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Brown KA, Donise KR, Cancilliere MK, Aluthge DP, Chen ES. Characterizing Autism Spectrum Disorder and Predicting Suicide Risk for Pediatric Psychiatric Emergency Services Encounters. AMIA ... ANNUAL SYMPOSIUM PROCEEDINGS. AMIA SYMPOSIUM 2024; 2023:864-873. [PMID: 38222397 PMCID: PMC10785882] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Subscribe] [Scholar Register] [Indexed: 01/16/2024]
Abstract
Individuals diagnosed with autism spectrum disorder (ASD) are at a higher risk for mental health concerns including suicidal thoughts and behaviors (STB). Limited studies have focused on suicidal risk factors that are more prevalent or unique to the population with ASD. This study sought to characterize and classify youth presenting to the psychiatric emergency department (ED) for a chief complaint of STB. The results of this study validated that a high number of patients with ASD present to the ED with STB. There were important differences in clinical characteristics to those with ASD versus those without. Clinical features that showed important impact in predicting high suicide risk in the ASD cases include elements of the mental status exam such as affect, trauma symptoms, abuse history, and auditory hallucinations. Focused attention is needed on these unique differences in ASD cases so that suicide risk level can be appropriately and promptly addressed.
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Affiliation(s)
| | - Kathleen R Donise
- Department of Psychiatry and Human Behavior at Alpert Medical School, Brown University, Providence RI
- Department of Child and Adolescent Psychiatry, Hasbro Children's Hospital, Providence RI
| | - Mary Kathryn Cancilliere
- Department of Psychiatry and Human Behavior at Alpert Medical School, Brown University, Providence RI
- Department of Child and Adolescent Psychiatry, Hasbro Children's Hospital, Providence RI
| | - Dilum P Aluthge
- Center for Biomedical Informatics, Brown University, Providence RI
| | - Elizabeth S Chen
- Center for Biomedical Informatics, Brown University, Providence RI
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2
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Rajamani S, Chen ES, Lindemann E, Aldekhyyel R, Wang Y, Melton GB. Representation of occupational information across resources and validation of the occupational data for health model. J Am Med Inform Assoc 2019; 25:197-205. [PMID: 28444213 DOI: 10.1093/jamia/ocx035] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/07/2016] [Accepted: 03/16/2016] [Indexed: 11/12/2022] Open
Abstract
Reports by the National Academy of Medicine and leading public health organizations advocate including occupational information as part of an individual's social context. Given recent National Academy of Medicine recommendations on occupation-related data in the electronic health record, there is a critical need for improved representation. The National Institute for Occupational Safety and Health has developed an Occupational Data for Health (ODH) model, currently in draft format. This study aimed to validate the ODH model by mapping occupation-related elements from resources representing recommendations, standards, public health reports and surveys, and research measures, along with preliminary evaluation of associated value sets. All 247 occupation-related items across 20 resources mapped to the ODH model. Recommended value sets had high variability across the evaluated resources. This study demonstrates the ODH model's value, the multifaceted nature of occupation information, and the critical need for occupation value sets to support clinical care, population health, and research.
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Affiliation(s)
- Sripriya Rajamani
- Public Health Informatics Program, University of Minnesota, Minneapolis, MN, USA.,Institute for Health Informatics, University of Minnesota
| | - Elizabeth S Chen
- Center for Biomedical Informatics, Brown University, Providence, RI, USA
| | | | - Ranyah Aldekhyyel
- Institute for Health Informatics, University of Minnesota.,Medical Education Department, College of Medicine, King Saud University, Riyadh, SA
| | - Yan Wang
- Institute for Health Informatics, University of Minnesota
| | - Genevieve B Melton
- Institute for Health Informatics, University of Minnesota.,Department of Surgery, University of Minnesota.,University of Minnesota Physicians, University of Minnesota
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3
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Mowery DL, Kawamoto K, Bradshaw R, Kohlmann W, Schiffman JD, Weir C, Borbolla D, Chapman WW, Del Fiol G. Determining Onset for Familial Breast and Colorectal Cancer from Family History Comments in the Electronic Health Record. AMIA JOINT SUMMITS ON TRANSLATIONAL SCIENCE PROCEEDINGS. AMIA JOINT SUMMITS ON TRANSLATIONAL SCIENCE 2019; 2019:173-181. [PMID: 31258969 PMCID: PMC6568127] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Subscribe] [Scholar Register] [Indexed: 05/11/2023]
Abstract
Background. Family health history (FHH) can be used to identify individuals at elevated risk for familial cancers. Risk criteria for common cancers rely on age of onset, which is documented inconsistently as structured and unstructured data in electronic health records (EHRs). Objective. To investigate a natural language processing (NLP) approach to extract age of onset and age of death from free-text EHR fields. Methods. Using 474,651 FHH entries from 89,814 patients, we investigated two methods - frequent patterns (baseline) and NLP classifier. Results. For age of onset, the NLP classifier outperformed the baseline in precision (96% vs. 83%; 95% CI [94, 97] and [80, 86]) with equivalent recall (both 93%; 95% CI [91, 95]). When applied to the full dataset, the NLP approach increased the percentage of FHH entries for which cancer risk criteria could be applied from 10% to 15%. Conclusion. NLP combined with structured data may improve the computation of familial cancer risk criteria for various use cases.
