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“The volume and complexity of what we know has exceeded our individual ability to deliver its benefits correctly, safely, or reliably. Knowledge has both saved us and burdened us.”—Atul Guwande.
I love that quote.
There’s possibly no clearer expression of the challenge that faces clinicians in medicine today. It’s ironic--we’re living in a time when we have more information available at our fingertips, more advanced medicine to deliver to our patients, and more automation in care delivery than we’ve ever had before….and yet, there is more dissatisfaction today with the electronic medical records and clinical decision support tools we use to deliver that care than ever before. Obviously, it wasn’t meant to be this way—the HITECH Act and implementation of these tools were intended to improve care delivery. But every day that I work clinically, it fails to feel that way. I’m usually only a few hours into my workday when I start to ask why these tools don’t do what HealthIT Analytics rightly notes we need them to do: “reduce variation and duplicative testing, [ensure] patient safety, [and avoid] complications that may result in expensive hospital readmissions.” I’ll make the argument that there’s now a fourth critical function for these systems: “Improving clinical regulatory compliance.”
In today’s clinical practice, particularly around issues of resource utilization and drug prescribing, a good clinician absolutely has to have an eye towards the regulations that govern our practices. Long gone are the days of complete independence where we practiced based on what we “thought” was best. Appropriately so, the increasing costs of healthcare mean that we must increasingly rely on these electronic systems to improve clinical inefficiencies. The question, then, is: “How do we design and implement clinical decision support tools that improve care delivery while also improving clinician satisfaction?” I believe the opioid epidemic offers an interesting case study in solution implementation.
We’ve previously written about the increasing regulations around opioid prescribing. As a digital health company, the question we try to answer is “how can we best implement these regulations at the clinician level to make them more user friendly?” We know that “alert fatigue and clinician burnout are common byproducts of poorly implemented clinical decision support features that overwhelm users with unimportant information or frustrating workflow freezes that require extra clicks.2” And similarly, we know that clinicians loathe the extra time they are being forced to spend with their EHRs instead of their patients. So how do we do more with fewer clicks?
CMS argues, and I would agree, that the first step in utilizing a decision support tool is to make sure the organization is ready to accept and implement the solution. Without a vested interest in the outcome, it’s unlikely that people will willingly accept changes to any existing processes. In the case of the opioid epidemic, this “readiness” in being increasingly pushed down onto the clinicians by federal and state authorities who are continuing to issue new mandates regularly.
Next, the organization has to be ready to allocate resources to implement the tool. Simply creating links to existing resources or webpages is unlikely to create the sort of systemic changes that a crisis like the opioid epidemic warrants. Instead, health systems and practices need to plan to make changes, devote resources to mapping clinical workflows and developing solutions that address existing problems (instead of creating problems for existing solutions), and finally, they need to measure pre- and post- solution implementation metrics for these problems. Clinicians are data driven professionals who will be more likely to engage in solution development when we can see demonstrable improvements in a critical process.
Building the Right Products with the Right Teams
As clinicians, the most hated solution is the one that doesn’t make patient care better. Often times, these are well intentioned, but poorly designed products created by people who don’t know what it’s like to actually deliver care at the bedside. It is critically important that new decision support solutions include health IT specialists, risk managers, physicians, nurses, and other users who are all part of the care delivery continuum. This can take many forms, but should include an internal development committee, an external user board to validate the solution, and clinical champions who can help navigate solution implementation with other clinicians. These clinical champions are “tech-savvy members of the organization who are trusted by their peers, understand the technical and workflow challenges of their colleagues, can provide balanced and informed perspective, and have strong interpersonal skills that can help them handle conflict, skepticism, or frustration.” I’ve lived through two health systems’ implementation of new EHRs and know that this last piece is often neglected.
Agreeing on a Common End State
Even within a crisis as big as the opioid epidemic, it’s surprising how much dissonance there is in the desired end state. Depending on who you ask, the solution can be anything from complete restriction of these medications to little, if any, oversight by third parties. A key to effective clinical support implementation in a crisis like this is ensuring that the relevant stakeholders are all in agreement about what the end state should be and then educating the users who will be charged with achieving those goals. Until the leadership can agree on the organization’s goals, whether they be internally drafted or driven by external regulation, it’s impossible to design support tools that will be satisfactory to all. Once there is agreement on a preferred end state, common benchmarks can be created that everyone can assess equally.
The opioid crisis is a unique challenge that creates an opportunity for us to build meaningful clinical decision support tools that can drive changes in inappropriate utilization of these medications. But from the clinician’s perspective, we can’t simply build out a “solution” utilizing the same approach we’ve used to create our existing electronic health records. If we do that, we will fail in our attempt to leverage the information gained from big data and population health. Instead, we should be including representation from across the clinical spectrum in step-wise solution development that identifies existing workflow problems that stand in the way of good clinical care. We should be developing internal consensus within our organizations about what we are working towards. And finally, we should continue to revise these solutions so that the process is one of constant improvement to make sure we can pivot as the epidemic evolves.
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