The research, led by Michelle S. Rockwell of the Department of Family and Community Medicine at Virginia Tech Carilion School of Medicine together with colleagues across Virginia Tech, Carilion Clinic, the Virginia Center for Health Innovation, the University of Michigan, UCLA, and the RAND Corporation, took an unusual comparative approach. Rather than surveying a random sample of primary care clinicians, the team deliberately sought out the extremes. Within a large health system in the southeastern United States, they ranked 48 primary care practices according to their historical rate of low-value acute low back pain imaging, determined using insurance claims data. From that ranking, they identified the six highest-performing practices, meaning those with the least low-value imaging, and the six lowest-performing practices, meaning those with the most.

From those twelve practices, the researchers invited 93 clinicians, including both physicians and advanced practice providers, to complete a survey. The response rate was 66 percent, a solid figure for clinician survey research. The questionnaire probed several distinct domains: attitudes and beliefs about low-value imaging for back pain, perceptions of the clinicians’ own performance relative to peers, views on what drives low-value imaging and how to fix it, and personal characteristics, including whether the clinician had personally experienced low back pain. The design’s key strength was the linkage of these self-reported attitudes and beliefs to objective, claims-based utilization data, allowing the researchers to see which subjective factors actually tracked with measured performance.

To analyze the results, the team used LASSO regression, a statistical technique well suited to situations where many candidate predictor variables must be winnowed down to a parsimonious set. The method applies a penalty that shrinks the coefficients of less informative variables toward zero, effectively selecting the responses most strongly associated with membership in a highest- versus lowest-performing practice, while controlling for clinician demographics. This approach matters because clinician surveys generate dozens of potential correlates, and naive comparisons can easily produce spurious associations. LASSO’s built-in variable selection provides a more disciplined filter.

The baseline findings were, in some ways, reassuring. Across all respondents, clinicians reported strong agreement with low back pain imaging guidelines, scoring 9.1 out of 10 on average with a standard deviation of 1.7. In other words, almost nobody in the sample believed the guidelines were wrong. Trust in the claims-based performance data used to rank practices was more lukewarm, averaging 4.1 out of 10 with a standard deviation of 1.9. That moderate skepticism is itself informative, because any de-implementation strategy that relies on clinicians accepting feedback from administrative data will have to contend with the fact that many of them do not fully trust that data in the first place.

The statistically significant differences between the groups were more surprising, and arguably more consequential. Two clinician characteristics were associated with lower odds of belonging to a highest-performing practice. The first was a personal history of low back pain, with an odds ratio of 0.70 and a 95 percent confidence interval of 0.64 to 0.76. The second was a greater number of years in practice, with an odds ratio of 0.87 and a 95 percent confidence interval of 0.79 to 0.96. Both confidence intervals exclude one, indicating associations unlikely to be due to chance. The direction of these effects is striking: clinicians who had personally suffered back pain, and clinicians with more experience, were more likely to work in practices that ordered more low-value imaging, not less.

The interpretation of these associations is not settled by the study’s cross-sectional design, which captures a snapshot rather than tracking change over time. One plausible reading is that clinicians who have endured back pain themselves develop a stronger intuitive sense of their patients’ distress and a greater desire to rule out structural causes, even when guidelines say imaging is unnecessary. Similarly, longer-tenured clinicians trained in an era when routine imaging was more accepted may carry ingrained habits that resist guideline updates. Alternatively, the associations could reflect sorting effects, in which clinicians with particular styles gravitate toward particular practices. The study cannot disentangle these mechanisms, but it does establish that experience and personal history are not protective factors against low-value care, and may even be risk factors.

Perhaps the most humbling result concerned self-assessment. Clinicians from the lowest-performing practices were significantly more likely to rate their own performance as better than that of other clinicians in their practice, compared with clinicians from the highest-performing practices, at 57 percent versus 40 percent, a difference the authors report as statistically significant with a p-value of 0.031. This is a textbook illustration of a well-documented cognitive bias: clinicians who order the most low-value care tend to believe they order less than their peers. The finding suggests that simply telling clinicians their raw performance numbers may not be enough, because many will assume the numbers are wrong or that their cases were exceptional. Notably, the overall trust in claims data was only moderate, which compounds the problem of getting accurate self-perceptions to stick.

