The Experts below are selected from a list of 327 Experts worldwide ranked by ideXlab platform
Jonathan B Kruskal - One of the best experts on this subject based on the ideXlab platform.
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impact of coronavirus disease 2019 covid 19 on the practice of Clinical Radiology
Journal of The American College of Radiology, 2020Co-Authors: Max P Rosen, Jonathan B Kruskal, Alexander Norbash, Carolyn C Meltzer, Judy Yee, James H ThrallAbstract:The speed at which coronavirus disease 2019 (COVID-19) spread quickly fractured the Radiology practice model in ways that were never considered. In March 2020, most practices saw an unprecedented drop in their volume of greater than 50%. The profound changes that have interrupted the arc of the Radiology narrative may substantially dictate how health care and Radiology services are delivered in the future. We examine the impact of COVID-19 on the future of Radiology practice across the following domains: employment, compensation, and practice structure; location and hours of work; workplace environment and safety; activities beyond the "usual scope" of Radiology practice; and CME, national meetings, and professional organizations. Our purpose is to share ideas that can help inform adaptive planning.
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quality initiatives lean approach to improving performance and efficiency in a Radiology department
Radiographics, 2012Co-Authors: Jonathan B Kruskal, Allen W Reedy, Laurie Pascal, Max P Rosen, Phillip M BoiselleAbstract:In anticipation of U.S. healthcare reforms, many Clinical Radiology departments are adopting “lean” principles from automobile manufacturing to improve efficiency, performance, safety, and employee commitment; however, to achieve lasting change, a lean transformation of workplace philosophy and culture is needed.
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application of failure mode and effect analysis in a Radiology department
Radiographics, 2011Co-Authors: Eavan Thornton, Olga R Brook, Mishal Mendirattalala, Donna Hallett, Jonathan B KruskalAbstract:Failure mode and effect analysis permits proactive identification of possible failures in complex processes, such as Clinical Radiology services, and provides a basis for continuous improvement by offering a tool for predicting failures and allowing changes to be implemented to prevent such failures.
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quality initiatives anatomy and pathophysiology of errors occurring in Clinical Radiology practice
Radiographics, 2010Co-Authors: Olga R Brook, Eavan Thornton, Mishal Mendirattalala, Anna Marie Oconnell, Ronald L Eisenberg, Jonathan B KruskalAbstract:The Joint Commission requires development of comprehensive error detection systems that incorporate root cause analyses for all sentinel events. To prevent medical errors from occurring, there is a need for a readily available and easy-to-implement system for detecting, classifying, and managing mistakes. The wide spectrum of interrelated contributing factors makes the classification of errors difficult. Contributors to and causes of radiologic errors can be classified under latent and active failures. Latent failures include technical and system-related failures, with a Radiology-specific subgroup of communication failures that includes documentation, inaccurate or incomplete information, and communication loop failures. Active failures may be ascribed to human failures (more specifically failure of execution of a task, inadequate planning, or behavior-related failures), patient-based failures, and external failures. Classification of an error should also include the impact of the error on the patient, staff, other customers, and Radiology practice. Further considerations should include nonmedical impact of the error, including legal, social, and economic effects on both the patient and the system. Rather than focusing the investigation on blaming individuals for active failures, the primary effort should be to discover latent system failures that can be remedied at a departmental level. Such an error classification system will decrease the likelihood of future errors and diminish their adverse impact.
Yang Huang - One of the best experts on this subject based on the ideXlab platform.
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a novel hybrid approach to automated negation detection in Clinical Radiology reports
Journal of the American Medical Informatics Association, 2007Co-Authors: Yang Huang, Henry J LoweAbstract:Objective: Negation is common in Clinical documents and is an important source of poor precision in automated indexing systems. Previous research has shown that negated terms may be difficult to identify if the words implying negations (negation signals) are more than a few words away from them. We describe a novel hybrid approach, combining regular expression matching with grammatical parsing, to address the above limitation in automatically detecting negations in Clinical Radiology reports. Design: Negations are classified based upon the syntactical categories of negation signals, and negation patterns, using regular expression matching. Negated terms are then located in parse trees using corresponding negation grammar. Measurements: A classification of negations and their corresponding syntactical and lexical patterns were developed through manual inspection of 30 Radiology reports and validated on a set of 470 Radiology reports. Another 120 Radiology reports were randomly selected as the test set on which a modified Delphi design was used by four physicians to construct the gold standard. Results: In the test set of 120 reports, there were a total of 2,976 noun phrases, of which 287 were correctly identified as negated (true positives), along with 23 undetected true negations (false negatives) and 4 mistaken negations (false positives). The hybrid approach identified negated phrases with sensitivity of 92.6% (95% CI 90.9- 93.4%), positive predictive value of 98.6% (95% CI 96.9-99.4%), and specificity of 99.87% (95% CI 99.7-99.9%). Conclusion: This novel hybrid approach can accurately locate negated concepts in Clinical Radiology reports not only when in close proximity to, but also at a distance from, negation signals.
