Zhao, H;
Bian, J;
Wei, L;
Li, L;
Ying, Y;
Zhang, Z;
Yao, X;
... Zhan, S; + view all
(2020)
Validation of an algorithm to evaluate the appropriateness of outpatient antibiotic prescribing using big data of Chinese diagnosis text.
BMJ Open
, 10
(3)
, Article e031191. 10.1136/bmjopen-2019-031191.
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Abstract
OBJECTIVE: We aimed to evaluate the validity of an algorithm to classify diagnoses according to the appropriateness of outpatient antibiotic use in the context of Chinese free text. SETTING AND PARTICIPANTS: A random sample of 10 000 outpatient visits was selected between January and April 2018 from a national database for monitoring rational use of drugs, which included data from 194 secondary and tertiary hospitals in China. RESEARCH DESIGN: Diagnoses for outpatient visits were classified as tier 1 if associated with at least one condition that 'always' justified antibiotic use; as tier 2 if associated with at least one condition that only 'sometimes' justified antibiotic use but no conditions that 'always' justified antibiotic use; or as tier 3 if associated with only conditions that never justified antibiotic use, using a tier-fashion method and regular expression (RE)-based algorithm. MEASURES: Sensitivity, specificity, positive predictive value (PPV) and negative predictive value (NPV) of the classification algorithm, using classification made by chart review as the standard reference, were calculated. RESULTS: The sensitivities of the algorithm for classifying tier 1, tier 2 and tier 3 diagnoses were 98.2% (95% CI 96.4% to 99.3%), 98.4% (95% CI 97.6% to 99.1%) and 100.0% (95% CI 100.0% to 100.0%), respectively. The specificities were 100.0% (95% CI 100.0% to 100.0%), 100.0% (95% CI 99.9% to 100.0%) and 98.6% (95% CI 97.9% to 99.1%), respectively. The PPVs for classifying tier 1, tier 2 and tier 3 diagnoses were 100.0% (95% CI 99.1% to 100.0%), 99.7% (95% CI 99.2% to 99.9%) and 99.7% (95% CI 99.6% to 99.8%), respectively. The NPVs were 99.9% (95% CI 99.8% to 100.0%), 99.8% (95% CI 99.7% to 99.9%) and 100.0% (95% CI 99.8% to 100.0%), respectively. CONCLUSIONS: The RE-based classification algorithm in the context of Chinese free text had sufficiently high validity for further evaluating the appropriateness of outpatient antibiotic prescribing.
| Type: | Article |
|---|---|
| Title: | Validation of an algorithm to evaluate the appropriateness of outpatient antibiotic prescribing using big data of Chinese diagnosis text |
| Location: | England |
| Open access status: | An open access version is available from UCL Discovery |
| DOI: | 10.1136/bmjopen-2019-031191 |
| Publisher version: | https://doi.org/10.1136/bmjopen-2019-031191 |
| Language: | English |
| Additional information: | This is an open access article distributed in accordance with the Creative Commons Attribution Non Commercial (CC BY-NC 4.0) license, which permits others to distribute, remix, adapt, build upon this work non-commercially, and license their derivative works on different terms, provided the original work is properly cited, appropriate credit is given, any changes made indicated, and the use is non-commercial. See: http://creativecommons.org/licenses/by-nc/4.0/. |
| UCL classification: | UCL UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Life Sciences UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Life Sciences > UCL School of Pharmacy UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Life Sciences > UCL School of Pharmacy > Practice and Policy |
| URI: | https://discovery.ucl.ac.uk/id/eprint/10094398 |
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