When asked what drives low-value imaging in the first place, clinicians across both groups converged on the same answer: patient demand was the most frequently identified driver. This attribution is common in the literature on medical overuse, and it frames the clinician as a gatekeeper responding to external pressure rather than an independent decision-maker. Interestingly, the two groups diverged on solutions. Clinicians from the highest-performing practices more frequently recommended health system-focused strategies, such as changes to workflows, decision support, or institutional policies, whereas clinicians from the lowest-performing practices more frequently recommended patient education. That split may reflect a self-serving logic, with lower performers locating the fix outside themselves, but it also carries practical weight: the strategies clinicians are willing to endorse are the strategies most likely to be implemented successfully in their own practices.

The study was conducted as part of the Virginia Center for Health Innovation’s Smarter Care Virginia initiative and a participating health system’s intervention to reduce low-value back pain imaging, both registered on ClinicalTrials.gov, with funding support in part from Arnold Ventures, which had no role in the study’s design, data collection, analysis, or manuscript preparation. The authors conclude that clinician characteristics, perceptions of performance, and preferred de-implementation strategies differ systematically by practice performance, and that these differences can help inform the selection and targeting of strategies to reduce low-value imaging. For health systems, the practical implication is that a one-size-fits-all campaign is unlikely to work. High-performing practices may respond best to system-level nudges, while low-performing practices may need interventions that confront the overconfidence gap directly, build trust in performance data, and address the patient-demand dynamic that clinicians themselves identify as the central pressure. As health systems worldwide grapple with the challenge of de-implementing low-value care, this study offers a reminder that the barriers are not ignorance of guidelines, which clinicians overwhelmingly endorse, but the subtler terrain of personal experience, habit, and self-perception.

Subject of Research: Clinician-level determinants of low-value acute back pain imaging in primary care

Article Title: Clinician-Level determinants of low-value acute back pain imaging in primary care

Article References: Rockwell, M. S., King, M., Mercogliano, E. H., Bortz, B. A., Karanjeet, R., Stewart, J., Fendrick, A. M., Mafi, J. N., & Epling, J. W. (2026). Clinician-Level determinants of low-value acute back pain imaging in primary care. BMC Health Services Research. https://doi.org/10.1186/s12913-026-15609-5

Image Credits: AI Generated

DOI: 10.1186/s12913-026-15609-5

Keywords: low-value care, acute low back pain, primary care, medical imaging, de-implementation, clinician survey, claims data, LASSO regression, health services research, patient demand, guideline adherence, overuse

The study, published in the Western Journal of Emergency Medicine, was led by co-senior authors David Thompson, MD, an associate professor of clinical emergency medicine and director of quality improvement and patient safety in the UC College of Medicine’s Department of Emergency Medicine, and Anita Goel, MD, also an associate professor of clinical emergency medicine. Over a 23-month campaign spanning the emergency departments of University of Cincinnati Medical Center and West Chester Hospital, the team tracked what happened when clinicians were given the training, tools and encouragement to apply validated clinical decision rules before ordering head and cervical spine CT scans in low-risk trauma patients. The outcome was striking: an estimated 3,542 CT scans were avoided, roughly $6.2 million in patient healthcare charges were saved, and more than 14,000 hours of cumulative patient waiting time were eliminated, all with no identifiable patient harm.

The technical foundation of the campaign rested on clinical decision rules that have become core components of emergency medicine training and standards of care nationwide. These rules, which guide clinicians in determining which head or neck trauma patients genuinely need cross-sectional imaging, incorporate carefully weighted risk indicators. Patients who fall from height, are ejected from motor vehicles, experience limb weakness or numbness, present with blood in the ear, vomit after the injury or show signs of confusion are considered high risk and proceed to imaging. Patients without such red flags, including many with concussions, are classified as low risk, and in those cases the rules support withholding the CT scan and monitoring the patient instead. The rationale is grounded in both radiation biology and health economics: each head or cervical spine CT delivers a meaningful ionizing radiation dose to tissues that are inherently radiosensitive, and the diagnostic yield in truly low-risk populations is exceptionally low.