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improved identification of noun phrases in Clinical Radiology reports using a high performance statistical natural language parser augmented with the umls specialist lexicon
Journal of the American Medical Informatics Association, 2005Co-Authors: Yang Huang, Henry J Lowe, Dan Klein, Russell J CucinaAbstract:Objective: The aim of this study was to develop and evaluate a method of extracting noun phrases with full phrase structures from a set of Clinical Radiology reports using natural language processing (NLP) and to investigate the effects of using the UMLS® Specialist Lexicon to improve noun phrase identification within Clinical Radiology documents. Design: The noun phrase identification (NPI) module is composed of a sentence boundary detector, a statistical natural language parser trained on a nonmedical domain, and a noun phrase (NP) tagger. The NPI module processed a set of 100 XML-represented Clinical Radiology reports in Health Level 7 (HL7)® Clinical Document Architecture (CDA)–compatible format. Computed output was compared with manual markups made by four physicians and one author for maximal (longest) NP and those made by one author for base (simple) NP, respectively. An extended lexicon of biomedical terms was created from the UMLS Specialist Lexicon and used to improve NPI performance. Results: The test set was 50 randomly selected reports. The sentence boundary detector achieved 99.0% precision and 98.6% recall. The overall maximal NPI precision and recall were 78.9% and 81.5% before using the UMLS Specialist Lexicon and 82.1% and 84.6% after. The overall base NPI precision and recall were 88.2% and 86.8% before using the UMLS Specialist Lexicon and 93.1% and 92.6% after, reducing false-positives by 31.1% and false-negatives by 34.3%. Conclusion: The sentence boundary detector performs excellently. After the adaptation using the UMLS Specialist Lexicon, the statistical parser's NPI performance on Radiology reports increased to levels comparable to the parser's native performance in its newswire training domain and to that reported by other researchers in the general nonmedical domain.
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a pilot study of contextual umls indexing to improve the precision of concept based representation in xml structured Clinical Radiology reports
Journal of the American Medical Informatics Association, 2003Co-Authors: Yang Huang, Henry J Lowe, William R HershAbstract:Objective: Despite the advantages of structured data entry, much of the patient record is still stored as unstructured or semistructured narrative text. The issue of representing Clinical document content remains problematic. The authors' prior work using an automated UMLS document indexing system has been encouraging but has been affected by the generally low indexing precision of such systems. In an effort to improve precision, the authors have developed a context-sensitive document indexing model to calculate the optimal subset of UMLS source vocabularies used to index each document section. This pilot study was performed to evaluate the utility of this indexing approach on a set of Clinical Radiology reports. Design: A set of Clinical Radiology reports that had been indexed manually using UMLS concept descriptors was indexed automatically by the SAPHIRE indexing engine. Using the data generated by this process the authors developed a system that simulated indexing, at the document section level, of the same document set using many permutations of a subset of the UMLS constituent vocabularies. Measurements: The precision and recall scores generated by simulated indexing for each permutation of two or three UMLS constituent vocabularies were determined. Results: While there was considerable variation in precision and recall values across the different subtypes of Radiology reports, the overall effect of this indexing strategy using the best combination of two or three UMLS constituent vocabularies was an improvement in precision without significant impact of recall. Conclusion: In this pilot study a contextual indexing strategy improved overall precision in a set of Clinical Radiology reports.
Henry J Lowe - One of the best experts on this subject based on the ideXlab platform.
