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The American Journal of Managed Care January 2017
Alignment of Breast Cancer Screening Guidelines, Accountability Metrics, and Practice Patterns
Tracy Onega, PhD; Jennifer S. Haas, MD; Asaf Bitton, MD; Charles Brackett, MD; Julie Weiss, MS; Martha Goodrich, MS; Kimberly Harris, MPH; Steve Pyle, BS; and Anna N. A. Tosteson, ScD
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Patricia J. Zettler, JD, and Erin C. Fuse Brown, JD, MPH
An Expanded Portfolio of Survival Metrics for Assessing Anticancer Agents
Jennifer Karweit, MS; Srividya Kotapati, PharmD; Samuel Wagner, PhD; James W. Shaw, PhD, PharmD, MPH; Steffan W. Wolfe, BA; and Amy P. Abernethy, MD, PhD
The Social Value of Childhood Vaccination in the United States
Tomas J. Philipson, PhD; Julia Thornton Snider, PhD; Ayman Chit, PhD; Sarah Green, BA; Philip Hosbach, BA; Taylor Tinkham Schwartz, MPH; Yanyu Wu, PhD; and Wade M. Aubry, MD
Value-Based Payment in Implementing Evidence-Based Care: The Mental Health Integration Program in Washington State
Yuhua Bao, PhD; Thomas G. McGuire, PhD; Ya-Fen Chan, PhD; Ashley A. Eggman, MS; Andrew M. Ryan, PhD; Martha L. Bruce, PhD, MPH; Harold Alan Pincus, MD; Erin Hafer, MPH; and Jürgen Unützer, MD, MPH, MA
Patient-Centered Care: Turning the Rhetoric Into Reality
Joel S. Weissman, PhD; Michael L. Millenson, BA; and R. Sterling Haring, DO, MPH
The Effect of Massachusetts Health Reform on Access to Care for Medicaid Beneficiaries
Laura G. Burke, MD, MPH; Thomas C. Tsai, MD, MPH; Jie Zheng, PhD; E. John Orav, PhD; and Ashish K. Jha, MD, MPH
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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.

Objectives: To determine whether electronic health record (EHR) access influences the number of laboratory and imaging tests ordered, which is a frequently cited mechanism for EHR-enabled cost savings.

Study Design: We analyzed data on non–federally employed office-based physicians from the 2008 to 2012 Electronic Health Medical Records Survey, a supplement to the National Ambulatory Medical Care Survey.

Methods: We estimated logistic regressions to determine the relationship between EHR utilization and the volume of laboratory and imaging tests ordered in our study population, controlling for age, sex, race, clinic type, payer type, health status, comorbidities, and new patients.

Results: Physicians who actively used an EHR system ordered more complete blood count (CBC) tests than physicians who did not (odds ratio [OR], 1.34; P <.001), even after adjusting for patient demographics, health status, and case mix. EHR-using physicians also ordered more computerized tomography scans (OR, 1.41; P <.001) and x-rays (OR, 1.39; P <.001); the difference for magnetic resonance imaging scans was not significant (OR, 1.08; P = .449). Subgroup analysis highlighted differences in ordering among various patient cohorts.

Conclusions: Using the most recent available nationally representative data, excluding federal and Veterans Affairs' hospitals, we found that physicians with EHR access ordered more tests than their non-EHR counterparts, thus contradicting a common rationale for EHR implementation. We argue that EHR use may actually increase healthcare expenditures by facilitating the ease of ordering tests. Whether these extra tests carry clinical utility requires further analysis.

Am J Manag Care. 2017;23(1):e16-e23
Takeaway Points

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. 
  • Against a backdrop of policies suggesting cost savings for EHR use, these results call for a reassessment of the unqualified expectation that EHRs will 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.
Over the last few years, the federal government has enacted seminal legislation to promote the adoption and use of health information technology (IT) among physicians. Most notably, in 2009, Congress passed the Health Information Technology for Economic and Clinical Health (HITECH) Act, which established incentive payments both for individual providers and hospital systems that demonstrate “Meaningful Use” of electronic health record (EHR) systems through 2015.1,2 An estimated $30 billion has been dedicated to this effort, with the goal of improving healthcare quality, cost-effectiveness, and efficiency.3 Much of this investment was predicated on an influential study by the RAND Corporation, a nonprofit global policy think tank. In 2005, RAND predicted that widespread EHR adoption would save $81 billion annually.4 A 2012 reassessment, however, altered this estimate: rather than decreasing, healthcare costs have climbed by over $800 billion since the first report was issued.5,6 Although EHRs are still thought to yield cost and quality benefits, recent studies have shown this effect to be inconsistent at best.7-9  