The numbers quantify just how much low-value imaging was being avoided. At an estimated cost of approximately $1,750 per scan, the 3,542 avoided CT examinations translated into $6.2 million in patient charges that were never incurred. At an average of about four hours of waiting per scan, the campaign spared patients a cumulative 14,168 hours of time spent in the emergency department. Perhaps most striking from a radiation safety perspective, the avoided scans eliminated an estimated 6,021.4 millisieverts of collective radiation dose, a figure the researchers note corresponds to roughly 2,000 years of typical natural background radiation exposure for a single person. In an era when medical imaging accounts for a substantial fraction of the population’s cumulative exposure to ionizing radiation, reductions of this magnitude carry genuine public health significance.

Crucially, the campaign’s safety review found no downside. When the team examined their data at the conclusion of the intervention period, they identified zero missed injuries, meaning every avoided scan had indeed been medically unnecessary and no patient had been sent home with an undetected serious head or spinal injury. This finding addresses the central concern that has historically slowed the adoption of imaging-reduction programs: the fear that trimming CT utilization will inevitably let dangerous pathology slip through. Thompson emphasized that the guidelines, when followed, reliably protect against missed serious injuries, and the campaign’s own outcome data provided direct, institution-level confirmation of that claim across two busy emergency departments.

The intervention itself was deliberately multifaceted, reflecting modern implementation science rather than a single blunt policy. Continuing education sessions were delivered to emergency medicine providers, reinforcing the decision rules and the research evidence behind them so that clinicians understood not just what to do but why. Simultaneously, the CT decision rules were embedded directly into the electronic health record used to order patient testing, so that the point of ordering became a structured prompt for appropriate test selection. This combination of provider education and clinical decision support embedded in the workflow is widely regarded as the most durable way to change ordering behavior, because it reduces reliance on individual memory or judgment at moments of time pressure and standardizes the application of evidence across the entire provider pool.

The campaign also extended its educational efforts to patients, recognizing that a significant fraction of unnecessary imaging is driven not by clinician uncertainty but by patient expectation. Some injured patients arrive convinced that a CT scan is required, even after a physician has determined that their risk profile does not warrant one. To address this, the team created informational handouts designed to reassure patients that declining a scan in their situation reflects careful, evidence-based medicine rather than corner-cutting. As Thompson explained, not everyone with a head or neck injury needs a CT scan; some do and some do not, and the department’s goal is to provide the right care for every patient using the tools available to make those distinctions accurately.

The setting in which these results were achieved amplifies their importance. University of Cincinnati Medical Center is the Greater Cincinnati region’s only academic medical center and its only Level I adult trauma center, functioning as a major tertiary referral hub with an 81-bed emergency department that Thompson described as almost always at capacity. West Chester Hospital adds further patient volume to the health system’s emergency care footprint. Demonstrating that imaging reduction can be achieved in such a demanding, high-acuity environment undercuts a common objection, namely that evidence-based imaging restraint works only in smaller or calmer settings. The UC experience suggests the opposite: precisely because high-volume departments face the greatest pressures of throughput, cost and radiation burden, they may have the most to gain from rigorously applied decision support.

By the end of the 23-month period, all measured outcome indicators showed reductions in the rate of head and cervical spine CT scanning at both participating hospitals, and the absence of any identified missed injuries confirmed that the avoided imaging was truly discretionary. The research team, which included co-first authors Jude C. Luke, an emergency medicine resident physician, and Rebecca N. Kubick, a medical student, alongside biostatistician Heidi J. Sucharew, Natalie E. Kreitzer, and performance improvement specialists Kayla Winkler and Mary L. Giles, framed the project as a win for both physicians and patients. Doctors retain the reassurance that validated rules protect against missed injuries, while patients receive faster care, smaller bills and less radiation. If replicated at scale, the model offers a template for how emergency departments everywhere can convert well-established clinical decision rules from guidelines on paper into measurable reductions in low-value care.

