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Original Research: HEALTH INFORMATION TECHNOLOGY |

Impact of a Clinical Decision Support System in an Electronic Health Record to Enhance Detection of α1-Antitrypsin DeficiencyElectronic Alert for α1-Antitrypsin Deficiency

Anil Jain, MD; Kevin McCarthy, RCPFT; Meng Xu, MS; James K. Stoller, MD
Author and Funding Information

From the Internal Medicine and Information Technology Division (Dr Jain); the Pulmonary Function Laboratory, the Respiratory Institute (Mr McCarthy); Quantitative Health Services (Ms Xu); Education Institute (Dr Stoller), Cleveland Clinic Lerner College of Medicine; and Cleveland Clinic Respiratory Therapy (Dr Stoller), Cleveland Clinic, Cleveland, OH.

Correspondence to: James K. Stoller, MD, FCCP, Cleveland Clinic-NA22, 9500 Euclid Ave, Cleveland, OH 44195; e-mail: stollej@ccf.org


Funding/support: This research was supported by the Alpha-1 Foundation.

Reproduction of this article is prohibited without written permission from the American College of Chest Physicians (http://www.chestpubs.org/site/misc/reprints.xhtml).


© 2011 American College of Chest Physicians


Chest. 2011;140(1):198-204. doi:10.1378/chest.10-1658
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Background:  Because α1-antitrypsin deficiency (AATD) is underrecognized, strategies to enhance guideline-based diagnostic testing are warranted.

Methods:  We studied the impact of issuing a guideline-based alert within an electronic health record (EHR) to test for AATD on the rate of testing for and detection of AATD. For patients at the Cleveland Clinic whose physician-ordered pulmonary function test results showed airflow obstruction, the rates of testing for and detection of AATD were determined during a baseline (prealert) period and again after implementing the alert.

Results:  During the baseline period, among 979 eligible subjects, 4.7% were tested; 8.9% of those who had phenotype testing performed were found to have AATD (serum levels < 100 mg/dL), of whom 3.2% had the PI*ZZ genotype. After the alert, among the 624 eligible subjects, the rate of testing increased severalfold (15.1%, P < .001), though neither the rate of detecting AATD (5.3%) nor PI*ZZ (2.6%) differed from the rates during the baseline period. Having an alternate diagnosis (other than asthma) that could explain airflow obstruction (eg, congestive heart failure, sarcoidosis, Langerhans cell histiocytosis) was associated with a lower rate of testing (P = .041), while carrying a diagnosis of asthma tended to increase the testing frequency (P = .15). Multivariate analysis showed that younger age and a smoking history were associated with being tested.

Conclusions:  Issuing an alert within an EHR was associated with a severalfold increase in the frequency of testing for α1-antitrypsin without a higher rate of diagnosing severe AATD. While the lack of more frequent diagnosis of AATD may reflect a high rate of baseline detection, these results prompt consideration of additional strategies to enhance detection of AATD.

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