The global market for EHRs has grown substantially since HITECH’s passage. In the United States alone, it is expected to expand further from $9.6 to $12.2 billion by 2021.10 Recently, however, physicians adopting EHRs have voiced their concerns about their limitations and cost-effectiveness.11,12 Given the role of health IT in health reform, it has become increasingly urgent that we determine the financial and clinical impact of the EHR rollout: do EHRs save money while improving quality and efficiency?

In this study, we examined whether the introduction of EHRs in ambulatory medical practices has reduced the ordering of diagnostic and imaging tests. Although some agree that EHRs make data more accessible and reduce the likelihood of duplicate orders, other studies show that the accessibility of EHRs makes tests easier to order thus increasing the number.3,4,6-8,13

Our study builds on earlier work.14 A 2012 Health Affairs study analyzed data from the 2008 National Ambulatory Medical Care Survey (NAMCS)—before the passage of the HITECH Act—and found an association between electronic access to results and increased ordering of imaging tests.14 Nevertheless, because EHR systems have evolved since 2008, an updated evaluation of their effect on test ordering is warranted. We extended this previous analysis to include years 2008 through 2012, the most recent publicly available NAMCS data. We also conducted an extensive subgroup analysis as the impact of EHRs can vary by patient risk and demographics. 


We analyzed data from the 2008 to 2012 Electronic Health Medical Records Survey, a mail survey supplement to the NAMCS. We included the years 2008 to 2012 to capture patient records both before and after the HITECH Act was implemented in 2009. For 2008 and 2009, the EHR supplement used the same sampling scheme as the NAMCS. In 2010, the NAMCS expanded the sampling scheme to better represent national EHR usage, and in 2012, it implemented 2 sampling designs: one for national, regional, and division-level analysis, and a second to enable state-based analyses for the 34 most populous states in the country. State-based analyses were not incorporated into our study because of their absence from the data prior to 2012. In aggregate, the NAMCS presents data on patient demographics, EHR usage, and laboratory tests for a random sample of office-based physicians with direct patient interaction, excluding pathologists, anesthesiologists, and radiologists.15

The basic sampling unit of the NAMCS is 1 physician–patient interaction with a non–federally employed physician. From 2008 to 2011, a multi-stage sampling design was used. First, the primary sampling unit (PSU) comprised counties and county equivalents; second, physicians were selected within PSUs according to their specialty; and third, specific patient encounters were selected within physician offices (eAppendix Figure A [eAppendices available at]). In 2012, this design was simplified to 2 stages: the first stage stratified physicians based on primary care status and the second stage selected individual patient encounters (eAppendix Figure B).

For the final stage of both sampling designs, physicians were each assigned a calendar week, and surveys were completed for a systematic random sample of patients that were seen during the assigned week.15 The CDC compiled survey data into the NAMCS database and, in 2012, they incorporated physicians practicing at community health clinics (CHCs) in a separate survey. Therefore, we excluded all CHCs from our study population.

Sampling and Analysis

We generated national estimates from the NAMCS, accounting for the complex sampling design. Demographic data were compiled for the total study population. Typical variables included, but were not limited to, age, sex, payer type, and clinic type. Median income was analyzed for individuals within the 2008 to 2011 dataset, but was then no longer collected by NAMCS in 2012. Clinic type was defined as private practice or nonprivate practice in the NAMCS.

We analyzed several individual test-ordering practices for our primary outcomes, including complete blood count (CBC) and radiographic studies, because they are common tests, expensive in aggregate, and readily available in EHR systems. For each test, we compared the probability of ordering by physicians using EHRs with that of physicians not using EHRs. We used a Bonferroni correction to adjust for multiple comparisons.  

