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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.
Overall, 7.7% of medical imaging procedures (n = 4316) were repeated within 90 days. As indicated in Table 1, that percentage varied widely by modality. The rate of repeat imaging was highest for ultrasounds: 15.5%. Similarly, 8.6% of radiographs, 4.7% of mammograms, 3.8% of CTs, and 2.5% of MRIs were repeated. Other procedures with high rates of repeats included echocardiography (12.5%) and urography (7.0%), although the absolute numbers of these tests were lower. For 2 other modalities, less than 1% each were repeated; another 8 modalities had no repeat imaging. The timing of the repeat imaging occurred at various points over the course of the 90-day follow-up period (Figure). Overall, half of the repeated procedures occurred within the first 30 days after the initial medical image. By 60 days, a total of 80% of the repeated procedures had occurred.

The majority of the imaging procedures (73%) were among women and among the privately insured (59%, Table 2). The average patient age was 57.2 years, and the average number of total healthcare encounters in the 90 days after the initial imaging procedure was 2.7.

Use of the HIE System

Overall, providers accessed the HIE system within 90 days after 11.8% of imaging procedures. As displayed in Table 2, when providers accessed the HIE system, those procedures were more likely to be for patients in Medicare managed care, for older patients, and for sicker patients, with higher counts of major ADGs. In addition, accessing the HIE system was more likely to occur with higher numbers of healthcare encounters.

Association Between HIE Usage and Repeat Medical Imaging

We found that if the HIE system was accessed within the 90 days following an initial imaging procedure, the imaging was less likely to be repeated (5.2% of imaging procedures were repeated when the HIE system was accessed versus 8.0% repeated when the HIE system was not accessed). The unadjusted odds of repeat imaging were 44% lower if the HIE system was accessed after the initial procedure (OR = 0.56; 95% CI, 0.49-0.65; Table 3). After controlling for patient characteristics and utilization, provider access of the HIE system after the initial imaging was independently associated with 25% lower odds of repeat imaging (OR = 0.75; 95% CI, 0.65-0.87). Given the rate of repeated imaging observed in this population (7.7%), out of every 36 images, HIE access would prevent 1 repeated image that would have occurred otherwise.

These results persisted when we considered ultrasounds alone and radiographs alone (Table 4). Provider access of the HIE system reduced the adjusted odds of both a repeat ultrasound by 44% and a repeat radiograph by 21%. HIE usage was not associated with the odds of a repeat CT, although the sample size of repeat CTs was much smaller than that of the other 2 tests, limiting power for that comparison.


In our community-based study, we found a rate of 28.7 imaging procedures completed for every 100 patients over a 6-month period. We also found that 7.7% of all imaging procedures were repeated within 90 days. When repeat imaging was done, it tended to occur quickly, with nearly 50% of all repeated imaging completed within 30 days and 80% completed by 60 days.

We found that if the community-based HIE system was accessed by providers within the 90 days following an initial imaging procedure, the imaging was significantly less likely to be repeated, with 5.2% of imaging procedures repeated when the HIE system was accessed, compared with 8% repeated when the HIE system was not accessed. Adjusting for potential confounders, the odds that an imaging procedure was repeated decreased by 25% with HIE access.

This study provides one of the few estimates of the frequency of repeat imaging for multiple modalities in a multi-payer, multi-provider community. Our finding of a 7.7% rate of repeat imaging is slightly lower than rates found by other investigators, such as 9%, 13%, and 20%.8,9,29 Unlike previous studies, our study included multiple settings of care and a broad, community-based patient population. Previous work supports this distinction, as imaging is overall less frequent in the ambulatory setting than in the inpatient or ED settings.30-32 Additionally, several previous estimates were derived from patient populations in which multiple and frequent imaging is to be expected (eg, trauma or neurological). Our sample is likely more representative of the overall adult population. There are few interventions that have been tried for reducing the frequency of medical imaging. Payers have tried prior authorization for certain imaging procedures, but it is not always clear that the cost of the prior authorization program is outweighed by savings from fewer images.33 Other interventions that have been tried are conceptually close to information exchange, such as electronic decision support for ordering physicians (which often includes access to prior results) and picture archiving and communication systems (PACS) for electronic sharing of actual images.34,35 Electronic decision support for ordering imaging is still an emerging tool, and PACS systems have typically been installed within a single institution. Other approaches to image sharing, like digital media transfers, may also be effective, but those approaches can be cumbersome and tend not to include breadth of clinical data about the patient, as is found in the Rochester RHIO system.4,7,36-38

