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Large-scale evaluation of automated clinical note de-identification and its impact on information extraction

JOURNAL OF THE AMERICAN MEDICAL INFORMATICS ASSOCIATION 2013年第1期20卷 84-94页

作者： Deleger, Louise Molnar, Katalin Savova, Guergana Xia, Fei Lingren, Todd Li, Qi Marsolo, Keith Jegga, Anil Kaiser, Megan Stoutenborough, Laura Solti, Imre Cincinnati Childrens Hosp Med Ctr Div Biomed Informat Cincinnati OH 45229 USA Childrens Hosp Boston Informat Program Boston MA USA Harvard Univ Sch Med Boston MA USA Univ Washington Dept Linguist Seattle WA 98195 USA

Objective (1) To evaluate a state-of-the-art natural language processing (NLP)-based approach to automatically de-identify a large set of diverse clinical notes. (2) To measure the impact of de-identification on the performance of information extraction algorithms on the de-identified documents. Material and methods A cross-sectional study that included 3503 stratified, randomly selected clinical notes (over 22 note types) from five million documents produced at one of the largest US pediatric hospitals. Sensitivity, precision, F value of two automated de-identification systems for removing all 18 HIPAA-defined protected health information elements were computed. Performance was assessed against a manually generated 'gold standard'. Statistical significance was tested. The automated de-identification performance was also compared with that of two humans on a 10% subsample of the gold standard. The effect of de-identification on the performance of subsequent medication extraction was measured. Results The gold standard included 30 815 protected health information elements and more than one million tokens. The most accurate NLP method had 91.92% sensitivity (R) and 95.08% precision (P) overall. The performance of the system was indistinguishable from that of human annotators (annotators' performance was 92.15%(R)/93.95%(P) and 94.55%(R)/88.45%(P) overall while the best system obtained 92.91%(R)/95.73%(P) on same text). The impact of automated de-identification was minimal on the utility of the narrative notes for subsequent information extraction as measured by the sensitivity and precision of medication name extraction. Discussion and conclusion NLP-based de-identification shows excellent performance that rivals the performance of human annotators. Furthermore, unlike manual de-identification, the automated approach scales up to millions of documents quickly and inexpensively.

关键词： Natural language processing privacy of patient data health insurance portability and accountability act automated de-identification protected health information NLP text mining human-computer interaction and human-centered computing providing just-in-time access to the biomedical literature and other health information applications that link biomedical knowledge from diverse primary sources (includes automated indexing) linking the genotype and phenotype discovery bionlp medical informatics biomedical informatics disease networks translational medicine drug repositioning rare diseases

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A system for coreference resolution for the clinical narrative

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JOURNAL OF THE AMERICAN MEDICAL INFORMATICS ASSOCIATION 2012年第4期19卷 660-667页

作者： Zheng, Jiaping Chapman, Wendy W. Miller, Timothy A. Lin, Chen Crowley, Rebecca S. Savova, Guergana K. Harvard Univ Sch Med Childrens Hosp Boston Informat Program Boston MA 02114 USA Univ Calif San Diego Div Biomed Informat San Diego CA 92103 USA Univ Pittsburgh Dept Biomed Informat Pittsburgh PA USA

Objective To research computational methods for coreference resolution in the clinical narrative and build a system implementing the best methods. Methods The Ontology Development and Information Extraction corpus annotated for coreference relations consists of 7214 coreferential markables, forming 5992 pairs and 1304 chains. We trained classifiers with semantic, syntactic, and surface features pruned by feature selection. For the three system components-for the resolution of relative pronouns, personal pronouns, and noun phrases-we experimented with support vector machines with linear and radial basis function (RBF) kernels, decision trees, and perceptrons. Evaluation of algorithms and varied feature sets was performed using standard metrics. Results The best performing combination is support vector machines with an RBF kernel and all features (MUC score=0.352, B-3=0.690, CEAF=0.486, BLANC=0.596) outperforming a traditional decision tree baseline. Discussion The application showed good performance similar to performance on general English text. The main error source was sentence distances exceeding a window of 10 sentences between markables. A possible solution to this problem is hinted at by the fact that coreferent markables sometimes occurred in predictable (although distant) note sections. Another system limitation is failure to fully utilize synonymy and ontological knowledge. Future work will investigate additional ways to incorporate syntactic features into the coreference problem. Conclusion We investigated computational methods for coreference resolution in the clinical narrative. The best methods are released as modules of the open source Clinical Text Analysis and Knowledge Extraction System and Ontology Development and Information Extraction platforms.

