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Improving Health Care Outcomes through Personalized Comparisons of Treatment Effectiveness Based on Electronic Health Records

Published online by Cambridge University Press:  01 January 2021

Extract

The unsustainable growth in U.S. health care costs is in large part attributable to the rising costs of pharmaceuticals and medical devices and to unnecessary medical procedures. This fact has led health reform advocates and policymakers to place considerable hope in the idea that increased government support for research on the comparative effectiveness of medical treatments will eventually help to reduce health care expenses by informing patients, health care providers, and payers about which treatments for common conditions are effective and which are not. Comparative effectiveness research (CER) has shown in some cases that expensive but commonly used treatments are significantly less effective than relatively inexpensive alternatives. Critics warn, however, that CER will homogenize patient care, limit patient choices, and lead to improper health care rationing and even to the denial of lifesaving treatments.

Type
Symposium
Copyright
Copyright © American Society of Law, Medicine and Ethics 2011

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References

Aaron, H. J. Ginsburg, P. B., “Is Health Spending Excessive? If So, What Can We Do about It?” Health Affairs 28, no. 5 (2009): 12601275, at 1266.CrossRefGoogle Scholar
Institute of Medicine, Initial National Priorities for Comparative Effectiveness Research (Washington, D.C., National Academies Press, 2009), available at <http://www.iom.edu/Reports/2009/ComparativeEffectivenessResearchPriorities.aspx> (last visited May 27, 2011).+(last+visited+May+27,+2011).>Google Scholar
ALLHAT Group, “Major Outcomes in High-Risk Hypertensive Patients Randomized to Angiotensin-Converting Enzyme Inhibitor or Calcium Channel Blocker vs. Diuretic: The Anti-hypertensive and Lipid-Lowering Treatment to Prevent Heart Attack Trial (ALLHAT),” JAMA 288, no. 23 (2002): 29812997; Keenan, S. P. Gregor, J. Sibbald, W. J. Cook, D. Gafni, A., “Noninvasive Positive Pressure Ventilation in the Setting of Severe, Acute Exacerbations of Chronic Obstructive Pulmonary Disease: More Effective and Less Expensive,” Critical Care Medicine 28, no. 6 (2000): 2094–2102.Google Scholar
Avorn, J., “Debate about Funding Comparative-Effectiveness Research,” New England Journal of Medicine 360, no. 19 (2009): 19271929.CrossRefGoogle Scholar
American Recovery and Reinvestment Act of 2009, tit. VIII, Pub. L. No. 111–5, 123 Stat. 115, 177–78 (2009).Google Scholar
Patient Protection and Affordable Care Act of 2010, Pub. L. No. 111–148, §6301, 124 Stat. 119 (2010), codified at 42 U.S.C. § 1320e (2010).Google Scholar
Wing, K. R. Mariner, W. K. Annas, G. J. Strouse, D. S., Public Health Law (LexisNexis, 2007): At vi.Google Scholar
Steyerberg, E. W., Clinical Prediction Models: A Practical Approach to Development, Validation, and Updating (New York: Springer, 2010): At 5382.Google Scholar
Gerber, A. S. Patashnik, E. M. Doherty, D. Dowling, C., “A National Survey Reveals Public Skepticism about Research-Based Treatment Guidelines,” Health Affairs 29, no. 10 (2010): 18821884.CrossRefGoogle Scholar
Evidence Based Medicine Working Group, “Evidence-Based Medicine: A New Approach to Teaching the Practice of Medicine,” JAMA 268, no. 17 (1992): 24202425; McColl, A. Smith, H. White, P. Field, J., “General Practitioners' Perceptions of the Route to Evidence Based Medicine: A Questionnaire Survey,” BMJ 316, no. 7128 (1998): 361–365.Google Scholar
See Avorn, , supra note 4, at 19271928.Google Scholar
Alexander, G. C. Stafford, R. S., “Does Comparative Effectiveness Have a Comparative Edge?” JAMA 301, no. 23 (2009): 24882490, at 2489.CrossRefGoogle Scholar
See Institute of Medicine, supra note 2.Google Scholar
