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Man vs machine in emergency medicine – a study on the effects of manual and automatic vital sign documentation on data quality and perceived workload, using observational paired sample data and questionnaires
BACKGROUND: Emergency medicine is characterized by a high patient flow where timely decisions are essential. Clinical decision support systems have the potential to assist in such decisions but will be dependent on the data quality in electronic health records which often is inadequate. This study e...
Autores principales: | , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
BioMed Central
2018
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6293611/ https://www.ncbi.nlm.nih.gov/pubmed/30545312 http://dx.doi.org/10.1186/s12873-018-0205-2 |
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author | Skyttberg, Niclas Chen, Rong Koch, Sabine |
author_facet | Skyttberg, Niclas Chen, Rong Koch, Sabine |
author_sort | Skyttberg, Niclas |
collection | PubMed |
description | BACKGROUND: Emergency medicine is characterized by a high patient flow where timely decisions are essential. Clinical decision support systems have the potential to assist in such decisions but will be dependent on the data quality in electronic health records which often is inadequate. This study explores the effect of automated documentation of vital signs on data quality and workload. METHODS: An observational study of 200 vital sign measurements was performed to evaluate the effects of manual vs automatic documentation on data quality. Data collection using questionnaires was performed to compare the workload on wards using manual or automatic documentation. RESULTS: In the automated documentation time to documentation was reduced by 6.1 min (0.6 min vs 7.7 min, p < 0.05) and completeness increased (98% vs 95%, p < 0.05). Regarding workflow temporal demands were lower in the automatic documentation workflow compared to the manual group (50 vs 23, p < 0.05). The same was true for frustration level (64 vs 33, p < 0.05). The experienced reduction in temporal demands was in line with the anticipated, whereas the experienced reduction in frustration was lower than the anticipated (27 vs 54, p < 0.05). DISCUSSION: The study shows that automatic documentation will improve the currency and the completeness of vital sign data in the Electronic Health Record while reducing workload regarding temporal demands and experienced frustration. The study also shows that these findings are in line with staff anticipations but indicates that the anticipations on the reduction of frustration may be exaggerated among the staff. The open-ended answers indicate that frustration focus will change from double documentation of vital signs to technical aspects of the automatic documentation system. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (10.1186/s12873-018-0205-2) contains supplementary material, which is available to authorized users. |
format | Online Article Text |
id | pubmed-6293611 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2018 |
publisher | BioMed Central |
record_format | MEDLINE/PubMed |
spelling | pubmed-62936112018-12-18 Man vs machine in emergency medicine – a study on the effects of manual and automatic vital sign documentation on data quality and perceived workload, using observational paired sample data and questionnaires Skyttberg, Niclas Chen, Rong Koch, Sabine BMC Emerg Med Research Article BACKGROUND: Emergency medicine is characterized by a high patient flow where timely decisions are essential. Clinical decision support systems have the potential to assist in such decisions but will be dependent on the data quality in electronic health records which often is inadequate. This study explores the effect of automated documentation of vital signs on data quality and workload. METHODS: An observational study of 200 vital sign measurements was performed to evaluate the effects of manual vs automatic documentation on data quality. Data collection using questionnaires was performed to compare the workload on wards using manual or automatic documentation. RESULTS: In the automated documentation time to documentation was reduced by 6.1 min (0.6 min vs 7.7 min, p < 0.05) and completeness increased (98% vs 95%, p < 0.05). Regarding workflow temporal demands were lower in the automatic documentation workflow compared to the manual group (50 vs 23, p < 0.05). The same was true for frustration level (64 vs 33, p < 0.05). The experienced reduction in temporal demands was in line with the anticipated, whereas the experienced reduction in frustration was lower than the anticipated (27 vs 54, p < 0.05). DISCUSSION: The study shows that automatic documentation will improve the currency and the completeness of vital sign data in the Electronic Health Record while reducing workload regarding temporal demands and experienced frustration. The study also shows that these findings are in line with staff anticipations but indicates that the anticipations on the reduction of frustration may be exaggerated among the staff. The open-ended answers indicate that frustration focus will change from double documentation of vital signs to technical aspects of the automatic documentation system. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (10.1186/s12873-018-0205-2) contains supplementary material, which is available to authorized users. BioMed Central 2018-12-13 /pmc/articles/PMC6293611/ /pubmed/30545312 http://dx.doi.org/10.1186/s12873-018-0205-2 Text en © The Author(s). 2018 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 | Research Article Skyttberg, Niclas Chen, Rong Koch, Sabine Man vs machine in emergency medicine – a study on the effects of manual and automatic vital sign documentation on data quality and perceived workload, using observational paired sample data and questionnaires |
title | Man vs machine in emergency medicine – a study on the effects of manual and automatic vital sign documentation on data quality and perceived workload, using observational paired sample data and questionnaires |
title_full | Man vs machine in emergency medicine – a study on the effects of manual and automatic vital sign documentation on data quality and perceived workload, using observational paired sample data and questionnaires |
title_fullStr | Man vs machine in emergency medicine – a study on the effects of manual and automatic vital sign documentation on data quality and perceived workload, using observational paired sample data and questionnaires |
title_full_unstemmed | Man vs machine in emergency medicine – a study on the effects of manual and automatic vital sign documentation on data quality and perceived workload, using observational paired sample data and questionnaires |
title_short | Man vs machine in emergency medicine – a study on the effects of manual and automatic vital sign documentation on data quality and perceived workload, using observational paired sample data and questionnaires |
title_sort | man vs machine in emergency medicine – a study on the effects of manual and automatic vital sign documentation on data quality and perceived workload, using observational paired sample data and questionnaires |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6293611/ https://www.ncbi.nlm.nih.gov/pubmed/30545312 http://dx.doi.org/10.1186/s12873-018-0205-2 |
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