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Big Data and Digital Solutions: Laying the Foundation for Cardiovascular Population ManagementCME Cover

Big Data and Digital Solutions: Laying the Foundation for Cardiovascular Population ManagementCME

Open Access
|Oct 2020

References

  1. CDC.gov [Internet]. Atlanta, GA: Centers for Disease Control and Prevention; c2020. Heart Disease Facts; 2020 Sep 8 [cited 2020 Nov 11]. Available from: https://www.cdc.gov/heartdisease/facts.htm.
  2. Khera R , Valero-Elizondo J , Nasir K . Financial Toxicity in Atherosclerotic Cardiovascular Disease in the United States: Current State and Future Directions. J Am Heart Assoc. 2020; 9( 19): e017793. DOI: 10.1161/JAHA.120.017793.
  3. Joynt KE . Health policy and cardiovascular medicine: rapid changes, immense opportunities. Circulation. 2015 Mar 24; 131( 12): 1098 105. doi: 10.1161/CIRCULATIONAHA.114.013606.
  4. Huang X Rosenthal MB . Overuse of Cardiovascular Services: Evidence, Causes, and Opportunities for Reform. Circulation. 2015 Jul 21; 132( 3): 205 14. doi: 10.1161/CIRCULATIONAHA.114.012668.
  5. Aaron KJ , Colantonio LD , Deng L , . Cardiovascular Health and Healthcare Utilization and Expenditures Among Medicare Beneficiaries: The Reasons for Geographic And Racial Differences in Stroke (REGARDS) Study. J Am Heart Assoc. 2017 Feb 1; 6( 2): e005106.
  6. Freeman AC Sweeney K . Why general practitioners do not implement evidence: qualitative study. BMJ. 2001 Nov 10; 323( 7321): 1100 2. doi: 10.1136/bmj.323.7321.1100.
  7. Flores G , Lee M , Bauchner H , Kastner B . Pediatricians' attitudes, beliefs, and practices regarding clinical practice guidelines: a national survey. Pediatrics. 2000 Mar; 105( 3 Pt 1): 496 501. DOI: 10.1542/peds.105.3.496.
  8. Hillman AL . Managing the physician: rules versus incentives. Health Aff (Mill-wood). 1991; 10( 4): 138 46. doi: 10.1377/hlthaff.10.4.138.
  9. Ventola CL . Challenges in evaluating and standardizing medical devices in health care facilities. P T. 2008 Jun; 33( 6): 348 359.
  10. Ghosh AK . On the challenges of using evidence-based information: the role of clinical uncertainty. J Lab Clin Med. 2004 Aug; 144( 2): 60 4. doi: 10.1016/j.lab.2004.05.013.
  11. Budrionis A Bellika JG . The Learning Healthcare System: Where are we now? A systematic review. J Biomed Inform. 2016 Dec; 64 87 92. doi: 10.1016/j.jbi.2016.09.018.
  12. Britto MT , Fuller SC , Kaplan HC , . Using a network organisational architecture to support the development of Learning Healthcare Systems. BMJ Qual Saf. 2018; 27( 11): 937 946. doi: 10.1136/bmjqs-2017-007219.
  13. IBM Big Data Hub [Internet]. Infographics & Animations: The Four V's of Big Data; 2019 [cited 2020 Nov 11]. Available from: https://www.ibmbigdatahub.com/infographic/four-vs-big-data.
  14. IBM.com [Internet]. Armonk, NY: IBM; c2020. Jain A. The 5 V's of big data; 2016 Sep 17 [cited 2020 Nov 11]. Available from: https://www.ibm.com/blogs/watson-health/the-5-vs-of-big-data/.
  15. McPadden J , Durant TJS , Bunch DR , . Health Care and Precision Medicine Research: Analysis of a Scalable Data Science Platform. J Med Internet Res. 2019 Apr 9; 21( 4): e13043. DOI: 10.1111/tid.13043.
  16. Schulz WL , Durant TJS , Torre CJ , Hsiao AL , Krumholz HM . Agile health care analytics: Enabling real-time disease surveillance with a computational health platform. J Med Internet Res. 2020 May 28; 22( 5): e18707. DOI: 10.2196/18707.
