270
Views
9
CrossRef citations to date
0
Altmetric
Research Article

A model to improve the accuracy of US Poison Center data collection

, , &
Pages 889-896 | Received 02 May 2014, Accepted 04 Aug 2014, Published online: 02 Sep 2014
 

Abstract

Context. Over 2 million human exposure calls are reported annually to United States regional poison information centers. All exposures are documented electronically and submitted to the American Association of Poison Control Center's National Poison Data System. This database represents the largest data source available on the epidemiology of pharmaceutical and non-pharmaceutical poisoning exposures. The accuracy of these data is critical; however, research has demonstrated that inconsistencies and inaccuracies exist. Objective. This study outlines the methods and results of a training program that was developed and implemented to enhance the quality of data collection using acetaminophen exposures as a model. Methods. Eleven poison centers were assigned randomly to receive either passive or interactive education to improve medical record documentation. A task force provided recommendations on educational and training strategies and the development of a quality-measurement scorecard to serve as a data collection tool to assess poison center data quality. Poison centers were recruited to participate in the study. Clinical researchers scored the documentation of each exposure record for accuracy. Results. Two thousand two hundred cases were reviewed and assessed for accuracy of data collection. After training, the overall mean quality scores were higher for both the passive (95.3%; + 1.6% change) and interactive intervention groups (95.3%; + 0.9% change). Data collection accuracy improved modestly for the overall accuracy score and significantly for the substance identification component. There was little difference in accuracy measures between the different training methods. Conclusion. Despite the diversity of poison centers, data accuracy, specifically substance identification data fields, can be improved by developing a standardized, systematic, targeted, and mandatory training process. This process should be considered for training on other important topics, thus enhancing the value of these data in relation to public health safety.

Acknowledgements

We thank the AAPCC Task Force members, Bruce Anderson, Leopoldo Artalejo III, Randy Badillo, Daniel J. Cobaugh, Rita Mrvos, and S. Rutherfoord Rose, for their assistance in advising the training program content and Vaishali Khatri for her help collecting data. We also thank the regional poison information centers who participated in this study: Drug and Poison Information Center, Cincinnati Children's Hospital; Carolinas Poison Center; Florida/USVI Poison Information Center, Jacksonville; Hennepin Regional Poison Center; Illinois Poison Center; Kentucky Regional Poison Control Center; Nebraska Regional Poison Center; Oklahoma Poison Control Center; VHS Children's Hospital of Michigan Regional Poison Control Center; Virginia Poison Center; and Washington Poison Center.

Declaration of interest

This project was funded by an Investigator-Initiated Grant from McNeil Consumer Healthcare. The sponsor had no role in the design, conduct, analysis, or manuscript preparation. The sponsor was allowed to review the manuscript prior to submission for the assessment of proprietary information only. The final content of the manuscript was determined solely by the authors.

Log in via your institution

Log in to Taylor & Francis Online

PDF download + Online access

  • 48 hours access to article PDF & online version
  • Article PDF can be downloaded
  • Article PDF can be printed
USD 65.00 Add to cart

Issue Purchase

  • 30 days online access to complete issue
  • Article PDFs can be downloaded
  • Article PDFs can be printed
USD 1,501.00 Add to cart

* Local tax will be added as applicable

Related Research

People also read lists articles that other readers of this article have read.

Recommended articles lists articles that we recommend and is powered by our AI driven recommendation engine.

Cited by lists all citing articles based on Crossref citations.
Articles with the Crossref icon will open in a new tab.