关键词： Coreference resolution natural language processing biomedical informatics information extraction nlp machine learning human-computer interaction and human-centered computing Intelligent tutoring and tailored information representation Improving the education and skills training of health professionals providing just-in-time access to the biomedical literature and other health information applications that link biomedical knowledge from diverse primary sources (includes automated indexing) linking the genotype and phenotype discovery

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The use of count data models in biomedical informatics evaluation research

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JOURNAL OF THE AMERICAN MEDICAL INFORMATICS ASSOCIATION 2012年第1期19卷 39-44页

作者： Du, Jing Park, Young-Taek Theera-Ampornpunt, Nawanan McCullough, Jeffrey S. Speedie, Stuart M. Univ Minnesota Div Hlth Policy & Management Minneapolis MN 55455 USA Univ Minnesota Inst Hlth Informat Minneapolis MN 55455 USA

Objectives Studies on the impact and value of health information technology (HIT) have often focused on outcome measures that are counts of such things as hospital admissions or the number of laboratory tests per patient. These measures with their highly skewed distributions (high frequency of 0s and 1s) are more appropriately analyzed with count data models than the much more frequently used variations of ordinary least squares (OLS). Use of a statistical procedure that does not properly fit the distribution of the data can result in significant findings being overlooked. The objective of this paper is to encourage greater use of count data models by demonstrating their utility with an example based on the authors' current work. Target audience Researchers conducting impact and outcome studies related to HIT. Scope We review and discuss count data models and illustrate their value in comparison to OLS using an example from a study of the impact of an electronic health record (EHR) on laboratory test orders. The best count data model reveals significant relationships that OLS does not detect. We conclude that comprehensive model checking is highly recommended to identify the most appropriate analytic model when the dependent variable being examined contains count data. This strategy can lead to more valid and precise findings in HIT evaluation studies.

关键词： Poisson regression negative binomial regression hurdle regression zero-inflated regression count data model phone number YT health IT adoption people and organizational issues measuring/improving patient safety and reducing medical errors classical experimental and quasi-experimental study methods (lab and field) methods for integration of information from disparate sources supporting practice at a distance (telehealth) data models data exchange communication and integration across care settings (inter- and intra-enterprise) human-computer interaction and human-centered computing

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The role of complementary bipartite visual analytical representations in the analysis of SNPs: a case study in ancestral informative markers

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JOURNAL OF THE AMERICAN MEDICAL INFORMATICS ASSOCIATION 2012年第E1期19卷 E5-E12页

作者： Bhavnani, Suresh K. Bellala, Gowtham Victor, Sundar Bassler, Kevin E. Visweswaran, Shyam Univ Texas Med Branch Inst Translat Sci Galveston TX 77555 USA Univ Michigan Dept Elect Engn & Comp Sci Ann Arbor MI 48109 USA Univ Houston Dept Phys Houston TX USA Univ Houston Texas Ctr Superconduct Houston TX USA Univ Pittsburgh Dept Biomed Informat Pittsburgh PA USA Univ Pittsburgh Intelligent Syst Program Pittsburgh PA USA

Objective Several studies have shown how sets of single-nucleotide polymorphisms (SNPs) can help to classify subjects on the basis of their continental origins, with applications to case-control studies and population genetics. However, most of these studies use dimensionality-reduction methods, such as principal component analysis, or clustering methods that result in unipartite (either subjects or SNPs) representations of the data. Such analyses conceal important bipartite relationships, such as how subject and SNP clusters relate to each other, and the genotypes that determine their cluster memberships. Methods To overcome the limitations of current methods of analyzing SNP data, the authors used three bipartite analytical representations (bipartite network, heat map with dendrograms, and Circos ideogram) that enable the simultaneous visualization and analysis of subjects, SNPs, and subject attributes. Results The results demonstrate (1) novel insights into SNP data that are difficult to derive from purely unipartite views of the data, (2) the strengths and limitations of each method, revealing the role that each play in revealing novel insights, and (3) implications for how the methods can be used for the analysis of SNPs in genomic studies associated with disease. Conclusion The results suggest that bipartite representations can reveal new patterns in SNP data compared with existing unipartite representations. However, the novel insights require multiple representations to discover, verify, and comprehend the complex relationships. The results therefore motivate the need for a complementary visual analytical framework that guides the use of multiple bipartite representations to analyze complex relationships in SNP data.

