biostudies-literatureAaron SNational Library of MedicineNLM NIH HHSNational Institutes of Health37-43https://www.ebi.ac.uk/biostudies/studies/S-EPMC6308015biostudies-literatureUnknown26(1)<h4>Background</h4>Rule-base clinical decision support alerts are known to malfunction, but tools for discovering malfunctions are limited.<h4>Objective</h4>Investigate whether user override comments can be used to discover malfunctions.<h4>Methods</h4>We manually classified all rules in our database with at least 10 override comments into 3 categories based on a sample of override comments: "broken," "not broken, but could be improved," and "not broken." We used 3 methods (frequency of comments, cranky word list heuristic, and a Naïve Bayes classifier trained on a sample of comments) to automatically rank rules based on features of their override comments. We evaluated each ranking using the manual classification as truth.<h4>Results</h4>Of the rules investigated, 62 were broken, 13 could be improved, and the remaining 45 were not broken. Frequency of comments performed worse than a random ranking, with precision at 20 of 8 and AUC = 0.487. The cranky comments heuristic performed better with precision at 20 of 16 and AUC = 0.723. The Naïve Bayes classifier had precision at 20 of 17 and AUC = 0.738.<h4>Discussion</h4>Override comments uncovered malfunctions in 26% of all rules active in our system. This is a lower bound on total malfunctions and much higher than expected. Even for low-resource organizations, reviewing comments identified by the cranky word list heuristic may be an effective and feasible way of finding broken alerts.<h4>Conclusion</h4>Override comments are a rich data source for finding alerts that are broken or could be improved. If possible, we recommend monitoring all override comments on a regular basis.Journal of the American Medical Informatics Association : JAMIACranky comments: detecting clinical decision support malfunctions through free-text override reasons.PMC6308015R01LM011966R01 LM011966Aaron SWright AMcEvoy DSRay SHickman TTfalseCranky comments: detecting clinical decision support malfunctions through free-text override reasons.<h4>Background</h4>Rule-base clinical decision support alerts are known to malfunction, but tools for discovering malfunctions are limited.<h4>Objective</h4>Investigate whether user override comments can be used to discover malfunctions.<h4>Methods</h4>We manually classified all rules in our database with at least 10 override comments into 3 categories based on a sample of override comments: "broken," "not broken, but could be improved," and "not broken." We used 3 methods (frequency of comments, cranky word list heuristic, and a Naïve Bayes classifier trained on a sample of comments) to automatically rank rules based on features of their override comments. We evaluated each ranking using the manual classification as truth.<h4>Results</h4>Of the rules investigated, 62 were broken, 13 could be improved, and the remaining 45 were not broken. Frequency of comments performed worse than a random ranking, with precision at 20 of 8 and AUC = 0.487. The cranky comments heuristic performed better with precision at 20 of 16 and AUC = 0.723. The Naïve Bayes classifier had precision at 20 of 17 and AUC = 0.738.<h4>Discussion</h4>Override comments uncovered malfunctions in 26% of all rules active in our system. This is a lower bound on total malfunctions and much higher than expected. Even for low-resource organizations, reviewing comments identified by the cranky word list heuristic may be an effective and feasible way of finding broken alerts.<h4>Conclusion</h4>Override comments are a rich data source for finding alerts that are broken or could be improved. If possible, we recommend monitoring all override comments on a regular basis.2019-01-01T00:00:00Z2019 Jan2024-12-04T04:23:40.242Z2019-03-26T22:29:43ZS-EPMC63080153059055710.1093/jamia/ocy139