eAppendix 1. Choosing Wisely Recommendations; Low-value back pain and headache imaging claims algorithms
eAppendix 2 and eTable 1. Sensitivity analyses, low-value headache predictors among clinicians who also saw back pain visits
eAppendix 3 and eTable 2. Sensitivity analyses, accounting for the expected patient Out-Of-Pocket (OOP) expenditure for imaging
eAppendix 4 and eTable 3. Sensitivity analyses, accounting for patients who contribute multiple visits to our sample
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Hong AS, Ross-Degnan D, Zhang F, Wharam JF. Clinician-Level Predictors for Ordering Low-Value Imaging. JAMA Intern Med. 2017;177(11):1577–1585. doi:https://doi.org/10.1001/jamainternmed.2017.4888
Given that information concerning geographic variation in health care usage and higher overall radiographic imaging among clinicians who own imaging equipment is well described, is there evidence that ownership of imaging equipment, prior ordering history, and ordering behavior in other clinical scenarios are associated with low-value imaging?
Clinician specialty, imaging for the prior patient, ownership of imaging equipment, and very high rates of low-value imaging for other clinical scenarios are strong predictors of low-value imaging, after adjusting for patient demographics.
There are several strong clinician predictors of low-value imaging that can guide intervention targets to improve the value of care for patients.
Clinicians who order unnecessary radiographic imaging may cause financial harm to patients who have increasing levels of cost sharing. Clinician predictors of low-value imaging are largely unknown.
To characterize clinician predictors of low-value imaging for acute uncomplicated back pain and headache, including clinicians who saw both conditions.
Design, Setting, and Participants
Multivariate logistic regression modeling of imaging rates after acute uncomplicated back pain and headache visits as indicated by January 2010 to December 2014 commercial insurance claims and demographic data from a large US health insurer. Participants included 100 977 clinicians (primary care physicians, specialist physicians, and chiropractors).
Main Outcomes and Measures
Imaging after acute uncomplicated back pain and headache visits was recorded. We identified whether the clinician’s prior patient received imaging, whether the clinician was an owner of imaging equipment, and the varying impact by clinician specialty. We then used high rates of low-value back imaging as a predictor for low-value headache imaging.
Clinicians conducted 1 007 392 visits for 878 720 adults ages 18 to 64 years with acute uncomplicated back pain; 52 876 primary care physicians conducted visits for 492 805 adults ages 18 to 64 years with acute uncomplicated headache; 34 190 primary care clinicians conducted 405 721 visits for 344 991 adults ages 18 to 64 years with headache and had also conducted at least 4 visits from patients with back pain. If a primary care physician’s prior patient received low-value back imaging, the patient had 1.81 higher odds of low-value imaging (95% CI, 1.77-1.85). This practice effect was larger for chiropractors (odds ratio [OR], 2.80; 95% CI, 2.74-2.86) and specialists (OR, 2.98; 95% CI, 2.88-3.07). For headache, a prior low-value head image predicted 2.00 higher odds of a subsequent head imaging order (95% CI, 1.95-2.06). Clinician ownership of imaging equipment was a consistent independent predictor of low-value imaging (OR, 1.65-7.76) across clinician type and imaging scenario. Primary care physicians with the highest rates of low-value back imaging also had 1.53 (95% CI, 1.45-1.61) higher odds of ordering low-value headache imaging.
Conclusions and Relevance
Clinician characteristics such as ordering low-value imaging on a prior patient, high rates of low-value imaging in another clinical scenario, and ownership of imaging equipment are strong predictors of low-value back and headache imaging. Findings should inform policies that target potentially unnecessary and financially burdensome care.
In the quest to contain the rapid growth of medical spending in the United States, policy makers have focused on clinically unnecessary, or low-value, services. Such wasteful care may account for up to one-third of all medical expenditures.1 Low-value services also can uncover findings that trigger downstream cascades of unnecessary care and clinical harm.2
Studies3-7 have found that individual clinician practice variation drives unnecessary care. For example, individual clinician-level variation has been noted in low-value cancer care in Medicare,7 and clinician ownership of imaging equipment has been associated with increased overall imaging use.8-13
Quiz Ref IDClinicians cite patient demand as a major driver of unnecessary medical care, particularly of imaging.14-17 Patient demand for services was the secondmost-cited barrier to avoiding low-value care in a recent physician survey, after malpractice concerns.18 However, commercially insured patients are now responsible for a rapidly growing proportion of their medical expenses,19 which may reduce patient demand for low-value services.