关键词： Visualization of data and knowledge human-computer interaction and human-centered computing networks systems to support and improve diagnostic accuracy linking the genotype and phenotype discovery text and data mining methods uncertain reasoning and decision theory

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Method of electronic health record documentation and quality of primary care

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JOURNAL OF THE AMERICAN MEDICAL INFORMATICS ASSOCIATION 2012年第6期19卷 1019-1024页

作者： Linder, Jeffrey A. Schnipper, Jeffrey L. Middleton, Blackford Brigham & Womens Hosp Div Gen Med & Primary Care Boston MA 02120 USA Harvard Univ Sch Med Brigham & Womens Hosp BWH Hosp Serv Boston MA USA Partners HealthCare Syst Clin Informat Res & Dev Boston MA USA

Objective Physicians who more intensively interact with electronic health records (EHRs) through their documentation style may pay greater attention to coded fields and clinical decision support and thus may deliver higher quality care. We measured the quality of care of physicians who used three predominating EH documentation styles: dictation, structured documentation, and free text. Methods We conducted a retrospective analysis of visits by patients with coronary artery disease and diabetes to the Partners Primary Care Practice Based Research Network. The main outcome measures were 15 EHR-based coronary artery disease and diabetes measures assessed 30 days after primary care visits. Results During the 9-month study period, 7000 coronary artery disease and diabetes patients made 18 569 visits to 234 primary care physicians of whom 20 (9%) predominantly dictated their notes, 68 (29%) predominantly used structured documentation, and 146 (62%) predominantly typed free text notes. In multivariable modeling adjusted for clustering by patient and physician, quality of care appeared significantly worse for dictators than for physicians using the other two documentation styles on three of 15 measures (antiplatelet medication, tobacco use documentation, and diabetic eye exam);better for structured documenters for three measures (blood pressure documentation, body mass index documentation, and diabetic foot exam);and better for free text documenters on one measure (influenza vaccination). There was no measure for which dictators had higher quality of care than physicians using the other two documentation styles. Conclusions EHR-assessed quality is necessarily documentation-dependent, but physicians who dictated their notes appeared to have worse quality of care than physicians who used structured EHR documentation.

关键词： Medical record systems computerized quality of healthcare physicians primary care human-computer interaction and human-centered computing developing/using clinical decision support (other than diagnostic) and guideline systems other specific EHR applications (results review) medication administration disease progression quality improvement patient safety clinical decision support hospital medicine uncertain reasoning and decision theory knowledge representations designing usable (responsive) resources and systems knowledge acquisition and knowledge management demonstrating return on IT investment

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The impact of electronic health records on care of heart failure patients in the emergency room

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JOURNAL OF THE AMERICAN MEDICAL INFORMATICS ASSOCIATION 2012年第3期19卷 334-340页

作者： Connelly, Donald P. Park, Young-Taek Du, Jing Theera-Ampornpunt, Nawanan Gordon, Bradley D. Bershow, Barry A. Gensinger, Raymond A., Jr. Shrift, Michael Routhe, Daniel T. Speedie, Stuart M. Univ Minnesota Dept Lab Med & Pathol Minneapolis MN 55455 USA Univ Minnesota Inst Hlth Informat Minneapolis MN 55455 USA Hlth Insurance Review & Assessment Serv Div Healthcare Finance Anal & Res Seoul South Korea Univ Minnesota Sch Publ Hlth Div Hlth Policy & Management Minneapolis MN 55455 USA Mahidol Univ Ramathibodi Hosp Fac Med Hlth Informat Div Bangkok 10400 Thailand Reg Hosp Dept Emergency Med St Paul MN USA HealthPartners Med Grp & Clin St Paul MN USA Bershow Consulting LLC Chanhassen MN USA Fairview Hlth Serv Corp Serv Minneapolis MN USA Univ Minnesota Dept Med Minneapolis MN 55455 USA Univ Minnesota Dept Nursing Minneapolis MN 55455 USA Allina Hosp & Clin Minneapolis MN USA