Stewart, W. F. Shah, N. R. Selna, M. J. Paulus, R. A. Walker, J. M., “Bridging the Inferential Gap: The Electronic Health Record and Clinical Evidence” Health Affairs 26, no. 2 (2007): W181–w191, at w181. For further discussion of observational trials, see infra Part III.C.Google Scholar
42 U.S.C. §1320e(a)(2)(A) (2010).Google Scholar
Id. at §1320e(d)(2)(A) (2010).Google Scholar
Manchikanti, L. Falco, F. J. E. Boswell, M. V. Hirsch, J. A., “Facts, Fallacies, and Politics of Comparative Effectiveness Research: Part I. Basic Consideration,” Pain Physician 13, no. 1 (2010): E23–E54, at E39.Google Scholar
42 U.S.C. §1320e(c) (2010).Google Scholar
Avon, J. Fischer, M., “‘Bench to Behavior’: Translating Comparative Effectiveness Research into Improved Clinical Practice,” Health Affairs 29, no. 10 (2010): 18911900; Bonham, A. C. Solomon, M. Z., “Moving Comparative Effectiveness Research into Practice: Implementation Science and the Role of Academic Medicine,” Health Affairs 29, no. 10 (2010): 1901–1905; Saver, R. S., “Health Care Reform's Wild Card: The Uncertain Effectiveness of Comparative Effectiveness Research,” University of Pennsylvania Law Review 159 (forthcoming 2011) (discussing comparative implementation research).Google Scholar
Blumenthal, D. Tavenner, M., “The “Meaningful Use’ Regulation for Electronic Health Records,” New England Journal of Medicine 363, no. 10 (2010): 501504, at 501.CrossRefGoogle Scholar
Hoffman, S. Podgurski, A., “Finding a Cure: The Case for Regulation and Oversight of Electronic Health Record Systems,” Harvard Journal of Law and Technology 22, no. 1 (2008) 103165, at 162–164.Google Scholar
PricewaterhouseCoopers, Transforming Healthcare through Secondary Use of Health Data (2009), available at <http://www.pwc.com/us/en/healthcare/publications/secondary-health-data.jhtml> (last visited May 27, 2011).+(last+visited+May+27,+2011).>Google Scholar
Atkins, D. Kupersmith, J. Eisen, S., “The Veterans Affairs Experience: Comparative Effectiveness Research in a Large Health System,” Health Affairs 29, no. 10 (2010): 19061912, at 1906–1907.CrossRefGoogle Scholar
U.S. Food and Drug Administration, Sentinel Initiative: Transforming How We Monitor the Safety of FDA-Regulated Products, available at <http://www.fda.gov/Safety/FDAsSentinelInitiative/default.htm> (last visited December 12, 2010).+(last+visited+December+12,+2010).>Google Scholar
Etheredge, L. M., “Creating a High-Performance System for Comparative Effectiveness Research,” Health Affairs 29, no. 10 (2010): 17611767, at 1765.CrossRefGoogle Scholar
See Atkins, Kupersmith, Eisen, , supra note 23, at 1911.Google Scholar
See Saver, , supra note 19 (“Accounting for Individual Patient Differences”); Coelho, T., “A Patient Advocate's Perspective On Patient-Centered Comparative Effectiveness Research,” Health Affairs 29, no. 10 (2010): 18851890, at 1888.Google Scholar
International Warfarin Pharmacogenetics Consortium, “Estimation of the Warfarin Dose with Clinical and Pharmacogenetic Data,” New England Journal of Medicine 360, no. 8 (2009): 753764.Google Scholar
Woodcock, J. Lesko, L. J., “Pharmacogenetics - Tailoring Treatment for the Outliers,” New England Journal of Medicine 360, no. 8 (2009): 811813, at 813.Google Scholar
See Avorn, , supra note 4; Alexander, Stafford, , supra note 10; Garber, A. M. Sox, H. C., “The Role of Costs in Comparative Effectiveness Research,” Health Affairs 29, no. 10 (2010): 18051811.Google Scholar
American College of Physicians, “Information on Cost-Effectiveness: An Essential Product of a National Comparative Effectiveness Program,” Annals of Internal Medicine 148, no. 12 (2008): 956961.CrossRefGoogle Scholar
42 U.S.C. §1320e-1(c)(1) (2010). See also 42 U.S.C. § 1320–1(e) (prohibiting the use of quality adjusted life years as a threshold for determining “what type of health care is cost effective or recommended”).Google Scholar