We examined clinician characteristics as predictors for 2 common low-value services: inappropriate back pain and headache imaging. In the absence of certain signs or symptoms indicating a serious underlying cause—such as neurologic problems, history of cancer, or unexplained fever—radiograph, computed tomography (CT), or magnetic resonance imaging (MRI) imaging within the first 6 weeks of acute uncomplicated back pain adds little to no clinical value.20 Similarly, for headache without neurologic symptoms or signs of a serious underlying cause—such as trauma or history of cancer—CT or MRI imaging are not recommended.21
The Harvard Pilgrim Health Care institutional review board approved the research protocol.
We used deidentified Optum data (OptumInsight), a medical claims data set from a large commercial health insurer. It includes enrollment and demographic information for nearly 29 million commercially insured members across all 50 states from January 2010 to December 2014.
Member demographic characteristics included sex and age. From 2000 US census member block group data and a surname-derived ethnicity variable, we used validated approaches to generate an indicator for race/ethnicity.22 As proxies for individual socioeconomic status, we classified neighborhoods as high-poverty and low-education based on the proportion of households below the poverty level or whose members had lower than a high-school education.23-28
Quiz Ref IDWe examined individual clinician predictors by modeling low-value back imaging (study sample 1) for different clinician specialties: primary care physician, chiropractor, and specialist physician. We then examined individual clinician predictors across clinical scenarios by modeling primary care physician ordering behavior for low-value headache imaging (study sample 2), and modeling how back image ordering is associated with headache image ordering (study sample 3).
We identified acute uncomplicated low-back pain visits among patients ages 18 to 64 years (eAppendix 1 in the Supplement). We were guided by the clinical recommendation against inappropriate back pain imaging defined by Choosing Wisely, a campaign developed by the American Board of Internal Medicine Foundation and other medical specialty groups to reduce unnecessary care.29 We also used a well-established claims-based algorithm for low-value back pain imaging in the Healthcare Effectiveness Data and Information Set30 adapted for use in the literature.31-34 The algorithm uses International Classification of Diseases, ninth revision (ICD-9), codes to identify low-back pain visits and exclude high-risk diagnoses, and Current Procedural Terminology (CPT) codes to identify radiograph, CT, and MRI back images.
Clinicians who billed for visits were categorized as primary care physicians if their specialty description included internal medicine, general medicine, or family practice; other nonradiologist, nonemergency physicians were categorized as specialist physicians. The most common specialties (accounting for two-thirds of specialist visits) were orthopedic surgery, neurosurgery, back and spine surgery, physical medicine, and rheumatology. Chiropractors were defined separately.
We used the vendor-generated anonymous identifier for the billing clinician on a visit claim. We attributed ordered images to clinicians by including only images that occurred within 7 days after an index visit with no intervening visits to another clinician. We trimmed to exclude clinicians who billed for more than the 99th percentile of visits in each scenario (62 visits for back pain, 61 visits for headache).
We flagged clinician ownership of imaging equipment by identifying nonradiologist clinicians who billed for technical or global claims for any imaging study, not just low-value imaging. Clinicians who see a patient for the clinic visit and also bill for subsequent imaging can either bill a global claim that includes both the technical component for obtaining the image and a professional fee for interpreting the image, or bill only for the technical component. In line with the literature, clinicians who billed for at least 3 instances of imaging (regardless of clinical appropriateness) with a global claim or technical component were considered to own imaging equipment.7-9,11,35 See Table 1 for rates of imaging equipment ownership.
We created a second study sample to analyze low-value headache imaging among primary care physicians. Our claims-based definition to identify uncomplicated headache visits (eAppendix 1 in the Supplement) was similarly applied to patients ages 18 to 64 years. We were guided by the definition of inappropriate headache imaging as outlined by Choosing Wisely, as well as the literature.31,33,36 We used ICD-9 codes to identify visits and exclude high-risk diagnoses, along with CPT codes for CT and MRI of head images.
We required patients in both samples to be continuously enrolled during the span used to scan for any red-flag diagnoses (12 months prior to the visit for back pain, 3 months prior for headache). We also excluded inpatient and emergency department visits.
