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CC BY-NC-ND 4.0 · Appl Clin Inform 2018; 09(03): 528-540
DOI: 10.1055/s-0038-1666994
Research Article
Georg Thieme Verlag KG Stuttgart · New York

Identifying Asthma Exacerbation-Related Emergency Department Visit Using Electronic Medical Record and Claims Data

Agnes S. Sundaresan
1   Department of Epidemiology and Health Services Research, Geisinger Health System, Danville, Pennsylvania, United States
2   Medicine Institute, Geisinger Health System, Danville, Pennsylvania, United States
,
Gargi Schneider
3   MedPeds Program, Geisinger Medical Center, Danville, Pennsylvania, United States
,
Joy Reynolds
4   Lewis Katz School of Medicine at Temple University, Temple University, Philadelphia, Pennsylvania, United States
,
H. Lester Kirchner
5   Department of Biomedical and Translational Informatics, Geisinger Health System, Danville, Pennsylvania, United States
6   Department of Clinical Sciences, Geisinger Commonwealth School of Medicine, Geisinger Health System, Scranton, Pennsylvania, United States
7   Department of Pediatrics, Global and Immigrant Health Section, Baylor College of Medicine, Houston, Texas, United States
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Publikationsverlauf

07. Dezember 2017

28. Mai 2018

Publikationsdatum:
18. Juli 2018 (online)

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Abstract

Background Asthma exacerbation leading to emergency department (ED) visit is prevalent, an indicator of poor control of asthma, and is a potentially preventable clinical outcome.

Objective We propose to utilize multiple data elements available in electronic medical records (EMRs) and claims database to create separate algorithms with high validity for clinical and research purposes to identify asthma exacerbation-related ED visit among the general population.

Methods We performed a retrospective study with inclusion criteria of patients aged 4 to 40 years, a visit to Geisinger ED from January 1, 2006, to October 28, 2013, with asthma on their problem list. Different electronic data elements including chief complaints, vitals, season, smoking, medication use, and discharge diagnoses were obtained to create the algorithm. A stratified random sample was generated to select the charts for review. Chart review was performed to classify patients with asthma-related ED visit, that is, the gold standard. Two reviewers performed the chart review and validation was done on a small subset.

Results There were 966 eligible ED visits in the EMR sample and 731 in the claims sample. Agreement between reviewers was 95.45% and kappa statistic was 0.91. Mean age of the EMR sample was 22 years, and mostly white (93%). Multiple models conventionally used in studies were evaluated and the final model chosen included principal diagnosis, bronchodilator, and steroid use for both algorithms, chief complaints for EMR, and secondary diagnosis for claims. Area under the curve was 0.93 (95% confidence interval: 0.91–0.94) and 0.94 (0.93–0.96), respectively, for EMR and claims data, with positive predictive value of > 94%. The algorithms are visually presented using nomograms.

Conclusion We were able to develop two separate algorithms for EMR and claims to identify asthma exacerbation-related ED visit with excellent diagnostic ability and varying discrimination threshold for clinical and research purposes.

Keywords

system improvement - disease management - clinical research informatics - allergy and immunology - clinical data management

Protection of Human and Animal Subjects

Human and/or animal subjects were not included in the project. The study was reviewed by the Geisinger Institutional Review Board.


Supplementary Material

 