Adjuvant Online! website, available at <http://www.adjuvantonline.com/index.jsp> (last visited May 27, 2011).+(last+visited+May+27,+2011).>Google Scholar
Downing, G. Boyle, S. Brinner, K. Osheroff, J., “Information Management to Enable Personalized Medicine: Stakeholder Roles in Building Clinical Decision Support,” BMC Medical Informatics and Decision Making 9, Supp. 1 (2009): 44.CrossRefGoogle Scholar
Burke, W. Psaty, B. M., “Personalized Medicine in the Era of Genomics,” JAMA 298, no. 14 (2007): 16821684.CrossRefGoogle Scholar
Izenman, A. J., Modern Multivariate Statistical Techniques: Regression, Classification, and Manifold Learning (New York: Springer, 2008): At 412413.CrossRefGoogle Scholar
Rosenbaum, P. R., Observational Studies (New York: Springer, 2002), at 295328; Rubin, D. B., “The Design Versus the Analysis of Observational Studies for Causal Effects: Parallels with the Design of Randomized Trials,” Statistics in Medicine 26, no. 1 (2007): 20–36.Google Scholar
Id. (Rubin); Morgan, S. L. Winship, C., Counterfactuals and Causal Inference: Methods and Principles for Social Research (New York: Cambridge University Press, 2007): At 87121.CrossRefGoogle Scholar
Austin, P. C., “Balance Diagnostics for Comparing the Distribution of Baseline Covariates between Treatment Groups in Propensity-Score Matched Samples,” Statistics in Medicine 28, no. 25 (2009): 30833107.CrossRefGoogle Scholar
Fleurence, R. L. Naci, H. Jansen, J. P., “The Critical Role of Observational Evidence in Comparative Effectiveness Research,” Health Affairs 29, no. 10 (2010): 18261832.CrossRefGoogle Scholar
See generally, Pearl, J., Causality: Models, Reasoning, and Inference (New York: Cambridge University Press, 2009); Rubin, , supra note 37; Morgan, Winship, , supra note 38.Google Scholar
See Pearl, , supra note 41, at 65–106;; Pearl, J., “Causal Inference in Statistics: An Overview,” Statistics Surveys 3 (2009): 96146.CrossRefGoogle Scholar
de Koning, J. Klazinga, N. Koudstaal, P. Prins, A. Borsboom, G. Mackenbach, J., “The Role of ‘Confounding by Indication’ in Assessing the Effect of Quality of Care on Disease Outcomes in General Practice: Results of a Case-Control Study,” BMC Health Services Research 5, no. 1 (2005): 1017; Sturmer, T. Glynn, R. J. Rothman, K. J. Avorn, J. M. Schneeweiss, S., “Adjustments for Unmeasured Confounders in Pharmacoepidemiologic Database Studies Using External Information,” Medical Care 45, no. 10, supp. 2 (2007): S158–S165.CrossRefGoogle Scholar
Conroy, S., “Defining Frailty – the Holy Grail of Geriatric Medicine,” Journal of Nutrition, Health & Aging 13, no. 4 (2009): 389; Williamson, A. Hoggart, B., “Pain: A Review of Three Commonly Used Pain Rating Scales,” Journal of Clinical Nursing 14, no. 7 (2005): 798804.CrossRefGoogle Scholar
Lash, T. L. Fox, M. P. Fink, A. K., Applying Quantitative Bias Analysis to Epidemiologic Data (New York: Springer, 2009): 132.CrossRefGoogle Scholar
Greenland, S. Brumback, B., “An Overview of Relations among Causal Modelling Methods,” International Journal of Epidemiology 31, no. 5 (2002): 10301037.CrossRefGoogle Scholar
U.S. v. Microsoft Corp., 253 F.3d 34, 69 (D.C. Cir. 2001) (per curiam) (“an exclusive contract does not violate the Clayton Act unless its probable effect is to ‘foreclose competition in a substantial share of the line of commerce affected.’”).Google Scholar
Agency for Healthcare Research and Quality, National Guideline Clearinghouse (2010), available at <http://www.guideline.gov/browse/index.aspx?alpha=A> (last visited May 27, 2011).+(last+visited+May+27,+2011).>Google Scholar
The POC overseeing a PCTE service should study estimated treatment-effect differences from actual PCTEs and compare them to published estimates from any randomized controlled trials that compared the same treatments to see if there is evidence of systematic bias in the PCTEs (or the RCTs). The data required for this purpose can be generated automatically as a by-product of completed PCTE queries. A similar bias assessment can be conducted even before a PCTE service is deployed by comparing RCT results to results obtained by conducting PCTEs for a sample of “virtual” subject patients whose EHRs were drawn at random from among all relevant EHRs in the database.Google Scholar
