due to data availability constraints. CONCLUSIONS: The phenotype definitions analyzed show significant variation in specific logical, arithmetic, and other operators but are all composed of the same high-level components, namely tabular data and logical expressions. A standard representation for phenotype definitions should support these formats and be modular to support localization and shared logic.
Pascal S. Brandt, Abel N. Kho, Yuan Luo 0001, Jennifer A. Pacheco, Theresa Walunas, Hakon Hakonarson, George Hripcsak, Cong Liu 0020, Ning Shang 0004, Chunhua Weng, Nephi Walton, David Carrell, Paul K. Crane, Eric B. Larson, Christopher G. Chute, Iftikhar J. Kullo, Robert J. Carroll, Joshua C. Denny, Andrea H. Ramirez, Wei-Qi Wei, Jyotishman Pathak, Laura K. Wiley, Rachel L. Richesson, Justin Starren, Luke V. Rasmussen
 J. Am. Medical Informatics Assoc.24
2022 Improving the Coverage of Food Allergy in Clinical Terminologies
Mark Wlodarski, Shruti Sehgal, Firas H. Wehbe, Lucy A. Bilaver, Justin Starren
 AMIA5
2022 Examining perspectives on the adoption and use of computer-based patient-reported outcomes among clinicians and health professionals: a Q methodology study
abstract
OBJECTIVE: To determine factors that influence the adoption and use of patient-reported outcomes (PROs) in the electronic health record (EHR) among users. MATERIALS AND METHODS: Q methodology, supported by focus groups, semistructured interviews, and a review of the literature was used for data collection about opinions on PROs in the EHR. An iterative thematic analysis resulted in 49 statements that study participants sorted, from most unimportant to most important, under the following condition of instruction: "What issues are most important or most unimportant to you when you think about the adoption and use of patient-reported outcomes within the electronic health record in routine clinical care?" Using purposive sampling, 50 participants were recruited to rank and sort the 49 statements online, using HTMLQ software. Principal component analysis and Varimax rotation were used for data analysis using the PQMethod software. RESULTS: Participants were mostly physicians (24%) or physician/researchers (20%). Eight factors were identified. Factors included the ability of PROs in the EHR to enable: efficient and reliable use; care process improvement and accountability; effective and better symptom assessment; patient involvement for care quality; actionable and practical clinical decisions; graphical review and interpretation of results; use for holistic care planning to reflect patients' needs; and seamless use for all users. DISCUSSION: The success of PROs in the EHR in clinical settings is not dependent on a "one size fits all" strategy, demonstrated by the diversity of viewpoints identified in this study. A sociotechnical approach for implementing PROs in the EHR may help improve its success and sustainability. CONCLUSIONS: PROs in the EHR are most important to users when the technology is used to improve patient outcomes. Future research must focus on the impact of embedding this EHR functionality on care processes.
Shirley Burton, Annette L. Valenta, Justin Starren, Joanna Abraham, Therese A. Nelson, Karl M. Kochendorfer, Ashley M. Hughes, Bhrandon Harris, Andrew D. Boyd
 J. Am. Medical Informatics Assoc.3
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.20
2021 A retrospective look at the predictions and recommendations from the 2009 AMIA policy meeting: did we see EHR-related clinician burnout coming?
abstract
Clinicians often attribute much of their burnout experience to use of the electronic health record, the adoption of which was greatly accelerated by the Health Information Technology for Economic and Clinical Health Act of 2009. That same year, AMIA's Policy Meeting focused on possible unintended consequences associated with rapid implementation of electronic health records, generating 17 potential consequences and 15 recommendations to address them. At the 2020 annual meeting of the American College of Medical Informatics (ACMI), ACMI fellows participated in a modified Delphi process to assess the accuracy of the 2009 predictions and the response to the recommendations. Among the findings, the fellows concluded that the degree of clinician burnout and its contributing factors, such as increased documentation requirements, were significantly underestimated. Conversely, problems related to identify theft and fraud were overestimated. Only 3 of the 15 recommendations were adjudged more than half-addressed.
Justin Starren, William M. Tierney, Marc S. Williams, Paul C. Tang, Charlene R. Weir, Ross Koppel, Philip R. O. Payne, George Hripcsak, Don E. Detmer
 J. Am. Medical Informatics Assoc.1
2020 Towards a Food Allergy Data Commons
Mark Wlodarski, Shruti Sehgal, Lucy A. Bilaver, Justin Starren, Firas H. Wehbe, Nicholas D. Soulakis
 AMIA5
2019 Pharmacogenomic clinical decision support design and multi-site process outcomes analysis in the eMERGE Network
abstract
To better understand the real-world effects of pharmacogenomic (PGx) alerts, this study aimed to characterize alert design within the eMERGE Network, and to establish a method for sharing PGx alert response data for aggregate analysis. Seven eMERGE sites submitted design details and established an alert logging data dictionary. Six sites participated in a pilot study, sharing alert response data from their electronic health record systems. PGx alert design varied, with some consensus around the use of active, post-test alerts to convey Clinical Pharmacogenetics Implementation Consortium recommendations. Sites successfully shared response data, with wide variation in acceptance and follow rates. Results reflect the lack of standardization in PGx alert design. Standards and/or larger studies will be necessary to fully understand PGx impact. This study demonstrated a method for sharing PGx alert response data and established that variation in system design is a significant barrier for multi-site analyses.