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Affiliation(s)
- Danielle L Mowery
- Biomedical Informatics
- Informatics, Decision-Enhancement, and Analytic Sciences (IDEAS) Center, Veterans Affairs Salt Lake City Health Care System, Salt Lake City, UT
- Biostatistics, Epidemiology, & Informatics
- Institute for Biomedical Informatics, University of Pennsylvania, Philadelphia, PA
| | | | | | | | | | | | | | - Wendy W Chapman
- Biomedical Informatics
- Informatics, Decision-Enhancement, and Analytic Sciences (IDEAS) Center, Veterans Affairs Salt Lake City Health Care System, Salt Lake City, UT
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Polubriaginof FCG, Shang N, Hripcsak G, Tatonetti NP, Vawdrey DK. Low Screening Rates for Diabetes Mellitus Among Family Members of Affected Relatives. AMIA ... ANNUAL SYMPOSIUM PROCEEDINGS. AMIA SYMPOSIUM 2018; 2018:1471-1477. [PMID: 30815192 PMCID: PMC6371358] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Subscribe] [Scholar Register] [Indexed: 06/09/2023]
Abstract
Cardiovascular disease is the leading cause of death in the United States, and abnormal blood glucose is an important risk factor. Delayed diagnosis of diabetes mellitus can increase patients' morbidity. In an urban academic medical center with a large clinical data warehouse, we used a novel algorithm to identify 56,794 family members of diabetic patients that were eligible for disease screening. We found that 30.6% of patients did not receive diabetes screening as recommended by current guidelines. Further, our analysis showed that having more than one family member affected and being a female were important contributors to being screened for diabetes mellitus. This study demonstrates that informatics methods applied to electronic health record data can be used to identify patients at risk for disease development, and therefore support clinical care.
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Affiliation(s)
| | - Ning Shang
- Department of Biomedical Informatics, Columbia University, New York, NY
| | - George Hripcsak
- Department of Biomedical Informatics, Columbia University, New York, NY
| | | | - David K Vawdrey
- Value Institute, NewYork-Presbyterian Hospital, New York, NY
- Department of Biomedical Informatics, Columbia University, New York, NY
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5
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Kennell TI, Willig JH, Cimino JJ. Clinical Informatics Researcher's Desiderata for the Data Content of the Next Generation Electronic Health Record. Appl Clin Inform 2017; 8:1159-1172. [PMID: 29270955 DOI: 10.4338/aci-2017-06-r-0101] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/29/2023] Open
Abstract
OBJECTIVE Clinical informatics researchers depend on the availability of high-quality data from the electronic health record (EHR) to design and implement new methods and systems for clinical practice and research. However, these data are frequently unavailable or present in a format that requires substantial revision. This article reports the results of a review of informatics literature published from 2010 to 2016 that addresses these issues by identifying categories of data content that might be included or revised in the EHR. MATERIALS AND METHODS We used an iterative review process on 1,215 biomedical informatics research articles. We placed them into generic categories, reviewed and refined the categories, and then assigned additional articles, for a total of three iterations. RESULTS Our process identified eight categories of data content issues: Adverse Events, Clinician Cognitive Processes, Data Standards Creation and Data Communication, Genomics, Medication List Data Capture, Patient Preferences, Patient-reported Data, and Phenotyping. DISCUSSION These categories summarize discussions in biomedical informatics literature that concern data content issues restricting clinical informatics research. These barriers to research result from data that are either absent from the EHR or are inadequate (e.g., in narrative text form) for the downstream applications of the data. In light of these categories, we discuss changes to EHR data storage that should be considered in the redesign of EHRs, to promote continued innovation in clinical informatics. CONCLUSION Based on published literature of clinical informaticians' reuse of EHR data, we characterize eight types of data content that, if included in the next generation of EHRs, would find immediate application in advanced informatics tools and techniques.
