Health services are inherently subject to several internal and external variabilities, both expected and unexpected, which demand slack resources (e.g., extra staff, materials, and money) to provide effective responses and preserve critical functionalities. Although slack resources are ubiquitous in health services (e.g., staff on standby in emergency departments; alternative drugs for the same disease), they often grow disorderly over time and are not deliberately designed into the system. Therefore, health services frequently do not have the right amount and type of slack resources at the right place and at the right time. Furthermore, the definition of what counts as slack and what counts as waste is both a political and a technical decision-making, which makes slack vulnerable to efficiency pressures. This essay discusses these and other facets of slack in health services, highlighting its key role for the delivery of safe and effective care while acknowledging its different manifestations and potential drawbacks. Several examples are presented to illustrate the importance and implications of slack in practice.

It is unsurprising that the evidence synthesis has become an increasingly useful tool in an implementation scientist's toolbox. Necessarily, the methodological horizons of evidence synthesis have expanded as we understand more about implementation practice and research. The importance of context, for example, has led to an increased focus on approaches such as realist synthesis – what works, for whom, and in what contexts is a seductive phrase for an implementation scientist. A move to a more democratized approach to research coproduction, and innovations around rapid and living reviews of evidence synthesis present further opportunities. If evidence syntheses are to provide a trustworthy foundation or catalyst for action, there needs to be greater multidisciplinary and co-productive effort galvanized towards maximizing the potential utility of evidence syntheses as a feature of implementation science.

The Plan, Do, Study, Act (PDSA) concept is ubiquitous in healthcare, following pioneering work over 100 years ago from Shewhart and colleagues who originally developed the control chart, another ubiquitous mechanism for improvement. We examine the historical underpinnings to the PDSA cycle and consider its implications for contemporary implementation science. The power of the PDSA rests in its simplicity, and it has been harnessed in many applications across the world. It has proven to be a useful guide for researchers and practitioners alike.

Integrating “best practices” into healthcare is a daunting task; healthcare delivery may be the most complex dynamic process ever crafted by humans in our efforts to modify the biology, physiology, and culture of health and illness. Simulation, particularly simulation in situ, involving patient care teams in patient care spaces, provides unique insights as it closely approaches clinical Work-as-Done. Simulation “in the wild” may use technology-enhanced manikins, simple or elaborate physical models, or virtual or augmented reality representations of patients. Initially, simulation efforts focused on improving provider knowledge and skills, but practice now includes integration into quality and safety programs. Simulation is a powerful tool that can inform and enrich implementation processes to improve healthcare delivery.

While initiating behaviour change is challenging, sustaining it is even more so; yet, failure to sustain successful implementation can have substantial consequences. Indeed, failure to address implementation sustainability and advance the science that underpins it was identified as the most important implementation problem of our time. By not addressing this challenge, research is wasted, and healthcare is impacted globally. In this essay, we will define sustainability, explore determinants of sustainability, outline models for sustainability, and identify research gaps in implementation sustainability to advance this field and strengthen our health systems.

Sensemaking is the process by which people give meaning to an experience that is somehow at odds with expectations. Giving structure to the unknown enables action. A dynamic process, effective sensemaking is built upon cognitive flexibility, analogic reasoning, systems thinking, and creativity. Sensemaking enables identification of salient cues, making connections between data, creating a rational account, and then acting upon it. Whether an individual cognitive exercise or a social process, sensemaking is critical in implementation when situations are usually only partially structured and partially predictable. Sensemaking is an inherent, if implicit, part of the change process and is thus a critical skill for both consultant and team member. The most effective leaders and implementors can figure out the rules of the game while they are playing it.

While clinical decision support (CDS) delivers much of the value from the electronic health record, obtaining that value can be difficult. This piece describes how to get value, in terms of quality, safety, and financial savings. A way to think about CDS is to divide it into two broad areas, getting the content right, and getting the delivery correct. To get value, both are critical – implementation science relates largely to delivery. While content is a prerequisite, both how it is delivered and what is done in the socio-technical context around delivery affect how likely it is to be accepted. For example, delivery is often accompanied by education and sometimes also by policy changes. The expected benefit also depends heavily how the warnings are delivered – whether they follow human factors principles in terms of how they appear and whether they are integrated into the user workflow. This piece uses a paper on experiences derived from implementation of CDS over many years to illustrate these lessons.