45 C.F.R. § 46.107 (2010).Google Scholar
See Hoffman, Podgurski, , supra note 21, at 150151.Google Scholar
Larman, C. Basili, V. R., “Iterative and Incremental Developments: A Brief History,” Computer 36, no. 6 (2003): 4756.CrossRefGoogle Scholar
Chute, C. G., “Medical Concept Representation,” in Chen, H. Fuller, S. S. Friedman, C. Hersh, W., eds., Medical Informatics (New York: Springer, 2005): At 163182.Google Scholar
See Sheth, A. P. Larson, J. A., “Federated Database Systems for Managing Distributed, Heterogeneous, and Autonomous Databases,” ACM Computing Surveys 22, no. 3 (1990): 183236.CrossRefGoogle Scholar
Diamond, C. C. Mostashari, F. Shirky, C., “Collecting and Sharing Data for Population Health: A New Paradigm,” Health Affairs 28, no. 2 (2009): 454466.CrossRefGoogle Scholar
Pace, W. D. Cifuentes, M. Valuck, R. J. Staton, E. W. Brandt, E. C. West, D. R., “An Electronic Practice-Based Network for Observational Comparative Effectiveness Research,” Annals of Internal Medicine 151, no. 5 (2009): 338340.CrossRefGoogle Scholar
Gelman, A. Hill, J., Data Analysis Using Regression and Multilevel/Hierarchical Models (Cambridge University Press, 2007), at 235342.Google Scholar
Majeed, A. Car, J. Sheikh, A., “Accuracy and Completeness of Electronic Patient Records in Primary Care,” Family Practice 25, no. 4 (2008): 213214; Terry, A. L. Chevendra, V. Thind, A. Stewart, M. Marshall, J. N. Cejic, S., “Using Your Electronic Medical Record for Research: A Primer for Avoiding Pitfalls,” Family Practice 27, no. 1 (2010): 121–126.CrossRefGoogle Scholar
See Id. (Terry et al.). at 122.Google Scholar
Wu, A. W. Snyder, C. Clancy, C. M. Steinwachs, D. M., “Adding the Patient Perspective to Comparative Effectiveness Research,” Health Affairs 29, no. 10 (2010): 18631871, at 1866 (discussing the “limited uptake of patient-reported outcome measures”).Google Scholar
Blanchet, K. D., “Remote Patient Monitoring,” Telemedicine and e-Health 14, no. 2 (2008): 127130.CrossRefGoogle Scholar
See Hoffman, Podgurski, , supra note 21, at 150155.Google Scholar
Rothstein, M., “Is Deidentification Sufficient to Protect Health Privacy in Research?” American Journal of Bioethics 10, no. 9 (2010): 311.Google Scholar
See 45 C.F.R. §§ 164.302-.318 (2010) (establishing the HIPAA Security Rule, which governs the security of electronic health information); 45 C.F.R § 164.514(b) (describing the HIPAA Privacy Rule's requirements for deidentification).Google Scholar
Hall, M. A. Schulman, K. A., “Ownership of Medical Information,” JAMA 301, no. 12 (2009): 12821284; Hall, M. A., “Property, Privacy, and the Pursuit of Interconnected Electronic Medical Records,” Iowa Law Review 95, no. 2 (2010): 631–663; Rodwin, M. A., “Patient Data: Property, Privacy & the Public Interest,” American Journal of Law & Medicine 36, no. 4 (2010): 586–618.CrossRefGoogle Scholar
See Rothstein, , supra note 63, at 56.Google Scholar
Hoffman, S. Podgurski, A., “In Sickness, Health, and Cyberspace: Protecting the Security of Electronic Private Health Information,” Boston College Law Review 48, no. 2 (2007): 331386, at 334–335.Google Scholar
45 C.F.R. § 160.103 (2010).Google Scholar
Vladeck, B. C. Rice, T., “Market Failure and the Failure of Discourse: Facing Up to the Power of Sellers,” Health Affairs 28, no. 5 (2009): 13051315.CrossRefGoogle Scholar
Jha, A. K. DesRoches, C. M. Kralover, P. D. Joshi, M. S., “A Progress Report on Electronic Health Records in U.S. Hospitals,” Health Affairs 29, no. 10 (2010): 19511957, at 1953 (finding that only “11.9 percent of U.S. hospitals had either a basic or a comprehensive electronic health record in 2009”).CrossRefGoogle Scholar