Data Migration: A Thorny Issue in Electronic Health Record Transitions—Case Studies and Review of the Literature
Open accessAbstract
Objective To review the existing literature regarding data migration during electronic health record (EHR)-to-EHR transitions and add two case studies on this topic. Methods Very few publications exist that detail the processes and potential pitfalls of data migration during EHR transitions. One of the authors participated in a panel discussion at the American Medical Informatics Association symposium in 2015; at the time, only five empiric or experiential research articles on any aspect of EHR transitions were available. Of those, only two mentioned their experiences with data migration or conversions. A detailed PubMed and CINAHL (Cumulative Index to Nursing and Allied Health Literature) search in March 2019 yielded only one more article giving details about data migration. Results The two new case studies contrast starkly: one relied on manual abstraction and data entry, whereas the other leveraged several electronic tools. The literature reflects this diversity of approach: no two sites have reported the same approaches. The authors identify nine domains of potential consequences of the currently available techniques and offer mitigating strategies. Discussion Very little empiric information exists in the peer-reviewed literature regarding data migrations during EHR-to-EHR transitions; yet the case studies reflect that much remains suitable for a prospective study. Conclusion This report adds two new case studies to the six already reported in the literature. There is a wide disparity in techniques of data migration, each with its own set of pros and cons, which sites must consider during an EHR-to-EHR transition. Such transitions would benefit from prospective research on evaluation and knowledge discovery.
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Challenges such as system construction variability, missing data, and dependence on original systems lead to conversion errors such as corrupted data and mismatched patient records, disrupting clinical workflows and compromising patient safety.
“However, challenges, such as system construction variability, missing data, and dependence on original systems, lead to conversion errors such as corrupted data and mismatched patient records [167], disrupting clinical workflows and compromising patient safety [168].”
From Toward Safer Diagnoses: A SEIPS-Based Narrative Review of Diagnostic Errors (2026) · cites this paper as [167]
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- Gettinger A, Csatari A. Transitioning from a legacy EHR to a commercial, vendor-supplied, EHR: one academic health system's experience. Appl Clin Inform 2012;3(04):367-376
- Abramson EL, Patel V, Malhotra S, et al. Physician experiences transitioning between an older versus newer electronic health record for electronic prescribing. Int J Med Inform 2012;81(08): 539-548
- Zandieh SO, Abramson EL, Pfoh ER, Yoon-Flannery K, Edwards A, Kaushal R. Transitioning between ambulatory EHRs: a study of practitioners' perspectives. J Am Med Inform Assoc 2012;19(03): 401-406
- Bornstein S. An integrated EHR at Northern California Kaiser Permanente: pitfalls, challenges, and benefits experienced in transitioning. Appl Clin Inform 2012;3(03):318-325
- Pfoh ER, Abramson E, Zandieh S, Edwards A, Kaushal R. Satisfac- tion after the transition between electronic health record systems at six ambulatory practices. J Eval Clin Pract 2012;18(06): 1133-1139
- Saleem JJ, Herout J. Transitioning from one electronic health record (EHR) to another: a narrative literature review. Proc HFES 2018;62(01):489-493
- Makar M. Dealing with Existing Data in Legacy Systems when Transitioning between Electronic Health Records in Three Swed- ish Counties [Master's thesis]. Stockholm, Sweden: Karolinska Institutet, Stockholm University; 2014 11
- Pageler NM, Grazier G'Sell MJ, Chandler W, Mailes E, Yang C, Longhurst CA. A rational approach to legacy data validation when transitioning between electronic health record systems. J Am Med Inform Assoc 2016;23(05):991-994
- Camacho Rodriguez JC, Stäubert S, Löbe M. Automated import of clinical data from HL7 messages into OpenClinica and tranSMART using Mirth Connect. Stud Health Technol Inform 2016; 228:317-321
- Bortis G. Experience with Mirth: an open source health care integration engine. Presented at the 30th International Conference on Software Engineering, Leipzig, Germany, May 10-18, 2008
- Yang YL, Zhang YH. Application of Mirth Connect interface integration engine in medical data transmission. Industrial Con- trol Computer. 2016;29:112-113
- Lin J, Ranslam K, Shi F, et al. Data migration from operating EMRs to OpenEMR with Mirth Connect. Stud Health Tech Inform 2019; 257:288-292
- Noreen N. Data migration lessons learned: one facility relates hard-learned best practices after migrating to a new EHR. J AHIMA 2013;84(09):40-42
- West S. Need versus cost: understanding EHR data migration options. J Med Pract Manage 2013;29(03):181-183
- van Ooijen PM, Aryanto KY, Broekema A, Horii S. DICOM data migration for PACS transition: procedure and pitfalls. Int J CARS 2015;10(07):1055-1064 20
- Agarwal TK, Sanjeev. Vendor neutral archive in PACS. Indian J Radiol Imaging 2012;22(04):242-245
- Berkowitz SJ, Wei JL, Halabi S. Migrating to the modern PACS: challenges and opportunities. Radiographics 2018;38(06): 1761-1772
- Epstein RH, Dexter F, Schwenk ES. Provider access to legacy electronic anesthesia records following implementation of an electronic health record system. J Med Syst 2019;43(05):105
- Barnett ML, Mehrotra A, Jena AB. Adverse inpatient outcomes during the transition to a new electronic health record system: observational study. BMJ 2016;354:i3835
- Graham J, Levick D, Schreiber R. AMDIS case conference: intrusive medication safety alerts. Appl Clin Inform 2010;1(01):68-78
- Michel J, Hsiao A, Fenick A. Using a scripted data entry process to transfer legacy immunization data while transitioning between electronic medical record systems. Appl Clin Inform 2014;5(01): 284-298
- Authors
- Schreiber, R., & Garber, L.
- Affiliations
- Geisinger College of Health Sciences (US)
- Reliant Medical Group (US)
- Corresponding author
- Schreiber, Richard
- Venue
- ACI Open
- Publisher
- Georg Thieme Verlag KG
- Published
- 2020-01
- Volume
- 04
- Issue
- 01
- Pages
- e48-e58
- ISSN
- 2566-9346 (ISSN-L)
- OpenAlex
- W3029182195
- Cited by
- 8
- References
- 20
- Citations by year
- 2026: 2 · 2025: 3 · 2024: 1 · 2023: 1 · 2020: 1
- FWCI
- 0.44
- Type
- journal article
- Language
- en
- Open access
- diamond
- doi.org
- Locations
- ACI Open (publishedVersion, cc-by)
- 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 › Issues, ethics and legal aspects › Nursing Diagnosis and Documentation
- Keywords
- data migration, electronic health records, health information exchange, data entry, data conversion, data quality, EHR implementation
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