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Big Bad Data: Law, Public Health, and Biomedical Databases

Published online by Cambridge University Press:  01 January 2021

Extract

The accelerating adoption of electronic health record (EHR) systems will have profound impacts on clinical care. It will also have far-reaching implications for public health research and surveillance, which in turn could lead to changes in public policy, statutes, and regulations. The public health benefits of EHR use can be significant. However, researchers and analysts who rely on EHR data must proceed with caution and understand the potential limitations of EHRs.

Much has been written about the risk of EHR privacy breaches. This paper focuses on a different set of concerns, those relating to data quality. Unlike clinical trial data, EHR data is not recorded primarily to meet the needs of researchers. Because of clinicians’ workloads, poor user-interface design, and other factors, EHR data is surprisingly likely to be erroneous, miscoded, fragmented, and incomplete. Although EHRs eliminate the problem of cryptic handwriting, other kinds of errors are more common with EHRs than with paper records.

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Copyright
Copyright © American Society of Law, Medicine and Ethics 2013

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References

See, e.g., Lee, L. M. and Gostin, L. O. “Ethical Collection, Storage, and Use of Public Health Data: A Proposal for a National Privacy Protection” JAMA 302, no. 1(2009):8284; O'Connor, J. and Matthews, G. “Informational Privacy, Public Health, and State Laws” American Journal of Public Health 101, no. 10(2011):1845–1850; Wilson, A., Note, “Missing the Mark: The Public Health Exception to the HIPAA Privacy Rule and Its Impact on Surveillance Activity” Houston Journal of Health Law & Policy 9, no. 1(2008): 131–156.CrossRefGoogle Scholar
Chretien, J. Tomich, N. E. Gaydos, J. C. and Kelley, P. W. “RealTime Public Health Surveillance for Emergency Preparedness” American Journal of Public Health 99, no. 8(2009):13601363; Smith, P. F. Hadler, J. L. Stanbury, M. Rolfs, R. T. Hopkins, R. S., and the CSTE Surveillance Strategy Group, “Blueprint Version 2.0’: Updating Public Health Surveillance for the 21st Century” Journal of Public Health Management Practice (2012) (Epub ahead of print), at 5; Klompas, M. Murphy, M. Lankiewicz, J. McVetta, J. Lazarus, R. Eggleston, E. Daly, P. Oppendisano, P. Beagan, B. Kirby, C. and Platt, R. “Harnessing Electronic Health Records for Public Health Surveillance” Online Journal of Public Health Informatics 3, no. 3(2011): 1–7.CrossRefGoogle Scholar
45 C.F.R. §170.205(c)-(e) (2011); Public Health Information Network, Meaningful Use Fact Sheet: Syndromic Surveillance, at <http://www.cdc.gov/phin/library/PHIN_Fact_Sheets/FS_MU_SS.pdf> (last visited January 9, 2013).+(last+visited+January+9,+2013).>Google Scholar
Cousens, S. Hargreaves, J. Bonell, C. Armstrong, B. Thomas, J. Kirkwood, B. R. and Hayes, R. “Alternatives to Randomisation in the Evaluation of Public-Health Interventions: Statistical Analysis and Causal Inference” Journal of Epidemiology and Community Health 65, no. 7(2011):576581; Guilbert, T. W. Arndt, B. Temte, J. Adams, A. Buckingham, W. Tandias, A. Tomasallo, C. Anderson, H. A. and Hanrahan, L. P. “The Theory and Application of UW e-Health-Phinex, A Clinical Electronic Health Record-Public Health Information Exchange” Wisconsin Medical Journal 111, no. 3(2012):124–133, at 124–125; Hoffman, S. and Podgurski, A. “Balancing Privacy, Autonomy, and Scientific Needs in Electronic Health Records Research” SMU Law Review 65, no. 1(2012):85–144, at 97–102. The latter article discusses the benefits of observational research and its limitations compared to randomized clinical studies. See also 42 U.S.C. §1320e (2010).CrossRefGoogle Scholar