Timothy M. Herr, Josh F. Peterson, Luke V. Rasmussen, Pedro J. Caraballo, Peggy L. Peissig, Justin Starren
 J. Am. Medical Informatics Assoc.6
2019 An ancillary genomics system to support the return of pharmacogenomic results
abstract
Existing approaches to managing genetic and genomic test results from external laboratories typically include filing of text reports within the electronic health record, making them unavailable in many cases for clinical decision support. Even when structured computable results are available, the lack of adopted standards requires considerations for processing the results into actionable knowledge, in addition to storage and management of the data. Here, we describe the design and implementation of an ancillary genomics system used to receive and process heterogeneous results from external laboratories, which returns a descriptive phenotype to the electronic health record in support of pharmacogenetic clinical decision support.
Luke V. Rasmussen, Maureen E. Smith, Federico Almaraz, Stephen D. Persell, Laura Rasmussen-Torvik, Jennifer A. Pacheco, Rex L. Chisholm, Carl Christensen, Timothy M. Herr, Firas H. Wehbe, Justin Starren
 J. Am. Medical Informatics Assoc.11
2018 Panel: Collaborative Science Within Academic Medical Centers: Opportunities and Challenges for Informatics
Justin Starren, William R. Hersh, Christopher A. Longhurst, Philip R. O. Payne
 AMIA1
2018 System Demonstration: Integration of Patient Reported Outcomes with Electronic Health Records - the EASI-PRO Project
Justin Starren, Daniella Meeker, Kenneth D. Mandl, Guo-Qiang Zhang 0001, Alyssa White, Raheel Sayeed, Daniel Gottlieb 0001, Alex Wormuth, Welmoed Van Deen, Shiqiang Tao
 AMIA1
2018 Characterizing Design Patterns of EHR-Driven Phenotype Extraction Algorithms
Yizhen Zhong, Luke V. Rasmussen, Jennifer A. Pacheco, Maureen E. Smith, Justin Starren, Wei-Qi Wei, Peter Speltz, Joshua C. Denny, Nephi Walton, George Hripcsak, Christopher G. Chute, Yuan Luo 0001
 BIBM6
2018 Segment convolutional neural networks (Seg-CNNs) for classifying relations in clinical notes
abstract
We propose Segment Convolutional Neural Networks (Seg-CNNs) for classifying relations from clinical notes. Seg-CNNs use only word-embedding features without manual feature engineering. Unlike typical CNN models, relations between 2 concepts are identified by simultaneously learning separate representations for text segments in a sentence: preceding, concept1, middle, concept2, and succeeding. We evaluate Seg-CNN on the i2b2/VA relation classification challenge dataset. We show that Seg-CNN achieves a state-of-the-art micro-average F-measure of 0.742 for overall evaluation, 0.686 for classifying medical problem-treatment relations, 0.820 for medical problem-test relations, and 0.702 for medical problem-medical problem relations. We demonstrate the benefits of learning segment-level representations. We show that medical domain word embeddings help improve relation classification. Seg-CNNs can be trained quickly for the i2b2/VA dataset on a graphics processing unit (GPU) platform. These results support the use of CNNs computed over segments of text for classifying medical relations, as they show state-of-the-art performance while requiring no manual feature engineering.
Yuan Luo 0001, Özlem Uzuner, Peter Szolovits, Justin Starren
 J. Am. Medical Informatics Assoc.5
2016 Assessment Center API: A Software Component Model for the Integration of Patient Reported Outcomes (PRO) into Clinical Care
Michael Bass, Paul A. Harris, Robert J. Taylor, Justin Starren, Joshua Spuhl, Jimmy Johnson, Jason Guattery
 AMIA4
2016 Design and Implementation of an Ancillary Genomics System for the Return of Pharmacogenetic Results
Luke V. Rasmussen, Maureen E. Smith, Federico Almaraz, Stephen D. Persell, Laura Rasmussen-Torvik, Jennifer A. Pacheco, Carl Christensen, Timothy M. Herr, Firas H. Wehbe, Justin Starren
 AMIA10
2016 Automatic identification and extraction of design patterns of EHR-driven phenotyping algorithms
Yizhen Zhong, Luke V. Rasmussen, Justin Starren, Yuan Luo 0001
 AMIA3
2016 Core informatics competencies for clinical and translational scientists: what do our customers and collaborators need to know?
abstract
Since the inception of the Clinical and Translational Science Award (CTSA) program in 2006, leaders in education across CTSA sites have been developing and updating core competencies for Clinical and Translational Science (CTS) trainees. By 2009, 14 competency domains, including biomedical informatics, had been identified and published. Since that time, the evolution of the CTSA program, changes in the practice of CTS, the rapid adoption of electronic health records (EHRs), the growth of biomedical informatics, the explosion of big data, and the realization that some of the competencies had proven to be difficult to apply in practice have made it clear that the competencies should be updated. This paper describes the process undertaken and puts forth a new set of competencies that has been recently endorsed by the Clinical Research Informatics Workgroup of AMIA. In addition to providing context and background for the current version of the competencies, we hope this will serve as a model for revision of competencies over time.