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Affiliation(s)
- Timothy I Kennell
- Informatics Institute, School of Medicine, University of Alabama at Birmingham, Birmingham, Alabama, United States
| | - James H Willig
- Informatics Institute, School of Medicine, University of Alabama at Birmingham, Birmingham, Alabama, United States.,Department of Medicine, University of Alabama at Birmingham, Birmingham, Alabama, United States
| | - James J Cimino
- Informatics Institute, School of Medicine, University of Alabama at Birmingham, Birmingham, Alabama, United States.,Department of Medicine, University of Alabama at Birmingham, Birmingham, Alabama, United States
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Wang Y, Wang L, Rastegar-Mojarad M, Liu S, Shen F, Liu H. Systematic Analysis of Free-Text Family History in Electronic Health Record. AMIA JOINT SUMMITS ON TRANSLATIONAL SCIENCE PROCEEDINGS. AMIA JOINT SUMMITS ON TRANSLATIONAL SCIENCE 2017; 2017:104-113. [PMID: 28815117 PMCID: PMC5543380] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 11/26/2022]
Abstract
Family history is an important component in modern clinical care especially in the era of precision medicine. Family history information in the Electronic Health Record (EHR) system is usually stored in structured format as well as in free-text format. In this study, we systematically analyzed a family history text corpus from 3 million clinical notes for the patients receiving their primary care at Mayo Clinic. Family members, medical problems, and their associations were analyzed and reported. Our findings showed a great agreement between positive/negated medical problems mentioned in the diagnosis report and the family history, as measured by observed agreement and random agreement. We also found that the family history of some medical problems existed up to 10~15 years prior to the diagnosis date of such problems. Finally two patient cases were studied to show the medical problems in the diagnosis and family history associated with the timeline.
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Chen ES, Melton GB, Wasserman RC, Rosenau PT, Howard DB, Sarkar IN. Mining and Visualizing Family History Associations in the Electronic Health Record: A Case Study for Pediatric Asthma. AMIA ... ANNUAL SYMPOSIUM PROCEEDINGS. AMIA SYMPOSIUM 2015; 2015:396-405. [PMID: 26958171 PMCID: PMC4765567] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Subscribe] [Scholar Register] [Indexed: 06/05/2023]
Abstract
Asthma is the most common chronic childhood disease and has seen increasing prevalence worldwide. While there is existing evidence of familial and other risk factors for pediatric asthma, there is a need for further studies to explore and understand interactions among these risk factors. The goal of this study was to develop an approach for mining, visualizing, and evaluating association rules representing pairwise interactions among potential familial risk factors based on information documented as part of a patient's family history in the electronic health record. As a case study, 10,260 structured family history entries for a cohort of 1,531 pediatric asthma patients were extracted and analyzed to generate family history associations at different levels of granularity. The preliminary results highlight the potential of this approach for validating known knowledge and suggesting opportunities for further investigation that may contribute to improving prediction of asthma risk in children.
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Affiliation(s)
- Elizabeth S Chen
- Center for Biomedical Informatics, Warren Alpert Medical School of Brown University, Providence, RI; Center for Clinical and Translational Science, University of Vermont, Burlington, VT
| | - Genevieve B Melton
- Institute for Health Informatics, University of Minnesota, Minneapolis, MN; Department of Surgery, University of Minnesota, Minneapolis, MN
| | - Richard C Wasserman
- Department of Pediatrics, University of Vermont, Burlington, VT; University of Vermont Children's Hospital, Burlington, VT
| | - Paul T Rosenau
- Department of Pediatrics, University of Vermont, Burlington, VT; University of Vermont Children's Hospital, Burlington, VT
| | - Diantha B Howard
- Center for Clinical and Translational Science, University of Vermont, Burlington, VT
| | - Indra Neil Sarkar
- Center for Biomedical Informatics, Warren Alpert Medical School of Brown University, Providence, RI; Center for Clinical and Translational Science, University of Vermont, Burlington, VT
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Polubriaginof F, Tatonetti NP, Vawdrey DK. An Assessment of Family History Information Captured in an Electronic Health Record. AMIA ... ANNUAL SYMPOSIUM PROCEEDINGS. AMIA SYMPOSIUM 2015; 2015:2035-2042. [PMID: 26958303 PMCID: PMC4765557] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Subscribe] [Scholar Register] [Indexed: 06/05/2023]
Abstract
Family history is considered a core element of clinical care. In this study we assessed the quality of family history data captured in an established commercial electronic health record (EHR) at a large academic medical center. Because the EHR had no centralized location to store family history information, it was collected as part of clinical notes in structured or free-text format. We analyzed differences between 10,000 free-text and 9,121 structured family history observations. Each observation was classified according to disease presence/absence and family member affected (e.g., father, mother, etc.). The structured notes did not collect a complete family history as defined by standards endorsed by the U.S. Agency for Healthcare Research and Quality; the free-text notes contained more information than the structured notes, but still not enough to be considered "complete." Several barriers remain for collecting complete, useful family history data in electronic health records.
