Section 1: Introduction to What It Means to Improve a Health System Based on Data

Section Overview

In this section, you will be introduced to the foundational definitions of implementation science, improvement science, and change management. This is an evolving field of study in the health sector where these bodies of literature intersect, sharing similarities while also possessing distinguishing characteristics. However, all have the goal of understanding how to further improve health services and programs – thus the health outcomes for the population.

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Introduction

Have you ever wondered how the research that is done in the university setting is realized in practice? Or how are new innovations in health care delivery implemented in an organization to help advance the health of a patient? Or maybe you have wondered about how you get someone to change the way they work after doing something a certain way for many years? These questions and many others are where implementation science, improvement science, and change management theories, principles, and concepts come into play.

It is important to acknowledge that there are many terms, theories, and concepts aimed at “making things better” in the health system. It is easy to become confused with trying to understand the differences between words like change, improvement, implementation, diffusion of innovation, and knowledge translation. It is noteworthy that they are not working in parallel, but rather, when looked at together, these concepts support each other in helping to achieve the goal of this book – using data to drive improvements in health services. This section review the areas of:

1. The body of research that studies how best to implement change – implementation science;
2. The practice of improvement science, which lends to the methods and tools to create change – improvement science; and
3. The people side of the change, which requires tailored and focused management – change management.

Understanding these three areas together provides a robust understanding of how best to use data to impact the way in which health services are delivered and to successfully implement changes that are based on the best available evidence.

CASE STUDY: A Demonstration of the Linkages of Implementation Science, Improvement Science, and Change Management – Peds-TECH

Niagara Health and McMasters Children’s Hospital embarked on a project to connect pediatric emergency specialists at McMaster with emergency physicians in Niagara Health through telemedicine for children in need of resuscitation. This collaboration, titled Pediatric Telemedicine Connecting Hospitals (Peds-TECH), started after an extensive focus on how to improve care for these patients. As the program rolled out, an improvement science lens was used to learn from the cases, report any technical issues, and obtain physician perceptions of the service so that these could be fixed in real time. This Model for Improvement approach allows the collaborators to improve the service over time. This process also enacted principles from change management to engage local health professionals in the development of the process, provide opportunities for feedback, and share successes of the program. This has helped to support the project to continue and the program to be used across the multi-site hospital system. With the program up and running successfully, there became an opportunity to study the implementation, hence implementation science. Assessing the program through rigorous qualitative and quantitative data to understand the factors associated with implementation can then lead to further enhancement while also lending to our scientific knowledge regarding factors that enhance implementation success of an innovative technology. This knowledge can then be used to support the sustainability and spread of this effective health service delivery model. To read more about Peds-TECH, go to this article.

Topics Defined and Explained

As outlined by Eccles and Mittman (2006) in the inaugural issue of Implementation Science, implementation science is defined as:

The scientific study of methods to promote the systematic uptake of research findings and other evidence-based practices into routine practice, and, hence, to improve the quality and effectiveness of health services and care. (p. 1)

This field of implementation science takes the idea of using data to drive change to another level. One can think of it as the study of that “middle ground” between data identifying the need for change and the outcome of achieving human behaviour change; implementation science allows us to understand individual and organizational factors that are more likely to enable successful change. Understanding the mechanisms in the process of implementation is its own science. It addresses the “know do gap”. As outlined by the University of Washington’s Implementation Science Program, the fundamental question of implementation science is: “How do we get ‘what works” to the people who need it, with greater speed, fidelity, efficiency, quality, and relevant coverage?” (University of Washington, 2022).

Watch the video below on implementation science:

Similarly, improvement science, which includes ideas such as the Model for Improvement, is an “applied science that emphasizes innovation, rapid-cycle testing in the field, and spread in order to generate learning about what changes, in which contexts, produce improvements” (Institute for Healthcare Improvement, 2022c, para. 2).

As you can see, these two fields are complementary, as both focus on improving something, are systematic in the approach, and are concerned with the context in which the change is being implemented. Implementation science is the actual “study of methods” to promote uptake of evidence. This might mean that there is a study of the perceptions of staff on how well the Model for Improvement was enacted to create change which would allow for an understanding of the Model for Improvement in that context.

