Skip to main content

Advertisement

SpringerLink
Log in

Strengthening the Learning Health System in Cardiovascular Disease Prevention: Time to Leverage Big Data and Digital Solutions

  • Evidence-Based Medicine, Clinical Trials and Their Interpretations (K. Nasir, Section Editor)
  • Published:
Current Atherosclerosis Reports Aims and scope Submit manuscript

Abstract

Purpose of Review

The past few decades have seen significant technologic innovation for the treatment and diagnosis of cardiovascular diseases. The subsequent growing complexity of modern medicine, however, is causing fundamental challenges in our healthcare system primarily in the spheres of patient involvement, data generation, and timely clinical implementation. The Institute of Medicine advocated for a learning health system (LHS) in which knowledge generation and patient care are inherently symbiotic. The purpose of this paper is to review how the advances in technology and big data have been used to further patient care and data generation and what future steps will need to occur to develop a LHS in cardiovascular disease.

Recent Findings

Patient-centered care has progressed from technologic advances yielding resources like decision aids. LHS can also incorporate patient preferences by increasing and standardizing patient-reported information collection. Additionally, data generation can be optimized using big data analytics by developing large interoperable datasets from multiple sources to allow for real-time data feedback.

Summary

Developing a LHS will require innovative technologic solutions with a patient-centered lens to facilitate symbiosis in data generation and clinical practice.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1

Similar content being viewed by others

References

Papers of particular interest, published recently, have been highlighted as: • Of importance •• Of major importance

  1. Green LW, et al. Diffusion theory and knowledge dissemination, utilization, and integration in public health. Annu Rev Public Health. 2009;30:151–74.

    Article  PubMed  Google Scholar 

  2. Zikmund-Fisher BJ, et al. Deficits and variations in patients’ experience with making 9 common medical decisions: the decisions survey. Med Decis Making. 2010;30(5 Suppl):85S–95S.

    Article  PubMed  Google Scholar 

  3. Bevan GH, et al. Level of scientific evidence underlying the Current American College of Cardiology/American Heart Association clinical practice guidelines. Circ Cardiovasc Qual Outcomes. 2019;12(2):e005293.

    Article  PubMed  Google Scholar 

  4. •• Institute of Medicine. Best care at lower cost: the path to continuously learning health care in America: National Academies Press; 2013. This landmark report delves into the current barriers of effective evidence generation and implementation as well as recommendations to improve this system.

  5. Elwyn G, et al. Investing in deliberation: a definition and classification of decision support interventions for people facing difficult health decisions. Med Decis Making. 2010;30(6):701–11.

    Article  PubMed  Google Scholar 

  6. Krumholz HM. Patient-centered medicine: the next phase in health care. Circ Cardiovasc Qual Outcomes. 2011;4(4):374–5.

    Article  PubMed  Google Scholar 

  7. Institute of Medicine. Crossing the quality chasm: a new health system for the 21st century. Washington, DC: National Academies Press; 2001.

    Google Scholar 

  8. Allen LA, et al. Decision making in advanced heart failure: a scientific statement from the American Heart Association. Circulation. 2012;125(15):1928–52.

    Article  PubMed  PubMed Central  Google Scholar 

  9. Legare F, et al. Interventions for improving the adoption of shared decision making by healthcare professionals. Cochrane Database Syst Rev. 2014;9:CD006732.

    Google Scholar 

  10. Lalonde L, et al. Development and preliminary testing of a patient decision aid to assist pharmaceutical care in the prevention of cardiovascular disease. Pharmacotherapy. 2004;24(7):909–22.

    Article  PubMed  Google Scholar 

  11. Thomson RG, et al. A patient decision aid to support shared decision-making on anti-thrombotic treatment of patients with atrial fibrillation: randomised controlled trial. Qual Saf Health Care. 2007;16(3):216–23.