Subject of Research: A quality improvement campaign reducing head and cervical spine CT imaging in low-risk trauma patients

Article Title: University of Cincinnati emergency medicine physicians safely eliminate thousands of unnecessary CT scans

Article References: University of Cincinnati emergency medicine physicians safely eliminate thousands of unnecessary CT scans. (n.d.). Original publication

Image Credits: AI Generated

DOI: Not provided

Keywords: emergency medicine, CT scans, quality improvement, clinical decision rules, trauma, radiation exposure, healthcare costs, University of Cincinnati, patient safety, low-value care, electronic health records, concussion

The article opens with a composite scenario that will feel familiar to many rheumatologists. A 40-year-old woman is referred for possible lupus after endorsing diffuse joint and muscle pain, headaches, fatigue, dry eyes and mouth, gastrointestinal distress, cognitive fog, and non-restorative sleep on a standardized review of systems. Her physical examination is entirely unremarkable: no synovitis, rash, ulcers, alopecia, edema, or neurologic deficits. Her antinuclear antibody titer is 1:160 with a dense fine speckled pattern, but antibodies to extractable nuclear antigens and double-stranded DNA are negative, complement levels are normal, and inflammatory markers are not elevated. Despite never meeting classification criteria for systemic lupus erythematosus, she carries the diagnosis in her record, has received hydroxychloroquine and repeated courses of glucocorticoids for flares, and has been counseled to delay pregnancy because of supposed disease activity. Years later, the label persists even after the medication is stopped for lack of benefit, and she has never been evaluated for or educated about fibromyalgia.

Chatterjee traces part of the problem to how modern medicine documents symptoms. Evaluation and Management guidelines introduced by the Centers for Medicare and Medicaid Services in 1995 and 1997 incentivized exhaustive checklist documentation over concise diagnostic synthesis, a shift the American College of Physicians later criticized for embedding clinically uninformative phrases such as a negative 10-point review of systems into routine notes purely to satisfy billing requirements. Within this environment of note bloat, the review of systems evolved from a focused diagnostic tool into an indiscriminate symptom inventory that can escalate concern for autoimmune disease. Yet autoimmune rheumatic diseases rarely announce themselves through diffuse symptom accumulation alone. Accurate diagnosis depends on coherent clinical phenotypes built from objective findings, such as clinical synovitis, characteristic rashes, Raynaud phenomenon, serositis, cytopenias, glomerulonephritis, inflammatory myopathy, or biopsy-proven organ involvement, accompanied by disease-specific autoantibodies.

The statistical core of the argument is Bayesian. Systemic autoimmune rheumatic diseases are collectively uncommon in the general population, whereas fibromyalgia and related central sensitization syndromes affect roughly 2 to 4 percent of adults. In a low-prevalence setting, the pretest probability of autoimmune disease is low even among highly symptomatic patients, and adding more non-specific findings does little to move it. Fatigue, diffuse pain, sleep disturbance, paresthesias, and cognitive complaints are intentionally sensitive but weakly specific symptoms that occur frequently in the absence of autoimmune disease, so each additional low-specificity finding contributes negligible diagnostic information and may obscure meaningful signals. By contrast, objective inflammatory and organ-specific findings substantially increase post-test probability. Failing to account for these base rates, the author contends, predictably produces diagnostic error.

Several well-described cognitive biases amplify the problem. Availability bias raises suspicion for autoimmune disease because these conditions are memorable, complex, and emphasized during training. Base-rate neglect leads clinicians to overestimate the likelihood of rare diseases. Anchoring bias occurs when an early finding, particularly a positive antinuclear antibody, dominates subsequent reasoning even as contradictory evidence accumulates. Once an autoimmune label is introduced, diagnostic momentum sustains it: subsequent clinicians inherit the diagnosis, interpret new symptoms through that lens, and hesitate to reverse course. Electronic health records and templated documentation perpetuate diagnoses long after their evidentiary basis has eroded, with problem-list clutter and indiscriminate review-of-systems templates reinforcing the appearance of chronic multisystem disease. The author also acknowledges that these errors are psychologically compelling, because both clinicians and patients often seek explanations proportional to the magnitude of suffering, and a multisystem autoimmune label can feel validating even when it is inaccurate.

Antinuclear antibody testing illustrates how bias and inappropriate test utilization intersect. Although the test is highly sensitive across autoimmune rheumatic diseases, it lacks specificity and performs poorly as a screening tool in low-prevalence populations. Low-titer antinuclear antibody, at 1:80 or below by indirect immunofluorescence, is present in 14 to 25 percent of healthy individuals and 10 to 12 percent of patients with fibromyalgia. Outside appropriate clinical contexts, the positive predictive value of the test for lupus is often in the single digits. When testing is ordered in low-probability settings, a positive result frequently triggers cascades of additional serologic work, incidental abnormalities, patient anxiety, and provisional labels such as possible lupus or undifferentiated connective tissue disease. Autoantibodies alone do not establish causality and may coexist with symptoms arising from entirely different processes. The test does retain real utility through its high negative predictive value, approaching 98 percent for excluding lupus, reflecting sensitivity of roughly 95 percent for systemic lupus erythematosus and systemic sclerosis and 85 to 90 percent for Sjögren syndrome and mixed connective tissue disease.