Annette L. Valenta, Emma A. Meagher, Umberto Tachinardi, Justin Starren
 J. Am. Medical Informatics Assoc.4
2015 CSER and eMERGE: current and potential state of the display of genetic information in the electronic health record
abstract
OBJECTIVE: Clinicians' ability to use and interpret genetic information depends upon how those data are displayed in electronic health records (EHRs). There is a critical need to develop systems to effectively display genetic information in EHRs and augment clinical decision support (CDS). MATERIALS AND METHODS: The National Institutes of Health (NIH)-sponsored Clinical Sequencing Exploratory Research and Electronic Medical Records & Genomics EHR Working Groups conducted a multiphase, iterative process involving working group discussions and 2 surveys in order to determine how genetic and genomic information are currently displayed in EHRs, envision optimal uses for different types of genetic or genomic information, and prioritize areas for EHR improvement. RESULTS: There is substantial heterogeneity in how genetic information enters and is documented in EHR systems. Most institutions indicated that genetic information was displayed in multiple locations in their EHRs. Among surveyed institutions, genetic information enters the EHR through multiple laboratory sources and through clinician notes. For laboratory-based data, the source laboratory was the main determinant of the location of genetic information in the EHR. The highest priority recommendation was to address the need to implement CDS mechanisms and content for decision support for medically actionable genetic information. CONCLUSION: Heterogeneity of genetic information flow and importance of source laboratory, rather than clinical content, as a determinant of information representation are major barriers to using genetic information optimally in patient care. Greater effort to develop interoperable systems to receive and consistently display genetic and/or genomic information and alert clinicians to genomic-dependent improvements to clinical care is recommended.
Brian H. Shirts, Joseph S. Salama, Samuel J. Aronson, Wendy K. Chung, Stacy W. Gray, Lucia Hindorff, Gail P. Jarvik, Sharon E. Plon, Elena M. Stoffel, Peter Tarczy-Hornoch, Eliezer M. Van Allen, Karen E. Weck, Christopher G. Chute, Robert R. Freimuth, Robert Grundmeier, Andrea L. Hartzler, Rongling Li, Peggy L. Peissig, Josh F. Peterson, Luke V. Rasmussen, Justin Starren, Marc S. Williams, Casey Overby Taylor
 J. Am. Medical Informatics Assoc.21
2014 PGS: a tool for association study of high-dimensional microRNA expression data with repeated measures
abstract
MOTIVATION: MicroRNAs (miRNAs) are short single-stranded non-coding molecules that usually function as negative regulators to silence or suppress gene expression. Owning to the dynamic nature of miRNA and reduced microarray and sequencing costs, a growing number of researchers are now measuring high-dimensional miRNA expression data using repeated or multiple measures in which each individual has more than one sample collected and measured over time. However, the commonly used univariate association testing or the site-by-site (SBS) testing may underutilize the longitudinal feature of the data, leading to underpowered results and less biologically meaningful results. RESULTS: We propose a penalized regression model incorporating grid search method (PGS), for analyzing associations of high-dimensional miRNA expression data with repeated measures. The development of this analytical framework was motivated by a real-world miRNA dataset. Comparisons between PGS and the SBS testing revealed that PGS provided smaller phenotype prediction errors and higher enrichment of phenotype-related biological pathways than the SBS testing. Our extensive simulations showed that PGS provided more accurate estimates and higher sensitivity than the SBS testing with comparable specificities. AVAILABILITY AND IMPLEMENTATION: R source code for PGS algorithm, implementation example and simulation study are available for download at https://github.com/feizhe/PGS.
Yinan Zheng, Zhe Fei, Wei Zhang 0253, Justin Starren, Lei Liu 0004, Andrea A. Baccarelli, Lifang Hou
 Bioinform.4
2014 Design patterns for the development of electronic health record-driven phenotype extraction algorithms
Luke V. Rasmussen, William K. Thompson, Jennifer A. Pacheco, Abel N. Kho, David Carrell, Jyotishman Pathak, Peggy L. Peissig, Gerard Tromp, Joshua C. Denny, Justin Starren
 J. Biomed. Informatics10
2012 Integrating Research Recruitment into a Clinical Patient Portal
Luke V. Rasmussen, David Were, Jeff Lunt, Steve Lee, Carl Christensen, Warren A. Kibbe, Justin Starren
 AMIA7
2012 Grouping and Translating Value Sets
Emre Motan, Luke V. Rasmussen, Andrew Winter, Justin Starren
 AMIA5
2012 Importance of multi-modal approaches to effectively identify cataract cases from electronic health records
abstract
OBJECTIVE: There is increasing interest in using electronic health records (EHRs) to identify subjects for genomic association studies, due in part to the availability of large amounts of clinical data and the expected cost efficiencies of subject identification. We describe the construction and validation of an EHR-based algorithm to identify subjects with age-related cataracts. MATERIALS AND METHODS: We used a multi-modal strategy consisting of structured database querying, natural language processing on free-text documents, and optical character recognition on scanned clinical images to identify cataract subjects and related cataract attributes. Extensive validation on 3657 subjects compared the multi-modal results to manual chart review. The algorithm was also implemented at participating electronic MEdical Records and GEnomics (eMERGE) institutions. RESULTS: An EHR-based cataract phenotyping algorithm was successfully developed and validated, resulting in positive predictive values (PPVs) >95%. The multi-modal approach increased the identification of cataract subject attributes by a factor of three compared to single-mode approaches while maintaining high PPV. Components of the cataract algorithm were successfully deployed at three other institutions with similar accuracy. DISCUSSION: A multi-modal strategy incorporating optical character recognition and natural language processing may increase the number of cases identified while maintaining similar PPVs. Such algorithms, however, require that the needed information be embedded within clinical documents. CONCLUSION: We have demonstrated that algorithms to identify and characterize cataracts can be developed utilizing data collected via the EHR. These algorithms provide a high level of accuracy even when implemented across multiple EHRs and institutional boundaries.