  17. Dash S , Shakyawar SK , Sharma M , Kaushik S . Big data in healthcare: management, analysis and future prospects. J Big Data. 2019 Jun; 6( 1): 1 25. doi: 10.1186/s40537-019-0217-0.
  18. Pastorino R , De Vito C , Migliara G , . Benefits and challenges of Big Data in healthcare: an overview of the European initiatives. Eur J Public Health. 2019 Oct 1; 29( Supplement_3): 23 7. DOI: 10.1093/eurpub/ckz168.
  19. Hekler EB , Klasnja P , Chevance G , Golaszewski NM , Lewis D , Sim I . Why we need a small data paradigm. BMC Med. 2019 Jul 17; 17( 1): 133.
  20. IBM.com [Internet]. Armonk, NY: IBM; c2020. Data infrastructure for managing Population Health; 2016 Jun [cited 2020 Nov 11]. Available from: https://www.ibm.com/downloads/cas/MOJDOKBW.
  21. Hulsen T , Jamuar SS , Moody AR , . From Big Data to Precision Medicine. Front Med (Lausanne). 2019 Mar 1; 6: 34. DOI: 10.3389/fmed.2019.00034.
  22. Google [Internet]. Mountain View, CA: Google; c2020. Dean J, Ghemawat S. MapReduce: Simplified Data Processing on Large Clusters; 2004 [cited 2020 Nov 11]. Available from: https://static.googleusercontent.com/media/research.google.com/en//archive/mapreduce-osdi04.pdf
  23. Wiley [Internet]. Hoboken, NJ: John Wiley & Sons, Inc.; c2020. Fernández A, del Río S, López V, et al. Big Data with Cloud Computing: an insight on the computing environment, MapReduce, and programming frameworks; 2014 Sep 29 [cited 2020 Nov 11]. Available from: https://onlinelibrary.wiley.com/doi/abs/10.1002/widm.1134. DOI: 10.1002/widm.1134.
  24. Inukollu VN , Arsi S , Rao Ravuri S . Security Issues Associated with Big Data in Cloud Computing. Int J Netw Secur Its Appl. 2014; 6( 3): 45 56. DOI: 10.5121/ijnsa.2014.6304
  25. Han A , Isaacson A , Muennig P . The promise of big data for precision population health management in the US. Public Health. 2020 Aug; 185 110 16. doi: 10.1016/j.puhe.2020.04.040.
  26. Gamache R , Kharrazi H , Weiner JP . Public and Population Health Informatics: The Bridging of Big Data to Benefit Communities. Yearb Med Inform. 2018 Aug; 27( 1): 199 206. doi: 10.1055/s-0038-1667081.
  27. Wells TS , Ozminkowski RJ , Hawkins K , Bhattarai GR , Armstrong DG . Leveraging big data in population health management. Big Data Anal. 2016 Jul 1; 1( 1): 1 14. doi: 10.1186/s41044-016-0001-5.
  28. Providence Health [Internet]. Renton, WA: Providence Health & Services; c2020. Population Health Dashboards: Data to drive change at the community level; 2020 Jul [cited 2020 Nov 11]. Available from: https://oregon.providence.org/our-services/c/center-for-outcomes-research-and-education-core/population-health-dashboards/.
  29. Woolf SH , Purnell JQ , Simon SM , . Translating Evidence into Population Health Improvement: Strategies and Barriers. Annu Rev Public Health. 2015 Mar 18; 36 463 82. doi: 10.1146/annurev-publhealth-082214-110901.
  30. AHRQ.gov [Internet]. Rockville, MD: Agency for Healthcare Research and Quality; c2020. Cardiovascular Treatment Outcomes Dashboard; 2020 [cited 2020 Nov 11]. Available from: https://www.ahrq.gov/evidencenow/tools/clinic-dashboard.html.
  31. Minnesota Department of Health [Internet]. St. Paul, MN: Minnesota Department of Health; c2020. Indicator Dashboard for Monitoring the Picture of Cardiovascular Health in Minnesota; 2018 [cited 2020 Nov 11]. Available from: https://www.health.state.mn.us/diseases/cardiovascular/cardio-dashboard/index.html.
