Documentation-based clinical decision support to improve antibiotic prescribing for acute respiratory infections in primary care: a cluster randomised controlled trial
<strong>Background</strong> and objective Clinical guidelines discourage antibiotic prescribing for many acute respiratory infections (ARIs), especially for non-antibiotic appropriate diagnoses. Electronic health record (EHR)-based clinical decision support has the potential to improve a...
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2009-12-01
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doaj-c1a069a3bb514dabbd02110a1e761f102020-11-24T23:12:21ZengBCS, The Chartered Institute for ITJournal of Innovation in Health Informatics2058-45552058-45632009-12-0117423124010.14236/jhi.v17i4.742684Documentation-based clinical decision support to improve antibiotic prescribing for acute respiratory infections in primary care: a cluster randomised controlled trialJeffrey LinderJeffrey SchnipperRuslana TsurikovaTony YuLynn VolkAndrea MelnikasMatvey PalchukMaya Olsha-YehiavBlackford Middleton<strong>Background</strong> and objective Clinical guidelines discourage antibiotic prescribing for many acute respiratory infections (ARIs), especially for non-antibiotic appropriate diagnoses. Electronic health record (EHR)-based clinical decision support has the potential to improve antibiotic prescribing for ARIs. <strong>Methods</strong> We randomly assigned 27 primary care clinics to receive an EHR-integrated, documentation based clinical decision support system for the care of patients with ARIs - the ARI Smart Form - or to offer usual care. The primary outcome was the antibiotic prescribing rate for ARIs in an intent-to-intervene analysis based on administrative diagnoses. <strong>Results</strong> During the intervention period, patients made 21 961 ARI visits to study clinics. Intervention clinicians used the ARI Smart Form in 6% of 11 954 ARI visits. The antibiotic prescribing rate in the intervention clinics was 39% versus 43% in the control clinics (odds ratio (OR), 0.8; 95% confidence interval (CI), 0.6_1.2, adjusted for clustering by clinic). For antibiotic appropriate ARI diagnoses, the antibiotic prescribing rate was 54% in the intervention clinics and 59% in the control clinics (OR, 0.8; 95% CI, 0.5_1.3). For non-antibiotic appropriate diagnoses, the antibiotic prescribing rate was 32% in the intervention clinics and 34% in the control clinics (OR, 0.9; 95% CI, 0.6_1.4). When the ARI Smart Form was used, based on diagnoses entered on the form, the antibiotic prescribing rate was 49% overall, 88% for antibiotic appropriate diagnoses and 27% for non-antibiotic appropriate diagnoses. In an as-used analysis, the ARI Smart Form was associated with a lower antibiotic prescribing rate for acute bronchitis (OR, 0.5; 95% CI, 0.3_0.8). <strong>Conclusions</strong> The ARI Smart Form neither reduced overall antibiotic prescribing nor significantly improved the appropriateness of antibiotic prescribing for ARIs, but it was not widely used. When used, the ARI Smart Form may improve diagnostic accuracy compared to administrative diagnoses and may reduce antibiotic prescribing for certain diagnoses.http://hijournal.bcs.org/index.php/jhi/article/view/742antibacterial agentscomputerised medical record systemsclinical decision support systemsrespiratory tract infections |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Jeffrey Linder Jeffrey Schnipper Ruslana Tsurikova Tony Yu Lynn Volk Andrea Melnikas Matvey Palchuk Maya Olsha-Yehiav Blackford Middleton |
spellingShingle |
Jeffrey Linder Jeffrey Schnipper Ruslana Tsurikova Tony Yu Lynn Volk Andrea Melnikas Matvey Palchuk Maya Olsha-Yehiav Blackford Middleton Documentation-based clinical decision support to improve antibiotic prescribing for acute respiratory infections in primary care: a cluster randomised controlled trial Journal of Innovation in Health Informatics antibacterial agents computerised medical record systems clinical decision support systems respiratory tract infections |
author_facet |
Jeffrey Linder Jeffrey Schnipper Ruslana Tsurikova Tony Yu Lynn Volk Andrea Melnikas Matvey Palchuk Maya Olsha-Yehiav Blackford Middleton |
author_sort |
Jeffrey Linder |
title |
Documentation-based clinical decision support to improve antibiotic prescribing for acute respiratory infections in primary care: a cluster randomised controlled trial |
title_short |
Documentation-based clinical decision support to improve antibiotic prescribing for acute respiratory infections in primary care: a cluster randomised controlled trial |
title_full |
Documentation-based clinical decision support to improve antibiotic prescribing for acute respiratory infections in primary care: a cluster randomised controlled trial |
title_fullStr |
Documentation-based clinical decision support to improve antibiotic prescribing for acute respiratory infections in primary care: a cluster randomised controlled trial |
title_full_unstemmed |
Documentation-based clinical decision support to improve antibiotic prescribing for acute respiratory infections in primary care: a cluster randomised controlled trial |
title_sort |
documentation-based clinical decision support to improve antibiotic prescribing for acute respiratory infections in primary care: a cluster randomised controlled trial |
publisher |
BCS, The Chartered Institute for IT |
series |
Journal of Innovation in Health Informatics |
issn |
2058-4555 2058-4563 |
publishDate |
2009-12-01 |
description |
<strong>Background</strong> and objective Clinical guidelines discourage antibiotic prescribing for many acute respiratory infections (ARIs), especially for non-antibiotic appropriate diagnoses. Electronic health record (EHR)-based clinical decision support has the potential to improve antibiotic prescribing for ARIs.
<strong>Methods</strong> We randomly assigned 27 primary care clinics to receive an EHR-integrated, documentation based clinical decision support system for the care of patients with ARIs - the ARI Smart Form - or to offer usual care. The primary outcome was the antibiotic prescribing rate for ARIs in an intent-to-intervene analysis based on administrative diagnoses.
<strong>Results</strong> During the intervention period, patients made 21 961 ARI visits to study clinics. Intervention clinicians used the ARI Smart Form in 6% of 11 954 ARI visits. The antibiotic prescribing rate in the intervention clinics was 39% versus 43% in the control clinics (odds ratio (OR), 0.8; 95% confidence interval (CI), 0.6_1.2, adjusted for clustering by clinic). For antibiotic appropriate ARI diagnoses, the antibiotic prescribing rate was 54% in the intervention clinics and 59% in the control clinics (OR, 0.8; 95% CI, 0.5_1.3). For non-antibiotic appropriate diagnoses, the antibiotic prescribing rate was 32% in the intervention clinics and 34% in the control clinics (OR, 0.9; 95% CI, 0.6_1.4). When the ARI Smart Form was used, based on diagnoses entered on the form, the antibiotic prescribing rate was 49% overall, 88% for antibiotic appropriate diagnoses and 27% for non-antibiotic appropriate diagnoses. In an as-used analysis, the ARI Smart Form was associated with a lower antibiotic prescribing rate for acute bronchitis (OR, 0.5; 95% CI, 0.3_0.8).
<strong>Conclusions</strong> The ARI Smart Form neither reduced overall antibiotic prescribing nor significantly improved the appropriateness of antibiotic prescribing for ARIs, but it was not widely used. When used, the ARI Smart Form may improve diagnostic accuracy compared to administrative diagnoses and may reduce antibiotic prescribing for certain diagnoses. |
topic |
antibacterial agents computerised medical record systems clinical decision support systems respiratory tract infections |
url |
http://hijournal.bcs.org/index.php/jhi/article/view/742 |
work_keys_str_mv |
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