Peggy L. Peissig, Luke V. Rasmussen, Richard L. Berg, James G. Linneman, Catherine A. McCarty, Carol Waudby, Joshua C. Denny, Russell A. Wilke, Jyotishman Pathak, David Carrell, Abel N. Kho, Justin Starren
 J. Am. Medical Informatics Assoc.13
2012 Development of an optical character recognition pipeline for handwritten form fields from an electronic health record
abstract
BACKGROUND: Although the penetration of electronic health records is increasing rapidly, much of the historical medical record is only available in handwritten notes and forms, which require labor-intensive, human chart abstraction for some clinical research. The few previous studies on automated extraction of data from these handwritten notes have focused on monolithic, custom-developed recognition systems or third-party systems that require proprietary forms. METHODS: We present an optical character recognition processing pipeline, which leverages the capabilities of existing third-party optical character recognition engines, and provides the flexibility offered by a modular custom-developed system. The system was configured and run on a selected set of form fields extracted from a corpus of handwritten ophthalmology forms. OBSERVATIONS: The processing pipeline allowed multiple configurations to be run, with the optimal configuration consisting of the Nuance and LEADTOOLS engines running in parallel with a positive predictive value of 94.6% and a sensitivity of 13.5%. DISCUSSION: While limitations exist, preliminary experience from this project yielded insights on the generalizability and applicability of integrating multiple, inexpensive general-purpose third-party optical character recognition engines in a modular pipeline.
Luke V. Rasmussen, Peggy L. Peissig, Catherine A. McCarty, Justin Starren
 J. Am. Medical Informatics Assoc.4
2011 Anticipating and addressing the unintended consequences of health IT and policy: a report from the AMIA 2009 Health Policy Meeting
abstract
Federal legislation (Health Information Technology for Economic and Clinical Health (HITECH) Act) has provided funds to support an unprecedented increase in health information technology (HIT) adoption for healthcare provider organizations and professionals throughout the U.S. While recognizing the promise that widespread HIT adoption and meaningful use can bring to efforts to improve the quality, safety, and efficiency of healthcare, the American Medical Informatics Association devoted its 2009 Annual Health Policy Meeting to consideration of unanticipated consequences that could result with the increased implementation of HIT. Conference participants focused on possible unintended and unanticipated, as well as undesirable, consequences of HIT implementation. They employed an input-output model to guide discussion on occurrence of these consequences in four domains: technical, human/cognitive, organizational, and fiscal/policy and regulation. The authors outline the conference's recommendations: (1) an enhanced research agenda to guide study into the causes, manifestations, and mitigation of unintended consequences resulting from HIT implementations; (2) creation of a framework to promote sharing of HIT implementation experiences and the development of best practices that minimize unintended consequences; and (3) recognition of the key role of the Federal Government in providing leadership and oversight in analyzing the effects of HIT-related implementations and policies.
Meryl Bloomrosen, Justin Starren, Nancy M. Lorenzi, Joan S. Ash, Vimla L. Patel, Edward H. Shortliffe
 J. Am. Medical Informatics Assoc.2
2010 Medicare payments, healthcare service use, and telemedicine implementation costs in a randomized trial comparing telemedicine case management with usual care in medically underserved participants with diabetes mellitus (IDEATel)
abstract
Objective To determine whether a diabetes case management telemedicine intervention reduced healthcare expenditures, as measured by Medicare claims, and to assess the costs of developing and implementing the telemedicine intervention. Design We studied 1665 participants in the Informatics for Diabetes Education and Telemedicine (IDEATel), a randomized controlled trial comparing telemedicine case management of diabetes to usual care. Participants were aged 55 years or older, and resided in federally designated medically underserved areas of New York State. Measurements We analyzed Medicare claims payments for each participant for up to 60 study months from date of randomization, until their death, or until December 31, 2006 (whichever happened first). We also analyzed study expenditures for the telemedicine intervention over six budget years (February 28, 2000- February 27, 2006). Results Mean annual Medicare payments (SE) were similar in the usual care and telemedicine groups, $9040 ($386) and $9669 ($443) per participant, respectively (p>0.05). Sensitivity analyses, including stratification by censored status, adjustment by enrollment site, and semi-parametric weighting by probability of dropping-out, rendered similar results. Over six budget years 28 821 participant/months of telemedicine intervention were delivered, at an estimated cost of $622 per participant/month. Conclusion Telemedicine case management was not associated with a reduction in Medicare claims in this medically underserved population. The cost of implementing the telemedicine intervention was high, largely representing special purpose hardware and software costs required at the time. Lower implementation costs will need to be achieved using lower cost technology in order for telemedicine case management to be more widely used.
