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Smashing the strict hierarchy: three cases of clinical decision support malfunctions involving carvedilol.
Wright A, Wright AP, Aaron S, Sittig DF
(2018) J Am Med Inform Assoc 25: 1552-1555
MeSH Terms: Adrenergic alpha-1 Receptor Antagonists, Adrenergic beta-Antagonists, Carvedilol, Decision Support Systems, Clinical, Electronic Health Records, Humans, Knowledge Management, Medication Errors, Terminology as Topic, Vocabulary, Controlled
Show Abstract · Added November 7, 2019
Clinical vocabularies allow for standard representation of clinical concepts, and can also contain knowledge structures, such as hierarchy, that facilitate the creation of maintainable and accurate clinical decision support (CDS). A key architectural feature of clinical hierarchies is how they handle parent-child relationships - specifically whether hierarchies are strict hierarchies (allowing a single parent per concept) or polyhierarchies (allowing multiple parents per concept). These structures handle subsumption relationships (ie, ancestor and descendant relationships) differently. In this paper, we describe three real-world malfunctions of clinical decision support related to incorrect assumptions about subsumption checking for β-blocker, specifically carvedilol, a non-selective β-blocker that also has α-blocker activity. We recommend that 1) CDS implementers should learn about the limitations of terminologies, hierarchies, and classification, 2) CDS implementers should thoroughly test CDS, with a focus on special or unusual cases, 3) CDS implementers should monitor feedback from users, and 4) electronic health record (EHR) and clinical content developers should offer and support polyhierarchical clinical terminologies, especially for medications.
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10 MeSH Terms
CATEGORICAL DIAGNOSIS OF EXTREME HYPERACTIVITY, IMPULSIVITY, AND INATTENTION IN VERY YOUNG CHILDREN.
Gleason MM, Humphreys KL
(2016) Infant Ment Health J 37: 476-85
MeSH Terms: Attention Deficit Disorder with Hyperactivity, Child, Child, Preschool, Humans, Impulsive Behavior, Infant, Vocabulary, Controlled
Show Abstract · Added March 3, 2020
Severe hyperactivity and impulsivity are common reasons for referral to infant mental health services. Past versions of ZERO TO THREE's () diagnostic nosology, the Diagnostic Classification of Mental and Developmental Disorders in Infancy and Early Childhood (DC:0-3), did not address this clinical issue because it had been addressed in other nosologies. These general diagnostic nosologies describe attention deficit hyperactivity disorder (ADHD), but with little attention to developmentally specific aspects of the diagnosis in very young children. Categorical diagnosis related to hyperactivity and impulsivity in very young children warrants careful review of existing literature. Explicit attention must be paid to ensure that categorical diagnoses serve to describe syndromes that cause significant impairment to the family to allow children and families to access effective supports and ensure that behaviors typical of the developmental level are not described as pathologic. This article reviews proposed diagnostic criteria for ADHD and overactivity disorder of toddlerhood as well as the rationale for the criteria and evidence supporting validity and reliability of the diagnoses in very young children. Clinical implications also are presented.
© 2016 Michigan Association for Infant Mental Health.
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MeSH Terms
A Standards-based Semantic Metadata Repository to Support EHR-driven Phenotype Authoring and Execution.
Jiang G, Solbrig HR, Kiefer R, Rasmussen LV, Mo H, Speltz P, Thompson WK, Denny JC, Chute CG, Pathak J
(2015) Stud Health Technol Inform 216: 1098
MeSH Terms: Databases, Factual, Electronic Health Records, Guidelines as Topic, Health Level Seven, Medical Record Linkage, Natural Language Processing, Semantics, United States, Vocabulary, Controlled
Show Abstract · Added March 14, 2018
This study describes our efforts in developing a standards-based semantic metadata repository for supporting electronic health record (EHR)-driven phenotype authoring and execution. Our system comprises three layers: 1) a semantic data element repository layer; 2) a semantic services layer; and 3) a phenotype application layer. In a prototype implementation, we developed the repository and services through integrating the data elements from both Quality Data Model (QDM) and HL7 Fast Healthcare Inteoroperability Resources (FHIR) models. We discuss the modeling challenges and the potential of our system to support EHR phenotype authoring and execution applications.
