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The American Journal of Managed Care Special Issue: Health Information Technology
Improving Adherence to Cardiovascular Disease Medications With Information Technology
William M. Vollmer, PhD; Ashli A. Owen-Smith, PhD; Jeffrey O. Tom, MD, MS; Reesa Laws, BS; Diane G. Ditmer, PharmD; David H. Smith, PhD; Amy C. Waterbury, MPH; Jennifer L. Schneider, MPH; Cyndee H. Yonehara, BS; Andrew Williams, PhD; Suma Vupputuri, PhD; and Cynthia S. Rand, PhD
Information Retrieval Pathways for Health Information Exchange in Multiple Care Settings
Patrick Kierkegaard, PhD; Rainu Kaushal, MD, MPH; and Joshua R. Vest, PhD, MPH
The 3 Key Themes in Health Information Technology
Julia Adler-Milstein, PhD
Leveraging EHRs to Improve Hospital Performance: The Role of Management
Julia Adler-Milstein, PhD; Kirstin Woody Scott, MPhil; and Ashish K. Jha, MD, MPH
Electronic Alerts and Clinician Turnover: The Influence of User Acceptance
Sylvia J. Hysong, PhD; Christiane Spitzmuller, PhD; Donna Espadas, BS; Dean F. Sittig, PhD; and Hardeep Singh, MD, MPH
Cost Implications of Human and Automated Follow-up in Ambulatory Care
Eta S. Berner, EdD; Jeffrey H. Burkhardt, PhD; Anantachai Panjamapirom, PhD; and Midge N. Ray, MSN, RN
Primary Care Capacity as Insurance Coverage Expands: Examining the Role of Health Information Technology
Renuka Tipirneni, MD, MSc; Ezinne G. Ndukwe, MPH; Melissa Riba, MS; HwaJung Choi, PhD; Regina Royan, MPH; Danielle Young, MPH; Marianne Udow-Phillips, MHSA; and Matthew M. Davis, MD, MAPP
Adoption of Electronic Prescribing for Controlled Substances Among Providers and Pharmacies
Meghan Hufstader Gabriel, PhD; Yi Yang, MD, PhD; Varun Vaidya, PhD; and Tricia Lee Wilkins, PharmD, PhD
Currently Reading
Health Information Exchange and the Frequency of Repeat Medical Imaging
Joshua R. Vest, PhD, MPH; Rainu Kaushal, MD, MPH; Michael D. Silver, MS; Keith Hentel, MD, MS; and Lisa M. Kern, MD
Automated Detection of Retinal Disease
Lorens A. Helmchen, PhD; Harold P. Lehmann, MD, PhD; and Michael D. Abràmoff, MD, PhD
Trending Health Information Technology Adoption Among New York Nursing Homes
Erika L. Abramson, MD, MS; Alison Edwards, MS; Michael Silver, MS; Rainu Kaushal, MD, MPH; and the HITEC investigators
Electronic Health Record Availability Among Advanced Practice Registered Nurses and Physicians
Janet M. Coffman, PhD, MPP, MA; Joanne Spetz, PhD; Kevin Grumbach, MD; Margaret Fix, MPH; and Andrew B. Bindman, MD
The Value of Health Information Technology: Filling the Knowledge Gap
Robert S. Rudin, PhD; Spencer S. Jones, PhD; Paul Shekelle, MD, PhD; Richard J. Hillestad, PhD; and Emmett B. Keeler, PhD
Overcoming Barriers to a Research-Ready National Commercial Claims Database
David Newman, JD, PhD; Carolina-Nicole Herrera, MA; and Stephen T. Parente, PhD
The Effects of Health Information Technology Adoption and Hospital-Physician Integration on Hospital Efficiency
Na-Eun Cho, PhD; Jongwha Chang, PhD; and Bebonchu Atems, PhD

Health Information Exchange and the Frequency of Repeat Medical Imaging

Joshua R. Vest, PhD, MPH; Rainu Kaushal, MD, MPH; Michael D. Silver, MS; Keith Hentel, MD, MS; and Lisa M. Kern, MD
Usage of a health information exchange system at the point of care reduced the occurrence of repeat imaging procedures in a multi-payer community sample.
Medical imaging, which is expensive, is frequently repeated for the same patient within a relatively short period of time due to lack of access to previous images. Health information exchange (HIE) may reduce repeat imaging by facilitating provider access to prior images and reports. We sought to determine the effect of an HIE system on the occurrence of repeat imaging.

Study Design and Methods
We conducted a cohort study of adult patients who consented to participate in a community-based HIE system in an 11-county region in New York. Using data from 2009-2010, we linked log files of provider HIE usage to administrative claims data from 2 commercial health plans. Using generalized estimation equations, we measured the association between HIE system access and repeat imaging within 90 days.

