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Evaluating Demographic Data to Improve Confidence in Equity Analytics in a Children’s Hospital
Healthcare institutions are placing greater emphasis on equitable care. To accurately track and validate equity metrics, Akron Children’s Hospital evaluated how key fields are collected, analyzed, and visualized throughout the organization. Standardized recommendations in this area vary, and this in...
Autores principales: | , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Lippincott Williams & Wilkins
2023
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10085515/ https://www.ncbi.nlm.nih.gov/pubmed/37051408 http://dx.doi.org/10.1097/pq9.0000000000000642 |
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author | Straus, Anna M. Hayes, Alissa Simon, Jodi Sims, Andrea Skerlong, Karen Wilmoth, Michele Bigham, Michael T. |
author_facet | Straus, Anna M. Hayes, Alissa Simon, Jodi Sims, Andrea Skerlong, Karen Wilmoth, Michele Bigham, Michael T. |
author_sort | Straus, Anna M. |
collection | PubMed |
description | Healthcare institutions are placing greater emphasis on equitable care. To accurately track and validate equity metrics, Akron Children’s Hospital evaluated how key fields are collected, analyzed, and visualized throughout the organization. Standardized recommendations in this area vary, and this investigation provided specific ways to advance analytics in this field. In addition, the technical infrastructure needed a comprehensive evaluation to increase confidence in using demographic data. METHODS: First, we reviewed how staff are trained to collect data at registration. Next, the electronic health record team standardized race and ethnicity fields with federal definitions. We found that fields were not consistently accessible across reporting tools. However, when present, all fields are sourced from the same electronic health record field. Finally, 6 months of encounters were analyzed and validated, with limitations to a seldom-populated Race 2 field. RESULTS: We compared data, including and excluding null values, to provide concise recommendations for standard visualizations. We uncovered many consistencies and a few inconsistencies that informed the next steps. CONCLUSIONS: The results informed 7 recommendations to align Akron Children’s Hospital’s advancement in analytics for health equity data: standardize race and ethnicity fields across all reporting tools, add Child Opportunity Index 2.0 to the enterprise data warehouse, utilize data at the time of the patient’s encounter, include null fields (patient refused, unknown, and not specified) in analysis, increase reporting capabilities for social determinants of health (SDOH), standardize multiracial data visualizations, and optimize reliable upstream data collection to increase reliability for all health equity measures. |
format | Online Article Text |
id | pubmed-10085515 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | Lippincott Williams & Wilkins |
record_format | MEDLINE/PubMed |
spelling | pubmed-100855152023-04-11 Evaluating Demographic Data to Improve Confidence in Equity Analytics in a Children’s Hospital Straus, Anna M. Hayes, Alissa Simon, Jodi Sims, Andrea Skerlong, Karen Wilmoth, Michele Bigham, Michael T. Pediatr Qual Saf Individual QI projects from single institutions Healthcare institutions are placing greater emphasis on equitable care. To accurately track and validate equity metrics, Akron Children’s Hospital evaluated how key fields are collected, analyzed, and visualized throughout the organization. Standardized recommendations in this area vary, and this investigation provided specific ways to advance analytics in this field. In addition, the technical infrastructure needed a comprehensive evaluation to increase confidence in using demographic data. METHODS: First, we reviewed how staff are trained to collect data at registration. Next, the electronic health record team standardized race and ethnicity fields with federal definitions. We found that fields were not consistently accessible across reporting tools. However, when present, all fields are sourced from the same electronic health record field. Finally, 6 months of encounters were analyzed and validated, with limitations to a seldom-populated Race 2 field. RESULTS: We compared data, including and excluding null values, to provide concise recommendations for standard visualizations. We uncovered many consistencies and a few inconsistencies that informed the next steps. CONCLUSIONS: The results informed 7 recommendations to align Akron Children’s Hospital’s advancement in analytics for health equity data: standardize race and ethnicity fields across all reporting tools, add Child Opportunity Index 2.0 to the enterprise data warehouse, utilize data at the time of the patient’s encounter, include null fields (patient refused, unknown, and not specified) in analysis, increase reporting capabilities for social determinants of health (SDOH), standardize multiracial data visualizations, and optimize reliable upstream data collection to increase reliability for all health equity measures. Lippincott Williams & Wilkins 2023-04-10 /pmc/articles/PMC10085515/ /pubmed/37051408 http://dx.doi.org/10.1097/pq9.0000000000000642 Text en Copyright © 2023 the Author(s). Published by Wolters Kluwer Health, Inc. https://creativecommons.org/licenses/by/4.0/This is an open access article distributed under the Creative Commons Attribution License 4.0 (CCBY (https://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 Straus, Anna M. Hayes, Alissa Simon, Jodi Sims, Andrea Skerlong, Karen Wilmoth, Michele Bigham, Michael T. Evaluating Demographic Data to Improve Confidence in Equity Analytics in a Children’s Hospital |
title | Evaluating Demographic Data to Improve Confidence in Equity Analytics in a Children’s Hospital |
title_full | Evaluating Demographic Data to Improve Confidence in Equity Analytics in a Children’s Hospital |
title_fullStr | Evaluating Demographic Data to Improve Confidence in Equity Analytics in a Children’s Hospital |
title_full_unstemmed | Evaluating Demographic Data to Improve Confidence in Equity Analytics in a Children’s Hospital |
title_short | Evaluating Demographic Data to Improve Confidence in Equity Analytics in a Children’s Hospital |
title_sort | evaluating demographic data to improve confidence in equity analytics in a children’s hospital |
topic | Individual QI projects from single institutions |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10085515/ https://www.ncbi.nlm.nih.gov/pubmed/37051408 http://dx.doi.org/10.1097/pq9.0000000000000642 |
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