Walter Palmas, Steven Shea, Justin Starren, Jeanne A. Teresi, Michael L. Ganz, Tanya M. Burton, Chris L. Pashos, Jan Blustein, Lesley Field, Philip C. Morin, Roberto E. Izquierdo, Stephanie Silver, Joseph P. Eimicke, Rafael A. Lantigua, Ruth S. Weinstock
 J. Am. Medical Informatics Assoc.3
2009 Ad-hoc association of pre-determined ZigBee devices
abstract
In pervasive sensor networks with high densities, similar networks might overlap, resulting in different coordinators for end devices to associate with. This can result in several problems, especially for home monitoring and hospital scenarios, where easy, fast, and accurate association of pre-dete
Patrick Seeling, Justin Starren
 MobiQuitous2
2009 Research Paper: A Randomized Trial Comparing Telemedicine Case Management with Usual Care in Older, Ethnically Diverse, Medically Underserved Patients with Diabetes Mellitus: 5 Year Results of the IDEATel Study
abstract
CONTEXT Telemedicine is a promising but largely unproven technology for providing case management services to patients with chronic conditions and lower access to care. OBJECTIVES To examine the effectiveness of a telemedicine intervention to achieve clinical management goals in older, ethnically diverse, medically underserved patients with diabetes. DESIGN, Setting, and Patients A randomized controlled trial was conducted, comparing telemedicine case management to usual care, with blinded outcome evaluation, in 1,665 Medicare recipients with diabetes, aged >/= 55 years, residing in federally designated medically underserved areas of New York State. Interventions Home telemedicine unit with nurse case management versus usual care. Main Outcome Measures The primary endpoints assessed over 5 years of follow-up were hemoglobin A1c (HgbA1c), low density lipoprotein (LDL) cholesterol, and blood pressure levels. RESULTS Intention-to-treat mixed models showed that telemedicine achieved net overall reductions over five years of follow-up in the primary endpoints (HgbA1c, p = 0.001; LDL, p < 0.001; systolic and diastolic blood pressure, p = 0.024; p < 0.001). Estimated differences (95% CI) in year 5 were 0.29 (0.12, 0.46)% for HgbA1c, 3.84 (-0.08, 7.77) mg/dL for LDL cholesterol, and 4.32 (1.93, 6.72) mm Hg for systolic and 2.64 (1.53, 3.74) mm Hg for diastolic blood pressure. There were 176 deaths in the intervention group and 169 in the usual care group (hazard ratio 1.01 [0.82, 1.24]). CONCLUSIONS Telemedicine case management resulted in net improvements in HgbA1c, LDL-cholesterol and blood pressure levels over 5 years in medically underserved Medicare beneficiaries. Mortality was not different between the groups, although power was limited. Trial Registration http://clinicaltrials.gov Identifier: NCT00271739.
Steven Shea, Ruth S. Weinstock, Jeanne A. Teresi, Walter Palmas, Justin Starren, James J. Cimino, Albert M. Lai, Lesley Field, Philip C. Morin, Robin Goland, Roberto E. Izquierdo, Susana Ebner, Stephanie Silver, Eva Petkova, Joseph P. Eimicke
 J. Am. Medical Informatics Assoc.5
2009 Understanding workflow in telehealth video visits: Observations from the IDEATel project
David R. Kaufman, Jenia Pevzner, Martha Rodriguez, James J. Cimino, Susana Ebner, Lesley Field, Vilma Moreno, Christina McGuiness, Ruth S. Weinstock, Steven Shea, Justin Starren
 J. Biomed. Informatics11
2008 Participatory design with children in the development of a support system for patient-centered care in pediatric oncology
Cornelia M. Ruland, Justin Starren, Torun M. Vatne
 J. Biomed. Informatics2
2007 Modeling Participant-Related Clinical Research Events Using Conceptual Knowledge Acquisition Techniques
Philip R. O. Payne, Eneida A. Mendonça, Justin Starren
 AMIA3
2007 Conceptual knowledge acquisition in biomedicine: A methodological review
Philip R. O. Payne, Eneida A. Mendonça, Stephen B. Johnson, Justin Starren
 J. Biomed. Informatics4
2006 Reliability of SNOMED-CT Coding by Three Physicians using Two Terminology Browsers
Michael F. Chiang, John C. Hwang, Alexander C. Yu, Daniel S. Casper, James J. Cimino, Justin Starren
 AMIA6
2006 Novel Techniques for Survey and Classification Studies to Improve Patient Centered Websites
Amy E. Chused, Philip R. O. Payne, Justin Starren
 AMIA3
2006 Coverage of Clinical Trials Tasks in Existing Ontologies
James R. Deitzer, Philip R. O. Payne, Justin Starren
 AMIA3
2006 A Methodological Framework for Evaluating Mobile Health Devices
David R. Kaufman, Justin Starren
 AMIA2
2006 Training Digital Divide Seniors to use a Telehealth System: A Remote Training Approach
Albert M. Lai, David R. Kaufman, Justin Starren
 AMIA3
2006 Consensus-based Construction of a Taxonomy of Clinical Trial Tasks
Philip R. O. Payne, James R. Deitzer, Eneida A. Mendonça, Justin Starren
 AMIA4
2006 Theater Style Demonstration: The Informatics for Diabetes Education And Telemedicine (IDEATel) Project
Justin Starren, Charlyn Hilliman, Ruth S. Weinstock, Steven Shea
 AMIA1
2006 Human Computer Interaction Issues in Clinical Trials Management Systems
Justin Starren, Philip R. O. Payne, David R. Kaufman
 AMIA1
2006 The Practice of Informatics: Design Features of Graphs in Health Risk Communication: A Systematic Review
abstract
This review describes recent experimental and focus group research on graphics as a method of communication about quantitative health risks. Some of the studies discussed in this review assessed effect of graphs on quantitative reasoning, others assessed effects on behavior or behavioral intentions, and still others assessed viewers' likes and dislikes. Graphical features that improve the accuracy of quantitative reasoning appear to differ from the features most likely to alter behavior or intentions. For example, graphs that make part-to-whole relationships available visually may help people attend to the relationship between the numerator (the number of people affected by a hazard) and the denominator (the entire population at risk), whereas graphs that show only the numerator appear to inflate the perceived risk and may induce risk-averse behavior. Viewers often preferred design features such as visual simplicity and familiarity that were not associated with accurate quantitative judgments. Communicators should not assume that all graphics are more intuitive than text; many of the studies found that patients' interpretations of the graphics were dependent upon expertise or instruction. Potentially useful directions for continuing research include interactions with educational level and numeracy and successful ways to communicate uncertainty about risk.
