Automated chart review utilizing natural language processing algorithm for asthma predictive index

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Abstract

Background

Thus far, no algorithms have been developed to automatically extract patients who meet Asthma Predictive Index (API) criteria from the Electronic health records (EHR) yet. Our objective is to develop and validate a natural language processing (NLP) algorithm to identify patients that meet API criteria.

Methods

This is a cross-sectional study nested in a birth cohort study in Olmsted County, MN. Asthma status ascertained by manual chart review based on API criteria served as gold standard. NLP-API was developed on a training cohort ( n = 87) and validated on a test cohort ( n = 427). Criterion validity was measured by sensitivity, specificity, positive predictive value and negative predictive value of the NLP algorithm against manual chart review for asthma status. Construct validity was determined by associations of asthma status defined by NLP-API with known risk factors for asthma.

Results

Among the eligible 427 subjects of the test cohort, 48% were males and 74% were White. Median age was 5.3 years (interquartile range 3.6–6.8). 35 (8%) had a history of asthma by NLP-API vs. 36 (8%) by abstractor with 31 by both approaches. NLP-API predicted asthma status with sensitivity 86%, specificity 98%, positive predictive value 88%, negative predictive value 98%. Asthma status by both NLP and manual chart review were significantly associated with the known asthma risk factors, such as history of allergic rhinitis, eczema, family history of asthma, and maternal history of smoking during pregnancy ( p value < 0.05). Maternal smoking [odds ratio: 4.4, 95% confidence interval 1.8–10.7] was associated with asthma status determined by NLP-API and abstractor, and the effect sizes were similar between the reviews with 4.4 vs 4.2 respectively.

Conclusion

NLP-API was able to ascertain asthma status in children mining from EHR and has a potential to enhance asthma care and research through population management and large-scale studies when identifying children who meet API criteria.

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Most cited references 31

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History of the Rochester Epidemiology Project: half a century of medical records linkage in a US population.

Walter A Rocca, Barbara P. Yawn, Jennifer St. Sauver … (2012)

The Rochester Epidemiology Project (REP) has maintained a comprehensive medical records linkage system for nearly half a century for almost all persons residing in Olmsted County, Minnesota. Herein, we provide a brief history of the REP before and after 1966, the year in which the REP was officially established. The key protagonists before 1966 were Henry Plummer, Mabel Root, and Joseph Berkson, who developed a medical records linkage system at Mayo Clinic. In 1966, Leonard Kurland established collaborative agreements with other local health care providers (hospitals, physician groups, and clinics [primarily Olmsted Medical Center]) to develop a medical records linkage system that covered the entire population of Olmsted County, and he obtained funding from the National Institutes of Health to support the new system. In 1997, L. Joseph Melton III addressed emerging concerns about the confidentiality of medical record information by introducing a broad patient research authorization as per Minnesota state law. We describe how the key protagonists of the REP have responded to challenges posed by evolving medical knowledge, information technology, and public expectation and policy. In addition, we provide a general description of the system; discuss issues of data quality, reliability, and validity; describe the research team structure; provide information about funding; and compare the REP with other medical information systems. The REP can serve as a model for the development of similar research infrastructures in the United States and worldwide. Copyright © 2012 Mayo Foundation for Medical Education and Research. Published by Elsevier Inc. All rights reserved.

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Automated identification of postoperative complications within an electronic medical record using natural language processing.

P Elkin, Theodore Speroff, Emery Brown … (2011)

Currently most automated methods to identify patient safety occurrences rely on administrative data codes; however, free-text searches of electronic medical records could represent an additional surveillance approach. To evaluate a natural language processing search-approach to identify postoperative surgical complications within a comprehensive electronic medical record. Cross-sectional study involving 2974 patients undergoing inpatient surgical procedures at 6 Veterans Health Administration (VHA) medical centers from 1999 to 2006. Postoperative occurrences of acute renal failure requiring dialysis, deep vein thrombosis, pulmonary embolism, sepsis, pneumonia, or myocardial infarction identified through medical record review as part of the VA Surgical Quality Improvement Program. We determined the sensitivity and specificity of the natural language processing approach to identify these complications and compared its performance with patient safety indicators that use discharge coding information. The proportion of postoperative events for each sample was 2% (39 of 1924) for acute renal failure requiring dialysis, 0.7% (18 of 2327) for pulmonary embolism, 1% (29 of 2327) for deep vein thrombosis, 7% (61 of 866) for sepsis, 16% (222 of 1405) for pneumonia, and 2% (35 of 1822) for myocardial infarction. Natural language processing correctly identified 82% (95% confidence interval [CI], 67%-91%) of acute renal failure cases compared with 38% (95% CI, 25%-54%) for patient safety indicators. Similar results were obtained for venous thromboembolism (59%, 95% CI, 44%-72% vs 46%, 95% CI, 32%-60%), pneumonia (64%, 95% CI, 58%-70% vs 5%, 95% CI, 3%-9%), sepsis (89%, 95% CI, 78%-94% vs 34%, 95% CI, 24%-47%), and postoperative myocardial infarction (91%, 95% CI, 78%-97%) vs 89%, 95% CI, 74%-96%). Both natural language processing and patient safety indicators were highly specific for these diagnoses. Among patients undergoing inpatient surgical procedures at VA medical centers, natural language processing analysis of electronic medical records to identify postoperative complications had higher sensitivity and lower specificity compared with patient safety indicators based on discharge coding.

