Appl Clin Inform 2017; 08(03): 826-831
DOI: 10.4338/ACI-2017-03-CR-0046
Case Report
Schattauer GmbH

Barriers to Achieving Economies of Scale in Analysis of EHR Data

A Cautionary Tale
Mark P. Sendak
1   Duke Institute for Health Innovation
,
Suresh Balu
1   Duke Institute for Health Innovation
,
Kevin A. Schulman
2   Duke Clinical Research Institute
3   Department of Medicine, Duke University School of Medicine, Durham, North Carolina
› Author Affiliations
This work was supported internally by the Duke Clinical Research Institute.
Further Information

Publication History

received: 22 March 2017

accepted in revised form: 15 June 2017

Publication Date:
20 December 2017 (online)

Summary

Signed in 2009, the Health Information Technology for Economic and Clinical Health Act infused $28 billion of federal funds to accelerate adoption of electronic health records (EHRs). Yet, EHRs have produced mixed results and have even raised concern that the current technology ecosystem stifles innovation. We describe the development process and report initial outcomes of a chronic kidney disease analytics application that identifies high-risk patients for nephrology referral. The cost to validate and integrate the analytics application into clinical workflow was $217,138. Despite the success of the program, redundant development and validation efforts will require $38.8 million to scale the application across all multihospital systems in the nation. We address the shortcomings of current technology investments and distill insights from the technology industry. To yield a return on technology investments, we propose policy changes that address the underlying issues now being imposed on the system by an ineffective technology business model.

Citation: Sendak MP, Balu S, Schulman KH. Barriers to Achieving Economies of Scale in Analysis of EHR Data. Appl Clin Inform 2017; 8: 826–831 https://doi.org/10.4338/ACI-2017-03-CR-0046

Additional Contributions

Damon M. Seils, MA, Duke University, assisted with manuscript preparation. Mr Seils did not receive compensation for his assistance apart from his employment at Duke University.


Clinical Relevance Statement

Health information technology can be used to improve the detection and management of chronic kidney disease at the population level, but requires significant investment. Unfortunately, existing electronic health record systems do not enable rapid and efficient use of data to drive population health management programs. Health care systems must transform their technology infrastructure to achieve efficiencies of scale and advance population health.


Human Subjects Protections

No human subjects were involved in this work. The study was approved by the institutional review board of the Duke University Health System.


 
  • References

  • 1 Henry J, Pylypchuk Y, Searcy T, Patel V. Data Brief 35: Adoption of Electronic Health Record Systems among U.S. Non-Federal Acute Care Hospitals: 2008–2015. Office of the National Coordinator for Health Information Technology, May 2016.
  • 2 Blumenthal D, Glaser JP. Information technology comes to medicine. N Engl J Med 2007; 356 (24) 2527-2534.
  • 3 Bates DW, Saria S, Ohno-Machado L, Shah A, Escobar G. Big Data In health care: using analytics to identify and manage high-risk and high-cost patients. Health Aff (Millwood) 2014; 33 (07) 1123-1131.
  • 4 Koppel R, Lehmann CU. Implications of an emerging EHR monoculture for hospitals and healthcare systems. J Am Med Inform Assoc 2015; 22 (02) 465-471.
  • 5 Mandl KD, Kohane IS. Escaping the EHR trap - the future of health IT. N Engl J Med 2012; 366 (24) 2240-2242.
  • 6 Lee BJ, Forbes K. The role of specialists in managing the health of populations with chronic illness: the example of chronic kidney disease. BMJ 2009; 339: b2395.
  • 7 Lee B, Turley M, Meng D, Zhou Y, Garrido T, Lau A, Radler L. Effects of proactive population-based nephrologist oversight on progression of chronic kidney disease: a retrospective control analysis. BMC Health Serv Res 2012; 12 (01) 1.
  • 8 Data Analytics Center, Perelman School of Medicine at the University of Pennsylvania. Epic Clarity. http://www.med.upenn.edu/dac/epic-clarity-data-warehousing.html Accessed June 11, 2017.
  • 9 Hersh WR, Weiner MG, Embi PJ, Logan JR, Payne PR, Bernstam EV, Lehmann HP, Hripcsak G, Hartzog TH, Cimino JJ, Saltz JH. Caveats for the use of operational electronic health record data in comparative effectiveness research. Med Care 2013; 51 8 Suppl 3 S30-S37.
  • 10 Fung V, Brand RJ, Newhouse JP, Hsu J. Using Medicare data for comparative effectiveness research: opportunities and challenges. Am J Manag Care 2011; 17 (07) 488-496.
  • 11 Adamusiak T, Shimoyama N, Shimoyama M. Next generation phenotyping using the unified medical language system. JMIR Med Inform 2014; 2 (01) e5.
  • 12 Tangri N, Stevens LA, Griffith J, Tighiouart H, Djurdjev O, Naimark D, Levin A, Levey AS. A predictive model for progression of chronic kidney disease to kidney failure. JAMA 2011; 305 (15) 1553-1559.
  • 13 Coresh J, Turin TC, Matsushita K, Sang Y, Ballew SH, Appel LJ, Arima H, Chadban SJ, Cirillo M, Djurdjev O, Green JA, Heine GH, Inker LA, Irie F, Ishani A, Ix JH, Kovesdy CP, Marks A, Ohkubo T, Shalev V, Shankar A, Wen CP, de Jong PE, Iseki K, Stengel B, Gansevoort RT, Levey AS. CKD Prognosis Consortium. Decline in estimated glomerular filtration rate and subsequent risk of end-stage renal disease and mortality. JAMA 2014; 311 (24) 2518-2531.
  • 14 Office of the National Coordinator for Health Information Technology.. Certified Health IT Vendors and Editions Reported by Hospitals Participating in the Medicare EHR Incentive Program. Health IT Quick-Stat 29. September 2016. dashboard.healthit.gov/quickstats/pages/FIG Vendors of EHRs to Participating Hospitals.php.
  • 15 Brown JS, Kahn M, Toh S. Data quality assessment for comparative effectiveness research in distributed data networks. Med Care 2013; 51 8 Suppl 3 S22-S29.
  • 16 Drawz PE, Archdeacon P, McDonald CJ, Powe NR, Smith KA, Norton J, Williams DE, Patel UD, Narva A. CKD as a model for improving chronic disease care through electronic health records. Clin J Am Soc Nephrol 2015; 10 (08) 1-12.
  • 17 Brajer N. CKD Population Health Cost Model. http://www.dihi.org/news/ckd-population-health-cost-model Accessed May 6, 2017.
  • 18 Rothenberg J. A Discussion of Data Quality for Verification, Validation, and Certification (VV&C) of Data to be used in Modeling. Rand Project Memorandum.; 1997
  • 19 Redman TC. Improve data quality for competitive advantage. Sloan Manag Rev 1995; 36 (02) 99-107.
  • 20 Redman TC. Data’s credibility problem. Harv Bus Rev. 2013 December; 84-88.
  • 21 Hripcsak G, Duke JD, Shah NH, Reich CG, Huser V, Schuemie MJ, Suchard MA, Park RW, Wong IC, Rijnbeek PR, van der Lei J, Pratt N, Norén GN, Li YC, Stang PE, Madigan D, Ryan PB. Observational health data sciences and informatics (OHDSI): opportunities for observational researchers. Stud Health Technol Inform 2015; 216: 574-578.