Health systems are multidimensional, multifaceted, dynamic, and complex, and implementation of interventions within them occurs across multiple levels. Systems research takes a holistic approach to health service interventions, using a broad perspective to study the different levels at which systems operate and the contexts within which they function. On the micro level, behaviour change must be a focus when designing and planning interventions, as individuals or teams functionally carry out the implementation of any particular intervention. On the meso (institutional) level, approaches and activities need to account for the continual evolution of health systems and can be facilitated by the implementation of appropriate technological and organizational infrastructures. Macro-level health systems research is crucial to understand the bigger, strategic or policy perspective; improve the uptake of research evidence; and meet the challenge of implementation from the top of the hierarchy. Using a health system research approach to implementation science promotes evidence-informed policy and decision-making while utilizing evidence-informed policies and decisions to improve intervention design. This allows for the participation, ownership, and engagement of stakeholders and key partners across the different levels of the systems for better designed interventions and more successful implementation.

BackgroundAccumulating evidence suggests that interventions to de-implement low-value services are urgently needed. While medical societies and educational campaigns such as Choosing Wisely have developed several guidelines and recommendations pertaining to low-value care, little is known about interventions that exist to de-implement low-value care in oncology settings. We conducted this review to summarize the literature on interventions to de-implement low-value care in oncology settings.MethodsWe systematically reviewed the published literature in PubMed, Embase, CINAHL Plus, and Scopus from 1 January 1990 to 4 March 2021. We screened the retrieved abstracts for eligibility against inclusion criteria and conducted a full-text review of all eligible studies on de-implementation interventions in cancer care delivery. We used the framework analysis approach to summarize included studies’ key characteristics including design, type of cancer, outcome(s), objective(s), de-implementation interventions description, and determinants of the de-implementation interventions. To extract the data, pairs of authors placed text from included articles into the appropriate cells within our framework. We analyzed extracted data from each cell to describe the studies and findings of de-implementation interventions aiming to reduce low-value cancer care.ResultsOut of 2794 studies, 12 met our inclusion criteria. The studies covered several cancer types, including prostate cancer (n = 5), gastrointestinal cancer (n = 3), lung cancer (n = 2), breast cancer (n = 2), and hematologic cancers (n = 1). Most of the interventions (n = 10) were multifaceted. Auditing and providing feedback, having a clinical champion, educating clinicians through developing and disseminating new guidelines, and developing a decision support tool are the common components of the de-implementation interventions. Six of the de-implementation interventions were effective in reducing low-value care, five studies reported mixed results, and one study showed no difference across intervention arms. Eleven studies aimed to de-implement low-value care by changing providers’ behavior, and 1 de-implementation intervention focused on changing the patients’ behavior. Three studies had little risk of bias, five had moderate, and four had a high risk of bias.ConclusionsThis review demonstrated a paucity of evidence in many areas of the de-implementation of low-value care including lack of studies in active de-implementation (i.e., healthcare organizations initiating de-implementation interventions purposefully aimed at reducing low-value care).

BackgroundShared decision-making (SDM) is preferred by many patients in cancer care. However, despite scientific evidence and promotion by health policy makers, SDM implementation in routine health care lags behind. This study aimed to evaluate an empirically and theoretically grounded implementation program for SDM in cancer care.MethodsIn a stepped wedge design, three departments of a comprehensive cancer center sequentially received the implementation program in a randomized order. It included six components: training for health care professionals (HCPs), individual coaching for physicians, patient activation intervention, patient information material/decision aids, revision of quality management documents, and reflection on multidisciplinary team meetings (MDTMs). Outcome evaluation comprised four measurement waves. The primary endpoint was patient-reported SDM uptake using the 9-item Shared Decision Making Questionnaire. Several secondary implementation outcomes were assessed. A mixed-methods process evaluation was conducted to evaluate reach and fidelity. Data were analyzed using mixed linear models, qualitative content analysis, and descriptive statistics.ResultsA total of 2,128 patient questionnaires, 559 questionnaires from 408 HCPs, 132 audio recordings of clinical encounters, and 842 case discussions from 66 MDTMs were evaluated. There was no statistically significant improvement in the primary endpoint SDM uptake. Patients in the intervention condition were more likely to experience shared or patient-lead decision-making than in the control condition (d=0.24). HCPs in the intervention condition reported more knowledge about SDM than in the control condition (d = 0.50). In MDTMs the quality of psycho-social information was lower in the intervention than in the control condition (d = − 0.48). Further secondary outcomes did not differ statistically significantly between conditions. All components were implemented in all departments, but reach was limited (e.g., training of 44% of eligible HCPs) and several adaptations occurred (e.g., reduced dose of coaching).ConclusionsThe process evaluation provides possible explanations for the lack of statistically significant effects in the primary and most of the secondary outcomes. Low reach and adaptations, particularly in dose, may explain the results. Other or more intensive approaches are needed for successful department-wide implementation of SDM in routine cancer care. Further research is needed to understand factors influencing implementation of SDM in cancer care.Trial registrationclinicaltrials.gov, NCT03393351, registered 8 January 2018.