Of the Importance of Motherhood and Apple Pie: What Big Data Can Learn from Small Data

Circulation: Cardiovascular Quality and Outcomes

Volumn 8, Issue 4, 2015, Pages 329-331

  Roger, Véronique L ^a  

^a MAYO CLINIC   (United States)

Author keywords

data science; epidemiology; population; registries

Indexed keywords

ACUTE CORONARY SYNDROME; ATRIAL FIBRILLATION; CARDIOVASCULAR DISEASE; CEREBROVASCULAR ACCIDENT; CODING ALGORITHM; ELECTRONIC MEDICAL RECORD; GOLD STANDARD; HOSPITAL DISCHARGE; HUMAN; INFORMED CONSENT; INTERNATIONAL CLASSIFICATION OF DISEASES; MEDICAL AUDIT; MEDICAL RECORD REVIEW; MEDICARE; OUTCOMES RESEARCH; PREDICTIVE VALUE; PRIORITY JOURNAL; QUALITY CONTROL; REGISTER; REVIEW; TEACHING HOSPITAL; TOTAL QUALITY MANAGEMENT; UNITED STATES; VALIDITY; ECONOMICS; HEALTH CARE POLICY; HEALTH MAINTENANCE ORGANIZATION; LEGISLATION AND JURISPRUDENCE;

CARDIOVASCULAR DISEASES; HEALTH MAINTENANCE ORGANIZATIONS; HEALTH POLICY; HUMANS; REGISTRIES; UNITED STATES;

EID: 84937715193     PISSN: 19417713     EISSN: 19417705     Source Type: Journal    
DOI: 10.1161/CIRCOUTCOMES.115.002115     Document Type: Review

References (21)