    Article  PubMed  PubMed Central  Google Scholar 

  12. Mahler SA, et al. The HEART Pathway randomized trial: identifying emergency department patients with acute chest pain for early discharge. Circ Cardiovasc Qual Outcomes. 2015;8(2):195–203.

    Article  PubMed  PubMed Central  Google Scholar 

  13. Coylewright M, et al. PCI choice decision aid for stable coronary artery disease: a randomized trial. Circ Cardiovasc Qual Outcomes. 2016;9(6):767–76.

    Article  PubMed  Google Scholar 

  14. Weymiller AJ, et al. Helping patients with type 2 diabetes mellitus make treatment decisions: statin choice randomized trial. Arch Intern Med. 2007;167(10):1076–82.

    Article  CAS  PubMed  Google Scholar 

  15. Man-Son-Hing M, et al. A patient decision aid regarding antithrombotic therapy for stroke prevention in atrial fibrillation: a randomized controlled trial. JAMA. 1999;282(8):737–43.

    Article  CAS  PubMed  Google Scholar 

  16. Hoefel L, et al. 20th Anniversary Update of the Ottawa Decision Support Framework Part 1: a systematic review of the decisional needs of people making health or social decisions. Med Decis Making. 2020;40(5):555–81.

    Article  PubMed  Google Scholar 

  17. Chewning B, et al. Patient preferences for shared decisions: a systematic review. Patient Educ Couns. 2012;86(1):9–18.

    Article  PubMed  Google Scholar 

  18. Wright JS, Wall HK, Ritchey MD. Million Hearts 2022: Small steps are needed for cardiovascular disease prevention. JAMA. 2018;320(18):1857–8.

    Article  PubMed  Google Scholar 

  19. • Rumsfeld JS, et al. Cardiovascular health: the importance of measuring patient-reported health status: a scientific statement from the American Heart Association. Circulation. 2013;127(22):2233–49 This scientific statement provides an overview of patient reported health methods and focuses on opportunities to broaden the inclusion of these metrics in research.

    Article  PubMed  Google Scholar 

  20. Hofer S, et al. The MacNew Heart Disease Health-Related Quality of Life Questionnaire in patients with angina and patients with ischemic heart failure. Value Health. 2012;15(1):143–50.

    Article  PubMed  Google Scholar 

  21. Spertus J, Dorian P, Bubien R, Lewis S, Godejohn D, Reynolds MR, et al. Development and validation of the Atrial Fibrillation Effect on QualiTy-of-Life (AFEQT) Questionnaire in patients with atrial fibrillation. Circ Arrhythm Electrophysiol. 2011;4(1):15–25.

    Article  PubMed  Google Scholar 

  22. Green CP, Porter CB, Bresnahan DR, Spertus JA. Development and evaluation of the Kansas City Cardiomyopathy Questionnaire: a new health status measure for heart failure. J Am Coll Cardiol. 2000;35(5):1245–55.

    Article  CAS  PubMed  Google Scholar 

  23. Beltrame JF, Weekes AJ, Morgan C, Tavella R, Spertus JA. The prevalence of weekly angina among patients with chronic stable angina in primary care practices: the Coronary Artery Disease in General Practice (CADENCE) Study. Arch Intern Med. 2009;169(16):1491–9.

    Article  PubMed  Google Scholar 

  24. Weintraub WS, S.J, Kolm P, Maron DJ, Zhang Z, Jurkovitz C, et al. COURAGE Trial Research Group. Effect of PCI on quality of life in patients with stable coronary disease. N Engl J Med. 2008;359:677–87.

    Article  CAS  PubMed  Google Scholar 

  25. Blumenthal DM, et al. Patient-reported outcomes in cardiology. Circ Cardiovasc Qual Outcomes. 2018;11(11):e004794.

    Article  PubMed  PubMed Central  Google Scholar 

  26. Perez MV, Mahaffey KW, Hedlin H, Rumsfeld JS, Garcia A, Ferris T, et al. Large-scale assessment of a smartwatch to identify atrial fibrillation. N Engl J Med. 2019;381(20):1909–17.

