patient care

This series has provided important perspectives regarding all stakeholders interested in reducing low-value care – researchers, patients, and providers. At an organizational or systems level, we know there are a number of factors influencing low-value care and our efforts to curtail it that differ from the factors that influence underuse of high-value care. For example, we know that the most widely tracked performance measures disproportionately address underuse; that payers historically have reimbursed based on care provided, not outcomes achieved; and the supply (over-supply) of health care resources. These are well-known, if not necessarily well understood. But there is another, less understood challenge at the organizational and systems level in how we scale-up and spread de-implementation strategies we believe are effective.

We have many promising strategies currently being tested in de-implementation, such as shared decision making, audit and feedback, and practice change management, which are promising approaches. While the mechanisms of these strategies (awareness raising, knowledge transfer, motivation and/or prioritization) are at the provider level, when scaling up at a systems level they potentially introduce system-level unintended consequences.

Perhaps the best example of this is the use of shared decision-making (SDM) to curb overuse. While SDM has been touted as the gold standard of care provision and has been required by some organizations, such the Centers for Medicare & Medicaid Services in the US, for example, SDM may not be uniformly ideal across contexts. There are patients who do not want to participate in the shared decision-making process because of personal characteristics, such as not wanting the responsibility of making potentially weighty decisions. Patients may also have low levels of health literacy or socioeconomic status, which can impact their ability to actively participate in decision-making. Additionally, some clinical situations, such as blood transfusions, do not require SDM because of their nature. Assuming that SDM is adopted across systems, however, there are other considerations to take into account. Health professionals system-wide will have to undergo additional training, which will be time intensive. Developing the support tools and curricula for SDM training will also be time and resource intensive. Lastly, once the training is carried out, the question arises: how will these systems be able to support the extra time needed to facilitate SDM on an ongoing basis?

Another consideration involves electronic health records. Part of management practice has become to prioritize their use, however, they are not in and of themselves a comprehensive solution. For instance, electronic health records do not aid a clinician in figuring out how to manage the time they have with a patient and what conversations to have. Electronic health records use reminders, audit and feedback and decision support tools, which are all methods that work to a degree. Nonetheless, when the volume of push notifications, reminders and decision-aids is too great to meaningfully attend to, they actually become an obstacle to delivering appropriate, patient-centered care. One solution is use design nudges that don’t overtly engage the clinician. For example, it has been shown that altering the default settings in an electronic health record is enough to significantly change prescribing behavior. Nudges have the potential to scale quickly, and if done well could make clinicians’ work easier not harder. However, there are ethical considerations when we attempt to alter people’s behavior without their knowledge, and this is particularly sensitive when we’re talking about trying to reduce the use of a service or practice.

The measurement of overuse is another relevant concern. Clinical performance measures overwhelmingly focus on underuse rather than overuse. By framing clinical quality nearly-exclusively in terms of ensuring patients receive care, clinical practice guidelines have almost certainly contributed to low-value care. Although it is on the agendas of decision makers and practitioners to identify and prioritize overuse for reduction, the methods of measurement may be overlooked. There has been one effort by Segal and colleagues, to create an index to measure overuse of services in the US using Medicare claims, however using an index may not be the answer to identifying system wide overuse. This methodology must be rigorously systematized and standardized to ensure that the evidence found is free of sampling and other biases. We are currently in need of sufficient datasets for relevant indicators of overuse. Additionally, sampling bias may be present. Within oncology, for instance, there appears to be more overuse within screening compared to other areas, however, this may simply reflect the volume of studies which have been conducted in this area because the indicators are easier to measure. One recent systematic review explores the kinds of measures that are being used to assess the impact of low-value care interventions, revealing that most studies focus on decreased use as opposed to other measures that may be more meaningful, such as appropriateness.

One final takeaway is that, although overuse has been identified as an issue across health systems, it is not something that we will be able to address with short-term solutions. Instead, we should take the long view and remember that, just as practice guidelines are forever evolving, so too will our outlook and implementation of techniques to reduce overuse. Complex interrelated factors, at multiple levels, affect how overuse is addressed across health systems. Technologies and procedures which are underutilized today may become overused in the future, thus, health systems will have to adapt in parallel. Evidence-based medicine is forever developing, and the evidence itself is so voluminous and extensive that the only way through is to create a culture across systems where not only is up-to-date evidence used to facilitate best practice, but it is also accepted that best practice is dynamic and evolving, and health systems can change to accommodate it. This change should be carried out through systematic, established procedures, which are cognizant of the landscape and how to navigate it to affect real change.

This four-part blog series is a product of a thematic working group on de-implementation convened by AcademyHealth and the ABIM Foundation as part of the Research Community on Low-Value Care. Access the first three posts in the series focused on viewing de-implementation as part of a learning health system and implications at the patient and provider levels.

Moriah Ellen

Moriah Ellen, M.B.A., Ph.D.

Senior Lecturer - Ben Gurion University

Dr. Ellen is currently a Senior Lecturer at Ben Gurion University’s Department of Health Systems Management in... Read Bio

Christian Helfrich

Christian D. Helfrich, M.P.H., Ph.D

Investigator - The Seattle-Denver Center of Innovation for Veteran-Centered and Value-Driven Care

Christian D. Helfrich, M.P.H., Ph.D., is an implementation scientist and core investigator at the Seattle-Denv... Read Bio

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