Jessica S. Ancker, Yalini Senathirajah, Rita Kukafka, Justin Starren
 J. Am. Medical Informatics Assoc.4
2006 Research Paper: Development, Validation, and Use of English and Spanish Versions of the Telemedicine Satisfaction and Usefulness Questionnaire
abstract
OBJECTIVES: To describe the development and validation of low literacy English and Spanish versions of the 26-item Telemedicine Satisfaction and Usefulness Questionnaire (TSUQ), report telemedicine satisfaction and usefulness ratings of urban and rural participants in the Informatics for Diabetes Education and Telemedicine (IDEATel) project, and explore relationships between utilization and perceptions of satisfaction and usefulness. METHODS: Data sources included TSUQ, utilization data from IDEATel log files, and sociodemographic data from the annual IDEATel interview. Psychometric analyses were conducted to examine the reliability and validity of TSUQ. Data were analyzed using descriptive, correlational techniques. RESULTS: The principal components factor analysis extracted two factors (Video Visits, alpha=.96, and Use and Impact, alpha=.92) that explained 63.6% of the variance in TSUQ satisfaction scores. All satisfaction and usefulness items had mean scores of greater than 4 on a 5-point scale. Those from urban areas reported significantly higher ratings on both factors than rural participants as did those who did not know how to use a computer at baseline. Mean frequency of utilization of IDEATel components was highest for blood sugar testing followed by web site for reviewing results, blood pressure testing, video visits, and ADA educational Web pages. Associations between utilization and perceptions of satisfaction and usefulness varied among IDEATel components. CONCLUSION: Psychometric analyses support the construct validity and internal consistency reliability of TSUQ, which is available in both English and Spanish at a readability level of 8th grade. Both rural and urban participants reported high levels of satisfaction and found all IDEATel components useful. Further work is needed to examine the relationships between utilization and perceptions of satisfaction and usefulness and to explore the effects of location (urban versus rural) and ethnicity on satisfaction with telemedicine services.
Suzanne Bakken, Lorena Grullon-Figueroa, Roberto E. Izquierdo, Nam-Ju Lee, Philip C. Morin, Walter Palmas, Jeanne A. Teresi, Ruth S. Weinstock, Steven Shea, Justin Starren
 J. Am. Medical Informatics Assoc.10
2006 Research Paper: A Randomized Trial Comparing Telemedicine Case Management with Usual Care in Older, Ethnically Diverse, Medically Underserved Patients with Diabetes Mellitus
abstract
BACKGROUND: Telemedicine is a promising but largely unproven technology for providing case management services to patients with chronic conditions who experience barriers to access to care or a high burden of illness. METHODS: The authors conducted a randomized, controlled trial comparing telemedicine case management to usual care, with blinding of those obtaining outcome data, in 1,665 Medicare recipients with diabetes, aged 55 years or greater, and living in federally designated medically underserved areas of New York State. The primary endpoints were HgbA1c, blood pressure, and low-density lipoprotein (LDL) cholesterol levels. RESULTS: In the intervention group (n = 844), mean HgbA1c improved over one year from 7.35% to 6.97% and from 8.35% to 7.42% in the subgroup with baseline HgbA1c > or =7% (n = 353). In the usual care group (n = 821) mean HgbA1c improved over one year from 7.42% to 7.17%. Adjusted net reductions (one-year minus baseline mean values in each group, compared between groups) favoring the intervention were as follows: HgbA1c, 0.18% (p = 0.006), systolic and diastolic blood pressure, 3.4 (p = 0.001) and 1.9 mm Hg (p < 0.001), and LDL cholesterol, 9.5 mg/dL (p < 0.001). In the subgroup with baseline HgbA1c > or =7%, net adjusted reduction in HgbA1c favoring the intervention group was 0.32% (p = 0.002). Mean LDL cholesterol level in the intervention group at one year was 95.7 mg/dL. The intervention effects were similar in magnitude in the subgroups living in New York City and upstate New York. CONCLUSION: Telemedicine case management improved glycemic control, blood pressure levels, and total and LDL cholesterol levels at one year of follow-up.
