Original article
Effectiveness of Clinical Decision Support in Controlling Inappropriate Imaging

https://doi.org/10.1016/j.jacr.2010.07.009Get rights and content

Background

Decision support systems for advanced imaging are being implemented with increased frequency and are mandated under some new governmental health care initiatives. However, evidence of effectiveness in reducing inappropriate imaging utilization is limited.

Methods

A retrospective cohort study was performed of the staged implementation of evidence-based clinical decision support built into ordering systems for selected high-volume imaging procedures: lumbar MRI, brain MRI, and sinus CT. Brain CT was included as a control. Imaging utilization rates (number of patients imaged as a proportion of patients with selected clinical conditions) and overall imaging utilization before and after the interventions were determined from billing data from a regional health plan and from the institutional radiology information system.

Results

The use of imaging clinical decision support was associated with substantial decreases in the utilization rate of lumbar MRI for low back pain (risk ratio, 0.77; 95% confidence interval, 0.87-0.67; P = .0001), head MRI for headache (risk ratio, 0.76; 95% confidence interval, 0.91-0.64; P = .001), and sinus CT for sinusitis (risk ratio, 0.73; 95% confidence interval, 0.82-0.65; P < .0001). Utilization rates for the head CT control group were not significantly changed. There was a corresponding significant decrease in overall imaging volumes (all diagnoses) for lumbar MRI, head MRI, and sinus CT, with no observed effect for the head CT control group.

Conclusion

Targeted use of imaging clinical decision support is associated with large decreases in the inappropriate utilization of advanced imaging tests.

Introduction

Health care expenses in the United States continue to spiral upward, now representing more than 17% of the gross domestic product [1]. Imaging is one of the most important contributors to health care costs, encompassing more than 14% of Medicare Part B expenditures [2, 3, 4]. Although identified as the most significant advance in medicine in the past several decades [5], imaging has become a target for cost containment. A major driver for increasing imaging cost is the inappropriate utilization of advanced imaging, including CT and MRI [4, 6, 7, 8]. Accordingly, health care providers are under increasing pressure to limit imaging to evidence-based applications.

Payers have initiated several approaches to control imaging utilization, including external authorization methods and clinical decision support systems [9]. Clinical decision support systems are point-of-order decision aids, usually through computer order entry systems, that provide real-time feedback to providers ordering imaging tests, including information on test appropriateness for specific indications. Such systems may be purely educational, or they may be restrictive in not allowing imaging test ordering to proceed when accepted indications are absent. Although data on the efficacy of imaging clinical decision support systems are limited [10], adoption is increasing and has spread to include state-level initiatives in Washington [11] and Minnesota [12]. Imaging clinical decision support systems can range from simple aids for small numbers of studies and indications to broad systems encompassing the thousands of possible pairs of indications and imaging procedures. To date, there are no published studies demonstrating decreased imaging utilization after implementation of imaging clinical decision support, though a decrease in the rate of growth of utilization of imaging has been reported. We hypothesized that imaging clinical decision support could decrease imaging utilization when targeted to select imaging studies and indications that included high volumes and high cost [13, 14].

The objective of this investigation was to identify changes in imaging utilization associated with the initiation of an imaging management program based on clinical decision support for selected CT and MRI studies at a single integrated health care delivery system.

Section snippets

Methods

The overall study design was a retrospective cohort evaluation of the effect of the staged implementation of a clinical decision support system on imaging utilization, with historical and concurrent controls. The study was granted a waiver from the institutional review board.

Results

We found clinically and statistically significant decreases in utilization rates for the targeted procedures after the intervention. Table 1 details the raw counts of imaging procedures, as well as the counts of patients with the corresponding diagnoses and the rate of imaging among affected individuals before and after the intervention. The rates of imaging after the intervention were 23.4% lower for low back pain lumbar MRI (RR, 0.77; 95% confidence interval [CI], 0.87-0.67; P < .001), 23.2%

Discussion

Clinical decision support is potentially an ideal method for improving the evidence-based use of imaging. Clinical decision support tools have the desired properties of being educational, transparent, efficient, practical, and consistent [4]. However, data on the effectiveness of clinical decision support is limited. Prior investigation has focused on the use of a global system encompassing virtually all CT and MRI studies and indications and has demonstrated only a relative attenuation in the

Acknowledgment

We gratefully acknowledge the collaboration of Premera Blue Cross in providing access to our institutional claims data.

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