Automated processing of electronic medical records is a reliable method of determining aspirin use in populations at risk for cardiovascular events

Informatics in Primary Care

Volumn 18, Issue 2, 2010, Pages 125-133

  Pakhomov, Serguei V S ^b   Shah, Nilay D ^a   Hanson, Penny ^a   Balasubramaniam, Saranya C ^a   Smith, Steven A ^a  

^a MAYO CLINIC   (United States)

^b UNIVERSITY OF MINNESOTA   (United States)

Author keywords

Aspirin; Natural language processing (NLP); Quality measurement

Indexed keywords

ACETYLSALICYLIC ACID; ANTITHROMBOCYTIC AGENT;

ARTICLE; CARDIOVASCULAR DISEASE; DRUG UTILIZATION; ELECTRONIC MEDICAL RECORD; HUMAN; NON INSULIN DEPENDENT DIABETES MELLITUS; PATIENT COMPLIANCE; STATISTICS;

ASPIRIN; CARDIOVASCULAR DISEASES; DIABETES MELLITUS, TYPE 2; DRUG UTILIZATION; HUMANS; MEDICAL RECORDS SYSTEMS, COMPUTERIZED; MEDICATION ADHERENCE; PLATELET AGGREGATION INHIBITORS;

EID: 78449302970     PISSN: 14760320     EISSN: None     Source Type: Journal    
DOI: 10.14236/jhi.v18i2.762     Document Type: Article

References (29)

1. Sinnott C, Rogers MA, Lehmann D and Weinstock RS. Bodily pain, poor physical functioning, and poor glycemic control in adults with diabetes. Diabetes Care 2005;28:1534.
2. Standards and Guidelines for the Recognition of Diabetes Physicians. USA, National Committee for Quality Assurance, 2004.
3. Birke JA, Horswell R, Patout CA Jr and Chen SL. The impact of a staged management approach to diabetes foot care in the Louisiana public hospital system. Journal of the Louisiana State Medical Society 2003;155:37-42.
4. Bruckner M, Mangan M, Godin S and Pogach L. Project LEAP of New Jersey: Lower extremity amputation prevention in persons with type 2 diabetes. American Journal of Managed Care 1999;5:609-16.
5. Kuo S, Fleming BB, Gittings NS et al. Trends in care practices and outcomes among Medicare beneficiaries with diabetes. American Journal of Preventive Medicine 2005;29:396-403.
6. Mangione CM, Gerzoff RB, Williamson DF et al. The association between quality of care and the intensity of diabetes diseasemanagement programs. Annals of Internal Medicine 2006;145:107-16.
7. Saaddine JB, Cadwell B, Gregg EW et al. Improvements in diabetes processes of care and intermediate outcomes: United States, 1988-2002. Annals of Internal Medicine 2006;144:465-74.
8. Fagot-Campagna A, Gary TL and Benjamin SM. Cardiovascular risk in diabetes: A story of missed opportunities? Diabetes Care 2001;24:2015-16.
9. Stafford RS and Radley DC. The underutilization of cardiac medications of proven benefit, 1990 to 2002. Journal of the American College of Cardiology 2003;41: 56-61.
10. Burton C, Pennington L, Roddam H et al. Assessing adherence to the evidence base in the management of post-stroke dysphagia. Clinical Rehabilitation 2006;20: 46-51.
11. Cassidy LD, Marsh GM, Holleran MK and Ruhl LS. Methodology to improve data quality from chart review in the managed care setting. American Journal of Managed Care 2002;8:787-93.
12. Jutley RS, McKinley A, Hobeldin M, Mohamed A and Youngson GG. Use of clinical audit for revalidation: Is it sufficiently accurate? Journal of Quality in Clinical Practice 2001;21:71-3.
13. Gompertz PH, Irwin P, Morris R et al. Reliability and validity of the Intercollegiate Stroke Audit Package. Journal of Evaluation in Clinical Practice 2001;7:1-11.
14. Ford E, Menacheni N and Phillips T. Predicting the adoption of electronic health records by physicians: When will health care be paperless? Journal of the American Medical Informatics Association 2006;13:106-12.
15. Persell SD, Wright JM, Thompson JA, Kmetik KS and Baker DW. Assessing the validity of national quality measures for coronary artery disease using an electronic health record. Archives of Internal Medicine 2006;166: 2272-7.
16. Fiszman M, Chapman WW, Aronsky D, Evans RS and Haug PJ. Automatic detection of acute bacterial pneumonia from chest x-ray reports. Journal of the American Medical Informatics Association 2000;7:593-604.
17. Fiszman M, Chapman WW, Evans SR and Haug PJ. Automatic identification of pneumonia related concepts on chest x-ray reports. American Medical Informatics Association Annual Symposium Proceedings 1999:67-71.
18. Friedman C. A broad-coverage natural language processing system. American Medical Informatics Association Annual Symposium Proceedings 2000:270-4.
19. Hazlehurst B, Sittig DF, Stevens VJ et al. Natural language processing in the electronic medical record: Assessing clinician adherence to tobacco treatment guidelines. American Journal of Preventive Medicine 2005; 29:434-9.
20. Melton GB and Hripcsak G. Automated detection of adverse events using natural language processing of discharge summaries. Journal of the American Medical Informatics Association 2005;12:448-57.
21. Pakhomov SS, Hemingway H, Weston SA, Jacobsen SJ, Rodeheffer R and Roger VL. Epidemiology of angina pectoris: Role of natural language processing of the medical record. American Heart Journal 2007;153:666-73.
22. Pakhomov SV, Buntrock J and Chute CG. Prospective recruitment of patients with congestive heart failure using an ad-hoc binary classifier. Journal of Biomedical Informatics 2005;38:145-53.
23. Pakhomov SV, Hanson PL, Bjornsen SS and Smith SA. Automatic classification of foot examination findings using clinical notes and machine learning. Journal of the American Medical Informatics Association 2008;15:198-202.
24. Chen ES, Hripcsak G, Xu H, MarkatouMand Friedman C. Automated acquisition of disease drug knowledge from biomedical and clinical documents: An initial study. Journal of the American Medical Informatics Association 2008;15:87-98.
25. Gold S, Elhadad N, Zhu X, Cimino JJ and Hripcsak G. Extracting structured medication event information from discharge summaries. American Medical Informatics Association Annual Symposium Proceedings 2008:237-41.
26. Jagannathan V, Mullett CJ, Arbogast JG et al. Assessment of commercial NLP engines for medication information extraction from dictated clinical notes. International Journal of Medical Informatics 2009;78: 284-91.
27. Melton LJ. History of the Rochester Epidemiology Project. Mayo Clinic Proceedings 1996;7:266-74.
28. Dolin RH, Alschuler L, Boyer S et al. HL7 Clinical Document Architecture, Release 2. Journal of the American Medical Informatics Association 2006;13:30-9.
29. Landis JR and Koch GG. The measurement of observer agreement for categorical data. Biometrics 1977;33:159-74.