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a novel hybrid approach to automated negation detection in Clinical Radiology reports
Journal of the American Medical Informatics Association, 2007Co-Authors: Yang Huang, Henry J LoweAbstract:Objective: Negation is common in Clinical documents and is an important source of poor precision in automated indexing systems. Previous research has shown that negated terms may be difficult to identify if the words implying negations (negation signals) are more than a few words away from them. We describe a novel hybrid approach, combining regular expression matching with grammatical parsing, to address the above limitation in automatically detecting negations in Clinical Radiology reports. Design: Negations are classified based upon the syntactical categories of negation signals, and negation patterns, using regular expression matching. Negated terms are then located in parse trees using corresponding negation grammar. Measurements: A classification of negations and their corresponding syntactical and lexical patterns were developed through manual inspection of 30 Radiology reports and validated on a set of 470 Radiology reports. Another 120 Radiology reports were randomly selected as the test set on which a modified Delphi design was used by four physicians to construct the gold standard. Results: In the test set of 120 reports, there were a total of 2,976 noun phrases, of which 287 were correctly identified as negated (true positives), along with 23 undetected true negations (false negatives) and 4 mistaken negations (false positives). The hybrid approach identified negated phrases with sensitivity of 92.6% (95% CI 90.9- 93.4%), positive predictive value of 98.6% (95% CI 96.9-99.4%), and specificity of 99.87% (95% CI 99.7-99.9%). Conclusion: This novel hybrid approach can accurately locate negated concepts in Clinical Radiology reports not only when in close proximity to, but also at a distance from, negation signals.
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improved identification of noun phrases in Clinical Radiology reports using a high performance statistical natural language parser augmented with the umls specialist lexicon
Journal of the American Medical Informatics Association, 2005Co-Authors: Yang Huang, Henry J Lowe, Dan Klein, Russell J CucinaAbstract:Objective: The aim of this study was to develop and evaluate a method of extracting noun phrases with full phrase structures from a set of Clinical Radiology reports using natural language processing (NLP) and to investigate the effects of using the UMLS® Specialist Lexicon to improve noun phrase identification within Clinical Radiology documents. Design: The noun phrase identification (NPI) module is composed of a sentence boundary detector, a statistical natural language parser trained on a nonmedical domain, and a noun phrase (NP) tagger. The NPI module processed a set of 100 XML-represented Clinical Radiology reports in Health Level 7 (HL7)® Clinical Document Architecture (CDA)–compatible format. Computed output was compared with manual markups made by four physicians and one author for maximal (longest) NP and those made by one author for base (simple) NP, respectively. An extended lexicon of biomedical terms was created from the UMLS Specialist Lexicon and used to improve NPI performance. Results: The test set was 50 randomly selected reports. The sentence boundary detector achieved 99.0% precision and 98.6% recall. The overall maximal NPI precision and recall were 78.9% and 81.5% before using the UMLS Specialist Lexicon and 82.1% and 84.6% after. The overall base NPI precision and recall were 88.2% and 86.8% before using the UMLS Specialist Lexicon and 93.1% and 92.6% after, reducing false-positives by 31.1% and false-negatives by 34.3%. Conclusion: The sentence boundary detector performs excellently. After the adaptation using the UMLS Specialist Lexicon, the statistical parser's NPI performance on Radiology reports increased to levels comparable to the parser's native performance in its newswire training domain and to that reported by other researchers in the general nonmedical domain.
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a pilot study of contextual umls indexing to improve the precision of concept based representation in xml structured Clinical Radiology reports
Journal of the American Medical Informatics Association, 2003Co-Authors: Yang Huang, Henry J Lowe, William R HershAbstract:Objective: Despite the advantages of structured data entry, much of the patient record is still stored as unstructured or semistructured narrative text. The issue of representing Clinical document content remains problematic. The authors' prior work using an automated UMLS document indexing system has been encouraging but has been affected by the generally low indexing precision of such systems. In an effort to improve precision, the authors have developed a context-sensitive document indexing model to calculate the optimal subset of UMLS source vocabularies used to index each document section. This pilot study was performed to evaluate the utility of this indexing approach on a set of Clinical Radiology reports. Design: A set of Clinical Radiology reports that had been indexed manually using UMLS concept descriptors was indexed automatically by the SAPHIRE indexing engine. Using the data generated by this process the authors developed a system that simulated indexing, at the document section level, of the same document set using many permutations of a subset of the UMLS constituent vocabularies. Measurements: The precision and recall scores generated by simulated indexing for each permutation of two or three UMLS constituent vocabularies were determined. Results: While there was considerable variation in precision and recall values across the different subtypes of Radiology reports, the overall effect of this indexing strategy using the best combination of two or three UMLS constituent vocabularies was an improvement in precision without significant impact of recall. Conclusion: In this pilot study a contextual indexing strategy improved overall precision in a set of Clinical Radiology reports.
Carol Friedman - One of the best experts on this subject based on the ideXlab platform.