  32. AHRQ.gov [Internet]. Rockville, MD: Agency for Healthcare Research and Quality; c2020. Health System Dashboards on Patient & Family Advisor Integration; 2020 [cited 2020 Nov 11]. Available from: https://www.ahrq.gov/evidencenow/tools/pfcc-dashboard.html.
  33. AHRQ.gov [Internet]. Rockville, MD: Agency for Healthcare Research and Quality; c2020. Data Visualization Best Practices for Primary Care Quality Improvement (QI) Dashboards; 2017 [cited 2020 Nov 11]. Available from: https://www.ahrq.gov/evidencenow/tools/dashboard-best-practice.html.
  34. Peterson PN , Rumsfeld JS , Liang L , . Treatment and risk in heart failure: gaps in evidence or quality? Circ Cardiovasc Qual Outcomes. 2010 May; 3( 3): 309 15. DOI: 10.1161/CIRCOUTCOMES.109.879478.
  35. Pavlović J , Greenland P , Deckers JW , . Assessing gaps in cholesterol treatment guidelines for primary prevention of cardiovascular disease based on available randomised clinical trial evidence: The Rotterdam Study. Eur J Prev Cardiol. 2018 Mar; 25( 4): 420 31. doi: 10.1177/2047487317743352.
  36. Maviglia SM , Teich JM , Fiskio J , Bates DW . Using an electronic medical record to identify opportunities to improve compliance with cholesterol guidelines. J Gen Intern Med. 2001 Aug; 16( 8): 531 7. doi: 10.1046/j.1525-1497.2001.016008531.x.
  37. The Community Guide [Internet]. Atlanta, GA: The Community Guide; c2020. CVD: Clinical Decision-Support Systems; 2013 Apr [cited 2020 Nov 11]. Available from: https://www.thecommunityguide.org/findings/cardiovascular-disease-clinical-decision-support-systems-cdss.
  38. HopkinsMedicine.org [Internet]. Baltimore, MD: The Johns Hopkins University; c2020. New Decision Support Tool Places Key Information Front and Center; 2018 Nov 2 [cited 2020 Nov 11]. Available from: https://www.hopkinsmedicine.org/office-of-johns-hopkins-physicians/best-practice-news/new-decision-support-tool-places-key-information-front-and-center.
  39. Clarke S , Wilson ML , Terhar M . Using Clinical Decision Support and Dashboard Technology to Improve Heart Team Efficiency and Accuracy in a Trans-catheter Aortic Valve Implantation (TAVI) Program. Stud Health Technol Inform. 2016; 225: 98 102. PMID: 27332170.
  40. Groenhof TKJ , Rittersma ZH , Bots ML , . A computerised decision support system for cardiovascular risk management 'live' in the electronic health record environment: development, validation and implementation-the Utrecht Cardiovascular Cohort Initiative. Neth Heart J. 2019 Sep; 27( 9): 435 42. DOI: 10.1007/s12471-019-01308-w.
  41. Leslie SJ , Hartswood M , Meurig C , . Clinical decision support software for management of chronic heart failure: development and evaluation. Comput Biol Med. 2006 May; 36( 5): 495 506. doi: 10.1016/j.compbiomed.2005.02.002.
  42. Khairat SS , Dukkipati A , Lauria HA , Bice T , Travers D , Carson SS . The Impact of Visualization Dashboards on Quality of Care and Clinician Satisfaction: Integrative Literature Review. JMIR Hum factors. 2018 May 31; 5( 2): e22. DOI: 10.2196/humanfactors.9328.
  43. HIT Consultant [Internet]. Atlanta, GA: HIT Consultant Media; c2020. Yale School of Medicine Launches Cloud-Based PHR Platform Hugo; 2016 May 10 [cited 2020 Nov 11]. Available from: https://hitconsultant.net/2016/05/10/yale-school-medicine-launches-cloud-based-phr-platform-hugo/#.X5o6GJpYY2w.
  44. Vawdrey DK , Wilcox LG , Collins SA , . A tablet computer application for patients to participate in their hospital care. AMIA Annu Symp Proc. 2011; 2011: 1428 1435. PMID: 22195206.