Objective To evaluate if electronic health records (EHR) have observable effects on care outcomes, we examined quality and efficiency measures for patients presenting to emergency departments (ED). Materials and methods We conducted a retrospective study of 5166 adults with heart failure in three metropolitan EDs. Patients were termed internal if prior information was in the EHR upon ED presentation, otherwise external. Associations of internality with hospitalization, mortality, length of stay (LOS), and numbers of tests, procedures, and medications ordered in the ED were examined after adjusting for age, gender, race, marital status, comorbidities and hospitalization as a proxy for acuity level where appropriate. Results At two EDs internals had lower odds of mortality if hospitalized (OR 0.55;95% CI 0.38 to 0.81 and 0.45;0.21 to 0.96), fewer laboratory tests during the ED visit (-4.6%;-8.9% to -0.1% and -14.0%;-19.5% to -8.1%) as well as fewer medications (-33.6%;-38.4% to -28.4% and -21.3%;-33.2% to -7.3%). At one of these two EDs, internals had lower odds of hospitalization (0.37;0.22 to 0.60). At the third ED, internal patients only experienced a prolonged ED LOS (32.3%;6.3% to 64.8%) but no other differences. There was no association with hospital LOS or number of procedures ordered. Discussion EHR availability was associated with salutary outcomes in two of three ED settings and prolongation of ED LOS at a third, but evidence was mixed and causality remains to be determined. Conclusions An EHR may have the potential to be a valuable adjunct in the care of heart failure patients.

关键词： Electronic health records emergency department evaluation heart failure clinical outcomes YT health IT adoption people and organizational issues emergency medicine medical informatics measuring/improving patient safety and reducing medical errors classical experimental and quasi-experimental study methods (lab and field) methods for integration of information from disparate sources supporting practice at a distance (telehealth) data models data exchange communication aintegration across care settings (inter- and intra-enterprise) human-computer interaction and human-centered computing

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Automated extraction of ejection fraction for quality measurement using regular expressions in Unstructured Information Management Architecture (UIMA) for heart failure

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JOURNAL OF THE AMERICAN MEDICAL INFORMATICS ASSOCIATION 2012年第5期19卷 859-866页

作者： Garvin, Jennifer H. DuVall, Scott L. South, Brett R. Bray, Bruce E. Bolton, Daniel Heavirland, Julia Pickard, Steve Heidenreich, Paul Shen, Shuying Weir, Charlene Samore, Matthew Goldstein, Mary K. SLC VA Healthcare Syst IDEAS Ctr Salt Lake City UT USA Univ Utah Sch Med Div Epidemiol Salt Lake City UT USA Univ Utah Sch Med Dept Biomed Informat Salt Lake City UT USA VA Palo Alto Hlth Care Syst Palo Alto CA USA Stanford Univ Ctr Primary Care & Outcomes Res PCOR Stanford CA 94305 USA SLC VA Healthcare Syst GRECC Salt Lake City UT USA

Objectives Left ventricular ejection fraction (EF) is a key component of heart failure quality measures used within the Department of Veteran Affairs (VA). Our goals were to build a natural language processing system to extract the EF from free-text echocardiogram reports to automate measurement reporting and to validate the accuracy of the system using a comparison reference standard developed through human review. This project was a Translational Use Case Project within the VA Consortium for Healthcare Informatics. Materials and methods We created a set of regular expressions and rules to capture the EF using a random sample of 765 echocardiograms from seven VA medical centers. The documents were randomly assigned to two sets: a set of 275 used for training and a second set of 490 used for testing and validation. To establish the reference standard, two independent reviewers annotated all documents in both sets;a third reviewer adjudicated disagreements. Results System test results for document-level classification of EF of <40% had a sensitivity (recall) of 98.41%, a specificity of 100%, a positive predictive value (precision) of 100%, and an F measure of 99.2%. System test results at the concept level had a sensitivity of 88.9% (95% CI 87.7% to 90.0%), a positive predictive value of 95% (95% CI 94.2% to 95.9%), and an F measure of 91.9% (95% CI 91.2% to 92.7%). Discussion An EF value of <40% can be accurately identified in VA echocardiogram reports. Conclusions An automated information extraction system can be used to accurately extract EF for quality measurement.

