A Framework for Leveraging "Big Data" to Advance Epidemiology and Improve Quality: Design of the VA Colonoscopy Collaborative. Issue 1 (13th April 2018)
- Record Type:
- Journal Article
- Title:
- A Framework for Leveraging "Big Data" to Advance Epidemiology and Improve Quality: Design of the VA Colonoscopy Collaborative. Issue 1 (13th April 2018)
- Main Title:
- A Framework for Leveraging "Big Data" to Advance Epidemiology and Improve Quality: Design of the VA Colonoscopy Collaborative
- Authors:
- Gupta, Samir
Liu, Lin
Patterson, Olga V.
Earles, Ashley
Bustamante, Ranier
Gawron, Andrew J.
Thompson, William K.
Scuba, William
Denhalter, Daniel
Martinez, M. Elena
Messer, Karen
Fisher, Deborah A.
Saini, Sameer D.
DuVall, Scott L.
Chapman, Wendy W.
Whooley, Mary A.
Kaltenbach, Tonya - Abstract:
- Objective: To describe a framework for leveraging big data for research and quality improvement purposes and demonstrate implementation of the framework for design of the Department of Veterans Affairs (VA) Colonoscopy Collaborative. Methods: We propose that research utilizing large-scale electronic health records (EHRs) can be approached in a 4 step framework: 1) Identify data sources required to answer research question; 2) Determine whether variables are available as structured or free-text data; 3) Utilize a rigorous approach to refine variables and assess data quality; 4) Create the analytic dataset and perform analyses. We describe implementation of the framework as part of the VA Colonoscopy Collaborative, which aims to leverage big data to 1) prospectively measure and report colonoscopy quality and 2) develop and validate a risk prediction model for colorectal cancer (CRC) and high-risk polyps. Results: Examples of implementation of the 4 step framework are provided. To date, we have identified 2, 337, 171 Veterans who have undergone colonoscopy between 1999 and 2014. Median age was 62 years, and 4.6 percent (n = 106, 860) were female. We estimated that 2.6 percent (n = 60, 517) had CRC diagnosed at baseline. An additional 1 percent (n = 24, 483) had a new ICD-9 code-based diagnosis of CRC on follow up. Conclusion: We hope our framework may contribute to the dialogue on best practices to ensure high quality epidemiologic and quality improvement work. As a result ofObjective: To describe a framework for leveraging big data for research and quality improvement purposes and demonstrate implementation of the framework for design of the Department of Veterans Affairs (VA) Colonoscopy Collaborative. Methods: We propose that research utilizing large-scale electronic health records (EHRs) can be approached in a 4 step framework: 1) Identify data sources required to answer research question; 2) Determine whether variables are available as structured or free-text data; 3) Utilize a rigorous approach to refine variables and assess data quality; 4) Create the analytic dataset and perform analyses. We describe implementation of the framework as part of the VA Colonoscopy Collaborative, which aims to leverage big data to 1) prospectively measure and report colonoscopy quality and 2) develop and validate a risk prediction model for colorectal cancer (CRC) and high-risk polyps. Results: Examples of implementation of the 4 step framework are provided. To date, we have identified 2, 337, 171 Veterans who have undergone colonoscopy between 1999 and 2014. Median age was 62 years, and 4.6 percent (n = 106, 860) were female. We estimated that 2.6 percent (n = 60, 517) had CRC diagnosed at baseline. An additional 1 percent (n = 24, 483) had a new ICD-9 code-based diagnosis of CRC on follow up. Conclusion: We hope our framework may contribute to the dialogue on best practices to ensure high quality epidemiologic and quality improvement work. As a result of implementation of the framework, the VA Colonoscopy Collaborative holds great promise for 1) quantifying and providing novel understandings of colonoscopy outcomes, and 2) building a robust approach for nationwide VA colonoscopy quality reporting. … (more)
- Is Part Of:
- EGEMS. Volume 6:Issue 1(2018)
- Journal:
- EGEMS
- Issue:
- Volume 6:Issue 1(2018)
- Issue Display:
- Volume 6, Issue 1 (2018)
- Year:
- 2018
- Volume:
- 6
- Issue:
- 1
- Issue Sort Value:
- 2018-0006-0001-0000
- Page Start:
- Page End:
- Publication Date:
- 2018-04-13
- Subjects:
- big data -- electronic health records -- epidemiology -- quality improvement -- veterans
Medical records -- Data processing -- Periodicals
Medical care -- Data processing -- Periodicals
Medical Records
Automatic Data Processing
Medical care -- Data processing
Medical records -- Data processing
Periodicals
Periodicals
651.504261 - Journal URLs:
- https://egems.academyhealth.org/ ↗
http://bibpurl.oclc.org/web/49556 ↗
http://repository.academyhealth.org/egems/ ↗
http://search.ebscohost.com/direct.asp?db=a9h&jid=GD7Z ↗
http://www.ncbi.nlm.nih.gov/pmc/journals/2686/ ↗ - DOI:
- 10.5334/egems.198 ↗
- Languages:
- English
- ISSNs:
- 2327-9214
- Deposit Type:
- Legaldeposit
- View Content:
- Available online (eLD content is only available in our Reading Rooms) ↗
- Physical Locations:
- British Library HMNTS - ELD Digital store
- Ingest File:
- 14678.xml