    Article  PubMed  Google Scholar 

  27. ClinicalTrial.gov [Internet]. Bethesda (MD): National Library of Medicine (US). 2020 August - . Identifier NCT04468321, Effect of wearable devices on patient-reported outcomes and clinical utilization; cited 2020 October 6. Available from: https://clinicaltrials.gov/ct2/show/NCT04468321 .

  28. Hoppe UC, Vanderheyden M, Sievert H, Brandt MC, Tobar R, Wijns W, et al. Chronic monitoring of pulmonary artery pressure in patients with severe heart failure: multicentre experience of the monitoring Pulmonary Artery Pressure by Implantable device Responding to Ultrasonic Signal (PAPIRUS) II study. Heart. 2009;95(13):1091–7.

    Article  CAS  PubMed  Google Scholar 

  29. Tricoci P, Allen JM, Kramer JM, Califf RM, Smith SC Jr. Scientific evidence underlying the ACC/AHA clinical practice guidelines. JAMA. 2009;301(8):831–41.

    Article  CAS  PubMed  Google Scholar 

  30. Angus DC. Fusing randomized trials with big data: the key to self-learning health care systems? JAMA. 2015;314(8):767–8.

    Article  CAS  PubMed  Google Scholar 

  31. • Institute of Medicine. Best care at lower cost: the path to continuously learning health care. Circ Cardiovasc Qual Outcomes. 2012;5(6):e93–4 This book explains the inefficiencies in healthcare that hinder improvements and advocates for transitioning to a continuously learning health care system.

    Google Scholar 

  32. • Raghupathi W, Raghupathi V. Big data analytics in healthcare: promise and potential. Health Inf Sci Syst. 2014;2:3 This article provides an overview of the advantages and disadvantages of using big data analytics in healthcare.

    Article  PubMed  PubMed Central  Google Scholar 

  33. U.S. Food and Drug Administration. 21st Century Cures Act. Available at: https://www.fda.gov/RegulatoryInformation/LawsEnforcedbyFDA/SignificantAmendmentstotheFDCAct/21stCenturyCuresAct/default.htm . Accessed October 1, 2020.

  34. Garvin JH, Y.K, Gobbel GT, et al. Automating quality measures for heart failure using natural language processing: a descriptive study in the Department of Veterans Affairs. JMIR Med Inform. 2018;6(1):e5.

    Article  PubMed  PubMed Central  Google Scholar 

  35. Weiskopf NG, Hripcsak G, Swaminathan S, Weng C. Defining and measuring completeness of electronic health records for secondary use. J Biomed Inform. 2013;46(5):830–6.

    Article  PubMed  Google Scholar 

  36. Ackerman MJ. Computer briefs: big data. J Med Pract Manage. 2012;28(2):153–4.

    PubMed  Google Scholar 

  37. Jensen PB, Jensen LJ, Brunak S. Mining electronic health records: towards better research applications and clinical care. Nat Rev Genet. 2012;13(6):395–405.

    Article  CAS  PubMed  Google Scholar 

  38. Ross MK, Wei W, Ohno-Machado L. “Big data” and the electronic health record. Yearb Med Inform. 2014;9:97–104.

    CAS  PubMed  PubMed Central  Google Scholar 

  39. Weis JM, Levy PC. Copy, paste, and cloned notes in electronic health records. Chest. 2014;145(3):632–8.

    Article  PubMed  Google Scholar 

  40. Connelly R, Playford CJ, Gayle V, Dibben C. The role of administrative data in the big data revolution in social science research. Soc Sci Res. 2016;59:1–12.

    Article  PubMed  Google Scholar 

  41. Frizzell JD, Liang L, Schulte PJ, Yancy CW, Heidenreich PA, Hernandez AF, et al. Prediction of 30-day all-cause readmissions in patients hospitalized for heart failure: comparison of machine learning and other statistical approaches. JAMA Cardiol. 2017;2(2):204–9.