1. Hoffman S, Podgurski A. The use and misuse of biomedical data: Is bigger really better? Am J Law Med. 2013; 39: 497-538.
2. Xian Y, Hammill BG, Curtis LH. Data sources for heart failure comparative effectiveness research. Heart Fail Clin. 2013; 9: 1-13. doi: 10.1016/j. hfc.2012.09.001.
3. Dunlay SM, Alexander KP, Melloni C, Kraschnewski JL, Liang L, Gibler WB, Roe MT, Ohman EM, Peterson ED. Medical records and quality of care in acute coronary syndromes: results from CRUSADE. Arch Intern Med. 2008; 168: 1692-1698. doi: 10.1001/archinte.168.15.1692.
4. Ferreira-González I, Marsal JR, Mitjavila F, Parada A, Ribera A, Cascant P, Soriano N, Sánchez PL, Arós F, Heras M, Bueno H, Marrugat J, Cuñat J, Civeira E, Permanyer-Miralda G. Patient registries of acute coronary syndrome: assessing or biasing the clinical real world data? Circ Cardiovasc Qual Outcomes. 2009; 2: 540-547. doi: 10.1161/CIRCOUTCOMES.108.844399.
5. Reeves MJ, Nickles AV, Roberts S, Hurst R, Lyon-Callo S. Assessment of the completeness and accuracy of case ascertainment in the Michigan Stroke Registry. Circ Cardiovasc Qual Outcomes. 2014; 7: 757-763. doi: 10.1161/CIRCOUTCOMES.113.000706.
6. Centers for Disease Control and Prevention. The Paul Coverdell National Acute Stroke Registry. http://www.cdc.gov/dhdsp/programs/stroke-registry.htm. Accessed June 19, 2015.
7. Lyu H, Cooper M, Patel K, Daniel M, Makary MA. Prevalence and data transparency of national clinical registries in the United States. [published online ahead of print]. J Healthc Qual. 2015. doi: 10.1097/JHQ.0000000000000001.
8. Kumamaru H, Judd SE, Curtis JR, Ramachandran R, Hardy NC, Rhodes JD, Safford MM, Kissela BM, Howard G, Jalbert JJ, Brott TG, Setoguchi S. Validity of claims-based stroke algorithms in contemporary Medicare data: reasons for geographic and racial differences in stroke (REGARDS) study linked with medicare claims. Circ Cardiovasc Qual Outcomes. 2014; 7: 611-619. doi: 10.1161/CIRCOUTCOMES.113.000743.
9. Thigpen JL, Dillon C, Forster KB, Henault L, Quinn EK, Tripodis Y, Berger PB, Hylek EM, Limdi NA. Validity of international classification of disease codes to identify ischemic stroke and intracranial hemorrhage among individuals with associated diagnosis of atrial fibrillation. Circ Cardiovasc Qual Outcomes. 2015; 8: 8-14. doi: 10.1161/CIRCOUTCOMES.113.000371.
10. Qualified Clinical Data Registry Reporting. http://www.cms.gov/medicare/quality-initiatives-patient-assessment-instruments/pqrs/qualifiedclinical-data-registry-reporting.html. Accessed June 19, 2015.
11. Messenger JC, Ho KK, Young CH, Slattery LE, Draoui JC, Curtis JP, Dehmer GJ, Grover FL, Mirro MJ, Reynolds MR, Rokos IC, Spertus JA, Wang TY, Winston SA, Rumsfeld JS, Masoudi FA; NCDR Science and Quality Oversight Committee Data Quality Workgroup. The National Cardiovascular Data Registry (NCDR) Data Quality Brief: the NCDR Data Quality Program in 2012. J Am Coll Cardiol. 2012; 60: 1484-1488. doi: 10.1016/j.jacc.2012.07.020.
12. Tu YK. Linear mixed model approach to network meta-analysis for continuous outcomes in periodontal research. J Clin Periodontol. 2015; 42: 204-212. doi: 10.1111/jcpe.12362.
13. Khoury MJ, Lam TK, Ioannidis JP, Hartge P, Spitz MR, Buring JE, Chanock SJ, Croyle RT, Goddard KA, Ginsburg GS, Herceg Z, Hiatt RA, Hoover RN, Hunter DJ, Kramer BS, Lauer MS, Meyerhardt JA, Olopade OI, Palmer JR, Sellers TA, Seminara D, Ransohoff DF, Rebbeck TR, Tourassi G, Winn DM, Zauber A, Schully SD. Transforming epidemiology for 21st century medicine and public health. Cancer Epidemiol Biomarkers Prev. 2013; 22: 508-516. doi: 10.1158/1055-9965.EPI-13-0146.
14. Roger VL, Boerwinkle E, Crapo JD, Douglas PS, Epstein JA, Granger CB, Greenland P, Kohane I, Psaty BM. Strategic transformation of population studies: recommendations of the working group on epidemiology and population sciences from the National Heart, Lung, and Blood Advisory Council and Board of External Experts. Am J Epidemiol. 2015; 181: 363-368. doi: 10.1093/aje/kwv011.
15. Chan KS, Fowles JB, Weiner JP. Review: electronic health records and the reliability and validity of quality measures: a review of the literature. Med Care Res Rev. 2010; 67: 503-527. doi: 10.1177/1077558709359007.
16. Parsons A, McCullough C, Wang J, Shih S. Validity of electronic health record-derived quality measurement for performance monitoring. J Am Med Inform Assoc. 2012; 19: 604-609. doi: 10.1136/amiajnl-2011-000557.
17. Bayley KB, Belnap T, Savitz L, Masica AL, Shah N, Fleming NS. Challenges in using electronic health record data for CER: experience of 4 learning organizations and solutions applied. Med Care. 2013; 51(8 Suppl 3): S80-S86. doi: 10.1097/MLR.0b013e31829b1d48.
18. Tyree PT, Lind BK, Lafferty WE. Challenges of using medical insurance claims data for utilization analysis. Am J Med Qual. 2006; 21: 269-275. doi: 10.1177/1062860606288774.
19. Abebe NA, Capozza KL, Des Jardins TR, Kulick DA, Rein AL, Schachter AA, Turske SA. Considerations for community-based mHealth initiatives: insights from three Beacon Communities. J Med Internet Res. 2013; 15: e221. doi: 10.2196/jmir.2803.
20. Adamson G. The well-oiled data machine: Data is the oil that keeps the business cogs turning. https://www.edq.com/uk/blog/the-well-oiled-datamachine/. Accessed June 19, 2015.
21. Wheatley M. Big, bad data: What's the big deal over data quality? http://siliconangle.com/blog/2014/07/25/big-bad-data-whats-the-big-deal-overdata-quality/. Accessed June 19, 2015.