After creating 2 separate samples of visits for uncomplicated low-back pain and headache, we explored whether primary care physicians with the highest rates of low-value back imaging were also more likely to order low-value headache imaging. We restricted our analysis to primary care physicians who had seen both uncomplicated headache and back pain visits. To define a high rate of low-value back imaging, we eliminated clinicians who had seen fewer than 4 uncomplicated back pain visits, due to the unreliability of imaging rates for clinicians with too few visits. We then defined high-imaging clinicians as those with a low-value back imaging rate at or above the 95th percentile (66.7%), and used this as an independent predictor of low-value headache imaging.
To determine whether individual clinician practice explained a significant proportion of low-value imaging variability, we used a nonlinear random-effects model with logit link function to estimate the level of unexplained variation in low-value imaging across clinicians.7,37,38 We used the clinician identifier as the random effect and generated the logit probability of low-value imaging. From this model, we calculated the median odds ratio of low-value imaging to quantify the between-clinician variation in the use of imaging.39
After developing this estimate of clinician-level variation, we quantified the strength of the clinician effect by developing a logistic regression model to predict low-value imaging after a visit. Drawing from the literature,7 we restricted the sample to clinicians who saw a condition more than once. We used the result of a clinician’s prior encounter as a predictor for low-value imaging in the subsequent patient with the respective condition, excluding a clinician’s first encounter from the sample.
The primary predictor of low-value back and headache imaging in the first 2 samples was the clinician’s prior patient result. The predictors for the third sample, low-value headache imaging among primary care physicians who saw both back pain and headache patients, were the clinician’s prior headache patient result, and also whether the clinician was at or above the 95th percentile for rate of low-value back imaging.
We then added clinician ownership of imaging equipment to each of the models to report its impact on the clinician predictors and its magnitude as an independent predictor.
We also used marginal effects methods with a binary distribution40 to adjust the imaging rate for available covariates (patient age, region, sex, race, poverty level, education level) and clinician predictors. This allowed us to compare imaging rates across clinician subtypes.
We used SAS software version 9.3 (SAS Institute Inc) and Stata/SE v12.1 (StataCorp) for statistical analyses.
Study sample 3 included only primary care physicians who saw an uncomplicated headache visit and 4 or more uncomplicated back pain visits. We conducted sensitivity analyses restricting the sample to clinicians who saw at least 5, 6, 7, or 8 uncomplicated back pain visits. We also reanalyzed after adjusting our definition of high rate of low-value back imaging by lowering to the 90th percentile (35.7% imaging rate).
We ran additional analyses controlling for patient out-of-pocket expenses and observed its impact on our results. See eAppendix 3 in the Supplement for a detailed explanation. To account for some patients contributing multiple acute episodes to the samples, we ran additional analyses using the same models but limiting the sample to the patient’s first visit, or a random visit for patients with multiple visits.
The cohort of acute uncomplicated low-back pain visits consisted of 1 007 392 visits from 878 720 patients. The sample included visits to 57 859 unique primary care physicians who saw a median (interquartile range [IQR]) of 7 (4-13) acute uncomplicated back pain visits; 27 583 chiropractors who saw a median (IQR) of 10 (5-19) back pain visits; and 15 535 specialist physicians who saw a median (IQR) of 5 (3-9) visits. Refer to Table 1 for a full reporting of clinician and patient visit characteristics.
Quiz Ref IDIn the mixed-effects model with random clinician effect, the median odds ratio was 2.59. This indicates that, compared with another random clinician, the odds that a clinician would order low-value imaging was 2.59 times higher if the clinician had ordered low-value back imaging for the prior patient.
In the multivariate logistic model (Table 2), if a primary care physician had ordered imaging for the prior patient seen for acute uncomplicated low-back pain, the odds of imaging were 2.08 times higher (95% CI, 2.04-2.12) than for clinicians who did not previously order imaging. Adjusting for clinician ownership of imaging equipment attenuated the effect to 1.81 (95% CI, 1.77-1.85).
For chiropractors and specialists, there were varying, but overall higher, odds of low-value back imaging if the prior patient had received imaging (chiropractors: 2.80; 95% CI, 2.74-2.86 vs specialists: 2.98; 95% CI, 2.88-3.07). For all 3 clinician types, ownership of imaging equipment was a significant independent predictor of low-value back imaging: primary care physicians had 2.06 increased odds (95% CI, 2.03-2.10); chiropractors had 7.76 increased odds (95% CI, 7.51-8.01); and specialist physicians had 4.96 increased odds (95% CI, 4.78-5.15). See Table 3 for predictors by clinician type.
