The Effect of Including Benchmark Prevalence Data of Common Imaging Findings in Spine Image Reports on Health Care Utilization Among Adults Undergoing Spine Imaging: A Stepped-Wedge Randomized Clinical Trial

Abstract

Importance: Lumbar spine imaging frequently reveals findings that may seem alarming but are likely unrelated to pain. Prior work has suggested that inserting data on the prevalence of imaging findings among asymptomatic individuals into spine imaging reports may reduce unnecessary subsequent interventions.

Objective: To evaluate the impact of including benchmark prevalence data in routine spinal imaging reports on subsequent spine-related health care utilization and opioid prescriptions.

Design, setting, and participants: This stepped-wedge, pragmatic randomized clinical trial included 250 401 adult participants receiving care from 98 primary care clinics at 4 large health systems in the United States. Participants had imaging of their backs between October 2013 and September 2016 without having had spine imaging in the prior year. Data analysis was conducted from November 2018 to October 2019.

Interventions: Either standard lumbar spine imaging reports (control group) or reports containing age-appropriate prevalence data for common imaging findings in individuals without back pain (intervention group).

Main outcomes and measures: Health care utilization was measured in spine-related relative value units (RVUs) within 365 days of index imaging. The number of subsequent opioid prescriptions written by a primary care clinician was a secondary outcome, and prespecified subgroup analyses examined results by imaging modality.

Results: We enrolled 250 401 participants (of whom 238 886 [95.4%] met eligibility for this analysis, with 137 373 [57.5%] women and 105 497 [44.2%] aged >60 years) from 3278 primary care clinicians. A total of 117 455 patients (49.2%) were randomized to the control group, and 121 431 patients (50.8%) were randomized to the intervention group. There was no significant difference in cumulative spine-related RVUs comparing intervention and control conditions through 365 days. The adjusted median (interquartile range) RVU for the control group was 3.56 (2.71-5.12) compared with 3.53 (2.68-5.08) for the intervention group (difference, -0.7%; 95% CI, -2.9% to 1.5%; P = .54). Rates of subsequent RVUs did not differ between groups by specific clinical findings in the report but did differ by type of index imaging (eg, computed tomography: difference, -29.3%; 95% CI, -42.1% to -13.5%; magnetic resonance imaging: difference, -3.4%; 95% CI, -8.3% to 1.8%). We observed a small but significant decrease in the likelihood of opioid prescribing from a study clinician within 1 year of the intervention (odds ratio, 0.95; 95% CI, 0.91 to 1.00; P = .04).

Conclusions and relevance: In this study, inserting benchmark prevalence information in lumbar spine imaging reports did not decrease subsequent spine-related RVUs but did reduce subsequent opioid prescriptions. The intervention text is simple, inexpensive, and easily implemented.

Trial registration: ClinicalTrials.gov Identifier: NCT02015455.

Conflict of interest statement

Conflict of Interest Disclosures: Dr Jarvik reported receiving royalties from Springer Publishing and Wolters/Kluwer/UpToDate and receiving travel reimbursement from GE–Association of University Radiologists outside the submitted work. Dr Tan reported being employed by Flatiron Health and owning stock in Roche outside the submitted work. Dr Deyo reported having an endowed professorship funded by Kaiser Permanente and receiving personal fees from UpToDate outside the submitted work. Dr Rundell reported receiving grants from the Institute of Translational Health Sciences, the Center for Large Data Research and Data Sharing in Rehabilitation, the Center on Health Services Training and Research, and the Scan│Design Foundation; having a contract with the Agency for Healthcare Research and Quality; and receiving personal fees from the Department of Defense and ATI Physical Therapy outside the submitted work. Dr Friedly reported receiving grants from the Department of Defense and salary support from the American Academy of Physical Medicine and Rehabilitation for serving as editor in chief outside the submitted work. No other disclosures were reported.

Figures

Figure 1.. CONSORT Stepped-Wedge Allocation of Trial Subjects

For clinics under the control condition, intervention indicates the intervention text was mistakenly included in the image report. For clinics under the intervention condition, intervention indicates that the intervention text was successfully included in the image report and no intervention indicates that the intervention text was not included. aTwo small clinics randomized to groups 2 and 5 were dropped before the first data submission because of clinic closure and are not included in the clinic counts. bBy pretrial design, for 1 clinic, step 0 extended through May 2014, and step 1 began June 1, 2014.

Figure 2.. Model Results for Spine-Related Relative Value Units (RVUs) at 1 Year

All models adjust for health system, clinic size, age range (ie, 18-39, 40-60, and ≥61 years), sex, imaging modality, Charlson Comorbidity Index category (ie, 0, 1, 2, and ≥3), and health system specific time trends. Models include hierarchical random effects for clinic (intercept and treatment) and primary care professional (intercept only). P values for subgroup models (ie, index imaging type and image finding type) are for Wald tests for effect modification. CI indicates clinically important, CT, computed tomography; RG, radiograph; and MR, magnetic resonance.

Figure 3.. Model Results for Opioid Prescriptions Within 12 months

All models adjust for health system, clinic size, age range (ie, 18-39, 40-60, and ≥61 years), sex, imaging modality, Charlson Comorbidity Index category (ie, 0, 1, 2, and ≥3), prior opioid use, and health system specific time trends. Models include hierarchical random effects for clinic (intercept and treatment) and primary care professional (intercept only). Prior opioid prescription is defined as having 1 or more prescriptions in the 120 days prior to index imaging. A Lumbar Imaging with Reporting of Epidemiology (LIRE) source is any health care professional who ordered an index lumbar spine image for 1 or more participants in the LIRE trial. It need not be the same individual who ordered the patient’s index image. A non-LIRE source is any other health care professional. Any source includes both LIRE and non-LIRE clinicians. NA indicates not applicable.

Figure 4.. Safety Outcomes

All models adjust for health system, clinic size, age range (ie, 18-39, 40-60, and ≥61 years), sex, imaging modality, Charlson Comorbidity Index category (ie, 0, 1, 2, and ≥3), seasonality, and health system specific time trends. The emergency department (ED) visit model includes hierarchical random effects for clinic (intercept and treatment) and primary care professional (intercept only). The mortality model uses general estimating equations with clustering on clinic.