  • References

  • 1 Global Initiative for Asthma. Global Strategy for Asthma Management and Prevention; 2016. Available at: www.ginasthma.org . Accessed June 19, 2018
    PubMedGoogle Scholar
  • 2 Rui P, Kang K, Albert M. National Hospital Ambulatory Medical Care Survey: 2013 Emergency Department Summary Tables. Available at: https://www.cdc.gov/nchs/data/ahcd/nhamcs_emergency/2013_ed_web_tables.pdf . Accessed June 19, 2018
    PubMedGoogle Scholar
  • 3 Sullivan PW, Ghushchyan VH, Slejko JF, Belozeroff V, Globe DR, Lin SL. The burden of adult asthma in the United States: evidence from the Medical Expenditure Panel Survey. J Allergy Clin Immunol 2011; 127 (02) 363-369
    CrossrefPubMedGoogle Scholar
  • 4 O'Byrne PM, Pedersen S, Lamm CJ, Tan WC, Busse WW. ; START Investigators Group. Severe exacerbations and decline in lung function in asthma. Am J Respir Crit Care Med 2009; 179 (01) 19-24
    CrossrefPubMedGoogle Scholar
  • 5 Sullivan PW, Smith KL, Ghushchyan VH, Globe DR, Lin SL, Globe G. Asthma in USA: its impact on health-related quality of life. J Asthma 2013; 50 (08) 891-899
    CrossrefPubMedGoogle Scholar
  • 6 Akinbami LJ, Moorman JE, Liu X. Asthma prevalence, health care use, and mortality: United States, 2005-2009. Natl Health Stat Rep 2011; 32 (32) 1-14
    PubMedGoogle Scholar
  • 7 Gold LS, Smith N, Allen-Ramey FC, Nathan RA, Sullivan SD. Associations of patient outcomes with level of asthma control. Ann Allergy Asthma Immunol 2012; 109 (04) 260-265
    CrossrefPubMedGoogle Scholar
  • 8 McDonald VM, Gibson PG. Exacerbations of severe asthma. Clin Exp Allergy 2012; 42 (05) 670-677
    CrossrefPubMedGoogle Scholar
  • 9 Sims EJ, Price D, Haughney J, Ryan D, Thomas M. Current control and future risk in asthma management. Allergy Asthma Immunol Res 2011; 3 (04) 217-225
    CrossrefPubMedGoogle Scholar
  • 10 Chipps BE, Zeiger RS, Borish L. , et al; TENOR Study Group. Key findings and clinical implications from The Epidemiology and Natural History of Asthma: Outcomes and Treatment Regimens (TENOR) study. J Allergy Clin Immunol 2012; 130 (02) 332-342
    CrossrefPubMedGoogle Scholar
  • 11 Suruki RY, Daugherty JB, Boudiaf N, Albers FC. The frequency of asthma exacerbations and healthcare utilization in patients with asthma from the UK and USA. BMC Pulm Med 2017; 17 (01) 74
    CrossrefPubMedGoogle Scholar
  • 12 Engelkes M, Janssens HM, de Jongste JC, Sturkenboom MC, Verhamme KM. Medication adherence and the risk of severe asthma exacerbations: a systematic review. Eur Respir J 2015; 45 (02) 396-407
    CrossrefPubMedGoogle Scholar
  • 13 FitzGerald JM, Gibson PG. Asthma exacerbations. 4: prevention. Thorax 2006; 61 (11) 992-999
    CrossrefPubMedGoogle Scholar
  • 14 Malone DC, Lawson KA, Smith DH. Asthma: an analysis of high-cost patients. Pharm Pract Manag Q 2000; 20 (01) 12-20
    PubMedGoogle Scholar
  • 15 Yong PL, Werner RM. Process quality measures and asthma exacerbations in the Medicaid population. J Allergy Clin Immunol 2009; 124 (05) 961-966
    CrossrefPubMedGoogle Scholar
  • 16 James T, Fine M. Monitoring asthma control using claims data and patient-reported outcomes measures. P&T 2008; 33 (08) 454-466
    PubMedGoogle Scholar
  • 17 Quality of Care Outcomes Research in CVD Stroke Working Groups. Measuring and improving quality of care: a report from the American Heart Association/American College of Cardiology First Scientific Forum on Assessment of Healthcare Quality in Cardiovascular Disease and Stroke. Circulation 2000; 101 (12) 1483-1493
    CrossrefPubMedGoogle Scholar
  • 18 Darrow LA, Hess J, Rogers CA, Tolbert PE, Klein M, Sarnat SE. Ambient pollen concentrations and emergency department visits for asthma and wheeze. J Allergy Clin Immunol 2012; 130 (03) 630-638.e4
    CrossrefPubMedGoogle Scholar
  • 19 Zeiger RS, Schatz M, Li Q, Zhang F, Purdum AS, Chen W. Step-up care improves impairment in uncontrolled asthma: an administrative data study. Am J Manag Care 2010; 16 (12) 897-906
    PubMedGoogle Scholar
  • 20 Tsai CL, Lee WY, Hanania NA, Camargo Jr CA. Age-related differences in clinical outcomes for acute asthma in the United States, 2006-2008. J Allergy Clin Immunol 2012; 129 (05) 1252-1258.e1
    CrossrefPubMedGoogle Scholar
  • 21 Rosychuk RJ, Voaklander DC, Klassen TP, Senthilselvan A, Marrie TJ, Rowe BH. Asthma presentations by children to emergency departments in a Canadian province: a population-based study. Pediatr Pulmonol 2010; 45 (10) 985-992
