Exploring electronic health records as a population health surveillance tool of cardiovascular disease risk factors

Population Health Management

Volumn 18, Issue 2, 2015, Pages 79-85

  Sidebottom, Abbey C ^a   Johnson, Pamela Jo ^b   Vanwormer, Jeffrey J ^c   Sillah, Arthur ^a   Winden, Tamara J ^a   Boucher, Jackie L ^d  

^a Applied Research   (United States)

^b UNIVERSITY OF MINNESOTA   (United States)

^c MARSHFIELD CLINIC   (United States)

^d MINNEAPOLIS HEART INSTITUTE FOUNDATION   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

ADULT; AGED; ARTICLE; BODY MASS; CARDIOVASCULAR DISEASE; CARDIOVASCULAR RISK; CHOLESTEROL BLOOD LEVEL; CONTROLLED STUDY; ELECTRONIC MEDICAL RECORD; FEMALE; HEART INFARCTION; HUMAN; MALE; MASS SCREENING; MIDDLE AGED; PUBLIC HEALTH; RISK FACTOR; RURAL POPULATION; SELECTION BIAS; SMOKING; SYSTOLIC BLOOD PRESSURE; CARDIOVASCULAR DISEASES; HEALTH STATUS INDICATOR; HEALTH SURVEY; INCIDENCE; ORGANIZATION AND MANAGEMENT; PROCEDURES; UNITED STATES;

ADULT; AGED; CARDIOVASCULAR DISEASES; ELECTRONIC HEALTH RECORDS; FEMALE; HEALTH STATUS INDICATORS; HEALTH SURVEYS; HUMANS; INCIDENCE; MALE; MIDDLE AGED; POPULATION SURVEILLANCE; RURAL POPULATION; UNITED STATES;

EID: 84927674483     PISSN: 19427891     EISSN: 19427905     Source Type: Journal    
DOI: 10.1089/pop.2014.0058     Document Type: Article

References (20)

1. Jha AK, Burke MF, DesRoches C, et al. Progress toward meaningful use: hospitals' adoption of electronic health records. Am J Manag Care. Dec 2011;17(12 Spec No.): SP117-SP124.
2. Arsand E, Walseth OA, Andersson N, et al. Using blood glucose data as an indicator for epidemic disease outbreaks. Stud Health Technol Inform. 2005;116:217-222.
3. Allen-Dicker J, Klompas M. Comparison of electronic laboratory reports, administrative claims, and electronic health record data for acute viral hepatitis surveillance. J Public Health Manag Pract. 2012;18:209-214.
4. Palaniappan LP, Maxwell AE, Crespi CM, Wong EC, Shin J, Wang EJ. Population colorectal cancer screening estimates: comparing self-report to electronic health record data in California. Int J Canc Prev. 2011;4(1). pii 28540.
5. Blumenthal D, Tavenner M. The ''meaningful use'' regulation for electronic health records. N Engl J Med. 2010; 363:501-504.
6. VanWormer JJ. Methods of using electronic health records for population-level surveillance of coronary heart disease risk in the Heart of New Ulm Project. Diabetes Spectrum. 2010;23:161-165.
7. Johnson PJ, Blewett LA, Davern M. Disparities in public use data availability for race, ethnic, and immigrant groups: national surveys for healthcare disparities research. Med Care. 2010;48:1122-1127.
8. Kochanek KD, Jiaquan X, Murphy SL, Minino AM, Kung HC. Deaths: Final Data for 2009. National Vital Statistics Reports. 2012;60(3):1-117.
9. Roger VL, Go AS, Lloyd-Jones DM, et al. Heart disease and stroke statistics-2012 update: a report from the American Heart Association. Circulation. 2012;125(1):e2-e220.
10. Boucher JL, Pereira RF, Graham KJ, Pettingill RR, Toscano JV, Henry TD. The Heart of New Ulm: a vision for the future. J Cardiovasc Transl Res. 2008;1:310-316.
11. VanWormer JJ, Johnson PJ, Pereira RF, et al. The Heart of New Ulm Project: using community-based cardiometabolic risk factor screenings in a rural population health improvement initiative. Popul Health Manag. 2012;15:135-143.
12. McLeroy KR, Bibeau D, Steckler A, Glanz K. An ecological perspective on health promotion programs. Health Educ Q. 1988;15:351-377.
13. VanWormer JJ, Fyfe-Johnson A, Boucher JL, et al. Stress and workplace productivity loss in the Heart of New Ulm project. J Occup Environ Med. 2011;53:1106-1109.
14. Blackwell DL, Lucas JW, Clarke TC. Summary health statistics for U.S. adults: national health interview survey, 2012. Vital and Health Statistics. 2014;10(260):95.
15. Green BB, Anderson ML, Cook AJ, et al. Using body mass index data in the electronic health record to calculate cardiovascular risk. Am J Prev Med. 2012;42:342-347.
16. Wei WQ, Leibson CL, Ransom JE, Kho AN, Chute CG. 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: 239-247.
17. Martinson BC, Crain AL, Sherwood NE, Hayes MG, Pronk NP, O'Connor PJ. Population reach and recruitment bias in a maintenance RCT in physically active older adults. J Phys Act Health. 2010;7(1):127-135.
18. Mechanic D. Population health: challenges for science and society. Milbank Q. 2007;85:533-559.
19. Pirie PL, Elias WS, Wackman DB, et al. Characteristics of participants and nonparticipants in a community cardiovascular disease risk factor screening: the Minnesota Heart Health Program. Am J Prev Med. 1986;2(1):20-25.
20. American Hospital Association. The Opportunities and Challenges for Rural Hospitals in the Era of Health Reform. Washington, DC: American Hospital Association; 2011:1-2.