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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
› InstitutsangabenThis work was supported internally by the Duke Clinical Research Institute.
Weitere Informationen

Publikationsverlauf

received: 22. März 2017

accepted in revised form: 15. Juni 2017

Publikationsdatum:
20. Dezember 2017 (online)

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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

Keywords

Chronic Kidney Diseases - Health Services Research - Informatics - Primary Health Care

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.


 

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