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9 MeSH Terms
Building bridges across electronic health record systems through inferred phenotypic topics.
Chen Y, Ghosh J, Bejan CA, Gunter CA, Gupta S, Kho A, Liebovitz D, Sun J, Denny J, Malin B
(2015) J Biomed Inform 55: 82-93
MeSH Terms: Electronic Health Records, Information Storage and Retrieval, Machine Learning, Medical Record Linkage, Natural Language Processing, Phenotype, United States, Vocabulary, Controlled
Show Abstract · Added April 10, 2018
OBJECTIVE - Data in electronic health records (EHRs) is being increasingly leveraged for secondary uses, ranging from biomedical association studies to comparative effectiveness. To perform studies at scale and transfer knowledge from one institution to another in a meaningful way, we need to harmonize the phenotypes in such systems. Traditionally, this has been accomplished through expert specification of phenotypes via standardized terminologies, such as billing codes. However, this approach may be biased by the experience and expectations of the experts, as well as the vocabulary used to describe such patients. The goal of this work is to develop a data-driven strategy to (1) infer phenotypic topics within patient populations and (2) assess the degree to which such topics facilitate a mapping across populations in disparate healthcare systems.
METHODS - We adapt a generative topic modeling strategy, based on latent Dirichlet allocation, to infer phenotypic topics. We utilize a variance analysis to assess the projection of a patient population from one healthcare system onto the topics learned from another system. The consistency of learned phenotypic topics was evaluated using (1) the similarity of topics, (2) the stability of a patient population across topics, and (3) the transferability of a topic across sites. We evaluated our approaches using four months of inpatient data from two geographically distinct healthcare systems: (1) Northwestern Memorial Hospital (NMH) and (2) Vanderbilt University Medical Center (VUMC).
RESULTS - The method learned 25 phenotypic topics from each healthcare system. The average cosine similarity between matched topics across the two sites was 0.39, a remarkably high value given the very high dimensionality of the feature space. The average stability of VUMC and NMH patients across the topics of two sites was 0.988 and 0.812, respectively, as measured by the Pearson correlation coefficient. Also the VUMC and NMH topics have smaller variance of characterizing patient population of two sites than standard clinical terminologies (e.g., ICD9), suggesting they may be more reliably transferred across hospital systems.
CONCLUSIONS - Phenotypic topics learned from EHR data can be more stable and transferable than billing codes for characterizing the general status of a patient population. This suggests that EHR-based research may be able to leverage such phenotypic topics as variables when pooling patient populations in predictive models.
Copyright © 2015 Elsevier Inc. All rights reserved.
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Analyzing differences between chinese and english clinical text: a cross-institution comparison of discharge summaries in two languages.
Wu Y, Lei J, Wei WQ, Tang B, Denny JC, Rosenbloom ST, Miller RA, Giuse DA, Zheng K, Xu H
(2013) Stud Health Technol Inform 192: 662-6
MeSH Terms: China, Documentation, Electronic Health Records, Medical Record Linkage, Natural Language Processing, Patient Discharge Summaries, Semantics, Translating, United States, Vocabulary, Controlled
Show Abstract · Added May 27, 2014
Worldwide adoption of Electronic Medical Records (EMRs) databases in health care have generated an unprecedented amount of clinical data available electronically. There has been an increasing trend in US and western institutions towards collaborating with China on medical research using EMR data. However, few studies have investigated characteristics of EMR data in China and their differences with the data in US hospitals. As an initial step towards differentiating EMR data in Chinese and US systems, this study attempts to understand system and cultural differences that may exist between Chinese and English clinical documents. We collected inpatient discharge summaries from one Chinese and from three US institutions and manually analyzed three major clinical components in text: medical problems, tests, and treatments. We reported comparison results at the document level and section level and discussed potential reasons for observed differences. Documenting and understanding differences in clinical reports from the US and China EMRs are important for cross-country collaborations. Our study also provided valuable insights for developing natural language processing tools for Chinese clinical text.
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10 MeSH Terms
Implementing an interface terminology for structured clinical documentation.