Brown, S. H. Fischetti, L. F. Graham, G. Bates, J. Lancaster, A. E. McDaniel, D. Gillon, J. Darbe, M. and Kolodner, R. M. “Use of Electronic Health Records in Disaster Response: The Experience of Department of Veterans Affairs After Hurricane Katrina” American Journal of Public Health 97, Supp. no. 1 (2007): S136S141.CrossRefGoogle Scholar
DeMers, G. Kah, C. Buono, C. Chan, T. Blair, P. Griswold, W. Johansson, P. Chipara, O. and Nilsson, A. “Secure Scalable Disaster Electronic Medical Record and Tracking System” 2011 IEEE International Conference on Technologies for Homeland Security (HST) (2011):402–406; Levy, G. Blumberg, N. Kreiss, Y. Ash, N. and Merin, O. “Application of Information Technology within a Field Hospital Deployment Following the January 2010 Haiti Earthquake Disaster” Journal of the American Medical Informatics Association 17, no. 6(2010): 626630.Google Scholar
Garrett, N. Mishra, N. Nichols, B. Staes, C. Akin, C. and Safran, C. “Characterization of Public Health Alerts and Their Suitability for Alerting in Electronic Health Record Systems,” Journal of Public Health Management Practice 17, no. 1(2011):7783; Lurio, J. Morrison, F. P. Pichardo, M. Berg, R. Buck, M. D. Wu, W. Kitson, K. Mostashari, F. and Calman, N. “Using Electronic Health Record Alerts to Provide Public Health Situational Awareness to Clinicians” Journal of the American Medical Informatics Association 17, no. 2(2010): 217–219.CrossRefGoogle Scholar
Botsis, T. Hartvigsen, G. Chen, F. and Weng, C. “Secondary Use of EHR: Data Quality Issues and Informatics Opportunities” AMIA Summits on Translational Science Proceedings 2010 (2010): 15.Google Scholar
Liaw, S. T. Taggart, J. Dennis, S. and Yeo, A. “Data Quality and Fitness for Purpose of Routinely Collected Data – A General Practice Case Study from an Electronic Practice-Based Research Network (ePBRN)” AMIA Annual Symposium Proceedings 2011 (2011):785794, at 789; see Botsis (id.)Google Scholar
Kukafka, R. Ancker, J. S. Chan, C. Chelico, J. Khan, S. Mortoti, S. Natarajan, K. Presley, K. and Stephens, K. “Redesigning Electronic Health Record Systems to Support Public Health” Journal of Biomedical Informatics 40, no. 4(2007): 398409, at 405.CrossRefGoogle Scholar
See Smith, , supra note 2, at 5; Brunt, C. S. “CPT Fee Differentials and Visit Upcoding Under Medicare Part B” Health Economics 20, no. 7(2011): 831841.Google Scholar
Newgard, C. D. Zive, D. Jui, J. Weathers, C. and Daya, M. “Electronic Versus Manual Data Processing: Evaluating the Use of Electronic Health Records in Out-of-Hospital Clinical Research” Academic Emergency Medicine 19, no. 2(2012): 217227, at 225.CrossRefGoogle Scholar
Diamond, C. C. Mostashari, F. and Shirky, C. “Collecting and Sharing Data for Population Health: A New Paradigm” Health Affairs 28, no. 2(2009):454–66, at 456–457; Beasley, J. W. Wetterneck, T. B. Temte, J. Lapin, J. A. Smith, P. Rivera-Rodriguez, J. and Karsh, B. T. “Information Chaos in Primary Care: Implications for Physician Performance and Patient Safety” Journal of the American Board of Family Medicine 24, no. 6(2011): 745–751, at 747.CrossRefGoogle Scholar
