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Paint the Stick Orange

Incentives, Rewards, and the Innovation Imperative

Section Editor(s): Ford, Eric W. PhD

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doi: 10.1097/JHM-D-19-00205
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Innovation, one of my favorite topics, figures prominently in this issue of the Journal of Healthcare Management.

E. M. Rogers’ diffusion of innovation theory describes how innovations are accepted or rejected in the marketplace. Specifically, Rogers (2003) states that “innovators” are the first to adopt and assess new technologies. If innovators promote the benefits of a new technology, then “early adopters” are the next group to assess the innovation’s value. If they confirm the technology’s utility, the new product crosses the innovation gap (the tipping point) and enjoys widespread adoption with the “early majority.”

The need to span a natural gap in the adoption cycle was noted in the publication of Crossing the Quality Chasm: A New Health System for the 21st Century (Institute of Medicine, 2001). That call to action recommended electronic health records (EHRs) as a critical part of the solution. Unfortunately, calls to action—no matter how practical or well reasoned and intentioned—often go unheeded because other factors drive the diffusion of innovations.

Two market forces drive the speed and levels of adoption: those that are internal to the customer and those that are external. Internal influences come from the adopter’s social system; they are a type of social contagion that makes a person want the innovation. A classic, mid-1970s example of an innovation with little actual value, but carrying a significant social contagion, was the pet rock. Nobody really needed it, but the social cachet was high. In contrast, forces outside of the consumer’s preferences drive external influences. With external influences, someone other than the consumer assesses the innovation’s value proposition to the consumer. Regulations either promoting or restricting a new technology are a common source of external forces. Many safety innovations in the auto industry were regulated into widespread adoption rather than voluntarily undertaken by either consumers or manufacturers. However, a paradox in the diffusion theory’s two factors often goes unnoticed.

Intuitively, it would seem that the external influence—mandates, in particular—would accelerate and increase adoption. However, that is not the case. Going back to the EHR as an innovation, hospitals resisted adoption because the systems were a significant added expense, their efficacy as a safety tool was unclear, and the paying patient was largely indifferent. The impetus to adopt an EHR had a low internal motivation factor. Internal influences or desires to adopt are far more powerful in accelerating adoption.

Diagnostic and treatment technologies have a high internal motivation factor. Doctors and patients both value technologies that they believe will improve outcomes. Hospitals value having state-of-the-art technology as a point of pride and as a marketing tool. Therefore, when a new clinical technology is introduced, it is often widely and rapidly adopted. In fact, some technologies are more widely adopted than is genuinely necessary, prompting certificate-of-need regulations to slow the spread of some innovations.

This issue’s Diversity and Inclusion column begins with a classic example of an innovation that has not been widely adopted despite the high likelihood that it can improve a health system’s operations: the hiring of underrepresented racial and ethnic minorities (UREMs). Raúl H. Zambrano, MD, FACHE, looks at the hiring of UREMs through a resource-based view of the firm. He deftly explains why adding diversity to a health system increases its flexibility and resilience in ever more challenging environments.

The Managing Risk column by Paul L. Epner and Dana Siegal, of the Society to Improve Diagnosis in Medicine and CRICO Strategies, respectively, discusses the problem of misdiagnosis and how much it costs all of us. They highlight many opportunities for improving the health system. In particular, they call for better metrics to assess the full scope of the problem. It is axiomatic that you cannot manage what is not measured. The need for better measurement of diagnostic errors is an opportunity for innovation.

The empirical articles section of this issue begins with the research of Justine Po; Thomas G. Rundall, PhD; Stephen M. Shortell, PhD; and Janet C. Blodgett. They examine the relationship between Lean processes and financial and patient outcomes. Interestingly, they find significant relationships between Lean adoption and financial metrics but no correlation with patient-reported outcomes. To my mind, this begs the question: What is the intended end of Lean adoption? Maybe it performs as intended, and health systems use it primarily to focus on efficiency.

The article by O. Elijah Asagbra, PhD, CPHQ; Ferhat D. Zengul, PhD; and Darrell Burke, PhD, looks at the adoption of health information technology by applying Rogers’ classic theory. Gratifyingly, they report that early adopters enjoyed improved financial performance compared to the early- and late-majority groups. In some ways, this finding is a surprise because early adopters often incur the costs of early learning and higher purchase prices. These extra costs often are referred to as the “bleeding edge” of the technology adoption cycle.

The topic of provider burnout is among the most popular for our readers lately. Beth A. Lown, MD; Andrew Shin, JD; and Richard N. Jones, ScD, have conducted national surveys of doctors and nurses to assess changes in their attitudes toward their respective professions. Sadly, but not surprisingly, their research confirms what many have been saying for some time: that dissatisfaction is on the rise. Lown et al. offer helpful suggestions for combating the burnout phenomenon with greater compassion.

Aske Skouboe; Zaza Hansen, PhD; and Jan Kloppenborg Møller, PhD, use a novel methodology to look at how improvement efforts perform over time. They apply accelerated longitudinal design with decomposition (ALDD) to foreshorten studies that would normally take years to complete. They do this by combining multiple patients’ experiences across the course of illness to create a synthetic episode of care. The results are encouraging, but many of my more conservative colleagues might have concerns about ALDD.

Value-based purchasing is one of the biggest innovations in healthcare in recent years. Sanjula Jain, PhD; Kenneth E. Thorpe, PhD; Jason M. Hockenberry, PhD; and Richard B. Saltman, PhD, combine large databases to study the topic. They find that changes to care management practices do lead to improved performance. However, as with other innovations, they express concerns that the benefits may be short-lived or dissipate over time.

In addition to our invited columns and research articles, we are delighted to feature an interview with Kenneth L. Johnson Jr., PhD, FACHE, associate dean and professor at Weber State University, as well as abstracts from the annual Forum on Advances in Healthcare Management Research that took place during the 2019 Congress on Healthcare Leadership of the American College of Healthcare Executives.

So, why paint the stick orange, so to speak? Policymakers and insurance companies are always trying to figure out how to either incentivize (with a stick) or reward (with a carrot) the adoption of innovations that improve care quality or control costs. Unfortunately, these schemes tend to focus on rewarding some at the expense of others. In effect, they have merely painted the stick orange to make it look like a carrot. These programs are the wrong type of influence if you want to promote adoption of innovations.


Institute of Medicine. (2001, March). Crossing the quality chasm: A new health system for the 21st century. Washington, DC: National Academies Press.
Rogers E. M. (2003). Diffusion of innovations. New York, NY: Free Press.
© 2019 Foundation of the American College of Healthcare Executives