Using Implementation Science to Enact Specific Ethical Norms: The Case of Code Status Policy


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Tag(s): Legacy post
Topic(s): Editorial-AJOB End of Life Care Ethics

This editorial presented by the American Journal of Bioethics. You can see the April 2020 issue by clicking here.

by Emily Shearer & David Magnus

In their article, “The ‘Ought-Is Problem:’ An Implementation Science Framework for Translating Ethical Norms into Practice,” Sisk et al. correctly draw a distinction between aspirational norms (“broad claims that are easily agreed upon”–e.g. “Everyone should have their goals of care met at the end of life”)–and specific norms (claims that provide “direct guidance” about specific actions that should ensue–e.g. “Doctors should complete Advance Health Care Directives with every patient to ascertain their goals of care for the end of life”). They further argue that, because there is a moral imperative to enact normative claims once they are developed, ethicists working in the healthcare sphere should make use of tools and frameworks from the field of implementation science to enact, sustain, and disseminate specific norms. Indeed, if specific norms are unattainable in practice, they argue, they will fail to achieve any real behavior change and thus “[fail] the overarching purpose of ethics.” Conversely, specific ethical norms that are successfully implemented can be studied to develop best practices, which can then be disseminated to improve ethical practice as a whole.

Healthcare ethics committees have been at least one avenue of specific policymaking within the province of ethics. These committees are often charged with authoring policies for hospitals and healthcare systems. Interestingly, with a few exceptions, most of this ethics policy work is done with little to no empirical grounding and in particular, with little attention to the insights from implementation science. At our institution, we ethicists are often engaged in going from “ought” to “is” as we draft our policies. But most of that work takes place without any empirical rigor. For example, less than a decade ago, based on our collective experiences and some empirical literature about code status orders, we decided to alter one of our policies regarding code status. This alteration was driven by an aspirational norm: namely, that all patients should be able to specify their preferences for treatment limitation. In US hospitals, limitations on life-sustaining treatments (LSTs) occur when patients and their providers agree such care would be ineffective, unnecessarily arduous, or inconsistent with their overall goals. Examples of treatment limitation preferences include “DNR” (Do Not Resuscitate) and “CMO” (Comfort Measures Only) and are operationalized via ordering mechanisms in an institution’s electronic health record (EHR). Our largely normative and experiential insights resulted in both a change in hospital policy and the corresponding EHR ordering system. But the complete lack of any of implementation science left us unsure if it was working as intended.

We began to fill this gap with qualitative work. Despite our intention to improve goal-concordant care for patients, particularly near the end of life, our qualitative work revealed discordance among providers at our institution in both ordering code status options and in interpreting these code status orders–and, as a result, some patients with specific preferences for treatment limitations are likely not having their care goals met. In other words, we now have evidence that ignoring a rigorous implementation science approach led us to policies and ordering systems that do not appear to achieve the goals of the policy. We thus began a project to understand the sources of discordance in code status ordering and interpretation, and to develop a new, improved code status system that would improve this concordance, and thus goal-concordant care. Though the final version of our new system will likely need to undergo several rounds of iteration before it is put into place, at each step of this process, we have utilized implementation science thinking to inform our approach and next steps, some of which we will summarize here.

Inner Setting
Before we began this project, we recognized it was key to have institutional support for such a large undertaking. As such, we presented our initial qualitative results to the executive leadership at our hospital, who agreed that our results represented a threat to goal-concordant care and perhaps even patient safety. With the executive committee’s help, we were able to escalate the issue and gain support among a wide range of stakeholders, including the informatics team responsible for creating changes to our institution’s EHR, who have been instrumental in helping us investigate and devise alternatives to our current code status ordering system. Further, during our qualitative work, we interviewed nurses and physicians at various levels of training across five departments with the most exposure to the code status system. Together, these individuals represented a diverse group of providers, each of whom had unique and key perspectives in understanding the needs of the code status system and the current sources of discordance at our institution.

Intervention Characteristics
As Sisk et al. correctly point out, in order to be sustainable, implementation science tells us that interventions should be simple, easily adoptable, and have little costs for participating individuals. In our project, we are working with our EHR informatics team to develop a new code status system that can replace our existing system as an order set within our EHR. Though there will of course be some training required once it is implemented, the ability to implement this intervention across our whole hospital in ways that do not change the current workflow or add additional work for providers are key advantages that will help ensure take-up and sustainability over the long term. Based on interview data from our qualitative work, we are also including an option to specify “Did Not Discuss” for all treatment limitation preferences in our new system. By including this “Did Not Discuss” checkbox, we believe we will further enhance the acceptability and sustainability of our new system by (1) encouraging a culture of honesty among providers engaged in goals of care discussions by allowing them to specify when a conversation has not yet been had, and (2) allowing providers the opportunity to leave certain topics for future conversation if it is not the appropriate time or place to discuss them with the patient. By contrast, a system that adds undue burdens on providers by forcing them to have unnecessary conversations with each patient at each encounter would likely not garner support or be sustainable over the long-term.

Finally, we know that any institutional change, particularly changes that affect each individual at an institution, will face resistance if effective strategies for engagement are not taken. In designing our new code status system, we purposefully paired up with our institution’s Resident Safety Council, a multidisciplinary group that matches providers with intervention or policy change projects across the hospital with key stakeholders. Each team on the Council also works with designated nurse and physician safety champions, who work to promote uptake in the departments affected by the work. Further, we chose to pilot the new system using surveys developed by our team. We plan to employ cognitive testing methods to assess users’ experience with the new system against pre-selected patient scenarios in these surveys, and to undergo several iterations based on this testing before the final roll-out of any new system developed. This is in stark contrast to the implementation of our previous policy, which was rolled out without any a priori testing on the individuals who would actually use the system.

Taken together, these experiences have given us first-hand insight into the role implementation science principles can play when designing interventions to enact specific ethical norms and the very real risks when ethicists ignore them. We believe this work will result in important and sustainable changes at our institution in improving goal-concordant care for our patients and that an implementation science approach, in which ethicists collaborate in multidisciplinary teams to enact specific, achievable goals, are essential in this project’s progress. In light of this experience, we agree with Sisk et al. that the field of healthcare ethics can be moved forward by more widespread adoption of this approach and encourage other applied ethicists to follow their call in doing so.

Sisk et al. raise the specter of normative ethics work that may not lead to concrete improvements. Ethics Committees are a logical locus for at least some implementation and where a lot of the “ought” to “is” translation is actually taking place. But failure to rigorously apply the lessons of implementation science is likely to lead to problematic policies that may harm patients. The good news is that if these committees adopt the lessons from Sisk et al. an important mechanism for policy implementation becomes available. Our experiences highlight that there has to be a robust empirical component utilizing implementation science to provide good evidence-based policy.

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