Steven Shea, Ruth S. Weinstock, Justin Starren, Jeanne A. Teresi, Walter Palmas, Lesley Field, Philip C. Morin, Robin Goland, Roberto E. Izquierdo, L. Thomas Wolff, Mohammed Ashraf, Charlyn Hilliman, Stephanie Silver, Suzanne Meyer, Douglas Holmes, Eva Petkova, Linnea Capps, Rafael A. Lantigua
 J. Am. Medical Informatics Assoc.3
2005 Assessment of Image-Based Technology: Impact of Referral Cutoff on Accuracy and Reliability of Remote Retinopathy of Prematurity Diagnosis
Michael F. Chiang, Jeremy D. Keenan, Yunling E. Du, William Schiff, Gaetano Barile, Joan Li, Ditte J. Hess, Rose Anne Johnson, John Flynn, Justin Starren
 AMIA10
2005 Web-based Educational Resources for Low Literacy Families in the NICU
Jeungok Choi, Justin Starren, Suzanne Bakken
 AMIA2
2005 Architecture for Remote Training of Home Telemedicine Patients
Albert M. Lai, Justin Starren, Steven Shea
 AMIA2
2005 A Systematic Review of User Interface Issues Related to PDA-based Decision Support Systems in Health Care
Nam-Ju Lee, Justin Starren, Suzanne Bakken
 AMIA2
2005 Developing Computer Skills and Competencies in Seniors
Jenia Pevzner, David R. Kaufman, Charlyn Hilliman, Steven Shea, Ruth S. Weinstock, Justin Starren
 AMIA6
2005 Research Paper: Quantifying Visual Similarity in Clinical Iconic Graphics
abstract
OBJECTIVE: The use of icons and other graphical components in user interfaces has become nearly ubiquitous. The interpretation of such icons is based on the assumption that different users perceive the shapes similarly. At the most basic level, different users must agree on which shapes are similar and which are different. If this similarity can be measured, it may be usable as the basis to design better icons. DESIGN: The purpose of this study was to evaluate a novel method for categorizing the visual similarity of graphical primitives, called Presentation Discovery, in the domain of mammography. Six domain experts were given 50 common textual mammography findings and asked to draw how they would represent those findings graphically. Nondomain experts sorted the resulting graphics into groups based on their visual characteristics. The resulting groups were then analyzed using traditional statistics and hypothesis discovery tools. Strength of agreement was evaluated using computational simulations of sorting behavior. MEASUREMENTS: Sorter agreement was measured at both the individual graphical and concept-group levels using a novel simulation-based method. "Consensus clusters" of graphics were derived using a hierarchical clustering algorithm. RESULTS: The multiple sorters were able to reliably group graphics into similar groups that strongly correlated with underlying domain concepts. Visual inspection of the resulting consensus clusters indicated that graphical primitives that could be informative in the design of icons were present. CONCLUSION: The method described provides a rigorous alternative to intuitive design processes frequently employed in the design of icons and other graphical interface components.
Philip R. O. Payne, Justin Starren
 J. Am. Medical Informatics Assoc.2
2005 Automating Content Extraction of HTML Documents
Suhit Gupta, Gail E. Kaiser, Peter Grimm, Michael F. Chiang, Justin Starren
 World Wide Web5
2003 An Experimental System for Comparing Speed, Accuracy, and Completeness of Physician Data Entry using Electronic and Paper Methods
Michael F. Chiang, Hui Cao 0002, Pallav Sharda, George Hripcsak, Justin Starren
 AMIA5
2003 A Cognitive Framework for Understanding Barriers to the Productive Use of a Diabetes Home Telemedicine System
David R. Kaufman, Justin Starren, Vimla L. Patel, Philip C. Morin, Charlyn Hilliman, Jenia Pevzner, Ruth S. Weinstock, Robin Goland, Steven Shea
 AMIA2
2003 Thin Client Performance for Remote 3-D Image Display
Albert M. Lai, Jason Nieh, Andrew F. Laine, Justin Starren
 AMIA4
2003 Quantifying Visual Similarity in Clinical Iconic Graphics
Justin Starren, Philip R. O. Payne
 AMIA1
2003 Usability in the real world: assessing medical information technologies in patients' homes
David R. Kaufman, Vimla L. Patel, Charlyn Hilliman, Philip C. Morin, Jenia Pevzner, Ruth S. Weinstock, Robin Goland, Steven Shea, Justin Starren
 J. Biomed. Informatics9
2002 Software engineering risk factors in the implementation of a small electronic medical record system: the problem of scalability
Michael F. Chiang, Justin Starren
 AMIA2
2002 Implementation Brief: Desiderata for Personal Electronic Communication in Clinical Systems
abstract
Electronic communication among clinicians and patients is becoming an essential part of medical practice. Evaluation and selection of these electronic systems, called personal clinical electronic communication (PCEC) systems, can be a difficult task in institutions that have no prior experience with such systems. It is particularly difficult in the clinical context. To directly address this point, the authors consulted a group of potential users affiliated with a nationally recognized telemedicine project, to determine important characteristics of a hypothetical PCEC system. They compiled a list of these characteristics and produced a desiderata, or list of desired features, for PCEC systems. Two conventional e-mail implementations and three Web-based PCEC systems were evaluated with respect to the features. The Web-based systems all scored higher than conventional e-mail. It is the hope of the authors that this paper will initiate further discussions about the features of PCEC systems and how to evaluate them.
Indra Neil Sarkar, Justin Starren
 J. Am. Medical Informatics Assoc.2
2002 Research Methods: Columbia University's Informatics for Diabetes Education and Telemedicine (IDEATel) Project: Rationale and Design
abstract
The Columbia University Informatics for Diabetes Education and Telemedicine (IDEATel) Project is a four-year demonstration project funded by the Centers for Medicare and Medicaid Services with the overall goals of evaluating the feasibility, acceptability, effectiveness, and cost-effectiveness of telemedicine in the management of older patients with diabetes. The study is designed as a randomized controlled trial and is being conducted by a state-wide consortium in New York. Eligibility requires that participants have diabetes, are Medicare beneficiaries, and reside in federally designated medically underserved areas. A total of 1,500 participants will be randomized, half in New York City and half in other areas of the state. Intervention participants receive a home telemedicine unit that provides synchronous videoconferencing with a project-based nurse, electronic transmission of home fingerstick glucose and blood pressure data, and Web access to a project Web site. End points include glycosylated hemoglobin, blood pressure, and lipid levels; patient satisfaction; health care service utilization; and costs. The project is intended to provide data to help inform regulatory and reimbursement policies for electronically delivered health care services.
