Within healthcare quality improvement circles there is a growing interest in Implementation Science, which seeks to understand how intentional change within healthcare delivery systems is performed and sustained. The National Institutes of Health define Implementation Science as:
“…the study of methods to promote the adoption and integration of evidence-based practices, interventions and policies into routine health care and public health settings.”
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The field is vast and touches on numerous disciplines and areas of study. By no means is it possible to describe the entire subject in a single article, but we can explore some of the theories and frameworks employed by Implementation Scientists to better understand how healthcare delivery systems (hospitals, clinics, EDs, SNFs, etc.) function and adapt to change. Much of what is presented below has appeared in some form in peer-reviewed papers and a recent book that I co-authored with colleagues at Indiana University’s Center for Healthcare Innovation and Implementation (CHIIS), Dr. Malaz Boustani and Dr. Jose Azar.
In order to understand how evidence-based solutions are accepted and incorporated into real-world clinical settings, it is necessary to understand:
1. How a healthcare delivery system functions, and
2. How the individuals within that system behave and make decisions.
Once that is accomplished, one can begin to explore how to set up the system to encourage the desired behaviors and decisions in an attempt to increase the likelihood of success and sustainability.
Any healthcare delivery system is made up of a physical space and individual members (i.e., doctors, nurses, techs, support staff, administrators, etc.); the system functions as a result of interactions between the members within that space. Therefore, the organizational structure, the social culture, the politics, as well as internal and external forces related to budgets, regulations, guidelines, and the like all influence what happens and how it happens.
Complex Adaptive Systems
Many Implementation Scientists believe that the appropriate model for any healthcare delivery system is that of a “complex network.” The fields of network theory and complexity science are used to describe all sorts of natural phenomena, such as biologic processes, ecologic systems, and social systems like cities, companies, and organizations. Networks are described by the structure and nature of how individuals are “linked” to one another: they may be highly spread out, randomly distributed, or more locally clustered with “hubs” (a handful of individuals with lots of connections). Depending on the structure, the network will display certain characteristics, such as how closely “connected” any two members are (i.e., the “6 degrees of separation” idea), and how adaptable or resilient the network is when faced with a change.
A particular kind of complex network that many feel is applicable to healthcare, including my colleagues listed above and others such as Philip Lambert, is that of a Complex Adaptive System. It’s “complex” because of the large number of members and the variety and depth of the type of connections between the members; it’s “adaptive” because it is dynamic in that its members can learn from previous experiences. As an aside, there is a wealth of information on these systems and their practical applications that, frankly, is fascinating (particularly Lambert’s assertions about how creativity and innovation are fostered and encouraged by dynamics within the system). For now, however, it is enough to to define a Complex Adaptive System as a dynamic network of individuals who are both semi-autonomous and interdependent. That is, they each can make their own decisions and have a “job to do,” but they also rely heavily on many other members of the system, both those in their immediate vicinity as well as those they may only be connected with through others. The behavior of the network is not defined by the individual members, but by the interactions between the members within the system as well as with external forces; the ability of the system to adapt depends on the members to grow and learn from their experiences. What this all implies for implementation is that evoking a desired change does not so much require a change to the “system” but to the decisions and interactions of the individuals who make up the system.
Behavioral Economics
To understand how and why individuals behave a certain way and make decisions, Implementation Scientists often turn to the ideas developed within behavioral economics. As explored by countless authors in many other settings, studies dating back to the 1970s have revealed cognitive biases that are inherent in all humans and that shape and color the decisions we make based on the information available to us. For example, we’re susceptible to how information is presented (“framing”), what is offered as a comparison (“anchoring”), and how readily we can conjure up a recent or similar situation or event (“availability” and “representativeness”). As a result, behavioral economists have uncovered ways to leverage these biases to “nudge” individuals to perform certain behaviors. For example, if we want school children to choose more fruits and veggies at lunch, placing them early and at eye level will make them more likely to select than if they appeared later and lower (by the way, marketers and store owners have also learned how to leverage these biases to sell more products). When there is an entire system built to encourage certain behaviors and decisions it is called a “choice architecture,” and if constructed correctly can make it much easier to induce change in behaviors and processes involved in care delivery. For example, if trying to reduce infections by incorporating an infection prevention bundle, aspects of a choice architecture may include: posters reminding staff of the frequency of infections and how they can occur, statements in front of colleagues about one’s intentions regarding adherence to the bundle, reminders that most staff are complying with the bundle, equipment placed in plain sight to remind staff to use them, and electronic reminders to remove central lines or urinary catheters after a number of days to prompt action.
Taken together, the theories and frameworks from complexity and network sciences and behavioral economics provide guidance for how to structure and implement evidence-based solutions when trying to change and improve care quality. From an introductory standpoint, however, it is enough to understand at this point there is a growing understanding of the interconnectedness of healthcare delivery systems and that the interplay of system dynamics, social pressures, individual skills and attributes, as well as the physical environment together shape and promote certain decisions and behaviors. At the same time, each system is unique in its characteristics and members, so that how these attributes combine to influence outcomes can vary from site to site. If we hope to be able to apply evidence-based solutions in ways that will consistently produce real and sustainable change, an understanding of the theories and frameworks that Implementation Science draws from will be crucial.