关键词： Natural language processing (NLP) heart failure left ventricular ejection fraction (EF) Improving healthcare workflow and process efficiency applied informatics Improving government and community policy relevant to informatics and health quality process modeling and hypothesis generation Informatics Enhancing the conduct of biological/clinical research and trials applications that link biomedical knowledge from diverse primary sources (includes automated indexing) visualization of data and knowledge uncertain reasoning and decision theory languages and computational methods statistical analysis of large datasets advanced algorithms discovery and text and data mining methods other methods of information extraction automated learning human-computer interaction and human-centered computing cognitive study (including experiments emphasizing verbal protocol analysis and usability) knowledge representations knowledge acquisition and knowledge management delivering health information and knowledge to the public processing and display analysis image representation controlled terminologies and vocabularies ontologies knowledge bases ejection fraction machine learning simulation of complex systems (at all levels: molecules to work groups to organizations) developing/using clinical decision support (other than diagnostic) and guideline systems detecting disease outbreaks and biological threats

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Clinical documentation: composition or synthesis?

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JOURNAL OF THE AMERICAN MEDICAL INFORMATICS ASSOCIATION 2012年第6期19卷 1025-1031页

作者： Mamykina, Lena Vawdrey, David K. Stetson, Peter D. Zheng, Kai Hripcsak, George Columbia Univ Dept Biomed Informat New York NY 10032 USA Univ Michigan Sch Informat Sch Publ Hlth Dept Hlth Management & Policy Ann Arbor MI 48109 USA

Objective To understand the nature of emerging electronic documentation practices, disconnects between documentation workflows and computing systems designed to support them, and ways to improve the design of electronic documentation systems. Materials and methods Time-and-motion study of resident physicians' note-writing practices using a commercial electronic health record system that includes an electronic documentation module. The study was conducted in the general medicine unit of a large academic hospital. Results During the study, 96 note-writing sessions by 11 resident physicians, resulting in close to 100 h of observations were seen. Seven of the 10 most common transitions between activities during note composition were between documenting, and gathering and reviewing patient data, and updating the plan of care. Discussion The high frequency of transitions seen in the study suggested that clinical documentation is fundamentally a synthesis activity, in which clinicians review available patient data and summarize their impressions and judgments. At the same time, most electronic health record systems are optimized to support documentation as uninterrupted composition. This mismatch leads to fragmentation in clinical work, and results in inefficiencies and workarounds. In contrast, we propose that documentation can be best supported with tools that facilitate data exploration and search for relevant information, selective reading and annotation, and composition of a note as a temporal structure. Conclusions Time-and-motion study of clinicians' electronic documentation practices revealed a high level of fragmentation of documentation activities and frequent task transitions. Treating documentation as synthesis rather than composition suggests new possibilities for supporting it more effectively with electronic systems.

关键词： Electronic documentation time and motion studies (F02.784.412.846.707) electronic health records (E05.318.308.940.968.625.500) workflow (L01.906.893) knowledge bases Ontologies human-computer interaction and human-centered computing delivering health information and knowledge to the public knowledge acquisition and knowledge management developing/using computerized provider order entry communications networking methods mobility developing/using clinical decision support (other than diagnostic) and guideline systems collaborative technologies personal health records and self-care systems systems supporting patient-provider interaction data mining

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The Triangle Model for evaluating the effect of health information technology on healthcare quality and safety

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JOURNAL OF THE AMERICAN MEDICAL INFORMATICS ASSOCIATION 2012年第1期19卷 61-65页

作者： Ancker, Jessica S. Kern, Lisa M. Abramson, Erika Kaushal, Rainu Weill Cornell Med Coll Dept Pediat New York NY 10065 USA Weill Cornell Med Coll Dept Publ Hlth New York NY 10065 USA HITEC New York NY USA Weill Cornell Med Coll Dept Med New York NY 10065 USA New York Presbyterian Hosp New York NY USA