Linking this back to the telemedicine case study, an implementation scientist might conclude that by using the Model for Improvement, where the team was able to obtain data on specific outcome measures each month, that this was the most effective strategy in enhancing the use of the telemedicine service over time. Therefore, if others were to implement a similar service at another hospital, there should be a consideration of the data systems before embarking on the implementation of this service.

Change management comes into play with a focus on supporting people through a change. People are inextricable from our organizations and our processes; when we introduce change, it often requires behaviour and attitude change, which can be challenging. According to the American Society for Quality (2022a), change management is defined as “the process, tools, and techniques used to manage change, including planning, validating and implementing change, and verifying effectiveness of change”. Furthermore, the World Health Organization (2019) defines change management as “the practice and process of supporting people through change, with the goal of ensuring that the change is successful in the long-term. Change management helps people to change their behaviors, attitudes, and/or work processes to achieve a desired business objective or outcome.” (p. 3)

The video below is a nice demonstration of the Large Scale Change Model, in which you will be able to appreciate the change management approach and see how improvement science and implementation science links into these concepts:

Change management is this overarching framework to which implementation science and improvement science can be situated.

All of this is to say that if an individual is looking to promote the uptake of evidence that is an inherent change to clinical practice or service delivery, they should look to implementation science and improvement science to understand how they can best do this and think about these approaches in a broader change management framework. The conscious application of these three perspectives is especially valuable in a complex environment like the health sector.

Motivating Change

Improving the health system seems like it should be a no-brainer! It should be easy to get buy-in for a great new clinical therapy or an innovative practice. But health systems are complex and human behaviour is notoriously hard to modify. Hence, asking someone to change their behaviour just because some researchers say it is better is not the carrot for change. Motivating change comes in the form of policy, funding, organizational structures, and appropriate improvement methods.

Over the years, health system researchers have come to understand that the creation of evidence through clinical trials, in very controlled settings, although important, is not the solution to closing the loop to ensuring that all patients are provided with evidence-based care. In fact, the uptake of research evidence to practice is so slow, it is estimated to take approximately 17 years for evidence to be fully adopted in practice (Morris et al., 2011), and only 50% of new evidence-based strategies even make it into practice (Bauer & Kirchner, 2020).

For years, much of the focus of researchers was to develop evidence and then have this published in scholarly journals as an indicator of their success. However, this minimalistic approach to knowledge translation of research findings and the relative lack of impact on practice that was arising out of research that was costing millions of dollars has been questioned by many. To this end, many funders now require that researchers demonstrate how they will act upon the results to ensure they are realized.

Extending Your Thinking to Implementation With a Health Equity Lens

Ensuring a health equity lens to all health system planning is essential. Kerkhoff et al. (2022) have provided an overview of the steps and questions that need to be asked as one begins an implementation process. This framework could be used to evaluate current efforts, but more ideally could be used to guide implementation. Considering health equity in the planning stages through these questions will help to ensure that vulnerable populations are considered throughout the planning process and in turn will help support the reduction of health inequities in our communities.

Please go to Chapter 2: Data for Equity? for more information about this topic.

Summary

What we know about working in the field of implementation, improvement, and change is that it is messy: It is never a straightforward process with easily defined boundaries. Acknowledging and being mindful of this messiness is one of the most important things we can do when trying to work on health systems change. There are numerous approaches that have been developed and tested to achieve the desired results, many of which will be discussed throughout the next two sections: Section 2: Frameworks for Improvement and Section 3: Factors Influencing Change Efforts.

To say that one approach is better than another other is wrong. What is important to consider is the context. This is what makes implementation and improvement so interesting (and so complex)! One size does not fit all. Health sectors are not controlled environments where you can simply take, for example, one intervention from a hospital in Northern Ontario and implement it the exact same way in an academic hospital site in downtown Toronto. Although the intervention is the same, the process by which the intervention is implemented may differ significantly. This is not to say that one is better than the other – it is to say that context matters and needs to be on the top of everyone’s mind when working to improve health systems.

In the next two sections, Section 2: Frameworks for Improvement and Section 3: Factors Influencing Change Efforts, you will be introduced to a number of frameworks that are used in improvement science together with factors that need to be considered when implementing change.

Test Your Knowledge

Complete the following activity to assess how much you learned about the content that was covered in this section.