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Prescription reporting with immediate medication utilization mapping (PRIMUM): development of an alert to improve narcotic prescribing

BACKGROUND: Prescription narcotic overdoses and abuse have reached alarming numbers. To address this epidemic, integrated clinical decision support within the electronic medical record (EMR) to impact prescribing behavior was developed and tested. METHODS: A multidisciplinary Expert Panel identified...

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Autores principales: Seymour, Rachel B., Leas, Daniel, Wally, Meghan K., Hsu, Joseph R.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2016
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4994311/
https://www.ncbi.nlm.nih.gov/pubmed/27549364
http://dx.doi.org/10.1186/s12911-016-0352-x
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author Seymour, Rachel B.
Leas, Daniel
Wally, Meghan K.
Hsu, Joseph R.
author_facet Seymour, Rachel B.
Leas, Daniel
Wally, Meghan K.
Hsu, Joseph R.
author_sort Seymour, Rachel B.
collection PubMed
description BACKGROUND: Prescription narcotic overdoses and abuse have reached alarming numbers. To address this epidemic, integrated clinical decision support within the electronic medical record (EMR) to impact prescribing behavior was developed and tested. METHODS: A multidisciplinary Expert Panel identified risk factors for misuse, abuse, or diversion of opioids or benzodiazepines through literature reviews and consensus building for inclusion in a rule within the EMR. We ran the rule “silently” to test the rule and collect baseline data. RESULTS: Five criteria were programmed to trigger the alert; based on data collected during a “silent” phase, thresholds for triggers were modified. The alert would have fired in 21.75 % of prescribing encounters (1.30 % of all encounters; n = 9998), suggesting the alert will have a low prescriber burden yet capture a significant number of at-risk patients. CONCLUSIONS: While the use of the EMR to provide clinical decision support is not new, utilizing it to develop and test an intervention is novel. We successfully built an alert system to address narcotic prescribing by providing critical, objective information at the point of care. The silent phase data were useful to appropriately tune the alert and obtain support for widespread implementation. Future healthcare initiatives can utilize similar methodology to collect data prospectively via the electronic medical record to inform the development, delivery, and evaluation of interventions.
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spelling pubmed-49943112016-08-24 Prescription reporting with immediate medication utilization mapping (PRIMUM): development of an alert to improve narcotic prescribing Seymour, Rachel B. Leas, Daniel Wally, Meghan K. Hsu, Joseph R. BMC Med Inform Decis Mak Technical Advance BACKGROUND: Prescription narcotic overdoses and abuse have reached alarming numbers. To address this epidemic, integrated clinical decision support within the electronic medical record (EMR) to impact prescribing behavior was developed and tested. METHODS: A multidisciplinary Expert Panel identified risk factors for misuse, abuse, or diversion of opioids or benzodiazepines through literature reviews and consensus building for inclusion in a rule within the EMR. We ran the rule “silently” to test the rule and collect baseline data. RESULTS: Five criteria were programmed to trigger the alert; based on data collected during a “silent” phase, thresholds for triggers were modified. The alert would have fired in 21.75 % of prescribing encounters (1.30 % of all encounters; n = 9998), suggesting the alert will have a low prescriber burden yet capture a significant number of at-risk patients. CONCLUSIONS: While the use of the EMR to provide clinical decision support is not new, utilizing it to develop and test an intervention is novel. We successfully built an alert system to address narcotic prescribing by providing critical, objective information at the point of care. The silent phase data were useful to appropriately tune the alert and obtain support for widespread implementation. Future healthcare initiatives can utilize similar methodology to collect data prospectively via the electronic medical record to inform the development, delivery, and evaluation of interventions. BioMed Central 2016-08-22 /pmc/articles/PMC4994311/ /pubmed/27549364 http://dx.doi.org/10.1186/s12911-016-0352-x Text en © The Author(s). 2016 Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.
spellingShingle Technical Advance
Seymour, Rachel B.
Leas, Daniel
Wally, Meghan K.
Hsu, Joseph R.
Prescription reporting with immediate medication utilization mapping (PRIMUM): development of an alert to improve narcotic prescribing
title Prescription reporting with immediate medication utilization mapping (PRIMUM): development of an alert to improve narcotic prescribing
title_full Prescription reporting with immediate medication utilization mapping (PRIMUM): development of an alert to improve narcotic prescribing
title_fullStr Prescription reporting with immediate medication utilization mapping (PRIMUM): development of an alert to improve narcotic prescribing
title_full_unstemmed Prescription reporting with immediate medication utilization mapping (PRIMUM): development of an alert to improve narcotic prescribing
title_short Prescription reporting with immediate medication utilization mapping (PRIMUM): development of an alert to improve narcotic prescribing
title_sort prescription reporting with immediate medication utilization mapping (primum): development of an alert to improve narcotic prescribing
topic Technical Advance
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4994311/
https://www.ncbi.nlm.nih.gov/pubmed/27549364
http://dx.doi.org/10.1186/s12911-016-0352-x
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