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PheKB: a catalog and workflow for creating electronic phenotype algorithms for transportability.

Jacqueline C. Kirby, Peter Speltz, Luke V Rasmussen … (2016)

Health care generated data have become an important source for clinical and genomic research. Often, investigators create and iteratively refine phenotype algorithms to achieve high positive predictive values (PPVs) or sensitivity, thereby identifying valid cases and controls. These algorithms achieve the greatest utility when validated and shared by multiple health care systems.Materials and Methods We report the current status and impact of the Phenotype KnowledgeBase (PheKB, http://phekb.org), an online environment supporting the workflow of building, sharing, and validating electronic phenotype algorithms. We analyze the most frequent components used in algorithms and their performance at authoring institutions and secondary implementation sites.

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Author and article information

Contributors

Harsheen Kaur: hakaur@salud.unm.edu

Sunghwan Sohn: sohn.sunghwan@mayo.edu

Chung-Il Wi: wi.chung@mayo.edu

Euijung Ryu: ryu.euijung@mayo.edu

Miguel A. Park: park.miguel@mayo.edu

Kay Bachman: bachman.kay@mayo.edu

Hirohito Kita: kita.hirohito@mayo.edu

Ivana Croghan: croghan.ivana@mayo.edu

Jose A. Castro-Rodriguez: jacastro17@hotmail.com

Gretchen A. Voge: Gretchen.Voge@childrensmn.org

Hongfang Liu: 507-293-0057 , liu.hongfang@mayo.edu

Young J. Juhn: 507-538-1642 , juhn.young@mayo.edu

Journal

Journal ID (nlm-ta): BMC Pulm Med

Journal ID (iso-abbrev): BMC Pulm Med

Title: BMC Pulmonary Medicine

Publisher: BioMed Central (London )

ISSN (Electronic): 1471-2466

Publication date (Electronic): 13 February 2018

Publication date PMC-release: 13 February 2018

Publication date Collection: 2018

Volume: 18

Electronic Location Identifier: 34

Affiliations

[1 ]ISNI 0000 0004 0459 167X, GRID grid.66875.3a, Department of Pediatric and Adolescent Medicine, , Mayo Clinic, ; 200 1st Street SW, Rochester, MN 55905 USA

[2 ]ISNI 0000 0004 0459 167X, GRID grid.66875.3a, Asthma Epidemiology Research Unit, , Mayo Clinic, ; Rochester, MN USA

[3 ]ISNI 0000 0001 2188 8502, GRID grid.266832.b, Departement of Pediatrics, , University of New Mexico, ; Albuquerque, NM USA

[4 ]ISNI 0000 0004 0459 167X, GRID grid.66875.3a, Division of Biomedical Statistics and Informatics, , Mayo Clinic, ; 200 1st Street SW, Rochester, MN 55905 USA

[5 ]ISNI 0000 0004 0459 167X, GRID grid.66875.3a, Division of Allergic Disease, , Mayo Clinic, ; Rochester, MN USA

[6 ]ISNI 0000 0004 0459 167X, GRID grid.66875.3a, Department of Medicine Research, , Mayo Clinic, ; Rochester, MN USA

[7 ]ISNI 0000 0001 2157 0406, GRID grid.7870.8, Division of Pediatrics, School of Medicine, , Pontificia Universidad Catolica de Chile, ; Santiago, Chile

[8 ]ISNI 0000 0004 0629 5022, GRID grid.418506.e, Division of Neonatology, , Children’s Hospitals and Clinics of Minnesota, ; Minneapolis, MN USA

Article

Publisher ID: 593

DOI: 10.1186/s12890-018-0593-9

PMC ID: 5812028

PubMed ID: 29439692

SO-VID: fdd2a617-6722-4f6f-a9f1-cc4fc187a931

License:

Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License ( http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver ( http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.

History

Date received : 6 November 2017

Date accepted : 22 January 2018

Funding

Funded by: FundRef http://dx.doi.org/10.13039/100000050, National Heart, Lung, and Blood Institute;

Award ID: R01 HL126667

Funded by: FundRef http://dx.doi.org/10.13039/100000049, National Institute on Aging;

Award ID: R01 AG034676

Custom metadata

ScienceOpen disciplines: Respiratory medicine

Keywords: asthma,api,epidemiology,informatics,nlp

Data availability:

ScienceOpen disciplines: Respiratory medicine

Keywords: asthma, api, epidemiology, informatics, nlp

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Automated chart review utilizing natural language processing algorithm for asthma predictive index

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Abstract

Background

Methods

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Conclusion

Related collections

New directions in predictive processing

Most cited references 31

History of the Rochester Epidemiology Project: half a century of medical records linkage in a US population.

Automated identification of postoperative complications within an electronic medical record using natural language processing.

PheKB: a catalog and workflow for creating electronic phenotype algorithms for transportability.

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Cited by 27

Most referenced authors 548