Steven Shea, Justin Starren, Ruth S. Weinstock, Paul E. Knudson, Jeanne A. Teresi, Douglas Holmes, Walter Palmas, Lesley Field, Robin Goland, Catherine Tuck, George Hripcsak, Linnea Capps, David Liss
 J. Am. Medical Informatics Assoc.2
2002 Application of Information Technology: Columbia University's Informatics for Diabetes Education and Telemedicine (IDEATel) Project: Technical Implementation
abstract
The Columbia University Informatics for Diabetes Education and Telemedicine IDEATel) project is a four-year demonstration project funded by the Centers for Medicare and Medicaid Services with the overall goal of evaluating the feasibility, acceptability, effectiveness, and cost-effectiveness of telemedicine. The focal point of the intervention is the home telemedicine unit (HTU), which provides four functions: synchronous videoconferencing over standard telephone lines, electronic transmission for fingerstick glucose and blood pressure readings, secure Web-based messaging and clinical data review, and access to Web-based educational materials. The HTU must be usable by elderly patients with no prior computer experience. Providing these functions through the HTU requires tight integration of six components: the HTU itself, case management software, a clinical information system, Web-based educational material, data security, and networking and telecommunications. These six components were integrated through a variety of interfaces, providing a system that works well for patients and providers. With more than 400 HTUs installed, IDEATel has demonstrated the feasibility of large-scale home telemedicine.
Justin Starren, George Hripcsak, Soumitra Sengupta, C. R. Abbruscato, Paul E. Knudson, Ruth S. Weinstock, Steven Shea
 J. Am. Medical Informatics Assoc.1
2001 Radiology and Medical Informatics Synergy
Donald P. Harrington, Justin Starren
 AMIA2
2001 Making grandma's data secure: a security architecture for home telemedicine
Justin Starren, Soumitra Sengupta, George Hripcsak, G. Ring, R. Klerer, Steven Shea
 AMIA1
2000 Knowledge-driven Highlighting of Clinical Texts: Does it Help or Distract?
Irina Shablinsky, Justin Starren, Carol Friedman
 AMIA2
2000 When seconds are counted: tools for mobile, high-resolution time-motion studies
Justin Starren, Faimah Tahil, Thomas White
 AMIA1
2000 A Web-based, secure, light weight clinical multimedia data capture and display system
Stephen S. Wang, Justin Starren
 AMIA2
2000 Review: An Object-oriented Taxonomy of Medical Data Presentations
abstract
A variety of methods have been proposed for presenting medical data visually on computers.Discussion of and comparison among these methods have been hindered by a lack of consistent terminology.A taxonomy of medical data presentations based on object-oriented user interface principles is presented.Presentations are divided into five major classes-list, table, graph, icon, and generated text.These are subdivided into eight subclasses with simple inheritance and four subclasses with multiple inheritance.The various subclasses are reviewed and examples are provided.Issues critical to the development and evaluation of presentations are also discussed.Ⅲ JAMIA.2000;7:1-20.Many different approaches have been taken to the presentation of medical data on computer screens.Every year more papers describing computer displays of medical data are published.Because different groups often use different terminology to describe similar methods, it is not always clear which of these are new methods, which are incremental improvements on existing methods, and which are existing methods applied to new data types.In addition, no single terminology has been identified that could inclusively address all the data presentation methods
Justin Starren, Stephen B. Johnson
 J. Am. Medical Informatics Assoc.1
1999 Use of the Java Speech API for Development of an Automated Radiology Reporting System
Michael L. Charney, Justin Starren
 AMIA2
1999 Use of the Extensible Stylesheet Language (XSL) for medical data transformation
Yoon-Ho Seol, Stephen B. Johnson, Justin Starren
 AMIA3
1999 What do ER physicians really want? A method for elucidating ER information needs
Irina Shablinsky, Justin Starren, Carol Friedman
 AMIA2
1999 New Dogs for Old Tricks: Finite State Modeling and XML for Access to Legacy Systems
Justin Starren, Stephen B. Johnson
 AMIA1
1999 Decision Modeling for Partial Left Ventriculectomy: An Experimental Alternative to Heart Transplantation for Cardiomyopathy?
Fatimah Ann Tahil, Elizabeth S. Chen, Justin Starren, Suzanne Bakken
 AMIA3
1999 A Java speech implementation of the Mini Mental Status Exam
Stephen S. Wang, Justin Starren
 AMIA2
1999 Time-Motion Timer: A Tool for Rapidly Defining and Running Usability Tests
Thomas White, Fatimah Ann Tahil, Justin Starren
 AMIA3
1998 Costs and benefits of connecting community physicians to a hospital WAN
Angelina Kouroubali, Justin Starren, Paul D. Clayton
 AMIA2
1997 Practical lessons in remote connectivity
Angelina Kouroubali, Justin Starren, Randolph C. Barrows Jr., Paul D. Clayton
 AMIA2
1997 Expressiveness of the Breast Imaging Reporting and Database System (BI-RADS)
Justin Starren, Stephen M. Johnson
 AMIA1