The cohort of acute uncomplicated headache visits consisted of 492 804 visits from 417 010 patients. The overall imaging rate was 7.5%. Patients were seen by 52 876 unique primary care physicians, who saw a median (IQR) of 7 (4-13) acute uncomplicated headache visits.
In the mixed-effects model with random clinician effect, the median odds ratio was 2.49. This indicates that, compared with another random clinician, the odds that a clinician would order low-value imaging was 2.49-fold higher if the clinician had ordered low-value headache imaging for the prior patient.
In the multivariate logistic model (Table 2), if a clinician had ordered imaging for the prior patient seen for uncomplicated headache, the odds of imaging were 2.20 higher (95% CI, 2.13-2.25). Adjusting for clinician ownership of imaging equipment attenuated the result to 2.00 (95% CI, 1.95-2.06). Ownership of imaging equipment independently predicted 1.88 higher odds of low-value imaging (95% CI, 1.82-1.94).
The 34 190 unique primary care physicians who saw patients with both conditions of interest accounted for 405 721 headache visits from 344 991 patients. The median (IQR) number of headache visits for these clinicians was 9 (5-17). Clinicians actively generated claims for a median (IQR) of 7 (5-8) quarters.
In the multivariate logistic model (Table 2), if a primary care physician had ordered imaging for the prior patient with a headache, the odds of head imaging were 1.91 higher (95% CI, 1.84-1.97). Adjusting for ownership of imaging equipment attenuated this effect to 1.80 (95% CI, 1.74-1.86), and ownership of imaging equipment remained an independent predictor of low-value headache imaging (odds ratio, 1.65; 95% CI, 1.59-1.72).
Finally, if the clinician exhibited a high rate of low-value back imaging (≥ 66.7%), then there were 1.78 higher odds of low-value headache imaging (95% CI, 1.69-1.86), an effect that persisted after adjusting for ownership (1.53; 95% CI, 1.45-1.61). See Table 4 for the impact of addition of ownership to the model across clinical scenarios.
With a concern for collinearity between high rates of low-value back imaging and imaging equipment ownership, we reanalyzed the model without the high rate of low-value back imaging and the odds ratio for ownership changed minimally, suggesting minimal collinearity (eAppendix 2 and eTable 1 in the Supplement).
Adjusted imaging rates are summarized in Table 5. The 1.81 increased odds of low-value back imaging for primary care physicians who ordered prior imaging translated to an adjusted imaging rate of 20.3% (95% CI, 20.0%-20.6%) among those who ordered prior imaging vs 12.5% (95% CI, 12.4%-12.6%) among those who did not. The 1.53 increased odds of low-value headache imaging for primary care physicians who had high rates of low-value back imaging translated to an adjusted rate of 11.2% (95% CI, 10.7%-11.7%) among frequent imagers vs 7.6% (95% CI, 7.5%-7.7%) among infrequent imagers.
Sensitivity analyses using higher minimum numbers of back pain visits (from 4 to 8 visits) to define clinicians with a high rate of low-value back imaging did not change results. Lowering the threshold to define high rate of low-value back imaging from the 95th percentile of clinicians to the 90th percentile led to small decreases in the magnitude of the clinician-level predictors that do not alter our overall findings (eAppendix 2 and eTable 1 in the Supplement).
Controlling for expected patient out-of-pocket expenditure for imaging led to nearly imperceptible changes to our results (eAppendix 3 and eTable 2 in the Supplement). Running the models on only a patient’s first visit or a random visit for patients with multiple visits also did not change our conclusions (eAppendix 4 and eTable 3 in the Supplement).
Quiz Ref IDAmong commercially insured patients, a clinician’s prior imaging pattern and ownership of imaging equipment were strong independent predictors of low-value back pain and headache imaging. The magnitude of these associations was greater among chiropractors and specialists. Primary care physicians who exhibited the highest rates of low-value back pain imaging also had higher odds of ordering low-value headache imaging.