Rosenbloom ST, Miller RA, Adams P, Madani S, Khan N, Shultz EK
(2013) J Am Med Inform Assoc 20: e178-82
MeSH Terms: Humans, Medical Records Systems, Computerized, Tennessee, User-Computer Interface, Vocabulary, Controlled
Show Abstract · Added December 10, 2013
Clinically oriented interface terminologies support interactions between humans and computer programs that accept structured entry of healthcare information. This manuscript describes efforts over the past decade to introduce an interface terminology called CHISL (Categorical Health Information Structured Lexicon) into clinical practice as part of a computer-based documentation application at Vanderbilt University Medical Center. Vanderbilt supports a spectrum of electronic documentation modalities, ranging from transcribed dictation, to a partial template of free-form notes, to strict, structured data capture. Vanderbilt encourages clinicians to use what they perceive as the most appropriate form of clinical note entry for each given clinical situation. In this setting, CHISL occupies an important niche in clinical documentation. This manuscript reports challenges developers faced in deploying CHISL, and discusses observations about its usage, but does not review other relevant work in the field.
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Extracting semantic lexicons from discharge summaries using machine learning and the C-Value method.
Jiang M, Denny JC, Tang B, Cao H, Xu H
(2012) AMIA Annu Symp Proc 2012: 409-16
MeSH Terms: Algorithms, Artificial Intelligence, Humans, Natural Language Processing, Patient Discharge, Semantics, Terminology as Topic, Vocabulary, Controlled
Show Abstract · Added May 27, 2014
Semantic lexicons that link words and phrases to specific semantic types such as diseases are valuable assets for clinical natural language processing (NLP) systems. Although terminological terms with predefined semantic types can be generated easily from existing knowledge bases such as the Unified Medical Language Systems (UMLS), they are often limited and do not have good coverage for narrative clinical text. In this study, we developed a method for building semantic lexicons from clinical corpus. It extracts candidate semantic terms using a conditional random field (CRF) classifier and then selects terms using the C-Value algorithm. We applied the method to a corpus containing 10 years of discharge summaries from Vanderbilt University Hospital (VUH) and extracted 44,957 new terms for three semantic groups: Problem, Treatment, and Test. A manual analysis of 200 randomly selected terms not found in the UMLS demonstrated that 59% of them were meaningful new clinical concepts and 25% were lexical variants of exiting concepts in the UMLS. Furthermore, we compared the effectiveness of corpus-derived and UMLS-derived semantic lexicons in the concept extraction task of the 2010 i2b2 clinical NLP challenge. Our results showed that the classifier with corpus-derived semantic lexicons as features achieved a better performance (F-score 82.52%) than that with UMLS-derived semantic lexicons as features (F-score 82.04%). We conclude that such corpus-based methods are effective for generating semantic lexicons, which may improve named entity recognition tasks and may aid in augmenting synonymy within existing terminologies.
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8 MeSH Terms
Detecting temporal expressions in medical narratives.
Reeves RM, Ong FR, Matheny ME, Denny JC, Aronsky D, Gobbel GT, Montella D, Speroff T, Brown SH
(2013) Int J Med Inform 82: 118-27
MeSH Terms: Electronic Health Records, Health Records, Personal, Narration, Natural Language Processing, Pattern Recognition, Automated, Software, Time Factors, United States, Vocabulary, Controlled
Show Abstract · Added May 27, 2014
BACKGROUND - Clinical practice and epidemiological information aggregation require knowing when, how long, and in what sequence medically relevant events occur. The Temporal Awareness and Reasoning Systems for Question Interpretation (TARSQI) Toolkit (TTK) is a complete, open source software package for the temporal ordering of events within narrative text documents. TTK was developed on newspaper articles. We extended TTK to support medical notes using veterans' affairs (VA) clinical notes and compared it to TTK.
METHODS - We used a development set consisting of 200 VA clinical notes to modify and append rules to TTK's time tagger, creating Med-TTK. We then evaluated the performances of TTK and Med-TTK on an independent random selection of 100 clinical notes. Evaluation tasks were to identify and classify time-referring expressions as one of four temporal classes (DATE, TIME, DURATION, and SET). The reference standard for this test set was generated by dual human manual review with disagreements resolved by a third reviewer. Outcome measures included recall and precision for each class, and inter-rater agreement scores.