    CrossrefPubMedGoogle Scholar
  • 22 Rowe BH, Voaklander DC, Wang D. , et al. Asthma presentations by adults to emergency departments in Alberta, Canada: a large population-based study. Chest 2009; 135 (01) 57-65
    CrossrefPubMedGoogle Scholar
  • 23 Héguy L, Garneau M, Goldberg MS, Raphoz M, Guay F, Valois MF. Associations between grass and weed pollen and emergency department visits for asthma among children in Montreal. Environ Res 2008; 106 (02) 203-211
    CrossrefPubMedGoogle Scholar
  • 24 Himes BE, Kohane IS, Ramoni MF, Weiss ST. Characterization of patients who suffer asthma exacerbations using data extracted from electronic medical records. AMIA Annual Symposium proceedings/AMIA Symposium; 2008:308–312
    PubMedGoogle Scholar
  • 25 Hirshon JM, Shardell M, Alles S. , et al. Elevated ambient air zinc increases pediatric asthma morbidity. Environ Health Perspect 2008; 116 (06) 826-831
    CrossrefPubMedGoogle Scholar
  • 26 Mohr LB, Luo S, Mathias E, Tobing R, Homan S, Sterling D. Influence of season and temperature on the relationship of elemental carbon air pollution to pediatric asthma emergency room visits. J Asthma 2008; 45 (10) 936-943
    CrossrefPubMedGoogle Scholar
  • 27 Moorman JE, Rudd RA, Johnson CA. , et al; Centers for Disease Control and Prevention (CDC). National surveillance for asthma--United States, 1980-2004. MMWR Surveill Summ 2007; 56 (08) 1-54
    PubMedGoogle Scholar
  • 28 Silverman RA, Ito K, Stevenson L, Hastings HM. The relationship of fall school opening and emergency department asthma visits in a large metropolitan area. Arch Pediatr Adolesc Med 2005; 159 (09) 818-823
    CrossrefPubMedGoogle Scholar
  • 29 Florin TA, Carron H, Huang G, Shah SS, Ruddy R, Ambroggio L. Pneumonia in children presenting to the emergency department with an asthma exacerbation. JAMA Pediatr 2016; 170 (08) 803-805
    CrossrefPubMedGoogle Scholar
  • 30 Sharifi M, Krishanswami S, McPheeters ML. A systematic review of validated methods to capture acute bronchospasm using administrative or claims data. Vaccine 2013; 31 (Suppl. 10) K12-K20
    CrossrefPubMedGoogle Scholar
  • 31 Sanders DL, Gregg W, Aronsky D. Identifying asthma exacerbations in a pediatric emergency department: a feasibility study. Int J Med Inform 2007; 76 (07) 557-564
    CrossrefPubMedGoogle Scholar
  • 32 Bronstein JM, Santer L, Johnson V. The use of Medicaid claims as a supplementary source of information on quality of asthma care. J Healthc Qual 2000; 22 (06) 13-18
    PubMedGoogle Scholar
  • 33 Dexheimer JW, Abramo TJ, Arnold DH. , et al. Implementation and evaluation of an integrated computerized asthma management system in a pediatric emergency department: a randomized clinical trial. Int J Med Inform 2014; 83 (11) 805-813
    CrossrefPubMedGoogle Scholar
  • 34 Oelsner EC, Loehr LR, Henderson AG. , et al. Classifying chronic lower respiratory disease events in epidemiologic cohort studies. Ann Am Thorac Soc 2016; 13 (07) 1057-1066
    CrossrefPubMedGoogle Scholar
  • 35 Minard J, Dostaler SM, Olajos-Clow JG. , et al. Development and implementation of an electronic asthma record for primary care: integrating guidelines into practice. J Asthma 2014; 51 (01) 58-68
    CrossrefPubMedGoogle Scholar
  • 36 Beard JD, Beck C, Graham R. , et al. Winter temperature inversions and emergency department visits for asthma in Salt Lake County, Utah, 2003-2008. Environ Health Perspect 2012; 120 (10) 1385-1390
    CrossrefPubMedGoogle Scholar
  • 37 May L, Carim M, Yadav K. Adult asthma exacerbations and environmental triggers: a retrospective review of ED visits using an electronic medical record. Am J Emerg Med 2011; 29 (09) 1074-1082
    CrossrefPubMedGoogle Scholar
  • 38 Berger WE, Legorreta AP, Blaiss MS. , et al. The utility of the Health Plan Employer Data and Information Set (HEDIS) asthma measure to predict asthma-related outcomes. Ann Allergy Asthma Immunol 2004; 93 (06) 538-545
    CrossrefPubMedGoogle Scholar
  • 39 Schatz M, Zeiger RS, Yang SJ. , et al. Persistent asthma defined using HEDIS versus survey criteria. Am J Manag Care 2010; 16 (11) e281-e288
    PubMedGoogle Scholar
  • 40 Hirsch AG, Stewart WF, Sundaresan AS. , et al. Nasal and sinus symptoms and chronic rhinosinusitis in a population-based sample. Allergy 2017; 72 (02) 274-281