Similar to prior studies, our findings confirm clinician-level variation in imaging,11,41,42 specifically low-value imaging.7 Although prior studies associated ownership of imaging equipment with higher rates of radiological imaging,8-13 we add the finding that such patterns persist specifically for low-value imaging and across clinician types. Delving into explanations for the different rates of low-value imaging across these clinician types deserves further study.
Our finding that primary care physicians with very high rates of low-value back pain imaging have increased odds of ordering low-value headache imaging adds to the literature by identifying clinicians who order more low-value imaging across multiple clinical scenarios. Some approaches to change clinician behavior have shown promise,43-46 particularly audit-feedback mechanisms, where clinicians receive regular feedback on specific performance metrics, in comparison to peers. However, targeting each clinical scenario individually might not address underlying drivers such as varying levels of discomfort with clinical uncertainty,47,48 overestimating the benefits of testing,49 group practice trends,50 and other practice-related factors.51 It also does not directly address the pervasive fear of malpractice. Other interventions are needed to improve communication between patients and clinicians, and to help both sides deal with uncertainty in clinical decision making.
Addressing ownership, previous legislation has limited imaging equipment ownership and clinician self-referral; however, exceptions have been made for patient convenience and evolving practice models. As a result, these laws may be less effective than intended.8,52 Although there are payment programs that hold health care provider groups responsible for the cost of care, early evidence shows that their effectiveness at reducing low-value care has been modest, even among self-selected provider groups.44 Finally, ongoing consolidation of practices and health systems may have an impact on low-value care usage that remains underexplored.53,54
Our findings also have implications for patients. Commercially insured patients have experienced large increases in cost sharing, particularly under high-deductible health plans.19 Cost sharing is promoted as a means of motivating patients to avoid unnecessary care,55 yet it is not clear that financially motivated patients are able to avoid low-value imaging.34,56 Previous research has found that patients gain little reassurance from a negative low-value study,57 and because of increasing patient cost sharing, low-value imaging will increasingly cause “financial toxicity”58 to patients. Future interventions to reduce low-value care could attempt to convey these important findings to patients and clinicians.
Our study had several limitations. Quiz Ref IDWe were unable to adjust for potentially relevant clinician characteristics such as training, demographics, practice type, and compensation structures. Although ownership of imaging equipment captures a very important financial incentive, we do not capture arrangements where clinicians might gain financially despite not directly billing for imaging. We focused our analysis on outpatient visits because clinician identifiers did not adequately distinguish individual clinicians in nonoutpatient settings, limiting generalizability of our findings to other settings such as the emergency department. Additionally, our findings apply only to commercially insured patients. Finally, claims data might not fully capture concerning signs or symptoms of low-back pain and headaches, but we used standard claims-based definitions and any misclassification is unlikely to be large enough to change our conclusions.
Among commercially insured patients, strong predictors of low-value back pain and headache imaging included a clinician’s history of ordering low-value imaging, a high rate of imaging in another low-value clinical scenario, and clinician ownership of imaging equipment. Stakeholders should use this information to design clinician-targeted interventions to reduce low-value care.
Corresponding Author: Arthur S. Hong, MD, MPH, Departments of Internal Medicine and Clinical Science, University of Texas Southwestern Medical Center, 5323 Harry Hines Blvd, Dallas, TX 75390-9169 (firstname.lastname@example.org).
Accepted for Publication: July 26, 2017.
Published Online: September 25, 2017. doi:10.1001/jamainternmed.2017.4888
Author Contributions: Dr Hong had full access to all the data in the study and takes responsibility for the integrity of the data and the accuracy of the data analysis.
Study concept and design: Hong, Ross-Degnan, Wharam.
Acquisition, analysis, or interpretation of data: All authors.
Drafting of the manuscript: Hong, Wharam.
Critical revision of the manuscript for important intellectual content: All authors.
Statistical analysis: Hong, Zhang.
Obtained funding: Hong, Wharam.
Administrative, technical, or material support: Wharam.
Study supervision: Ross-Degnan, Wharam.
Conflict of Interest Disclosures: None reported.
Funding/Support: This work was supported by an Institutional National Research Service Award (award No. T32HP12706), the Ryoichi Sasakawa Fellowship Fund, and the Harvard Pilgrim Health Care Institute.
Role of the Funder/Sponsor: The funders had no role in the design and conduct of the study; collection, management, analysis, and interpretation of the data; and preparation, review, or approval of the manuscript; and decision to submit the manuscript for publication.
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