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Electronic Health Records and the Frequency of Diagnostic Test Orders
Ibrahim Hakim, BBA; Sejal Hathi, BS; Archana Nair, MS; Trishna Narula, MPH; and Jay Bhattacharya, MD, PhD

Electronic Health Records and the Frequency of Diagnostic Test Orders

Ibrahim Hakim, BBA; Sejal Hathi, BS; Archana Nair, MS; Trishna Narula, MPH; and Jay Bhattacharya, MD, PhD
Using the most recently available national data, physicians with electronic health record (EHR) access ordered more tests than their non-EHR counterparts, thus contradicting a common rationale for EHR implementation.
The availability of an EHR system is associated with a measurable increase in the ordering of CBC and imaging tests in outpatient settings, even after adjusting for an extensive set of demographic and case-mix variables. Physicians with EHR access exhibited a higher probability of ordering an imaging study (OR, 1.26; P <.001) and a CBC test (OR, 1.34; P <.001). This difference is particularly pronounced for Medicare patients and patients with private insurance. These findings contradict one of the most common arguments for EHR implementation: that EHRs reduce excessive testing and, subsequently, unnecessary costs.16-18 Although our results do not differentiate between clinically indicated and redundant tests, rates of both expensive and inexpensive tests are higher in practices with EHRs.

EHR systems have been federally subsidized since 2009, when the HITECH Act earmarked billions of dollars in reimbursement to early adopters. Proponents argued that EHR use would improve care coordination, increase efficiency, expose duplicate testing, and, thereby, reduce costs. Preliminary evidence upheld this potential; however, these studies typically examined health technology systems developed in-house in highly controlled single-clinic or emergency department environments.19,20 

Evidence on the quality and cost-effectiveness of EHRs beyond these benchmark hospitals has been mixed.21,22 One study found hospitals across the country with advanced EHRs had a 9.66% lower cost per admission than those without advanced EHRs.23 By contrast, another study found that inpatient cases cost 7% more in hospitals with advanced EHRs than in those without.24 A third, analyzing Medicare claims data from 1998 to 2005, found an initial 1.3% increase in billed charges with no evidence of cost savings—even 5 years after adoption.25 The initial promises of EHRs to “cut waste…reduce the need to repeat expensive medical tests” and “save billions of dollars,” have yet to be achieved.26,27

We propose 2 possible interpretations of the observed correlation between EHR access and test ordering: one in which computerized access simplifies the ordering process leading to more frequent ordering, and another in which the same physicians who readily adopt EHRs also order more tests for their patients. Our results support the former interpretation. First, we found striking increases in both test and imaging orders for EHR-equipped physicians across nearly every subgroup; no variable, from patient demographics to insurance type to comorbidities, eliminated this effect. Second, from 2008 to 2011—when this data was available—we found that the largest effect of EHRs on test ordering was in large practice settings, such as HMOs, in which individual physicians are least likely to influence institutional IT decisions. In those settings, the argument that doctors who are most likely to adopt EHRs are the same doctors who are most likely to order excessive tests bears less relevance. If the selection bias interpretation was correct, we would have expected a larger difference in test ordering between EHR and non-EHR doctors in small practice settings.28,29 Because we observed the opposite, selection bias is a less likely interpretation of our results.

Limitations

Our study has a few limitations. Although we used CBC ordering as our single measure for all laboratory testing, other laboratory tests may be affected differently by EHRs. Nonetheless, as CBC is typically among the first-ordered laboratory tests in many clinical situations, it arguably reflects the overall trend for laboratory tests.30,31 Our measure for EHR implementation represents, at minimum, the capacity to order and view patient diagnostic information, not necessarily advanced clinical decision support—helpful for filtering vast quantities of patient information—because such distinctions were not available from our data source. Still, our results are suggestive of the broader impact of EHRs as it is reasonable to conclude that ordering of laboratory tests and imaging are basic functions of all EHR systems.

Another limitation stems from our lack of data beyond 2012. In 2011, the federal government implemented Meaningful Use regulations, which tied federal incentive payments to specific care delivery improvements enabled by EHRs.32 Because we reviewed years 2008 through 2012 only, we cannot be certain whether additional functionalities developed in the last 3 years might have reduced the quantity of laboratory and imaging tests ordered. Still, cost data for evolved functionalities like clinical decision support, one of the most publicized of Meaningful Use, remain conflicted to modest at best.33 Moreover, at a time when less than one-third of office-based providers are meeting Stage 2 Meaningful Use requirements, perhaps it is the EHR programs studied—however rudimentary—that most accurately reflect the current usage and usability of EHRs nationwide.34 It remains to future studies to evaluate EHR systems as they continue to evolve.

Finally we were limited by the constraints of our data source. Given that our basic sampling unit was a single patient encounter and not the patient, long-term outcome variables, such as mortality and complications, could not be included. Moreover, our study does not cleanly distinguish between clinically necessary and unnecessary tests. We can infer clinical utility for some subsets of patients: those with a primary diagnosis of cancer, for example, for whom imaging was 47% (P <.001) more likely to be ordered if EHRs were available. Nonetheless, from our analysis, it also appears that EHRs may simply promote excessive testing more generally. It is interesting, for instance, that this effect holds true—across both imaging and laboratory testing—even for patients seen primarily for depression and mental disorders, diagnoses typically not associated with CBC or imaging requirements. Furthermore, those diagnoses that would almost necessitate CBC testing—specifically, infection and blood diseases—saw no significant difference in ordering frequency between EHR and non-EHR practices. This suggests that physicians will order critical diagnostic tests and imaging regardless of EHR status.

CONCLUSIONS

Our results demonstrate a positive relationship between EHR implementation and the volume of laboratory and imaging tests that physicians order. Against a backdrop of policies suggesting cost savings for EHRs, these results call for reassessment of the hope that EHRs can reduce medical expenditures and increase clinical efficiency. Adopting EHRs is not enough: providers must also foster the organizational and delivery processes required to realize systemwide efficiencies. Implementing EHR systems may become cost-effective only when complemented by models of care that emphasize quality, value, and efficiency.

Acknowledgments

Ibrahim Hakim, BBA; Sejal Hathi, BS; Archana Nair, MS; and Trishna Narula, MPH, contributed equally and are all joint first authors on the paper. The authors alone are responsible for the statements in the paper and for any errors; all had equal access to the data. 

Author Affiliations: School of Medicine (IH, SH, AN, TN), and Institute for Economic Policy Research (JB), and Graduate School of Business (SH), Stanford University, Stanford, CA; National Bureau of Economic Research (JB), Cambridge, MA.

Source of Funding: Dr Bhattacharya acknowledges funding from the National Institute on Aging (NIA) for his work on this project (R37 AG036791 and P30 AG17253).

Author Disclosures: The authors report no relationship or financial interest with any entity that would pose a conflict of interest with the subject matter of this article.

Authorship Information: Concept and design (IH, SH, AN, TN, JB); acquisition of data (SH, AN, TN); analysis and interpretation of data (IH, SH, AN, TN, JB); drafting of the manuscript (IH, SH, AN, TN, JB); critical revision of the manuscript for important intellectual content (IH, SH, AN, JB); statistical analysis (IH, SH, AN, TN, JB); provision of patients or study materials (TN); obtaining funding (JB); administrative, technical, or logistic support (SH); and supervision (JB).

Address Correspondence to: Sejal Hathi, BS, Stanford School of Medicine, Stanford Graduate School of Business, 291 Campus Dr, Stanford, CA 94305. E-mail: shathi@stanford.edu. 
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