    Article  Google Scholar 

  42. Gavrielov-Yusim N, Friger M. Use of administrative medical databases in population-based research. J Epidemiol Community Health. 2014;68(3):283–7.

    Article  PubMed  Google Scholar 

  43. Shahian DM, Silverstein T, Lovett AF, Wolf RE, Normand SLT. Comparison of clinical and administrative data sources for hospital coronary artery bypass graft surgery report cards. Circulation. 2007;115(12):1518–27.

    Article  PubMed  Google Scholar 

  44. Sanborn TA, Tcheng JE, Anderson HV, Chambers CE, Cheatham SL, DeCaro MV, et al. ACC/AHA/SCAI 2014 health policy statement on structured reporting for the cardiac catheterization laboratory: a report of the American College of Cardiology Clinical Quality Committee. J Am Coll Cardiol. 2014;63(23):2591–623.

    Article  PubMed  Google Scholar 

  45. Carbo A, Gupta M, Tamariz L, Palacio A, Levis S, Nemeth Z, et al. Mobile technologies for managing heart failure: a systematic review and meta-analysis. Telemed J E Health. 2018;24:958–68.

    Article  Google Scholar 

  46. Hung G, et al. Mobile health application platform ‘Corrie’ personalises and empowers the heart attack recovery patient experience in the hospital and at home for an underserved heart attack survivor. BMJ Case Rep. 2020;13(2).

  47. ClinicalTrial.gov [Internet]. Bethesda (MD): National Library of Medicine (US). 2020 February - . Identifier NCT04276441, A study to investigate if early atrial fibrillation (AF) diagnosis reduces risk of events like stroke in the real-world; cited 2020 October 6. Available from: https://clinicaltrials.gov/ct2/show/NCT04276441.

Download references

Author information

Authors and Affiliations

  1. Department of Medicine, Johns Hopkins University School of Medicine, 600 North Wolfe Street, Harvey Building, Suite 808, Baltimore, MD, 21287, USA

    Anjali A Wagle & Seth Shay Martin

  2. Division of Cardiology, Johns Hopkins University School of Medicine, Baltimore, MD, USA

    Nino Isakadze & Seth Shay Martin

  3. Division of Cardiology, Houston Methodist Hospital, Houston, TX, USA

    Khurram Nasir

Authors
  1. Anjali A Wagle
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Nino Isakadze
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Khurram Nasir
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Seth Shay Martin
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Anjali A Wagle.

Ethics declarations

Human and Animal Rights and Informed Consent

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

Conflict of Interest

Khurram Nasir is on the Advisory Boards of Amgen, Novartis, and The Medicines Company, and his research is partly supported by the Jerold B. Katz Academy of Translational Research.

Seth Shay Martin reports personal fees from Amgen, AstraZeneca, Esperion, REGENXBIO, and 89bio; grants from Apple, Google, iHealth, Nokia, Maryland Innovation Initiative, American Heart Association, Aetna Foundation, PJ Schafer Memorial Fund, David and June Trone Family Foundation, Akcea Therapeutics,

and the National Institutes of Health; and is co-founder of Corrie Health, LLC. In addition, Dr. Martin has a pending patent on System of LDL-C Estimation.

The other authors declare no conflict of interest.

Additional information

Publisher’s Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

This article is part of the Topical Collection on Evidence-Based Medicine, Clinical Trials and Their Interpretations

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Wagle, A.A., Isakadze, N., Nasir, K. et al. Strengthening the Learning Health System in Cardiovascular Disease Prevention: Time to Leverage Big Data and Digital Solutions. Curr Atheroscler Rep 23, 19 (2021). https://doi.org/10.1007/s11883-021-00916-5

Download citation

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1007/s11883-021-00916-5

Keywords

Search

Navigation