Annually in the United States, about 250 000 patients with advanced cancer receive palliative radiotherapy to lessen pain, control bleeding, or improve quality of life. To ensure reproducible positioning, patients are immobilized on laser-aligned treatment tables and have standard weekly imaging during treatment. Daily imaging, using radiography or computed tomography, can augment positioning. Although daily imaging is often used for curative radiotherapy, national guidelines consider it unnecessary for palliative radiotherapy.1,2 Unnecessary imaging can increase treatment time and expense for patients in distress.
Default options, which leverage insights from behavioral economics, can change physician behavior but have focused less on deadoption of unnecessary care.3,4 We conducted a stepped-wedge cluster randomized clinical trial to test the effectiveness of introducing a default imaging order in the electronic health record (EHR) vs usual practice to reduce unnecessary daily imaging during palliative radiotherapy.
The trial (NCT03110692) was conducted in the University of Pennsylvania Health System from February 10, 2016, to February 9, 2018, including a 1-year preintervention period. The trial protocol is available in Supplement 1. The sample comprised physicians from 5 radiation oncology practices (1 university practice in Philadelphia and 4 community practices in Pennsylvania and New Jersey). Eligible physicians prescribed at least 10 courses of palliative radiotherapy during the trial. Eligible patients were aged 18 years or older with bone, soft tissue, or brain metastases receiving 3-dimensional conformal radiotherapy. Single fraction radiotherapy was excluded. This study was approved as a quality improvement project by the University of Pennsylvania institutional review board and informed consent was waived.
The intervention introduced a default imaging order in the EHR that specified no daily imaging during palliative radiotherapy. Physicians could opt out, selecting another imaging frequency. Practices were classified into 2 groups: university or community based. Groups were randomly assigned by coin flip to cross over to the intervention in two 4-month predefined wedges (analyses included 1-month washout periods).
The primary outcome was a binary indicator of radiotherapy courses with daily imaging (defined as imaging during ≥80% of treatments). In intention-to-treat primary analyses, we fit models using generalized estimating equations clustering on physicians, using group and period (4-month increments) fixed effects and adjusting for monthly temporal trends. In secondary analyses, we adjusted for age, sex, race, performance status, insurance type, fraction count, dose per fraction, prior radiotherapy, and target. We examined effects at university and community practices by interacting group with the intervention periods. We bootstrapped to obtain adjusted differences in percentage points. Analyses were conducted in SAS statistical software (version 9.4; SAS Institute Inc).
The sample comprised 21 radiation oncologists and 1019 patients who received 1188 palliative radiotherapy courses (n = 747 at the university practice; n = 441 at the community-based practices) to bone (52.2%), soft tissue (19.9%), brain (15.7%), or multiple sites (12.3%). Table 1 shows the flow of patients through the trial. Daily imaging was used in 68.2% (463/679) of courses in preintervention periods and 32.4% (165/509) of courses in intervention periods. The default intervention led to a significant reduction in daily imaging (adjusted odds ratio, 0.43; 95% CI, 0.24-0.77; adjusted difference in percentage points, −18.6; 95% CI, −34.1 to −2.1; P = .004) (Table 2). These findings were similar in analyses also adjusted for patient and treatment characteristics and across both university and community practices.
In a network of 5 radiation oncology practices, introducing a default order in the EHR reduced unnecessary daily imaging during palliative radiotherapy. There was potential for spillover to community practices during the university intervention period; however, this would bias results toward the null. Our findings suggest that simple nudges, such as setting default orders, can meaningfully reduce unnecessary care.
Corresponding Author: Sonam Sharma, MD, Department of Radiation Oncology, Mount Sinai Medical Center, 1184 Fifth Ave, 1st Flr, PO Box 1236, New York, NY 10029 (sonam.sharma@mountsinai.org).
Accepted for Publication: March 29, 2019.
Published Online: June 27, 2019. doi:10.1001/jamaoncol.2019.1432
Author Contributions: Dr Sharma had full access to all of the data in the study and takes responsibility for the integrity of the data and the accuracy of the data analysis. Drs Patel and Bekelman contributed equally.
Study concept and design: Sharma, Jones, Patel, Bekelman.
Acquisition, analysis, or interpretation of data: Sharma, Guttmann, Small, Rareshide, Patel, Bekelman.
Drafting of the manuscript: Sharma, Rareshide, Bekelman.
Critical revision of the manuscript for important intellectual content: All authors.
Statistical analysis: Guttmann, Small, Rareshide, Patel, Bekelman.
Obtained funding: Patel, Bekelman.
Administrative, technical, or material support: Sharma, Guttmann, Patel.
Study supervision: Sharma, Patel, Bekelman.
Conflict of Interest Disclosures: Dr Bekelman reports serving as a consultant for the Centers for Medicare and Medicaid Services. Dr Patel reports ownership of Catalyst Health and serving on medical advisory boards for Life.io, HealthMine Services, and Holistic Industries. No other conflicts are reported.
Funding/Support: This work was funded in part by grants from the National Cancer Institute (K07-CA163616) and by the University of Pennsylvania Health System through the Penn Medicine Nudge Unit and the Department of Radiation Oncology.
Role of the Funder/Sponsor: The National Cancer Institute and the University of Pennsylvania Health System had no role in the design and conduct of the study; collection, management, analysis, and interpretation of the data; preparation, review, or approval of the manuscript; and decision to submit the manuscript for publication.
Data Sharing Statement: See Supplement 2.
Additional Contributions: We acknowledge the following individuals for extensive comments, insights and assistance during this research initiative: Michelle Alonso-Basanta, MD, PhD; Nathan Anderson, MS; Mary Cohen, BS, RT(T); Jarod Finlay, PhD (died April 11, 2018); Peter Gabriel, MD, MSE; Sarah Lowitz, MBA, RT(T); Robert Lustig, MD; Amit Maity, MD, PhD; James Metz, MD; Julie Mozes, MBA; and Jacob Shabason, MD, MTR, all at the University of Pennsylvania. These individuals were not compensated for their contributions.
Trial Registration: clinicaltrials.gov Identifier: NCT03110692.
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