Invited commentary: Observational research in the age of the electronic health record

American Journal of Epidemiology

Volumn 179, Issue 6, 2014, Pages 759-761

  Chute, Christopher G ^a  

^a Mayo Clinic College of Medicine   (United States)

Author keywords

clinical case retrieval; electronic medical records; high throughput clinical phenotyping; natural language processing

Indexed keywords

CANCER; DATA ACQUISITION; ELECTRONIC EQUIPMENT; EPIDEMIOLOGY; HEALTH MONITORING; HEALTH SURVEY; HOSPITAL SECTOR; WOMENS HEALTH;

ELECTRONIC MEDICAL RECORD; HUMAN; INFORMED CONSENT; MEDICAL RESEARCH; REVIEW;

CLINICAL CASE RETRIEVAL; ELECTRONIC MEDICAL RECORDS; HIGH-THROUGHPUT CLINICAL PHENOTYPING; NATURAL LANGUAGE PROCESSING;

BREAST NEOPLASMS; ELECTRONIC HEALTH RECORDS; FEMALE; HUMANS; NATURAL LANGUAGE PROCESSING; NEOPLASM RECURRENCE, LOCAL;

EID: 84895867454     PISSN: 00029262     EISSN: 14766256     Source Type: Journal    
DOI: 10.1093/aje/kwt443     Document Type: Review

References (18)

1. Data and Informatics Working Group of the Advisory Committee to the NIH Director.Data and Informatics Working Group Report.Bethesda, MD: National Institutes of Health; 2012:5.
2. NIH Office of the Director.NIH proposes critical initiatives to sustain future of US biomedical research.http://www.nih.gov/news/health/dec2012/od-07. htm.Accessed September 28, 2013.
3. Chute CG, Ullman-Cullere M, Wood GM, et al.Some experiences and opportunities for big data in translational research.Genet Med.2013;15(10):802- 809.
4. Carrell DS, Halgrim S, Tran D-T, et al.Using natural language processing to improve efficiency of manual chart abstraction in research: the case of breast cancer recurrence.Am J Epidemiol.2014;179(6);749-758.
5. McCarty CA, Chisholm RL, Chute CG, et al.The eMERGE Network: a consortium of biorepositories linked to electronic medical records data for conducting genomic studies.BMC Med Genomics.2011;4:13.
6. Gottesman O, Kuivaniemi H, Tromp G, et al.The Electronic Medical Records and Genomics (eMERGE) Network: past, present, and future.Genet Med.2013;15(10):761-771.
7. Kullo IJ, Fan J, Pathak J, et al.Leveraging informatics for genetic studies: use of the electronic medical record to enable a genome-wide association study of peripheral arterial disease.J Am Med Inform Assoc.2010;17(5):568-574.
8. Kho AN, Hayes MG, Rasmussen-Torvik L, et al.Use of diverse electronic medical record systems to identify genetic risk for type 2 diabetes within a genome-wide association study.J Am Med Inform Assoc.2012;19(2):212-218.
9. Kho AN, Pacheco JA, Peissig PL, et al.Electronic medical records for genetic research: results of the eMERGE consortium.Sci Transl Med.2011;3(79):79.
10. Newton KM, Peissig PL, Kho AN, et al.Validation of electronic medical record-based phenotyping algorithms: results and lessons learned from the eMERGE network.J Am Med Inform Assoc.2013;20(e1):e147-e154.
11. Savova GK, Fan J, Ye Z, et al.Discovering peripheral arterial disease cases from radiology notes using natural language processing.AMIA Annu Symp Proc.2010;2010:722-726.
12. Savova GK, Masanz JJ, Ogren PV, et al.Mayo clinical Text Analysis and Knowledge Extraction System (cTAKES): architecture, component evaluation and applications.J Am Med Inform Assoc.2010;17(5):507-513.
13. Conway M, Berg RL, Carrell D, et al.Analyzing the heterogeneity and complexity of electronic health record oriented phenotyping algorithms.AMIA Annu Symp Proc.2011;2011:274-283.
14. Wei WQ, Leibson CL, Ransom JE, et al.Impact of data fragmentation across healthcare centers on the accuracy of a high-throughput clinical phenotyping algorithm for specifying subjects with type 2 diabetes mellitus.J Am Med Inform Assoc.2012;19(2):219-224.
15. Wei WQ, Leibson CL, Ransom JE, et al.The absence of longitudinal data limits the accuracy of high-throughput clinical phenotyping for identifying type 2 diabetes mellitus subjects.Int J Med Inform.2013;82(4):239-247.
16. Blumenthal D, Tavenner M.The "meaningful use" regulation for electronic health records.N Engl J Med.2010;363(6): 501-504.
17. Chute CG, Pathak J, Savova GK, et al.The SHARPn Project on Secondary Use of Electronic Medical Record Data: progress, plans, and possibilities.AMIA Annu Symp Proc.2011;2011: 248-256.
18. Rea S, Pathak J, Savova G, et al.Building a robust, scalable and standards-driven infrastructure for secondary use of EHR data: the SHARPn Project.J Biomed Inform.2012;45(4): 763-771.