With the proliferation of relatively mature health information technology (IT) systems with large numbers of users, it becomes increasingly important to evaluate the effect of these systems on the quality and safety of healthcare. Previous research on the effectiveness of health IT has had mixed results, which may be in part attributable to the evaluation frameworks used. The authors propose a model for evaluation, the Triangle Model, developed for designing studies of quality and safety outcomes of health IT. This model identifies structure-level predictors, including characteristics of: (1) the technology itself;(2) the provider using the technology;(3) the organizational setting;and (4) the patient population. In addition, the model outlines process predictors, including (1) usage of the technology, (2) organizational support for and customization of the technology, and (3) organizational policies and procedures about quality and safety. The Triangle Model specifies the variables to be measured, but is flexible enough to accommodate both qualitative and quantitative approaches to capturing them. The authors illustrate this model, which integrates perspectives from both health services research and biomedical informatics, with examples from evaluations of electronic prescribing, but it is also applicable to a variety of types of health IT systems.

关键词： Evaluation studies research design quality of healthcare medical errors medical informatics applications cognitive study (including experiments emphasizing verbal protocol analysis and usability) classical experimental and quasi-experimental study methods (lab and field) uncertain reasoning and decision theory delivering health information and knowledge to the public human-computer interaction and human-centered computing quality of care measuring/improving patient safety and reducing medical errors

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Automated discovery of drug treatment patterns for endocrine therapy of breast cancer within an electronic medical record

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JOURNAL OF THE AMERICAN MEDICAL INFORMATICS ASSOCIATION 2012年第E1期19卷 E83-E89页

作者： Savova, Guergana K. Olson, Janet E. Murphy, Sean P. Cafourek, Victoria L. Couch, Fergus J. Goetz, Matthew P. Ingle, James N. Suman, Vera J. Chute, Christopher G. Weinshilboum, Richard M. Mayo Clin Rochester MN USA Childrens Hosp Boston Boston MA 02115 USA Harvard Univ Sch Med Boston MA 02115 USA

Objective To develop an algorithm for the discovery of drug treatment patterns for endocrine breast cancer therapy within an electronic medical record and to test the hypothesis that information extracted using it is comparable to the information found by traditional methods. Materials The electronic medical charts of 1507 patients diagnosed with histologically confirmed primary invasive breast cancer. Methods The automatic drug treatment classification tool consisted of components for: (1) extraction of drug treatment-relevant information from clinical narratives using natural language processing (clinical Text Analysis and Knowledge Extraction System);(2) extraction of drug treatment data from an electronic prescribing system;(3) merging information to create a patient treatment timeline;and (4) final classification logic. Results Agreement between results from the algorithm and from a nurse abstractor is measured for categories: (0) no tamoxifen or aromatase inhibitor (AI) treatment;(1) tamoxifen only;(2) AI only;(3) tamoxifen before AI;(4) AI before tamoxifen;(5) multiple AIs and tamoxifen cycles in no specific order;and (6) no specific treatment dates. Specificity (all categories): 96.14%-100%;sensitivity (categories (0)-(4)): 90.27%-99.83%;sensitivity (categories (5)-(6)): 0-23.53%;positive predictive values: 80%-97.38%;negative predictive values: 96.91%-99.93%. Discussion Our approach illustrates a secondary use of the electronic medical record. The main challenge is event temporality. Conclusion We present an algorithm for automated treatment classification within an electronic medical record to combine information extracted through natural language processing with that extracted from structured databases. The algorithm has high specificity for all categories, high sensitivity for five categories, and low sensitivity for two categories.

关键词： Clinical natural language processing electronic medical record phenotype extraction endocrine therapy breast cancer treatment human-computer interaction and human-centered computing providing just-in-time access to the biomedical literature and other health information applications that link biomedical knowledge from diverse primary sources (includes automated indexing) linking the genotype and phenotype discovery NLP asdf d pet biostatistics ontologies knowledge representations controlled terminologies and vocabularies information retrieval HIT data standards pharmacogenomics

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