RESULTS - There were 3146 temporal expressions in the reference standard. TTK identified 1595 temporal expressions. Recall was 0.15 (95% confidence interval [CI] 0.12-0.15) and precision was 0.27 (95% CI 0.25-0.29) for TTK. Med-TTK identified 3174 expressions. Recall was 0.86 (95% CI 0.84-0.87) and precision was 0.85 (95% CI 0.84-0.86) for Med-TTK.
CONCLUSION - The algorithms for identifying and classifying temporal expressions in medical narratives developed within Med-TTK significantly improved performance compared to TTK. Natural language processing applications such as Med-TTK provide a foundation for meaningful longitudinal mapping of patient history events among electronic health records. The tool can be accessed at the following site: http://code.google.com/p/med-ttk/.
Copyright © 2012 Elsevier Ireland Ltd. All rights reserved.
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Comparison of SNOMED CT versus Medcin terminology concept coverage for mild Traumatic Brain Injury.
Montella D, Brown SH, Elkin PL, Jackson JC, Rosenbloom ST, Wahner-Roedler D, Welsh G, Cotton B, Guillamondegui OD, Lew H, Taber KH, Tupler LA, Vanderploeg R, Speroff T
(2011) AMIA Annu Symp Proc 2011: 969-78
MeSH Terms: Brain Injuries, Humans, Medical Records Systems, Computerized, Systematized Nomenclature of Medicine, United States, United States Department of Defense, United States Department of Veterans Affairs, Vocabulary, Controlled
Show Abstract · Added December 10, 2013
BACKGROUND - Traumatic Brain Injury (TBI) is a "signature" injury of the current wars in Iraq and Afghanistan. Structured electronic data regarding TBI findings is important for research, population health and other secondary uses but requires appropriate underlying standard terminologies to ensure interoperability and reuse. Currently the U.S. Department of Veterans Affairs (VA) uses the terminology SNOMED CT and the Department of Defense (DOD) uses Medcin.
METHODS - We developed a comprehensive case definition of mild TBI composed of 68 clinical terms. Using automated and manual techniques, we evaluated how well the mild TBI case definition terms could be represented by SNOMED CT and Medcin, and compared the results. We performed additional analysis stratified by whether the concepts were rated by a TBI expert panel as having High, Medium, or Low importance to the definition of mild TBI.
RESULTS - SNOMED CT sensitivity (recall) was 90% overall for coverage of mild TBI concepts, and Medcin sensitivity was 49%, p < 0.001 (using McNemar's chi square). Positive predictive value (precision) for each was 100%. SNOMED CT outperformed Medcin for concept coverage independent of import rating by our TBI experts.
DISCUSSION - SNOMED CT was significantly better able to represent mild TBI concepts than Medcin. This finding may inform data gathering, management and sharing, and data exchange strategies between the VA and DOD for active duty soldiers and veterans with mild TBI. Since mild TBI is an important condition in the civilian population as well, the current study results may be useful also for the general medical setting.
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8 MeSH Terms
Testing Three Problem List Terminologies in a simulated data entry environment.
Fung KW, Xu J, Rosenbloom ST, Mohr D, Maram N, Suther T
(2011) AMIA Annu Symp Proc 2011: 445-54
MeSH Terms: Clinical Coding, Electronic Health Records, Medical Records, Problem-Oriented, Systematized Nomenclature of Medicine, Vocabulary, Controlled
Show Abstract · Added December 10, 2013
Three Problem List Terminologies (PLT) were tested using a web-based application simulating a clinical data entry environment to evaluate coverage and coding efficiency. The three PLTs were: the CORE Problem List Subset of SNOMED CT, a clinical subset extracted from the full SNOMED CT and the PLT currently used at the Mayo Clinic. Candidate problem statements were randomly extracted from free text problem list entries contained in two electronic medical record systems. Physician reviewers searched for concepts in one of the three PLTs that most closely matched a problem statement. Altogether 45 reviewers reviewed 15 problems each. The coverage of the much smaller CORE Subset was comparable to Clinical SNOMED for combined exact or partial matches. The CORE Subset required the shortest time to find a concept. This may be related to the smaller size of the pick lists for the CORE Subset.
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5 MeSH Terms