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Electronic Medical Records and Their Use in Health Promotion and Population Research of Cardiovascular Disease

  • Cardiovascular Risk Health Policy (W Rosamond, Section Editor)
  • Published:
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Abstract

The primary use of electronic medical records (EMRs) is to record ongoing interaction between patients and the health systems in which they participate. Secondary uses of the EMR continue to emerge providing opportunities for high-quality population health research as well as health promotion efforts. Research and health promotion activities involving the EMR may be passive and/or active. Secondary EMR activities are being focused on improving patient and provider management of chronic diseases, such as cardiovascular disease (CVD). CVD affects over 30 % of American adults, and the EMR contains information relevant to this multifaceted disease. Secondary EMR use related to CVD research and awareness includes functioning as a data repository, recruiting study participants, building predictive analytics, developing algorithms for disease screening, and delivering disease management tools. Diverse secondary EMR applications have revealed successes, challenges, and limitations highlighting new lessons learned and opportunities in health promotion and population research.

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References

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  1. Garret P, Sideman, J. EMR vs EHR—what is the difference? HealthIT.gov Buzz. 2011; http://www.healthit.gov/buzz-blog/electronic-health-and-medical-records/emr-vs-ehr-difference/.

  2. Lloyd-Jones D, Adams RJ, Brown TM, et al. Heart disease and stroke statistics—2010 update a report from the American heart association. Circulation. 2010;121(7):e46–e215.

    Article  PubMed  Google Scholar 

  3. Cohen JD, Aspry KE, Brown AS, et al. Use of health information technology (HIT) to improve statin adherence and low-density lipoprotein cholesterol goal attainment in high-risk patients: proceedings from a workshop. J Clin Lipidol. 2013;7(6):573–609.

    Article  PubMed  Google Scholar 

  4. Heidenreich PA, Trogdon JG, Khavjou OA, et al. Forecasting the future of cardiovascular disease in the United States. Circulation. 2011;1(8):933–44.

    Article  Google Scholar 

  5. Roger VL, Go AS, Lloyd-Jones DM, et al. Heart disease and stroke statistics—2012 update. Circulation. 2012;125(1):e2.

    Article  PubMed  Google Scholar 

  6. Foraker RE, Olivo-Marston S, Allen NB. Lifestyle and primordial prevention of cardiovascular disease: challenges and opportunities. Curr Cardiovasc Risk Rep. 2012;6(6):520–7.

    Article  Google Scholar 

  7. Hammermeister K, Bronsert M, Henderson WG, et al. Risk-adjusted comparison of blood pressure and low-density lipoprotein (LDL) noncontrol in primary care offices. J Am Board Fam Med. 2013;26(6):658–68.

    Article  PubMed  Google Scholar 

  8. Foraker RE, Shoben AB, Lopetegui MA, et al. Assessment of lifes simple 7™ in the primary care setting. Contemp Clin Trials. 2014;38(2):182–9.

    Article  PubMed  Google Scholar 

  9. Green BB, Anderson ML, Cook AJ, et al. E-care for heart wellness: a feasibility trial to decrease blood pressure and cardiovascular risk. Am J Prev Med. 2014;46(4):368–77.

    Article  PubMed  Google Scholar 

  10. Roumia M, Steinhubl S. Improving cardiovascular outcomes using electronic health records. Curr Cardiol Rep. 2014;16(2):1–6.

    Article  Google Scholar 

  11. Aspry KE, Furman R, Karalis DG, et al. Effect of health information technology interventions on lipid management in clinical practice: a systematic review of randomized controlled trials. J Clin Lipidol. 2013;7(6):546–60.

    Article  PubMed  Google Scholar 

  12. Goudev A. New insights into the management of hypertension and cardiovascular risk with angiotensin receptor blockers: observational studies help us? Open Cardiovasc Med J. 2014;8:35. This meta-analysis by Goudev sheds light on how EMRs allow for the possibility of increased sample sizes, enable faster algorithm generation, and intervention testing. These studies cover multiple countries and illustrate how the increased dissemination of findings from observational studies may help fill the knowledge gap created by the longevity of randomized control trial results.

    Article  PubMed Central  PubMed  Google Scholar 

  13. Kleinberg S, Elhadad N. Lessons learned in replicating data-driven experiments in multiple medical systems and patient populations. AMIA Annual Symposium Proceedings. 2013:786 The authors’ research examined the longitudinal raw EMR data of 46299 patients to study CVD risk factors in both a rural and urban environment. Their study looked for mutual findings from the differing populations through the common EMR infrastructure.

  14. Bardach NS, Wang JJ, De Leon SF, et al. Effect of pay-for-performance incentives on quality of care in small practices with electronic health records: a randomized trial. J Am Med Assoc. 2013;310(10):1051–9.

    Article  CAS  Google Scholar 

  15. Begum R, Smith RM, Winther CH, et al. Small practices’ experience with EHR, quality measurement, and incentives. Am J Manag Care. 2013;19(10 Spec No):eSP12–8.

    PubMed  Google Scholar 

  16. Wang JJ, Sebek KM, McCullough CM, et al. Sustained improvement in clinical preventive service delivery among independent primary care practices after implementing electronic health record systems. Preventing Chronic Disease. 2013;10.

  17. Danford CP, Navar-Boggan AM, Stafford J, et al. The feasibility and accuracy of evaluating lipid management performance metrics using an electronic health record. Am Heart J. 2013;166(4):701–8.