The existing literature on the effects of HIE on patient healthcare utilization, in general, is sparse,39 and the few studies that examine the effects on imaging do not present a consistent picture. A series of studies among ED patients at one RHIO reported similar reductions in imaging utilization for select patients, modalities, and locations when an HIE system was utilized at the point of care.14,16,17 Other reports of information-sharing technology also suggest that reductions in repeated and overall imaging usage are possible.40,41 Conversely, other examinations have found that adoption of exchange-capable health information technology is not associated with reductions in the rates of imaging ordering.18,19 However, those studies differ from this investigation, as they did not measure actual usage of the system.

Our study has several limitations. First, from our secondary sources, we could not determine the appropriateness of the imaging procedures. Our study measured all repeat imaging observed; we were not able to distinguish between procedures that were clinically appropriate and those that were potentially unnecessary. Some of the repeated procedures are clearly clinically appropriate and expected. For example, clinicians may need to determine changes in status or decide if new interventions are warranted. Further research could move toward separating the potentially unnecessary from the potentially appropriate imaging. Second, we were not able to adjust for all potential confounders at the provider level due to the fact that claims data do not consistently include the ordering provider. However, we tried to overcome this limitation through our procedure-level analysis, and it is likely that the same providers accessed the HIE for some of their patients and not for others; this would minimize the impact of any provider variables. Third, we know that providers accessed the HIE, but we cannot tell which particular data element may have affected their medical decision making. Understanding which pieces of information influenced changes to decision making would require alternative study designs.

Our study has several strengths, including objective measure of technology usage. We do not rely on self-reported usage, which is a different and independent construct that does not always accurately reflect actual system usage.42,43 We are also not using aggregated organizational-level measures of adoption, which can obscure individual usage of systems.44 Our study represents care for nearly 200,000 patients in a multi-county area with multiple payers. We capture healthcare utilization in multiple settings, including outpatient, inpatient, ED, and long-term care settings. Our study also reflects the effectiveness of a commercially available HIE product as used in a real-world setting.


When a patient comes to a radiology facility for an imaging procedure, previous similar studies often exist but are inaccessible at the point of care. The federal government and many states are investing heavily in health information technology that can address this issue. Strong incentives exist for providers to adopt and meaningfully use electronic health records that have the capacity to exchange data. Also, the federal government has supported state-level programs to implement HIE (which can be community-wide portals like the one studied here). This study demonstrates that a community-wide portal is effective for reducing the frequency of repeat imaging. Thus suggesting a technology-driven improvement in care that represents both higher quality and potentially lower costs.


The authors wish to thank the Rochester RHIO for requesting this evaluation and providing access to data. We also thank Ted Kremer, Jill Eisenstein, Sara Abrams, and Gloria Hitchcock of the Rochester RHIO; and Thomas Campion Jr of The Center for Healthcare Informatics & Policy, Weill Cornell Medical College, for their assistance with this project.

Author Affiliations: The Center for Healthcare Informatics & Policy (JRV, RK, MDS, LMK), the Department of Healthcare Policy and Research (JRV, RK, MDS, LMK), the Department of Medicine (RK, LMK), the Department of Pediatrics (RK), and the Department of Radiology (RK), Weill Cornell Medical College, New York, NY; and NewYork-Presbyterian Hospital (RK, KH), New York, NY.

Source of Funding: The project was funded by the New York State Department of Health’s Healthcare Efficiency and Affordability Law for New Yorkers Program (HEAL NY)—Phase 5 Evaluation (Contract #C023699).

Author Disclosures: Dr Vest has presented these findings at the Rochester Regional Health Information Organization board meeting. Drs Kaushal, Hentel, and Kern, and Mr Silver report no relationship or financial interest with any entity that might be a conflict of interest with the subject of this paper.

Authorship Information: Concept and design (JRV, RK, KH, LMK); acquisition of data (RK, LMK); analysis and interpretation of data (JRV, RK, MS, LMK); drafting of the manuscript (JRV, RK, KH, LMK); critical revision of the manuscript for important intellectual content (JRV, RK, KH, MS, LMK); statistical analysis (JRV, MS); obtaining funding (RK); administrative, technical, or logistic support (RK); and supervision (RK).

Address correspondence to: Joshua R. Vest, PhD, MPH, Department of Healthcare Policy and Research, Weill Cornell Medical College, 402 E 67th St, New York, NY 10065. E-mail:
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