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Affiliation(s)
| | | | - David K Vawdrey
- NewYork-Presbyterian Hospital, New York, NY, USA; Department of Biomedical Informatics, Columbia University, New York, NY, USA
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Carter EW, Sarkar IN, Melton GB, Chen ES. Representation of Drug Use in Biomedical Standards, Clinical Text, and Research Measures. AMIA ... ANNUAL SYMPOSIUM PROCEEDINGS. AMIA SYMPOSIUM 2015; 2015:376-385. [PMID: 26958169 PMCID: PMC4765691] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Subscribe] [Scholar Register] [Indexed: 06/05/2023]
Abstract
Drug misuse is a prominent cause of morbidity and mortality in the United States. Recent focus on behavioral and social domains in the electronic health record (EHR) has highlighted the need for comprehensive examination of social history information, such as drug use. In this study, representation of drug use was examined in three types of sources: (1) standards from HL7 and openEHR, (2) clinical text from publicly accessible clinical notes and a local EHR, and (3) research measures from the PhenX Toolkit and CDE Browser. In total, 27 elements were identified across the examined sources, revealing a diverse set of values that were found to be associated with drug use type, frequency, method, time frame, and amount. The findings of this study provide insight into the representation of drug use information that may contribute to efforts for standardizing collection and use of these data to support clinical care and research.
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Affiliation(s)
- Elizabeth W Carter
- Center for Clinical & Translational Science, University of Vermont, Burlington, VT
| | - Indra Neil Sarkar
- Center for Clinical & Translational Science, University of Vermont, Burlington, VT; Department of Microbiology & Molecular Genetics, University of Vermont, Burlington, VT
| | - Genevieve B Melton
- Institute for Health Informatics, University of Minnesota, Minneapolis, MN; Department of Surgery, University of Minnesota, Minneapolis, MN
| | - Elizabeth S Chen
- Center for Clinical & Translational Science, University of Vermont, Burlington, VT; Department of Medicine, University of Vermont, Burlington, VT
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Melton GB, Wang Y, Arsoniadis E, Pakhomov SV, Adam TJ, Kwaan MR, Rothenberger DA, Chen ES. Analyzing Operative Note Structure in Development of a Section Header Resource. Stud Health Technol Inform 2015; 216:821-826. [PMID: 26262166 PMCID: PMC4781788] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/04/2023]
Abstract
Operative notes contain essential details of surgical procedures and are an important form of clinical documentation. Sections within operative notes segment provide high level note structure. We evaluated the HL7 Implementation Guide for Clinical Document Architecture Release 2.0 Operative Note Draft Standard for Trial Use (HL7-ON DSTU) Release 1 as well as Logical Observation Identifiers Names and Codes (LOINC®) section names on 384 unique section headers from 362,311 operative notes. Overall, HL7-ON DSTU alone and HL7-ON DSTU with LOINC® section headers covered 66% and 79% of sections headers (93% and 98% of header instances), respectively. Section headers contained large numbers of synonyms, formatting variation, and variation of word forms, as well as smaller numbers of compound sections and issues with mismatches in header granularity. Robust operative note section mapping is important for clinical note interoperability and effective use of operative notes by natural language processing systems. The resulting operative note section resource is made publicly available.
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Affiliation(s)
- Genevieve B. Melton
- Department of Surgery, University of Minnesota, Minneapolis, MN, USA
- Institute for Health Informatics, University of Minnesota, Minneapolis, MN, USA
| | - Yan Wang
- Institute for Health Informatics, University of Minnesota, Minneapolis, MN, USA
| | - Elliot Arsoniadis
- Department of Surgery, University of Minnesota, Minneapolis, MN, USA
- Institute for Health Informatics, University of Minnesota, Minneapolis, MN, USA
| | - Serguei V.S. Pakhomov
- Institute for Health Informatics, University of Minnesota, Minneapolis, MN, USA
- College of Pharmacy, University of Minnesota, Minneapolis, MN, USA
| | - Terrence J. Adam
- Institute for Health Informatics, University of Minnesota, Minneapolis, MN, USA
- College of Pharmacy, University of Minnesota, Minneapolis, MN, USA
| | - Mary R. Kwaan
- Department of Surgery, University of Minnesota, Minneapolis, MN, USA
| | | | - Elizabeth S. Chen
- Center for Clinical and Translational Science, University of Vermont, Burlington, VT, USA
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