In their article, “Trends in Pediatric Imaging from 1997–2024 in an Integrated Healthcare Setting,” Mahendra et al describe the growth of the use of magnetic resonance imaging (MRI), computed tomography (CT), and ultrasonography imaging of children in the Kaiser Permanente Northern California region1 as a representation of likely national trends. Their research found that CT use declined during the 2010s, whereas the use of MRI and ultrasonography increased, suggesting that imaging may have been shifting from CT to MRI and ultrasonography during this time. Imaging in all modalities then leveled off by the end of the decade, suggesting that overall use of advanced pediatric imaging had appeared to stabilize. Since 2020, however, imaging experienced renewed exponential growth beyond what would be expected from post-pandemic rebound alone. The authors’ findings have significant implications from a pediatric imaging health services research perspective.In general, innovation—such as the use of imaging technology—tends to spread (or be adopted) across a population according to a sigmoid growth model, with 4 general phases2 (Figure 1): Introduction phaseGrowth phaseDeceleration phaseStationary phaseIn reality, the pattern of diffusion of technology can vary, with temporary plateaus, periods of decline, or cycles of decline and renewal, depending on the underlying drivers. Furthermore, even when the growth follows the sigmoid curve precisely, it is usually difficult to predict the saturation rate of use while the technology is still in the growth phase. Nevertheless, the model is helpful to understand the current state and project into the future.The fact that advanced pediatric imaging volumes seemed to have plateaued by the late 2010s suggested that it may have reached the stationary phase. However, this article suggests that the plateau was, in retrospect, only temporary.This renewed increase raises concern because of associated risks—particularly radiation exposure from CT and anesthesia for MRI—as well as both direct and indirect costs of imaging, including system strain and provider burnout.The findings raise 2 pressing questions: what underlying forces are driving renewed growth, and—assuming increased use does not improve outcomes—what can be done to dampen it?The authors attribute the decrease in CT use in the 2010s to campaigns to raise awareness about radiation risks.3 They then attribute the renewed increase in MRI and CT use to 1) increasing complexity and severity of illness, 2) decreasing time that clinicians have to evaluate their patients, 3) a paucity of evidence-based guidelines, 4) defensive medical practice, and 5) low-value imaging use (which is a euphemism for systematic ordering of imaging that is not indicated).4 These proposed drivers are plausible but necessarily speculative because the study was not designed to establish causation. In the authors’ defense, identifying causal mechanisms in complex use patterns is notoriously difficult, particularly when multiple small forces interact over time.5 Although identifying drivers is difficult, designing effective interventions is harder still.Pediatric imaging use reflects a phenomenon described by Thomas Schelling in Micromotives and Macrobehavior: large-scale patterns emerge in unpredictable ways from many individually rational local decisions.6 Although image volumes are measured in the aggregate, imaging studies are ordered 1 patient at a time under conditions of uncertainty, time pressure, incentives, expectations, and availability. Macrobehavior does not reflect macro-intent; rather, subtle shifts in local decisions, repeated at scale, produce what can be dramatic aggregate effects. It is difficult to predict the aggregate results by examining the local decisions and even harder to identify the factors driving local decisions based on the aggregate results.One hypothesis is that the plateau of the 2010s may have reflected temporary countervailing pressures—such as heightened awareness of radiation risk—rather than a durable shift in underlying incentives. If local microincentives continued to favor imaging through scanner availability, clinical time pressure, or defensive practice, the aggregate pattern would be expected to eventually reassert itself. Regardless of the precise drivers, what is to be done about the findings?At its core, imaging is a tool to reduce clinical uncertainty.7 Reframing rising use as increasing reliance on imaging to reduce clinical uncertainty shifts attention upstream to the factors shaping ordering decisions. Therefore, the answer to inappropriate use may lie less in curbing imaging and more in developing other means of managing clinical uncertainty.Schelling reminds us that addressing the accumulated effect of many small decisions requires altering the conditions under which those decisions are made. Several broad strategies for controlling imaging use follow from this framework.Not every actor in a system is the same. Levels of experience, tolerance for uncertainty, and amenability to decision support differ between a newly trained nurse practitioner in an outpatient clinic, a time-pressured emergency medicine physician, and a seasoned subspecialist. Change strategies should account for these distinct archetypes rather than assuming that all ordering providers use a uniform decision calculus.Almost all behavior is influenced by local leaders and peers, even when this influence is not recognized. Therefore, as much attention should be paid to the social dynamics of local systems surrounding ordering of imaging, including committed leadership, group norms, and peer reinforcement, as is paid to policy or technical infrastructure.Simply telling clinicians to image less does not solve the underlying problem of clinical uncertainty. Investing in other local context-specific mechanisms to reduce clinical certainty, such as decision support, clinical pathways, or rapid access to subspecialty consultation, may decrease the need for imaging to answer clinical questions.A significant part of the actors’ decisions occurs at the point of local interaction with “the system.” Small design choices at the point of ordering can have large impacts on behavior. For example, details such as order set defaults, embedded appropriateness criteria, and automatic display of recent past imaging can meaningfully shift ordering decision thresholds.When a behavior is fast, easy, and immediately rewarding, its use tends to expand; the converse is also true. Carefully designed mechanisms to readily facilitate appropriate ordering and impose pauses for inappropriate ordering can meaningfully influence aggregate outcomes.Over time, feedback is among the most powerful drivers of results in a complex system. Transparent reporting of ordering patterns at the clinician or group level can create self-regulating dynamics, especially when accompanied by group discussion and commitment.Health care is local—and ordering of imaging is hyperlocal, involving single or small groups of individuals. Therefore, durable change likely requires engagement at the level of local clinical teams and service lines. This means that meaningful changes are likely to come more from pediatric providers, administrative leaders, and information technology systems managers than from radiologists (although your local radiologist is almost certainly happy to partner with you to improve the value of imaging).The Schelling framework suggests that as imaging becomes more readily available, clinicians’ time pressure becomes greater, and as subspecialty expertise becomes less broadly distributed, the use of imaging is not likely to curb itself. The stationary phase represents an equilibrium where the drivers of increased use are in balance with the limiting factors. As Paul Batalden observed, every system is perfectly designed to get the results that it gets.8 Structural aspects, such as throughput pressures or easier access, tend to outweigh generalized ethical exhortations. Whether the system’s implicit priorities are appropriate cannot be judged in the aggregate; that determination requires case-level assessment.That work is inherently local and more difficult than assessing aggregate outcomes and issuing broad directives. For that reason, solutions will likely need to operate at a higher structural level, such as establishing institutional mechanisms for imaging stewardship, with ongoing monitoring, feedback, and shared accountability. Modern approaches to antibiotic stewardship can serve as a guide.9The persistence of growth in imaging suggests that the system has not reached a stable equilibrium, indicating that shifts in local incentives continue to spur increasing use of imaging.This reality should motivate action while cautioning against both complacency and alarmism—complacency risks lack of urgency to solve the problem whereas alarmism risks rushing to overly blunt mechanisms to restrict imaging. The more effective path lies in systematic study and deliberate redesign of the decision environment—aligning local incentives with stated priorities and supporting professional self-regulation—accompanied by feedback and engagement with local leaders and provider groups.
David B. Larson (Mon,) studied this question.