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Impact of computerised provider order entry on the quality and quantity of clinical information included with investigation requests: an interrupted time series analysis

INTRODUCTION: Relevant clinical information is vital to inform the analytical and interpretative phases of most investigations. The aim of this study is to evaluate the impact of implementation of computerised provider order entry (CPOE), featuring order-specific electronic order entry forms (eOEFs)...

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Autores principales: Weiand, Daniel, Cullerton, Caroline, Oxley, Robert, Plummer, Chris J
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BMJ Publishing Group 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9890764/
https://www.ncbi.nlm.nih.gov/pubmed/36720495
http://dx.doi.org/10.1136/bmjoq-2022-002143
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author Weiand, Daniel
Cullerton, Caroline
Oxley, Robert
Plummer, Chris J
author_facet Weiand, Daniel
Cullerton, Caroline
Oxley, Robert
Plummer, Chris J
author_sort Weiand, Daniel
collection PubMed
description INTRODUCTION: Relevant clinical information is vital to inform the analytical and interpretative phases of most investigations. The aim of this study is to evaluate the impact of implementation of computerised provider order entry (CPOE), featuring order-specific electronic order entry forms (eOEFs), on the quality and quantity of clinical information included with investigation requests. METHODS: The CPOE module of a commercially available electronic health record (Cerner Millennium) was implemented at a large, tertiary care centre. The laboratory information management system was interrogated to collect data on specimens sent for microbiological culture 1 year before implementation of CPOE (2018), immediately post implementation (2019) and 6 months post implementation (2020). An interrupted time series analysis was performed, using text mining, to evaluate the quality and quantity of free-text clinical information. RESULTS: In total, 39 919 specimens were collected from 16 458 patients. eOEFs were used to place 10 071 out of 13 735 orders in 2019 (73.3%), and 9155 out of 12 229 orders in 2020 (74.9%). No clinical details were included with 653 out of 39 919 specimens (1.6%), of which 22 (3.4%) were ordered using eOEFs. The median character count increased from 14 in 2018, to 41 in 2019, and 38 in 2020. An anti-infective agent was specified in 581 out of 13 955 requests (4.2%) in 2018; 5545 out of 13 735 requests (40.4%) in 2019; and 5215 out of 12 229 requests (42.6%) in 2020. Ciprofloxacin or piperacillin-tazobactam (Tazocin) were mentioned in the clinical details included with 421 out of 15 335 urine culture requests (2.7%), of which 406 (96.3%) were ordered using eOEFs. Subsequent detection of in vitro non-susceptibility led to a change in anti-infective therapy for five patients. CONCLUSIONS: Implementation of CPOE, featuring order-specific eOEFs, significantly and sustainably improves the quality and quantity of clinical information included with investigation requests, resulting in changes to patient management that would not otherwise have occurred.
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spelling pubmed-98907642023-02-02 Impact of computerised provider order entry on the quality and quantity of clinical information included with investigation requests: an interrupted time series analysis Weiand, Daniel Cullerton, Caroline Oxley, Robert Plummer, Chris J BMJ Open Qual Original Research INTRODUCTION: Relevant clinical information is vital to inform the analytical and interpretative phases of most investigations. The aim of this study is to evaluate the impact of implementation of computerised provider order entry (CPOE), featuring order-specific electronic order entry forms (eOEFs), on the quality and quantity of clinical information included with investigation requests. METHODS: The CPOE module of a commercially available electronic health record (Cerner Millennium) was implemented at a large, tertiary care centre. The laboratory information management system was interrogated to collect data on specimens sent for microbiological culture 1 year before implementation of CPOE (2018), immediately post implementation (2019) and 6 months post implementation (2020). An interrupted time series analysis was performed, using text mining, to evaluate the quality and quantity of free-text clinical information. RESULTS: In total, 39 919 specimens were collected from 16 458 patients. eOEFs were used to place 10 071 out of 13 735 orders in 2019 (73.3%), and 9155 out of 12 229 orders in 2020 (74.9%). No clinical details were included with 653 out of 39 919 specimens (1.6%), of which 22 (3.4%) were ordered using eOEFs. The median character count increased from 14 in 2018, to 41 in 2019, and 38 in 2020. An anti-infective agent was specified in 581 out of 13 955 requests (4.2%) in 2018; 5545 out of 13 735 requests (40.4%) in 2019; and 5215 out of 12 229 requests (42.6%) in 2020. Ciprofloxacin or piperacillin-tazobactam (Tazocin) were mentioned in the clinical details included with 421 out of 15 335 urine culture requests (2.7%), of which 406 (96.3%) were ordered using eOEFs. Subsequent detection of in vitro non-susceptibility led to a change in anti-infective therapy for five patients. CONCLUSIONS: Implementation of CPOE, featuring order-specific eOEFs, significantly and sustainably improves the quality and quantity of clinical information included with investigation requests, resulting in changes to patient management that would not otherwise have occurred. BMJ Publishing Group 2023-01-31 /pmc/articles/PMC9890764/ /pubmed/36720495 http://dx.doi.org/10.1136/bmjoq-2022-002143 Text en © Author(s) (or their employer(s)) 2023. Re-use permitted under CC BY-NC. No commercial re-use. See rights and permissions. Published by BMJ. https://creativecommons.org/licenses/by-nc/4.0/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/ (https://creativecommons.org/licenses/by-nc/4.0/) .
spellingShingle Original Research
Weiand, Daniel
Cullerton, Caroline
Oxley, Robert
Plummer, Chris J
Impact of computerised provider order entry on the quality and quantity of clinical information included with investigation requests: an interrupted time series analysis
title Impact of computerised provider order entry on the quality and quantity of clinical information included with investigation requests: an interrupted time series analysis
title_full Impact of computerised provider order entry on the quality and quantity of clinical information included with investigation requests: an interrupted time series analysis
title_fullStr Impact of computerised provider order entry on the quality and quantity of clinical information included with investigation requests: an interrupted time series analysis
title_full_unstemmed Impact of computerised provider order entry on the quality and quantity of clinical information included with investigation requests: an interrupted time series analysis
title_short Impact of computerised provider order entry on the quality and quantity of clinical information included with investigation requests: an interrupted time series analysis
title_sort impact of computerised provider order entry on the quality and quantity of clinical information included with investigation requests: an interrupted time series analysis
topic Original Research
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9890764/
https://www.ncbi.nlm.nih.gov/pubmed/36720495
http://dx.doi.org/10.1136/bmjoq-2022-002143
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