Of 196,314 patients in the cohort, 34,604 (18%) of patients had at least 1 imaging procedure, which was equivalent to a rate of 28.7 imaging procedures per 100 patients. Overall, 7.7% of images were repeated within 90 days. If the HIE system was accessed within the 90 days following an initial imaging procedure, imaging was significantly less likely to be repeated (5% repeated with HIE access vs 8% repeated without HIE access, P <.001). HIE system access reduced the adjusted odds of a repeat image by 25% (95% CI, 13%-35%).

Use of the HIE system to access previous patient information was associated with a reduction in repeated imaging.

Am J Manag Care. 2014;20(11 Spec No. 17):eSP16-eSP24
Health information exchange (HIE) systems allow providers electronic access to patient information from other sources. Current federal policies support the adoption of these types of systems in order to change healthcare utilization patterns. Repeated imaging is a type of utilization that is potentially common and costly.
  • In an 11-county community, 7.7% of medical imaging procedures were repeated within 90 days.
  • The community has a regional health information organization that facilitates access to prior patient information at the point of care.
  • Use of the HIE system was associated with a 25% lower odds of repeat imaging.
Medical imaging is common and costly,1,2 and is also often repeated over time for a given patient. Repeat imaging may be appropriate if it is being used to determine a change in a patient’s clinical condition. However, some repeat imaging is ordered because providers do not have easy access to previous medical images. 3-5 In those cases, repeat images may be ordered even if access to existing images would have provided sufficient clinical information.6,7

Previous studies estimate that between 9% and 40% of all medical images are repeated, regardless of the reason for the repeat imaging.8-12 These estimates have some limitations because they were derived from the experiences of single institutions, consider only 1 type of imaging, or are based on consensus reports. The frequency and timing of repeat imaging in typical community-based settings, where the majorityof healthcare is delivered, are not clear. These are important statistics, however, because knowing the frequency and timing of repeat imaging could inform the design of interventions to reduce repeat imaging and inform expectations of their effects.

One important intervention that could reduce the frequency of repeat medical imaging is electronic health information exchange (HIE). HIE allows providers electronic access to their patients’ clinical information, including images and radiologists’ reports interpreting those images, even if it was collected by providers in other healthcare organizations.13 HIE has been implemented in several communities across the United States, but the effectiveness of HIE for reducing the frequency of repeat medical images is not clear. The results of previous studies have been mixed: some studies found that technology that enabled access to prior patient information was associated with fewer repeat images while other studies found no effect.14-19 Previous studies have also not compared the relative effects of HIE across different imaging modalities (eg, computed tomography [CT] and ultrasound).

Our objectives were: 1) to measure the frequency and timing of repeat imaging in a community-based setting, and 2) to determine the association between provider usage of an HIE system and repeat imaging, including by type of medical imaging. Our study may be highly generalizable because it took place in a multi-payer, multi-provider community that used a commercially available HIE system.


We conducted a longitudinal cohort study of patients and their medical imaging procedures in 2009-2010 in the Rochester, New York, region. This study was part of a broader evaluation of New York’s Health Care Efficiency and Affordability Law for New Yorkers (HEAL NY) Capital Grants program by the multi-institutional Health Information Technology Collaborative.20 The Institutional Review Boards of Weill Cornell Medical College and the University of Rochester approved the study protocol.


This study evaluates an HIE system implemented by the Rochester Regional Health Information Organization (RHIO).21 Supported in part with funding from the New York State Department of Health under the HEAL NY Capital Grants program, the Rochester RHIO is a nonprofit organization that facilitates information exchange among more than 70 healthcare organizations in western New York.22 The Rochester RHIO has implemented an HIE system to enable authorized user access at the point of care to patient information collected from multiple providers and locations.

Users access the HIE system via a Web-based portal and can view data on patients’ demographic information, diagnoses, medication history, radiology reports, laboratory results, and discharge summaries from participating providers.23,24 The HIE receives data from a variety of sources, including insurance providers, hospital systems, ambulatory practices, radiology groups, reference laboratories,and others. The system, which operates with an opt-in model for patient consent, became fully operational in March 2009 and now includes more than 800,000 patients. At the time of the study, the system had 1318 users in 156 different outpatient, emergency, inpatient, and long-term care settings. More than two-thirds of the region’s hospitals and physicians participate.25


First, we used healthcare claims from 2 commercial health plans, which insure approximately 60% of the Rochester area population. We included patients 18 years and older who were continuously enrolled in one of these plans from 2009 to 2010. Additionally, patients had to have provided consent during the same period to the Rochester RHIO to have their data in the HIE system viewed by providers. Providers may only access data after patients provide consent, except in emergency situations. The claims data were then submitted by the plans to a third-party data aggregator. Then the data aggregator used a roster of consented patients, provided by the Rochester RHIO, to select patients’ claims. We required that patients have at least 1 encounter (eg, an office visit, hospitalization, or emergency department [ED] visit) with a provider participating in the HIE in the 6 months following the patient’s date of consent. The data aggregation company categorized Current Procedural Terminology (CPT) codes by modality (such as ultrasound) and body region (see eAppendix).