    CrossrefPubMedGoogle Scholar
  • 41 Maeng DD, Snyder SR, Davis TW, Tomcavage JF. Impact of a complex care management model on cost and utilization among adolescents and young adults with special care and health needs. Popul Health Manag 2017; 20 (06) 435-441
    CrossrefPubMedGoogle Scholar
  • 42 Pitblado J. Survey Data Analysis in Stata: Canadian Stata Users Group Meeting; 2009. Available at: https://www.google.com/url?sa=t&rct=j&q=&esrc=s&source=web&cd=1&ved=0ahUKEwjs6_2NzKPOAhXDwiYKHbMqCD4QFggcMAA&url=http%3A%2F%2Fwww.stata.com%2Fmeeting%2Fcanada09%2Fca09_pitblado_handout.pdf&usg=AFQjCNFHePahdy3vJwjndc5-asNndJdb3Q&bvm=bv.128617741,d.eWE&cad=rja . Accessed August 2, 2016
    PubMedGoogle Scholar
  • 43 Mansournia MA, Altman DG. Inverse probability weighting. BMJ 2016; 352: i189 . Doi: 10.1136/bmj.i189
    PubMedGoogle Scholar
  • 44 Chantala K, Blanchette D, Suchindran CM. Software to Compute Sampling Weights for Multilevel Analysis. Chapel Hill, NC: Carolina Population Center, UNC at Chapel Hill; 2011
    Google Scholar
  • 45 Little RJ, Lewitzky S, Heeringa S, Lepkowski J, Kessler RC. Assessment of weighting methodology for the National Comorbidity Survey. Am J Epidemiol 1997; 146 (05) 439-449
    CrossrefPubMedGoogle Scholar
  • 46 Dargatz DA, Hill GW. Analysis of survey data. Prev Vet Med 1996; 28 (04) 225-237
    CrossrefPubMedGoogle Scholar
  • 47 DeLong ER, DeLong DM, Clarke-Pearson DL. Comparing the areas under two or more correlated receiver operating characteristic curves: a nonparametric approach. Biometrics 1988; 44 (03) 837-845
    CrossrefPubMedGoogle Scholar
  • 48 Meng Y, Lou H, Wang C, Zhang L. Predictive significance of computed tomography in eosinophilic chronic rhinosinusitis with nasal polyps. Int Forum Allergy Rhinol 2016; 6 (08) 812-819
    CrossrefPubMedGoogle Scholar
  • 49 Fischer JE, Bachmann LM, Jaeschke R. A readers' guide to the interpretation of diagnostic test properties: clinical example of sepsis. Intensive Care Med 2003; 29 (07) 1043-1051
    CrossrefPubMedGoogle Scholar
  • 50 Zlotnik A, Abraira V. A general-purpose nomogram generator for predictive logistic regression models. Stata J 2015; 15 (02) 537-546
    PubMedGoogle Scholar
  • 51 Iasonos A, Schrag D, Raj GV, Panageas KS. How to build and interpret a nomogram for cancer prognosis. J Clin Oncol 2008; 26 (08) 1364-1370
    CrossrefPubMedGoogle Scholar
  • 52 Newton KM, Peissig PL, Kho AN. , et al. Validation of electronic medical record-based phenotyping algorithms: results and lessons learned from the eMERGE network. J Am Med Inform Assoc 2013; 20 (e1): e147-e154
    CrossrefPubMedGoogle Scholar
  • 53 2015 National Health Interview Survey (NHIS). Data National Center for Health Statistics, CDC; updated February 10, 2017. Available at: https://www.cdc.gov/asthma/nhis/2015/table6-1.htm . Accessed June 19, 2018
    PubMed
  • 54 Smith K, Warholak T, Armstrong E, Leib M, Rehfeld R, Malone D. Evaluation of risk factors and health outcomes among persons with asthma. J Asthma 2009; 46 (03) 234-237
    CrossrefPubMedGoogle Scholar
  • 55 Murphy KR, Meltzer EO, Blaiss MS, Nathan RA, Stoloff SW, Doherty DE. Asthma management and control in the United States: results of the 2009 Asthma Insight and Management survey. Allergy Asthma Proc 2012; 33 (01) 54-64
    CrossrefPubMedGoogle Scholar
  • 56 Bahadori K, Doyle-Waters MM, Marra C. , et al. Economic burden of asthma: a systematic review. BMC Pulm Med 2009; 9: 24
    CrossrefPubMedGoogle Scholar
  • 57 Okelo SO, Butz AM, Sharma R. , et al. Interventions to modify health care provider adherence to asthma guidelines: a systematic review. Pediatrics 2013; 132 (03) 517-534
    CrossrefPubMedGoogle Scholar
  • 58 Kharrazi H, Chi W, Chang HY. , et al. Comparing population-based risk-stratification model performance using demographic, diagnosis and medication data extracted from outpatient electronic health records versus administrative claims. Med Care 2017; 55 (08) 789-796
    CrossrefPubMedGoogle Scholar
  • 59 Rowan CG, Flory J, Gerhard T. , et al. Agreement and validity of electronic health record prescribing data relative to pharmacy claims data: a validation study from a US electronic health record database. Pharmacoepidemiol Drug Saf 2017; 26 (08) 963-972
    CrossrefPubMedGoogle Scholar