    Article  PubMed  Google Scholar 

  18. Ferrario CM, Joyner J, Colby C, et al. The COSEHC™ global vascular risk management quality improvement program: first follow-up report. Vasc Health Risk Manag. 2013;9:391.

    Article  PubMed Central  PubMed  Google Scholar 

  19. Hissett J, Folks B, Coombs L, et al. Effects of changing guidelines on prescribing aspirin for primary prevention of cardiovascular events. J Am Board Fam Med. 2014;27(1):78–86.

    Article  PubMed  Google Scholar 

  20. Persell SD, Eder M, Friesema E, et al. EHR‐based medication support and nurse‐led medication therapy management: rationale and design for a three‐arm clinic randomized trial. J Am Heart Assoc. 2013;2(5):e000311.

    Article  PubMed Central  PubMed  Google Scholar 

  21. Benson GA, Sidebottom A, VanWormer JJ, et al. Heart beat connections: a rural community of solution for cardiovascular health. J Am Board Fam Med. 2013;26(3):299–310. The authors illustrate a common challenge related to EMR use. The original study aim was to identify individuals at CVD risk and manage through intervention through the EMR, but the EMR could not be utilized as planned. This was in large part because although it theoretically had the capability of tracking interventions, in reality the EMR did not function well for this aim in practical application.

    Article  PubMed  Google Scholar 

  22. Catalán-Ramos A, Verdú JM, Grau M, et al. Population prevalence and control of cardiovascular risk factors: what electronic medical records tell us. Aten Primaria. 2014;46(1):15–24. This study shows the capability of EMR collected data for use in determining CVD prevalence rates. This study in Spain included 2.1 million patient’s data to determine a 40% rate of hypertension and hypercholesterolemia prevalence. It was also discovered that 66% of the patient population had adequately controlled hypertension and hypercholesterolemia.

    Article  PubMed  Google Scholar 

  23. Rapsomaniki E, Timmis A, George J, et al. Blood pressure and incidence of twelve cardiovascular diseases: lifetime risks, healthy life-years lost, and age-specific associations in 1·25 million people. Lancet. 2014;383(9932):1899. This study was conducted in the UK and examined over 1.25 million patients from 225 primary care practices looking at the association between blood pressure and incidence of CVD. Their results surrounding associations between blood pressure and CVD were inconsistent with previous studies and suggested that blood pressure does not hold the association with as many CVD occurrences as was previously believed. Their results revealed that even with treatment through medication, the burden of hypertension is substantial.

    Article  PubMed Central  PubMed  Google Scholar 

  24. Bielinski SJ, Olson JE, Pathak J, et al. Preemptive genotyping for personalized medicine: design of the right drug, right dose, right time—using genomic data to individualize treatment protocol. Circulation. 2014;89(1):25–33.

    Google Scholar 

  25. Violán C, Foguet-Boreu Q, Hermosilla-Pérez E, et al. Comparison of the information provided by electronic health records data and a population health survey to estimate prevalence of selected health conditions and multimorbidity. BMC Public Health. 2013;1:251.

    Article  Google Scholar 

  26. Cross DS, McCarty CA, Steinhubl SR, et al. Development of a multi‐institutional cohort to facilitate cardiovascular disease biomarker validation using existing biorepository samples linked to electronic health records. Clin Cardiol. 2013;36(8):486–91.

    Article  PubMed Central  PubMed  Google Scholar 

  27. NIH. Risk assessment tool. National Heart, Lung, and Blood Institute. 2013. http://cvdrisk.nhlbi.nih.gov/.

  28. Samal L, Linder JA, Lipsitz SR, et al. Electronic health records, clinical decision support, and blood pressure control. Am J Manag Care. 2011;17(9):626–32.

    PubMed  Google Scholar 

  29. Katzan I, Speck M, Dopler C, et al. The knowledge program: an innovative, comprehensive electronic data capture system and warehouse. AMIA Annual Symposium Proceedings. 2011:683.

  30. McCoy AB, Thomas EJ, Krousel-Wood M, et al. Clinical decision support alert appropriateness: a review and proposal for improvement. Ochsner J. 2014;14(2):195–202.

    PubMed Central  PubMed  Google Scholar 

  31. Battaglia L, Aronson MD, Neeman N, et al. A “Smart” heart failure sheet: using electronic medical records to guide clinical decision making. Am J Med. 2011;124(2):118–20.

    Article  PubMed  Google Scholar 

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Conflict of Interest

Randi Foraker reports grants from Pfizer, Inc., during the conduct of the study; grants from Pfizer, Inc., outside the submitted work. In addition, Foraker has a patent Electronic medical record web application pending. Bobbie Kite is on grant #T15LM011270-02 through the National Library of Medicine. Wilkister Tangasi, Marjorie Kelley, and Julie Bower have no conflicts of interest.

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This article does not contain any studies with human or animal subjects performed by any of the authors.

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Correspondence to Bobbie J. Kite or Randi E. Foraker.

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This article is part of the Topical Collection on Cardiovascular Risk Health Policy

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Kite, B.J., Tangasi, W., Kelley, M. et al. Electronic Medical Records and Their Use in Health Promotion and Population Research of Cardiovascular Disease. Curr Cardiovasc Risk Rep 9, 422 (2015). https://doi.org/10.1007/s12170-014-0422-5

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  • DOI: https://doi.org/10.1007/s12170-014-0422-5

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