Cargando…
Unlocking the Potential of Electronic Health Records for Health Research
Electronic health records (EHRs), originally designed to facilitate health care delivery, are becoming a valuable data source for health research. EHR systems have two components, both of which have various components, and points of data entry, management, and analysis. The “front end” refers to whe...
Autores principales: | , , , , , , |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Swansea University
2020
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7473254/ https://www.ncbi.nlm.nih.gov/pubmed/32935049 http://dx.doi.org/10.23889/ijpds.v5i1.1123 |
_version_ | 1783579147684020224 |
---|---|
author | Lee, S Xu, Y D'Souza, AG Martin, EA Doktorchik, C Zhang, Z Quan, H |
author_facet | Lee, S Xu, Y D'Souza, AG Martin, EA Doktorchik, C Zhang, Z Quan, H |
author_sort | Lee, S |
collection | PubMed |
description | Electronic health records (EHRs), originally designed to facilitate health care delivery, are becoming a valuable data source for health research. EHR systems have two components, both of which have various components, and points of data entry, management, and analysis. The “front end” refers to where the data are entered, primarily by healthcare workers (e.g. physicians and nurses). The second component of EHR systems is the electronic data warehouse, or “back-end,” where the data are stored in a relational database. EHR data elements can be of many types, which can be categorized as structured, unstructured free-text, and imaging data. The Sunrise Clinical Manager (SCM) EHR is one example of an inpatient EHR system, which covers the city of Calgary (Alberta, Canada). This system, under the management of Alberta Health Services, is now being explored for research use. The purpose of the present paper is to describe the SCM EHR for research purposes, showing how this generalizes to EHRs in general. We further discuss advantages, challenges (e.g. potential bias and data quality issues), analytical capacities, and requirements associated with using EHRs in a health research context. |
format | Online Article Text |
id | pubmed-7473254 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2020 |
publisher | Swansea University |
record_format | MEDLINE/PubMed |
spelling | pubmed-74732542020-09-14 Unlocking the Potential of Electronic Health Records for Health Research Lee, S Xu, Y D'Souza, AG Martin, EA Doktorchik, C Zhang, Z Quan, H Int J Popul Data Sci Population Data Science Electronic health records (EHRs), originally designed to facilitate health care delivery, are becoming a valuable data source for health research. EHR systems have two components, both of which have various components, and points of data entry, management, and analysis. The “front end” refers to where the data are entered, primarily by healthcare workers (e.g. physicians and nurses). The second component of EHR systems is the electronic data warehouse, or “back-end,” where the data are stored in a relational database. EHR data elements can be of many types, which can be categorized as structured, unstructured free-text, and imaging data. The Sunrise Clinical Manager (SCM) EHR is one example of an inpatient EHR system, which covers the city of Calgary (Alberta, Canada). This system, under the management of Alberta Health Services, is now being explored for research use. The purpose of the present paper is to describe the SCM EHR for research purposes, showing how this generalizes to EHRs in general. We further discuss advantages, challenges (e.g. potential bias and data quality issues), analytical capacities, and requirements associated with using EHRs in a health research context. Swansea University 2020-01-30 /pmc/articles/PMC7473254/ /pubmed/32935049 http://dx.doi.org/10.23889/ijpds.v5i1.1123 Text en https://creativecommons.org/licences/by/4.0/ This work is licenced under a Creative Commons Attribution 4.0 International License. |
spellingShingle | Population Data Science Lee, S Xu, Y D'Souza, AG Martin, EA Doktorchik, C Zhang, Z Quan, H Unlocking the Potential of Electronic Health Records for Health Research |
title | Unlocking the Potential of Electronic Health Records for Health Research |
title_full | Unlocking the Potential of Electronic Health Records for Health Research |
title_fullStr | Unlocking the Potential of Electronic Health Records for Health Research |
title_full_unstemmed | Unlocking the Potential of Electronic Health Records for Health Research |
title_short | Unlocking the Potential of Electronic Health Records for Health Research |
title_sort | unlocking the potential of electronic health records for health research |
topic | Population Data Science |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7473254/ https://www.ncbi.nlm.nih.gov/pubmed/32935049 http://dx.doi.org/10.23889/ijpds.v5i1.1123 |
work_keys_str_mv | AT lees unlockingthepotentialofelectronichealthrecordsforhealthresearch AT xuy unlockingthepotentialofelectronichealthrecordsforhealthresearch AT dapossouzaag unlockingthepotentialofelectronichealthrecordsforhealthresearch AT martinea unlockingthepotentialofelectronichealthrecordsforhealthresearch AT doktorchikc unlockingthepotentialofelectronichealthrecordsforhealthresearch AT zhangz unlockingthepotentialofelectronichealthrecordsforhealthresearch AT quanh unlockingthepotentialofelectronichealthrecordsforhealthresearch |