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a schema for representing medical language applied to Clinical Radiology
Journal of the American Medical Informatics Association, 1994Co-Authors: Carol Friedman, James J. Cimino, Stephen B. JohnsonAbstract:Objective : Develop a representational schema for Clinical concepts and apply it to the task of encoding Radiology reports of the chest. Design : The schema was developed following a manual analysis of sample reports from the domain. The schema has two main components: the Medical Entities Dictionary (MED), which specifies the formal representation of the concepts in the domain and of their structures, and the natural-language processor, which specifies the linguistic expressions of the concepts. The schema was evaluated by applying it to a test set of 7,500 reports. Two-hundred reports from the test set were manually analyzed by a medical expert to determine the accuracy and success rate of the system. Results : 82% of the 7,500 reports that contained relevant Clinical information were successfully structured automatically. For the smaller set of 200 reports, 80% were structured successfully with an accuracy rate of 97%. Conclusions : The schema is a formal representation for Clinical concepts in Radiology reports, and provides domain coverage that is particularly well-suited for natural-language processing of Radiology for use in a decision support system.
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a general natural language text processor for Clinical Radiology
Journal of the American Medical Informatics Association, 1994Co-Authors: Carol Friedman, James J. Cimino, Philip O Alderson, John H M Austin, Stephen B. JohnsonAbstract:Objective : Development of a general natural-language processor that identifies Clinical information in narrative reports and maps that information into a structured representation containing Clinical terms. Design : The natural-language processor provides three phases of processing, all of which are driven by different knowledge sources. The first phase performs the parsing. It identifies the structure of the text through use of a grammar that defines semantic patterns and a target form. The second phase, regularization, standardizes the terms in the initial target structure via a compositional mapping of multi-word phrases. The third phase, encoding, maps the terms to a controlled vocabulary. Radiology is the test domain for the processor and the target structure is a formal model for representing Clinical information in that domain. Measurements : The impression sections of 230 Radiology reports were encoded by the processor. Results of an automated query of the resultant database for the occurrences of four diseases were compared with the analysis of a panel of three physicians to determine recall and precision. Results : Without training specific to the four diseases, recall and precision of the system(combined effect of the processor and query generator) were 70% and 87%. Training of the query component increased recall to 85% without changing precision.
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A conceptual model for Clinical Radiology reports.
Proceedings. Symposium on Computer Applications in Medical Care, 1993Co-Authors: Carol Friedman, James J. Cimino, Stephen B. JohnsonAbstract:Abstract The structural and informational content of Clinical Radiology reports was examined to develop a comprehensive representational schema of the concepts in the domain. The model involves several different conceptual levels, ranging from the high level description of the report to the lower level description of the Clinical concepts contained in the reports and the specification of the terms used to express the concepts. The design of an adequate structured representation for the domain has important implications for the design of the electronic patient record, for the unification of different controlled vocabularies by enabling them to be mapped to one common representation, and for the facilitation of natural language processing of Clinical reports so that coded data may be obtained.
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The UMLS coverage of Clinical Radiology.
Proceedings. Symposium on Computer Applications in Medical Care, 1992Co-Authors: Carol FriedmanAbstract:The informational content of Clinical Radiology reports was examined to determine the coverage of the Unified Medical Language System (UMLS) in relation to the terminology used by physicians in the Radiology Department of Columbia Presbyterian Medical Center (CPMC). The UMLS semantic network contained 17 semantic types which were compatible with the types of Clinical information in the reports. The type of semantic categories missing from the UMLS consisted mainly of modifier information relating to certainty, degree, and change type of information. This type of information formed a substantial part of the domain. Although most of the informational categories were found in the UMLS semantic network, most of the domain terms were not. Our results strongly suggest that the UMLS could be a significant tool for developing Clinical text processing applications if it were extended to cover Clinical domains.
Magnus Ath - One of the best experts on this subject based on the ideXlab platform.
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multi institutional evaluation of digital tomosynthesis dual energy radiography and conventional chest radiography for the detection and management of pulmonary nodules
Radiology, 2017Co-Authors: James T Dobbins, Page H Mcadams, Joh M Sabol, Dev P Chakraborty, Ella A Kazerooni, Gautham P Reddy, Jenny Vikgre, Magnus AthAbstract:Tomosynthesis, when used in a Clinical Radiology setting with both thoracic and nonthoracic specialists, can improve detection of pulmonary nodules and case management decisions when compared with conventional chest radiography.