Terhune, C. “U.S. Pushes Healthcare Providers to Share Records Electronically” Los Angeles Times, March 10, 2012, available at <http://articles.latimes.com/2012/mar/10/business/la-fi-health-tech-20120310> (last visited January 9, 2013). Shortliffe, E. H. and Cimino, J. J., eds., Biomedical Informatics: Computer Applications in Health Care and Biomedicine (New York: Springer, 2006): at 952 (defining interoperability).Google Scholar
Chute, C. G. “Medical Concept Representation” in Chen, H. Fuller, S. S. Friedman, C. and Hersh, W., eds., Medical Informatics: Knowledge Management and Data Mining in Biomedicine (New York: Springer-Verlag 2005): at 170; Gold, M. R. McLaughlin, C. G. Devers, K. J. Berenson, R. A. and Bovbjerg, R. R. “Obtaining Providers’ ‘Buy-In’ and Establishing Effective Means of Information Exchange Will Be Critical to HITECH's Success” Health Affairs 31, no. 3(2012): 514–526, at 519.Google Scholar
Ahern, J. Hubbard, A. and Galea, S. “Estimating the Effects of Potential Public Health Interventions on Population Disease Burden: A Step-by-Step Illustration of Causal Inference Methods” American Journal of Epidemiology 169, no. 9(2009):11401147; see Cousens, et al., supra note 4.CrossRefGoogle Scholar
Faigman, D. Blumenthal, J. Cheng, E. K. Mnookin, J. L. Murphy, E. E. and Sanders, J., Modern Scientific Evidence: The Law and Science of Expert Testimony (Minnesota: Thomson Reuters/West, 2011): at §5:16, pp, 281282.Google Scholar
Greenland, S. “Quantifying Biases in Causal Models: Classical Confounding vs. Collider-Stratification Bias” Epidemiology 14, no. 3(2003): 300306, at 306.CrossRefGoogle Scholar
See Beasley, et al., supra note 13, at 747; Faigman, et al., supra note 17, at §5:10, at 277; Hammer, G. P. Prel, J. B. du and Blettner, M. “Avoiding Bias in Observational Studies” Deutsches Ärzteblatt International 106, no. 41(2009): 664668, at 665.Google Scholar
Turner, K. and Ferland, L. “State Electronic Disease Surveillance Systems – United States, 2007 and 2010” Morbidity and Mortality Weekly Report 60, no. 41(2011):14211423, at 1421; Rolka, H. Walker, D. W. English, R. Katzoff, M. Scogin, G. and Neuhaus, E. “Analytical Challenges for Emerging Public Health Surveillance” Morbidity and Mortality Weekly Report 61, Supp. (2012): 35–39, at 36.Google Scholar
Sachdeva, S. and Bhalla, S. “Semantic Interoperability in Standardized Electronic Health Record Databases” Association for Computing Machinery Journal of Data and Information Quality 3, no. 1(2012):11:37, at 1:5.CrossRefGoogle Scholar
Klompas, M. McVetta, J. Lazarus, R. Eggleston, E. Haney, G. Kruskal, B. A. Yih, W. K. Daly, P. Oppendisano, P. Beagan, B. Lee, M. Kirby, C. Heisey-Grove, D. DeMaria, A. and Platt, R. “Integrating Clinical Practice and Public Health Surveillance Using Electronic Medical Record Systems” American Journal of Preventive Medicine 42, no. 6, Supp. 2 (2012): S154S162. The ESP platform “automatically execute[s] complex disease-detection algorithms to provide meaningful surveillance without requiring clinicians to manually parse potential cases.” Id., at S154.CrossRefGoogle Scholar
Kahn, M. G. Raebel, M. A. Glanz, J. M. Riedlinger, K. and Steiner, J. F. “A Pragmatic Framework for Single-Site and Multisite Data Quality Assessment in Electronic Health Record-Based Clinical Research” Medical Care 50, Supp. (2012): S21S29.CrossRefGoogle Scholar
Pearl, J., Causality, 2d ed. (New York: Cambridge University Press, 2009): at 6568; VanderWeele, T. J. and Staudt, N. C. “Causal Diagrams for Empirical Legal Research: Methodology for Identifying Causation, Avoiding Bias, and Interpreting Results” Law, Probability and Risk 10, no. 4(2011): 329–354.CrossRefGoogle Scholar
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