Understand how we got here — and how to move forward.

Blog Post

Why health care leaders must distinguish between technology enablers and goals

By Katie SchmalkucheJohn League

April 1, 2022

    "We need to invest in AI."

    "We need better data."

    At Advisory Board we frequently hear sentiments like these in our conversations with health care executives—especially in the last few years, as leaders across the ecosystem feel an urgency to strengthen their digital capabilities and close the digital transformation gap.

    But in many of our conversations, we find that executives often conflate the goals of digital transformation with the underlying technology enablers or tools that help achieve those goals.


    Our take is that such a distinction is necessary for guiding digitally-enabled strategies in health care. Making this distinction not only clarifies the purpose of a given technology investment, but also helps anticipate the downstream, ripple-effect impacts of that investment. By failing to clearly distinguish goals and enablers—in our language, in our conversations, and in our strategic plans—leaders can stymie progress on digital transformation in two big ways:

    1. They risk investing in the wrong things, for the wrong reasons.

    This risk plays out differently for vendors (e.g., digital health companies, tech solution providers) vs purchasers and users (e.g., health systems, plans). Many vendors fall into the "hammer looking for a nail" trap. That is, they invest time and money advancing their products' underlying technology, but fail to critically assess whether and how it solves a customer problem; they focus on bells and whistles without understanding which ones make a meaningful difference to the end user.

    For example, many clinical decision support (CDS) companies innovate new ways to aggregate, analyze, curate, and present information to clinicians at the point of care, but don't assess whether and when that information is valuable. The result, for many clinicians, is alert fatigue. With so many flags and alerts in the EHR attempting to grab their attention and influence a decision, the opposite happens: clinicians tune the alerts out altogether.

    The purchasers and users of technology, on the other hand, fall into a different trap: FOMO, or "fear of missing out." They feel an urgency to invest in technology enablers that are "hyped" and often do so without a clear sense of the problem they need to solve. Their rationale for investing in the first place is largely based on observing what's been successful at other organizations without accounting for how differences in patient population, payer mix, geography, etc. can impact the viability of the investment.

    AI is one of the most prominent examples of this. A low-hanging fruit application of AI in health care is revenue cycle management (RCM), particularly the use of robotic process automation (RPA) to automate elements of the front, middle, and back end of RCM. But provider organizations that make these investments without careful examination of their own unique needs consistently end up automating bad processes or creating new inefficiencies, rather than actually solving the problem.  

    The overall result is a lot of activity and investment (on both the vendor and user sides) in technologies that don't solve a problem. This means leaders not only divert resources away from more impactful investments, but also risk frustrating their end-users and discouraging them from adopting similar technologies in the future.

    Because the onslaught of CDS tools has created alert fatigue, any new tools that involve EHR alerts, even if they would be valuable to a clinician, are more likely to be met with resistance. Similarly, the implementation of an AI tool that doesn't solve an end user's problem provides fuel for the skepticism and mistrust surrounding the use of AI in health care writ large.

    2. They fail to anticipate how the need for (and the use of) technology will evolve in the long term.

    Understanding health care technology through the lens of enablers vs goals also helps anticipate the downstream, ripple-effect impacts of that investment. This is because technology goals and enablers are interdependent. That is, as the goals of health care stakeholders shift, the market demands that guide technology innovation shift in tandem.

    And as enabling technologies become more sophisticated, the goals for technology inevitably change—either because technology solves one problem and allows other problems to be prioritized, or because one solved problem creates net new challenges.

    Take the implementation of EHRs as an example. A key health care goal in the 1990s and early 2000s was to digitize the medical record to improve communication across providers; the enabling technology was the EHR. As EHR adoption in the United States grew, one goal was solved (digitizing the medical record) and an entirely new set of opportunities and challenges emerged: a wealth of data that could be leveraged for medical research and performance improvement was being created, but the data is unstructured, not standardized, and organizations became incentivized to hoard rather than share data.

    The more sophisticated the EHR technology becomes, the bigger the opportunity for data-driven insight—and the associated challenges getting it—become. Fast forward to present day, when one of the most prominent ripple effects of EHR adoption is that an entirely new industry of data aggregators has sprung up to start to solve these challenges.

    In the future, a similar co-evolution of technology enablers and goals could play out with technologies like AI. Once an AI becomes proficient enough at automating a given task, it could eliminate the need for that task entirely. For example, if automation for revenue cycle management (RCM) becomes sophisticated enough to drastically reduce or even eliminate claims denials, it could eliminate the need to focus on denials management as a priority in general.

    Similar scenarios could play out with AI that's applied toward clinical use cases. If AI tools are eventually able to automate (at least some) clinical decisions, what current processes could  be eliminated? How would that shift the goals around evidence-based medicine? What new goals could be created?

    By recognizing not just the distinction, but also the interdependencies of enablers and goals of technology in health care, leaders across the ecosystem are better equipped to make smart investment decisions and anticipate future needs on the journey toward a more digitally-enabled health care ecosystem. 

    Have a Question?


    Ask our experts a question on any topic in health care by visiting our member portal, AskAdvisory.