Screening for medication errors using an outlier detection system.

Autor:	Schiff GD; Division of General Internal Medicine and Primary Care, Brigham and Women's Hospital, Boston, MA, USA.; Center for Patient Safety Research and Practice, Brigham and Women's Hospital, Boston, MA, USA.; Harvard Medical School, Boston, MA, USA., Volk LA; Clinical and Quality Analysis, Partners HealthCare, Boston, MA, USA., Volodarskaya M; Division of General Internal Medicine and Primary Care, Brigham and Women's Hospital, Boston, MA, USA., Williams DH; Division of General Internal Medicine and Primary Care, Brigham and Women's Hospital, Boston, MA, USA., Walsh L; Division of General Internal Medicine and Primary Care, Brigham and Women's Hospital, Boston, MA, USA., Myers SG; Clinical and Quality Analysis, Partners HealthCare, Boston, MA, USA., Bates DW; Division of General Internal Medicine and Primary Care, Brigham and Women's Hospital, Boston, MA, USA.; Center for Patient Safety Research and Practice, Brigham and Women's Hospital, Boston, MA, USA.; Harvard Medical School, Boston, MA, USA., Rozenblum R; Division of General Internal Medicine and Primary Care, Brigham and Women's Hospital, Boston, MA, USA.; Center for Patient Safety Research and Practice, Brigham and Women's Hospital, Boston, MA, USA.; Harvard Medical School, Boston, MA, USA.
Jazyk:	angličtina
Zdroj:	Journal of the American Medical Informatics Association : JAMIA [J Am Med Inform Assoc] 2017 Mar 01; Vol. 24 (2), pp. 281-287.
DOI:	10.1093/jamia/ocw171
Abstrakt:	Objective: The study objective was to evaluate the accuracy, validity, and clinical usefulness of medication error alerts generated by an alerting system using outlier detection screening. Materials and Methods: Five years of clinical data were extracted from an electronic health record system for 747 985 patients who had at least one visit during 2012-2013 at practices affiliated with 2 academic medical centers. Data were screened using the system to detect outliers suggestive of potential medication errors. A sample of 300 charts was selected for review from the 15 693 alerts generated. A coding system was developed and codes assigned based on chart review to reflect the accuracy, validity, and clinical value of the alerts. Results: Three-quarters of the chart-reviewed alerts generated by the screening system were found to be valid in which potential medication errors were identified. Of these valid alerts, the majority (75.0%) were found to be clinically useful in flagging potential medication errors or issues. Discussion: A clinical decision support (CDS) system that used a probabilistic, machine-learning approach based on statistically derived outliers to detect medication errors generated potentially useful alerts with a modest rate of false positives. The performance of such a surveillance and alerting system is critically dependent on the quality and completeness of the underlying data. Conclusion: The screening system was able to generate alerts that might otherwise be missed with existing CDS systems and did so with a reasonably high degree of alert usefulness when subjected to review of patients' clinical contexts and details. (© The Author 2017. Published by Oxford University Press on behalf of the American Medical Informatics Association. All rights reserved. For Permissions, please email: journals.permissions@oup.com)
Databáze:	MEDLINE
Externí odkaz:	Zobrazit plný text záznamu