Central sensitization syndromes complicate this picture because they produce real, often disabling symptoms without consistent structural or inflammatory correlates. The absence of a definitive biomarker can create discomfort around diagnostic uncertainty, prompting clinicians to substitute non-specific serologic abnormalities as explanatory anchors. Chatterjee argues that diagnosing fibromyalgia should be viewed not as diagnostic failure but as a conclusion grounded in probability, pattern recognition, and outcome-based evidence, and that recognizing symptom amplification rather than inflammation is a learned clinical skill requiring deliberate teaching. Social determinants of health, including chronic stress, early-life adversity, repetitive occupational strain, nutritional deficiencies, and socioeconomic disadvantage, can amplify pain processing and produce diffuse symptoms resembling fibromyalgia, broadening diagnostic reasoning beyond autoimmune paradigms. Fibromyalgia also commonly coexists with confirmed autoimmune disease, occurring in about 25 percent of rheumatoid arthritis, 30 percent of lupus, and up to 50 percent of primary Sjögren’s syndrome cases, where its symptom burden can be mistaken for inflammatory flare.

Fear of missing early lupus often drives ongoing surveillance, but longitudinal data are reassuring. In patients with fibromyalgia, including those with positive antinuclear antibodies, the risk of developing lupus is approximately 0.0027 percent per year, similar to the general population, and low-titer antinuclear antibody does not predict future autoimmune rheumatic disease. Among antibody-positive individuals without established disease, progression is uncommon and is driven by evolving objective clinical features, disease-specific autoantibodies, and interferon signatures rather than antibody positivity alone. Serial autoantibody testing adds little value in the absence of new clinical findings. The author notes that some diseases, including the spondyloarthritis spectrum, polymyalgia rheumatica, large- and medium-vessel vasculitis, and Still’s disease, are seronegative, so diagnosis in early or atypical presentations requires longitudinal reassessment rather than point-in-time evaluation.

The harms of misdiagnosis are not benign. Patients labeled with autoimmune disease may face prolonged anxiety, repeated testing, unnecessary referrals, and exposure to glucocorticoids, hydroxychloroquine, biologics, and other immunosuppressants that carry metabolic, skeletal, ophthalmologic, infectious, and financial risks without addressing the true driver of symptoms. Life decisions around pregnancy, employment, insurance, and identity may be altered, disproportionately affecting women. At the systems level, indiscriminate testing and referrals increase costs without improving outcomes. Clear communication is essential to the solution: framing fibromyalgia as a disorder of pain processing rather than tissue damage helps validate symptoms and explain the lack of response to immunosuppression, while diagnostic restraint should be presented as probability-based rather than inattentive. The article closes with principles for generalists, including reserving screening labs for targeted indications, refusing to let isolated low-titer results drive labeling, diagnosing functional disorders with validated criteria rather than by exclusion, and favoring longitudinal clinical assessment over serial laboratory testing. A pan-positive review of systems, the author concludes, should prompt diagnostic restraint rather than escalation, because precision in diagnosis requires resisting serologic noise and returning to reasoning grounded in probability, pattern recognition, and humility.

Subject of Research: Diagnostic anchoring on positive autoimmune serologies and its role in misdiagnosis of systemic autoimmune rheumatic disease in polysymptomatic patients

Article Title: Diagnostic Anchoring on Positive Autoimmune Serologies in Polysymptomatic Patients

Article References: Chatterjee, S. (2026). Diagnostic Anchoring on Positive Autoimmune Serologies in Polysymptomatic Patients. Journal of General Internal Medicine. https://doi.org/10.1007/s11606-026-10727-6

Image Credits: AI Generated

DOI: 10.1007/s11606-026-10727-6

Keywords: diagnostic anchoring, antinuclear antibody, autoimmune serologies, base-rate neglect, fibromyalgia, central sensitization, systemic lupus erythematosus, misdiagnosis, iatrogenic harm, overdiagnosis, rheumatology, low-value care