The second data source was the Rochester RHIO itself, which provided us with the system log files from the HIE system for the same time period as the claims file data set. The HIE system automatically records user activities, such as the patient record viewed and date of access. We matched these log files to the claims based on a common patient identifier, the dates of usage, and the dates of the imaging procedures.


We defined a single imaging procedure as the unique combination of modality and body region on a calendar day for a given patient. Because one imaging procedure may be documented by multiple claims, we created a single indicator for a procedure regardless of the number of CPT codes used in billing. For example, if a female patient had 3 different CPT codes associated with mammography on a single calendar day, we classified these discrete CPT codes as belonging to a single imaging procedure. In contrast, if a patient had multiple imaging procedures for different body regions on the same day (eg, a CT of the pelvis and a CT of the abdomen), these were classified as 2 different procedures. Only those procedures during the first 3 months (out of the rolling 6-month time period for each patient) were eligible to be index procedures; this strategy ensured that every imaging procedure could be followed for 90 days. The selection of imaging procedures for inclusion in the study is illustrated in the Appendix.

Our outcome of interest was a repeat imaging procedure. We applied a 90-day follow-up period to every index imaging procedure, and looked for the first occurrence of an additional imaging procedure using the same modality for the same body region. We selected 90 days as the primary time period for repeat imaging based on previous studies from the literature.11,26

The primary independent variable for our analysis was any usage of the HIE system for a patient who received imaging. We defined usage as any access of the HIE system after the initial imaging procedure (starting on the next calendar day) and before either the repeated procedure date (if any) or the 90-day mark, whichever came first.

From the claims files, we also collected patient characteristics and healthcare utilization. Patient characteristics included age, gender, and insurance status (grouped into private payer, Medicare managed care, or Medicaid managed care/state-subsidized private insurance product). We measured patient disease severity as the count of major Aggregated Diagnostic Groups (ADGs) in the 12-month period prior to consent, using the Johns Hopkins AdjustedClinical Groups Case-Mix System.27,28 ADGs are non–mutually exclusive groupings of diagnoses, so we did not include diagnoses elsewhere in our models. Additionally, we calculated the number of primary care visits, specialty care visits, ED/urgent care visits, and admissions that occurred in the 90 days after the initial procedure or up until the imaging procedure was repeated.


We structured the data set as a procedure-level data set, allowing each patient to contribute multiple imaging procedures. We calculated both the frequency of imaging and repeat imaging overall and by modality. We also calculated when (in days) during the follow-up period any repeat imaging occurred.

We measured the frequency with which providers accessed HIE data overall. We then determined whether the characteristics of patients whose data were accessed via the HIE were different from the characteristics of patients whose data were not accessed, using t tests for continuous variables and χ2 tests for dichotomous variables.

We modeled the binary outcome of repeat imaging using a binary logit model with generalized estimating equations. We chose this method because it accounts for the clustering that occurs with repeated measures. With the exception of the patient, we treated all other measures as fixed effects. We exponentiated parameter coefficients to express odds ratios (ORs). We adjusted for the following clinically relevant patient-level variables: payer type, age, gender, number of primary care visits, number of specialty care visits, number of ED/urgent care visits, number of admissions, and the count of major ADGs. We conducted stratified analyses for the 3 modalities with sufficient sample sizes: CT, ultrasound, and radiographs.


Frequency of Medical Imaging and Repeat Imaging

The entire cohort consisted of 196,314 patients. The first 3 months of claims for the patient cohort included 68,296 claims for imaging procedures. After de-duplication (removing multiple claims associated with the same imaging procedure and those that had no body region identified), we were left with 56,306 imaging procedures. Overall, 17.6% (n = 34,604) of patients had at least 1 imaging procedure, equivalent to a rate of 28.7 imaging procedures per 100 patients. As displayed in Table 1, the most common imaging modalities were radiographs (43.7% of all imaging procedures), CT (16.4%), ultrasound (16.4%), mammography (10.0%), and magnetic resonance imaging (MRI) (6.8%). Although we considered 23 different modalities, these 5 accounted for more than 90% of all imaging procedures.

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