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Improving Early Discharges With an Electronic Health Record Discharge Optimization Tool
INTRODUCTION: Delays in hospital discharge can negatively impact patient care, bed availability, and patient satisfaction. There are limited studies examining how the electronic health record (EHR) can be used to improve discharge timeliness. This study aimed to implement an EHR discharge optimizati...
Autores principales: | , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Wolters Kluwer Health
2020
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7297394/ https://www.ncbi.nlm.nih.gov/pubmed/32607458 http://dx.doi.org/10.1097/pq9.0000000000000301 |
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author | Perry, Michael F. Macias, Charlie Chaparro, Juan D. Heacock, Allison C. Jackson, Kenneth Bode, Ryan S. |
author_facet | Perry, Michael F. Macias, Charlie Chaparro, Juan D. Heacock, Allison C. Jackson, Kenneth Bode, Ryan S. |
author_sort | Perry, Michael F. |
collection | PubMed |
description | INTRODUCTION: Delays in hospital discharge can negatively impact patient care, bed availability, and patient satisfaction. There are limited studies examining how the electronic health record (EHR) can be used to improve discharge timeliness. This study aimed to implement an EHR discharge optimization tool (DOT) successfully and achieve a discharge before noon (DBN) percentage of 25%. METHODS: We conducted a single-center quality improvement study of patients discharged from 3 pediatric hospital medicine teaching service teams at a quaternary care academic children’s hospital. The multidisciplinary team created a DOT centrally embedded within the care team standard workflow to communicate anticipated time until discharge. The primary outcome was the monthly percentage of patients discharged before noon. Secondary outcomes included provider utilization of the DOT, tool accuracy, and patient length of stay. Balancing measures were 7- and 30-day readmission rates. RESULTS: The DBN percentage increased from 16.4% to an average of 19.3% over the 13-month intervention period (P = 0.0005). DOT utilization was measured at 87.2%, and the overall accuracy of predicting time until discharge was 75.6% (P < 0.0001). Median length of stay declined from 1.75 to 1.68 days (P = 0.0033), and there was no negative impact on 7- or 30-day readmission rates. CONCLUSION: This initiative demonstrated that a highly utilized and accurate discharge tool could be created in the EHR to assist medical care teams with improving DBN percentage on busy, academic teaching services. |
format | Online Article Text |
id | pubmed-7297394 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2020 |
publisher | Wolters Kluwer Health |
record_format | MEDLINE/PubMed |
spelling | pubmed-72973942020-06-29 Improving Early Discharges With an Electronic Health Record Discharge Optimization Tool Perry, Michael F. Macias, Charlie Chaparro, Juan D. Heacock, Allison C. Jackson, Kenneth Bode, Ryan S. Pediatr Qual Saf Individual QI Projects from Single Institutions INTRODUCTION: Delays in hospital discharge can negatively impact patient care, bed availability, and patient satisfaction. There are limited studies examining how the electronic health record (EHR) can be used to improve discharge timeliness. This study aimed to implement an EHR discharge optimization tool (DOT) successfully and achieve a discharge before noon (DBN) percentage of 25%. METHODS: We conducted a single-center quality improvement study of patients discharged from 3 pediatric hospital medicine teaching service teams at a quaternary care academic children’s hospital. The multidisciplinary team created a DOT centrally embedded within the care team standard workflow to communicate anticipated time until discharge. The primary outcome was the monthly percentage of patients discharged before noon. Secondary outcomes included provider utilization of the DOT, tool accuracy, and patient length of stay. Balancing measures were 7- and 30-day readmission rates. RESULTS: The DBN percentage increased from 16.4% to an average of 19.3% over the 13-month intervention period (P = 0.0005). DOT utilization was measured at 87.2%, and the overall accuracy of predicting time until discharge was 75.6% (P < 0.0001). Median length of stay declined from 1.75 to 1.68 days (P = 0.0033), and there was no negative impact on 7- or 30-day readmission rates. CONCLUSION: This initiative demonstrated that a highly utilized and accurate discharge tool could be created in the EHR to assist medical care teams with improving DBN percentage on busy, academic teaching services. Wolters Kluwer Health 2020-05-18 /pmc/articles/PMC7297394/ /pubmed/32607458 http://dx.doi.org/10.1097/pq9.0000000000000301 Text en Copyright © 2020 the Author(s). Published by Wolters Kluwer Health, Inc. This is an open access article distributed under the Creative Commons Attribution License 4.0 (CCBY) (http://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. |
spellingShingle | Individual QI Projects from Single Institutions Perry, Michael F. Macias, Charlie Chaparro, Juan D. Heacock, Allison C. Jackson, Kenneth Bode, Ryan S. Improving Early Discharges With an Electronic Health Record Discharge Optimization Tool |
title | Improving Early Discharges With an Electronic Health Record Discharge Optimization Tool |
title_full | Improving Early Discharges With an Electronic Health Record Discharge Optimization Tool |
title_fullStr | Improving Early Discharges With an Electronic Health Record Discharge Optimization Tool |
title_full_unstemmed | Improving Early Discharges With an Electronic Health Record Discharge Optimization Tool |
title_short | Improving Early Discharges With an Electronic Health Record Discharge Optimization Tool |
title_sort | improving early discharges with an electronic health record discharge optimization tool |
topic | Individual QI Projects from Single Institutions |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7297394/ https://www.ncbi.nlm.nih.gov/pubmed/32607458 http://dx.doi.org/10.1097/pq9.0000000000000301 |
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