Literature DB >> 9250251

Assessment of asthma using automated and full-text medical records.

J G Donahue1, S T Weiss, M A Goetsch, J M Livingston, D K Greineder, R Platt.   

Abstract

Automated medical records systems are used to study clinical outcomes and quality of care, but this requires accurate disease identification and assessment of severity. We sought to determine the reliability of identifying asthmatics through automated medical and pharmacy records, and the adequacy of such data for severity assessment. All adult health maintenance organization (HMO) members who received at least one asthma drug and an asthma diagnosis between April 1988 and September 1991 were identified. Records of a random sample were reviewed to validate the diagnosis and extract clinical information. Asthma drugs were dispensed to 15,491 individuals; 7583 (49%) also received an asthma diagnosis. Asthma drug use was three times greater for persons with diagnosed asthma compared to those with no diagnosis. Record review revealed that a coded asthma diagnosis had a positive predictive value of 86%. Nearly 4000 ambulatory encounters were reviewed, 10% of which were for asthma; the median number of encounters was two. Asthma symptoms were mentioned in 9% of all encounters; wheezing was most common. Peak flow and spirometry were measured in 4% and 1% of encounters, respectively. Records from recipients of asthma drugs who lacked an asthma diagnosis showed that 79% did not have asthma. Automated medical and pharmacy records from an HMO were relatively accurate when used to identify individuals with asthma. Similarly, most asthma drug recipients who lacked a coded diagnosis of asthma did not have asthma. However, conventional full-text records usually do not contain sufficient information to assess asthma severity, limiting the utility of such records for research and quality improvement.

Entities:  

Mesh:

Year:  1997        PMID: 9250251     DOI: 10.3109/02770909709067217

Source DB:  PubMed          Journal:  J Asthma        ISSN: 0277-0903            Impact factor:   2.515


  12 in total

1.  A highly specific algorithm for identifying asthma cases and controls for genome-wide association studies.

Authors:  Jennifer A Pacheco; Pedro C Avila; Jason A Thompson; May Law; Jihan A Quraishi; Alyssa K Greiman; Eric M Just; Abel Kho
Journal:  AMIA Annu Symp Proc       Date:  2009-11-14

Review 2.  Biomedical informatics applications for asthma care: a systematic review.

Authors:  David L Sanders; Dominik Aronsky
Journal:  J Am Med Inform Assoc       Date:  2006-04-18       Impact factor: 4.497

3.  Managing data quality for a drug safety surveillance system.

Authors:  Abraham G Hartzema; Christian G Reich; Patrick B Ryan; Paul E Stang; David Madigan; Emily Welebob; J Marc Overhage
Journal:  Drug Saf       Date:  2013-10       Impact factor: 5.606

4.  Automated chart review for asthma cohort identification using natural language processing: an exploratory study.

Authors:  Stephen T Wu; Sunghwan Sohn; K E Ravikumar; Kavishwar Wagholikar; Siddhartha R Jonnalagadda; Hongfang Liu; Young J Juhn
Journal:  Ann Allergy Asthma Immunol       Date:  2013-08-12       Impact factor: 6.347

5.  Identifying patients with asthma in primary care electronic medical record systems Chart analysis-based electronic algorithm validation study.

Authors:  Nancy Xi; Rebecca Wallace; Gina Agarwal; David Chan; Andrea Gershon; Samir Gupta
Journal:  Can Fam Physician       Date:  2015-10       Impact factor: 3.275

6.  A computable phenotype for asthma case identification in adult and pediatric patients: External validation in the Chicago Area Patient-Outcomes Research Network (CAPriCORN).

Authors:  Majid Afshar; Valerie G Press; Rachel G Robison; Abel N Kho; Sindhura Bandi; Ashvini Biswas; Pedro C Avila; Harsha Vardhan Madan Kumar; Byung Yu; Edward T Naureckas; Sharmilee M Nyenhuis; Christopher D Codispoti
Journal:  J Asthma       Date:  2017-11-10       Impact factor: 2.515

Review 7.  Validation of asthma recording in electronic health records: a systematic review.

Authors:  Francis Nissen; Jennifer K Quint; Samantha Wilkinson; Hana Mullerova; Liam Smeeth; Ian J Douglas
Journal:  Clin Epidemiol       Date:  2017-12-01       Impact factor: 4.790

8.  Validation of asthma recording in the Clinical Practice Research Datalink (CPRD).

Authors:  Francis Nissen; Daniel R Morales; Hana Mullerova; Liam Smeeth; Ian J Douglas; Jennifer K Quint
Journal:  BMJ Open       Date:  2017-08-11       Impact factor: 2.692

9.  Accuracy of Asthma Computable Phenotypes to Identify Pediatric Asthma at an Academic Institution.

Authors:  Mindy K Ross; Henry Zheng; Bing Zhu; Ailina Lao; Hyejin Hong; Alamelu Natesan; Melina Radparvar; Alex A T Bui
Journal:  Methods Inf Med       Date:  2021-07-14       Impact factor: 1.800

10.  A longitudinal study of adult-onset asthma incidence among HMO members.

Authors:  Susan R Sama; Phillip R Hunt; C I H Priscilla Cirillo; Arminda Marx; Richard A Rosiello; Paul K Henneberger; Donald K Milton
Journal:  Environ Health       Date:  2003-08-07       Impact factor: 5.984

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