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How the Provenance of Electronic Health Record Data Matters for Research: A Case Example Using System Mapping
Open accessAbstract
Introduction: The use of electronic health records (EHRs) for research is proceeding rapidly, driven by computational power, analytical techniques, and policy. However, EHR-based research is limited by the complexity of EHR data and a lack of understanding about data provenance, meaning the context under which the data were collected. This paper presents system flow mapping as a method to help researchers more fully understand the provenance of their EHR data as it relates to local workflow. We provide two specific examples of how this method can improve data identification, documentation, and processing.Background: EHRs store clinical and administrative data, often in unstructured fields. Each clinical system has a unique and dynamic workflow and EHR, which may be influenced by broader context such as documentation required for billing.Methods: We present a case study with two examples of using system flow mapping to characterize EHR data for a local colorectal cancer screening process.Findings: System flow mapping demonstrated that information entered into the EHR during clinical practice required interpretation and transformation before it could be accurately applied to research. We illustrate how system flow mapping shaped our knowledge of the quality and completeness of data in two examples: 1) determining colonoscopy indication as recorded in the EHR, and 2) discovering a specific EHR form that captured family history.Discussion: Researchers who do not consider data provenance risk compiling data that are systematically incomplete or incorrect. For example, researchers who are not familiar with the clinical workflow under which data were entered might miss or misunderstand patient information or procedure and diagnostic codes.Conclusions/Next steps: Data provenance is a fundamental characteristic of research data from EHRs. Given the diversity of EHR platforms and system workflows, researchers need tools for evaluating and reporting data availability, quality, and transformations. Our case study illustrates how system mapping can inform researchers about the provenance of their data as it pertains to local workflows.
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“Differences in how data are captured and stored in the original source-data system—usually the institutional EHR or enterprise data warehouse, how data are extracted and transformed into the analytic data, and the impact of data workflows or provenance can cause significant challenges in ensuring common data formats (syntax) and meaning (semantics).”
From A harmonized data quality assessment terminology and framework for the secondary use of electronic health record data (2016) · cites this paper as [69]
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Kahn, M. G., Callahan, T. J., Barnard, J., Bauck, A. E., Brown, J., Davidson, B. N., Estiri, H., Goerg, C., Holve, E., Johnson, S. G., et al. (2016) · eGEMs (Generating Evidence & Methods to improve patient outcomes) · doi:10.13063/2327-9214.1244 · Cited by 697
“in the original source-data system—usually the institutional EHR or enterprise data warehouse, how data are extracted and transformed into the analytic data, and the impact of data workflows or provenance can cause significant challenges in ensuring common data formats (syntax) and meaning (semantics).(69) Currently,…”
BackgroundPfaff, E., Girvin, A., Gabriel, D., Kostka, K., Morris, M., Palchuk, M., Lehmann, H., Amor, B., Bissell, M. M., Bradwell, K., et al. (2021) · J. Am. Medical Informatics Assoc. · doi:10.1093/jamia/ocab217 · Cited by 65
“19,23,24 Each of the aforementioned federated networks has methods to promote local DQ and adherence to data model conventions; these methods vary in maturity.”
A basic model for assessing primary health care electronic medical record data quality
Terry, A. L., Stewart, M., Cejic, S., Marshall, J. N., de Lusignan, S., Chesworth, B. M., Chevendra, V., Maddocks, H. L., Shadd, J., Burge, F., et al. (2019) · BMC Medical Informatics and Decision Making · doi:10.1186/s12911-019-0740-0 · Cited by 67
“…the “ fitness ” of the data for its intended purpose [9] one needs to apply both broad considerations captured in the aforementioned frameworks, including the provenance of the data [43], and narrow ones – applying specific quality measures to the data elements that are to be used [8, 37, 44].”
BackgroundFu, S., Leung, L. Y., Raulli, A.-O., Kallmes, D. F., Kinsman, K. A., Nelson, K. B., Clark, M. S., Luetmer, P. H., Kingsbury, P., Kent, D. M., et al. (2020) · BMC Medical Informatics and Decision Making · doi:10.1186/s12911-020-1072-9 · Cited by 51
Dziadkowiec, O., Callahan, T., Ozkaynak, M., Reeder, B., & Welton, J. (2016) · eGEMs · doi:10.13063/2327-9214.1201 · Cited by 41
Metadata
- Authors
- Johnson, K. E., Kamineni, A., Fuller, S., Olmstead, D., & Wernli, K. J.
- Affiliations
- Kaiser Permanente Washington Health Research Institute (US)
- Corresponding author
- Johnson, Karin E.
- Venue
- eGEMs (Generating Evidence & Methods to improve patient outcomes)
- Publisher
- Ubiquity Press, Ltd.
- Published
- 2014-04-16
- Volume
- 2
- Issue
- 1
- Pages
- 4
- ISSN
- 2327-9214 (ISSN-L)
- PMC
- PMC4371416
- OpenAlex
- W2155931339
- Cited by
- 39
- References
- 23
- Citations by year
- 2025: 1 · 2024: 2 · 2023: 5 · 2022: 4 · 2021: 6 · 2020: 4 · 2019: 2 · 2018: 4 · 2017: 4 · 2016: 5 · 2014: 2
- FWCI
- 6.70
- Type
- journal article
- Language
- en
- Open access
- diamond
- doi.org
- Licence
- cc-by-nc-nd
- Locations
- eGEMs (Generating Evidence & Methods to improve patient outcomes) (publishedVersion, cc-by-nc-nd)
- Europe PMC (PubMed Central) (publishedVersion, cc-by-nc-nd)
- PubMed Central (publishedVersion, cc-by-nc-nd)
- PubMed (publishedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion)
- Funders
- Agency for Healthcare Research and Quality (U13HS019564)
- National Cancer Institute (U54CA163261)
- Topics
- Health Sciences › Health Professions › Health Information Management › Electronic Health Records Systems
- Health Sciences › Health Professions › Health Information Management › Medical Coding and Health Information
- Health Sciences › Nursing › Research and Theory › Nursing Diagnosis and Documentation
- Keywords
- electronic health records, data provenance, clinical workflow, EHR data quality, data completeness, colorectal cancer screening, colonoscopy indications, family history documentation, research data documentation
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