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Optimising computerised decision support to transform medication safety and reduce prescriber burden: study protocol for a mixed-methods evaluation of drug–drug interaction alerts
INTRODUCTION: Drug–drug interaction (DDI) alerts in hospital electronic medication management (EMM) systems are generated at the point of prescribing to warn doctors about potential interactions in their patients’ medication orders. This project aims to determine the impact of DDI alerts on DDI rate...
Autores principales: | , , , , , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
BMJ Publishing Group
2019
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6701635/ https://www.ncbi.nlm.nih.gov/pubmed/31427312 http://dx.doi.org/10.1136/bmjopen-2018-026034 |
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author | Baysari, Melissa T Zheng, Wu Yi Li, Ling Westbrook, Johanna Day, Richard O Hilmer, Sarah Van Dort, Bethany Annemarie Hargreaves, Andrew Kennedy, Peter Monaghan, Corey Doherty, Paula Draheim, Michael Nair, Lucy Samson, Ruby |
author_facet | Baysari, Melissa T Zheng, Wu Yi Li, Ling Westbrook, Johanna Day, Richard O Hilmer, Sarah Van Dort, Bethany Annemarie Hargreaves, Andrew Kennedy, Peter Monaghan, Corey Doherty, Paula Draheim, Michael Nair, Lucy Samson, Ruby |
author_sort | Baysari, Melissa T |
collection | PubMed |
description | INTRODUCTION: Drug–drug interaction (DDI) alerts in hospital electronic medication management (EMM) systems are generated at the point of prescribing to warn doctors about potential interactions in their patients’ medication orders. This project aims to determine the impact of DDI alerts on DDI rates and on patient harm in the inpatient setting. It also aims to identify barriers and facilitators to optimal use of alerts, quantify the alert burden posed to prescribers with implementation of DDI alerts and to develop algorithms to improve the specificity of DDI alerting systems. METHODS AND ANALYSIS: A controlled pre-post design will be used. Study sites include six major referral hospitals in two Australian states, New South Wales and Queensland. Three hospitals will act as control sites and will implement an EMM system without DDI alerts, and three as intervention sites with DDI alerts. The medical records of 280 patients admitted in the 6 months prior to and 6 months following implementation of the EMM system at each site (total 3360 patients) will be retrospectively reviewed by study pharmacists to identify potential DDIs, clinically relevant DDIs and associated patient harm. To identify barriers and facilitators to optimal use of alerts, 10–15 doctors working at each intervention hospital will take part in observations and interviews. Non-identifiable DDI alert data will be extracted from EMM systems 6–12 months after system implementation in order to quantify alert burden on prescribers. Finally, data collected from chart review and EMM systems will be linked with clinically relevant DDIs to inform the development of algorithms to trigger only clinically relevant DDI alerts in EMM systems. ETHICS AND DISSEMINATION: This research was approved by the Hunter New England Human Research Ethics Committee (18/02/21/4.07). Study results will be published in peer-reviewed journals and presented at local and international conferences and workshops. |
format | Online Article Text |
id | pubmed-6701635 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2019 |
publisher | BMJ Publishing Group |
record_format | MEDLINE/PubMed |
spelling | pubmed-67016352019-09-02 Optimising computerised decision support to transform medication safety and reduce prescriber burden: study protocol for a mixed-methods evaluation of drug–drug interaction alerts Baysari, Melissa T Zheng, Wu Yi Li, Ling Westbrook, Johanna Day, Richard O Hilmer, Sarah Van Dort, Bethany Annemarie Hargreaves, Andrew Kennedy, Peter Monaghan, Corey Doherty, Paula Draheim, Michael Nair, Lucy Samson, Ruby BMJ Open Health Informatics INTRODUCTION: Drug–drug interaction (DDI) alerts in hospital electronic medication management (EMM) systems are generated at the point of prescribing to warn doctors about potential interactions in their patients’ medication orders. This project aims to determine the impact of DDI alerts on DDI rates and on patient harm in the inpatient setting. It also aims to identify barriers and facilitators to optimal use of alerts, quantify the alert burden posed to prescribers with implementation of DDI alerts and to develop algorithms to improve the specificity of DDI alerting systems. METHODS AND ANALYSIS: A controlled pre-post design will be used. Study sites include six major referral hospitals in two Australian states, New South Wales and Queensland. Three hospitals will act as control sites and will implement an EMM system without DDI alerts, and three as intervention sites with DDI alerts. The medical records of 280 patients admitted in the 6 months prior to and 6 months following implementation of the EMM system at each site (total 3360 patients) will be retrospectively reviewed by study pharmacists to identify potential DDIs, clinically relevant DDIs and associated patient harm. To identify barriers and facilitators to optimal use of alerts, 10–15 doctors working at each intervention hospital will take part in observations and interviews. Non-identifiable DDI alert data will be extracted from EMM systems 6–12 months after system implementation in order to quantify alert burden on prescribers. Finally, data collected from chart review and EMM systems will be linked with clinically relevant DDIs to inform the development of algorithms to trigger only clinically relevant DDI alerts in EMM systems. ETHICS AND DISSEMINATION: This research was approved by the Hunter New England Human Research Ethics Committee (18/02/21/4.07). Study results will be published in peer-reviewed journals and presented at local and international conferences and workshops. BMJ Publishing Group 2019-08-18 /pmc/articles/PMC6701635/ /pubmed/31427312 http://dx.doi.org/10.1136/bmjopen-2018-026034 Text en © Author(s) (or their employer(s)) 2019. Re-use permitted under CC BY-NC. No commercial re-use. See rights and permissions. Published by BMJ. 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/. |
spellingShingle | Health Informatics Baysari, Melissa T Zheng, Wu Yi Li, Ling Westbrook, Johanna Day, Richard O Hilmer, Sarah Van Dort, Bethany Annemarie Hargreaves, Andrew Kennedy, Peter Monaghan, Corey Doherty, Paula Draheim, Michael Nair, Lucy Samson, Ruby Optimising computerised decision support to transform medication safety and reduce prescriber burden: study protocol for a mixed-methods evaluation of drug–drug interaction alerts |
title | Optimising computerised decision support to transform medication safety and reduce prescriber burden: study protocol for a mixed-methods evaluation of drug–drug interaction alerts |
title_full | Optimising computerised decision support to transform medication safety and reduce prescriber burden: study protocol for a mixed-methods evaluation of drug–drug interaction alerts |
title_fullStr | Optimising computerised decision support to transform medication safety and reduce prescriber burden: study protocol for a mixed-methods evaluation of drug–drug interaction alerts |
title_full_unstemmed | Optimising computerised decision support to transform medication safety and reduce prescriber burden: study protocol for a mixed-methods evaluation of drug–drug interaction alerts |
title_short | Optimising computerised decision support to transform medication safety and reduce prescriber burden: study protocol for a mixed-methods evaluation of drug–drug interaction alerts |
title_sort | optimising computerised decision support to transform medication safety and reduce prescriber burden: study protocol for a mixed-methods evaluation of drug–drug interaction alerts |
topic | Health Informatics |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6701635/ https://www.ncbi.nlm.nih.gov/pubmed/31427312 http://dx.doi.org/10.1136/bmjopen-2018-026034 |
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