  45. Riso B , Tupasela A , Vears DF , . Ethical sharing of health data in online platforms - which values should be considered? Life Sci Soc Policy. 2017 Aug 21; 13( 1): 12. DOI: 10.1186/s40504-017-0060-z.
  46. Huang W , Li J , Alem L . Towards Preventative Healthcare: A Review of Wearable and Mobile Applications. Stud Health Technol Inform. 2018; 251 11 14. doi: 10.3233/978-1-61499-880-8-11.
  47. Pliakos I , Kefaliakos A , Kalokerinou A , Al-Fantel K , Mechili A , Diomidous M . m-Health: Integration of Mobile Phones and Applications for a Better Healthcare System. Stud Health Technol Inform. 2014 Jul; 202: 315.
  48. Ricciardi W , Pita Barros P , Bourek A , . How to govern the digital transformation of health services. Eur J Public Health. 2019 Oct 1; 29( Supplement_3): 7 12. DOI: 10.1093/eurpub/ckz165.
  49. McCoy AB , Thomas EJ , Krousel-Wood M , Sittig DF . Clinical decision support alert appropriateness: A review and proposal for improvement. Ochsner J. Summer 2014; 14( 2): 195 202. PMID: 24940129.
  50. Anderson JL , Heidenreich PA , Barnett PG , . ACC/AHA statement on cost/value methodology in clinical practice guidelines and performance measures: A report of the American college of cardiology/American heart association task force on performance measures and task force on practice guidelines. Circulation. 2014 Jun 3; 129( 22): 2329 45. doi: 10.1161/CIR.0000000000000042.
  51. Ghazisaeidi M , Safdari R , Torabi M , Mirzaee M , Farzi J , Goodini A . Development of Performance Dashboards in Healthcare Sector: Key Practical Issues. Acta Inform Medica. 2015 Oct; 23( 5): 317 21. doi: 10.5455/aim.2015.23.317-321.
  52. Ferver K , Burton B , Jesilow P . The Use of Claims Data in Healthcare Research. Open Public Health J. 2009; 2: 11 24.
  53. Lee CH Yoon HJ . Medical big data: promise and challenges. Kidney Res Clin Pract. 2017 Mar; 36( 1): 3 11. doi: 10.23876/j.krcp.2017.36.1.3.
  54. Khairat S , Marc D , Crosby W , Al Sanousi A . Reasons For Physicians Not Adopting Clinical Decision Support Systems: Critical Analysis. JMIR Med Inform. 2018 Apr 18; 6(c 2): e24. DOI: 10.2196/medinform.8912.
  55. Ash JS , Sittig DF , Campbell EM , Guappone KP , Dykstra RH . Some unintended consequences of clinical decision support systems. AMIA Annu Symp Proc. 2007 Oct 11; 2007: 26 30. PMID: 18693791.
  56. NursingCenter.com [Internet]. South Holland, Netherlands: Wolters Kluwer; c2020. Castillo RS, Kelemen A. Considerations for a Successful Clinical Decision Support System; 2020 [cited 2020 Nov 11]. Available from: https://www.nursingcenter.com/lnc/ce_articleprint?an=00024665-201307000-00003.
  57. Sutton RT , Pincock D , Baumgart DC , Sadowski DC , Fedorak RN , Kroeker KI . An overview of clinical decision support systems: benefits, risks, and strategies for success. npj Digit Med. 2020 Feb 6; 3: 17. DOI: 10.1038/s41746-020-0221-y.
  58. Sujansky W . Heterogeneous database integration in biomedicine. J Biomed Inform. 2001 Aug; 34( 4): 285 98. doi: 10.1006/jbin.2001.1024.
Language: English
Page range: 272 - 282
Published on: Oct 1, 2020
Published by: Houston Methodist DeBakey Heart & Vascular Center
In partnership with: Paradigm Publishing Services

© 2020 Khurram Nasir, Zulqarnain Javed, Safi U. Khan, Stephen L. Jones, Julia Andrieni, published by Houston Methodist DeBakey Heart & Vascular Center
This work is licensed under the Creative Commons Attribution-NonCommercial 4.0 License.