We estimated a patient-level multivariate logistic regression to determine the relationship between the ability to order laboratory tests electronically and the probability of ordering each test. We then computed an adjusted estimate for EHR and non-EHR physicians in which confounders were held at mean values. As covariates, all multivariate logistic regressions included, as available: age, sex, race, clinic type, payer type, health status, major International Classification of Diseases, Ninth Revision, Clinical Modification (ICD-9-CM) diagnosis categories, and whether the physician had seen the patient before. The ICD-9-CM categories included were: infectious/parasitic, neoplasms, endocrine/metabolic/immune disease, blood disease, mental disorders, nervous disease, sense organs disease, circulatory disease, respiratory disease, genitourinary disease, pregnancy/childbirth complications, skin disease, musculoskeletal disease, congenital anomalies, perinatal conditions, ill-defined conditions, injury/poisoning, and external injury.

To assess the overall effect of EHRs on imaging orders, 1 variable combined all major imaging modalities, such as, but not limited to, x-ray, computed tomography (CT), magnetic resonance imaging (MRI), electrocardiogram, ultrasound, and bone mineral density. We also analyzed 3 modalities individually: x-ray, CT, and MRI. For imaging, EHR usage was defined as the presence and usage of an electronic system for viewing imaging results, encompassing both the imaging study and its interpretation. Such computerized systems might be either part or independent of a full EHR, which is assumed to contain patient medical histories, previous laboratory and imaging results, and point-of-decision support. For those occasional patients seen at facilities where the EHR system was “turned off” or “not used routinely,” we categorized observations as occurring at facilities without an EHR system.


The study population (n = 183,519), as described in Table 1, includes a higher proportion of female (58.4%) and white patients (84.5%). The modal patient was between 45 and 64 years old (29.6%). Approximately half of the study population had private insurance (53.4%), one-third had a median family income above $52,387 (32.5%), and most were seen in private practice office settings (92.1%).

Table 2 illustrates the health status of our study population, with just over a quarter of patients diagnosed with hypertension (27.4%). Other notable chronic health conditions included hyperlipidemia (16.5%), arthritis (13.4%), diabetes (11.6%), and depression (9.2%). The most common primary diagnoses were “external causes of injury” (19.9%), “diseases of the respiratory system” (9.8%), and “musculoskeletal disease” (9%).

For physician practices with and without EHRs, Table 3 shows the probabilities of CBC test ordering. Superscripted notes indicate that the difference between these groups was significant at the 95% confidence interval (CI) after the Bonferroni correction. In subgroup analysis, we found that availability of EHRs was associated with higher rates of CBC testing regardless of sex, race, median household income, or new patient status, and for all individuals over 25 years of age. This correlation was additionally seen in those receiving either Medicare or private insurance and in private or freestanding clinic settings. Furthermore, several comorbid conditions and primary ICD-9-CM diagnoses—which are further detailed in Table 3—were associated with higher rates of CBC testing. The availability of an EHR did not statistically significantly reduce the probability of ordering a CBC test in any subgroup.

At the 95% CI using the Bonferroni correction, the probability that a provider would order imaging studies was significantly higher with an EHR system, again, regardless of sex, race, median household income, new patient status, or even age, as shown in Table 4. It was higher for those seen in private, health maintenance organization (HMO), and faculty practice plan settings, as well as for all insurance types except worker’s compensation and “other,” for which the data were not statistically significant. Several comorbid conditions and primary ICD-9-CM  diagnoses (further detailed in Table 4) were associated with higher rates of CBC testing. In no single subgroup did the availability of EHRs statistically significantly reduce the probability of ordering imaging studies. 

Multivariate logistic regressions demonstrated that the probability of ordering a CBC test is significantly greater for physicians with EHR access (odds ratio [OR], 1.34; P <.001)—a 30% increase in the adjusted likelihood of ordering a CBC test compared with physicians not using an EHR after adjusting for patient demographic information and a detailed set of clinical risk-adjusters (Table 5). Although the difference for MRIs is not significant, physicians using EHRs have a greater probability of ordering CT scans (OR, 1.41; P <.001) and x-rays (OR, 1.39; P <.001), at a 41% and 37% increase, respectively. Considering imaging in aggregate, physicians using EHRs have a greater probability (OR, 1.26) and a 23% increased adjusted likelihood of ordering imaging than physicians not using EHRs (P <.001). A multivariate logistic subgroup analysis shows statistically significant differences among various subgroups (eAppendix Table).


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