Why Analyze Electronic Health Record (EHR) Deployments?

The Unfulfilled Promise

The expectations around EHRs are lofty indeed:

“The broad use of EHRs has the potential to improve health care quality, prevent medical errors and increase the efficiency of care provision and reduce unnecessary health care costs, increase administrative efficiencies, decrease paperwork, expand access to affordable care, and improve population health.”
- Office of the National Coordinator for Health IT (ONC), Department of Health and Human Services (HHS)
 

But there are key indications that EHRs are not coming close to fulfilling these promises:
  • Only 4% of physicians in the U.S. currently use full-featured EHRs.1
  • HHS estimates that 30-50% of EHR implementations ultimately fail.2
  • MGMA predicts that more than 50% of physician practices will not achieve CMS-mandated EHR Meaningful Use, resulting in substantial missed incentives and imposed penalties.3


    • EHR deployments are highly-complex and not easy to get right.  A myriad of interrelated factors spanning systems, workflow redesign, and user behavior determine whether an EHR deployment is successful.   For instance, even if an EHR system and workflows are properly designed, inadequate user training or lack of user compliance to best practices can doom the entire deployment.  In practice, EHRs too often are hard-to-use, require too many clicks, and are not designed with the most efficient clinician workflows in mind. It is no wonder that deployment failures and low user satisfaction are so prevalent.

    Negative financial impacts of sub-par EHR deployments are also substantial.  On average, more than $40,000 per provider per year is lost in productivity for every minute unnecessarily wasted by EHR inefficiencies during each patient visit.   More than $10,000 per provider per year is at risk of being lost if Meaningful Use is not achieved.  Just as EHRs, when properly implemented, can bring great gains to practices, they can also result in big losses.

    Fixing an EHR Deployment Hinges On Proper Analysis

    Swapping out an EHR can be very expensive and time-consuming and, therefore, never a first option.  Providers spend countless dollars and staff hours trying to fix their existing EHR deployments.  The chances for success are greatly dependent upon having enough of the right kind of data about the EHR's performance and usage patterns to identify the issues most worth solving.  For example, if users complain about the EHR being slow, is the root cause network performance, EHR software performance, a hardware issue, or a combination of these?  If users say that an EHR workflow is cumbersome, are they using the system in the way it was designed to be used? If not, how many more users are not using the system properly or not taking advantage of built-in shortcuts?

    Gathering sufficient qualitative and quantitative data about all the issues is a critical step to get right at the outset.  Since providers have limited time and resources to upgrade  systems, change workflows, or alter user behaviors, accurate prioritization of issues based on good data is absolutely critical.  Significant time and resources should only be spent solving those issues that have the greatest potential returns-on-investment (the upper-left corner in the EHR Issues Landscape shown below).   In addition, it is essential to have identified root causes, not just symptoms, so that issues can be completely solved.  The ability to find root causes also crucially depends upon the volume and types of data gathered about the issues in question.



    But identifying and solving an issue once is not enough.  On-going analyses in the forms of periodic monitoring and reporting are needed to detect whether issues re-emerge. For instance, if the remedy to an issue is better user training, then user behavior after the new training is given should be monitored to check whether users revert to old habits.  An EHR deployment is dynamic and complex, warranting continuous analysis and monitoring to ensure its overall health.

    EHR vendors have the same need for good analysis.  Software development cycles to change user interfaces or add new features can be long and costly.  Similar to providers, vendors need to gain a comprehensive understanding of all the issues and customer requests they can potentially address.  Making prioritization decisions is notoriously hard in the absence of enough of the right kind of data about how their systems are actually performing and being used in clinical practice.

    The Problem: "The Analysis Gap"

    Despite the critical importance of proper analysis, current methods for analyzing EHR deployments are woefully inadequate, causing the entire process of fixing EHRs to be prone to failure.   Traditional methods such as user focus groups, interviews, and surveys are expensive and limited in the amount and breadth of data that can be captured.  Current means of collecting workflow performance data, such as equipping staff with stopwatches or shadowing clinicians, are obtrusive, inefficient, and unreliable .  What further complicates matters is that research has shown that what users say about their EHR usage in interviews and surveys is often different from how they actually use EHRs in clinical practice. 4

    Current analysis tools also fall short.  EHR systems' built-in analysis modules have limited data-capture capabilities and generate reports that often only system administrators can understand.  Tracking user behavior in a clinical environment at a detailed level is almost non-existent.  The situation is similar to the early days of e-commerce during which internet shopping sites rapidly increased in complexity without the right tools to capture large-scale usage patterns to identify where and why users were having difficulties.

    These factors explain why identifying EHR issues and isolating root causes are currently so expensive, time-consuming, and unreliable.  In short, there is an Analysis Gap.

    A symptom of the Analysis Gap is commonly seen in how prioritization decisions are made in trying to fix an EHR deployment.  Due to the lack of data about identified issues, often the "Tyranny of the Squeaky Wheel" takes hold.  Those providers who are the most vocal or hold the greatest sway in the practice get their particular complaints addressed, overshadowing other issues.  In fact, these other issues may be more pervasive and cause bigger financial losses for the practice. There is often not enough of the right type of objective data about the issues to override this pattern.  Furthermore, the lack of data results in issues that remain hidden and, hence, never even get the chance to be discussed.  What results from these hidden and mis-prioritized  issues is that, in trying to fix its EHR deployment, a practice makes misguided investment decisions based upon an incorrect view of the EHR Issues Landscape (see figure below). Investments are made on issues that seem more important than others, but really aren't. And, some highly-impactful issues are never even discovered.



    The Solution

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    Learn More

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    1. Office of Health IT Adoption, ONC, 2008.
    2. Charette, R. What Happened To Do No Harm? CIO Magazine, April 1, 2006.
    3. Ready or Not? Probably not, says MGMA. Healthcare IT News, October 13, 2009.
    4. Zheng, et al. JAMIA 16(2):228-237, March/April 2009.
     
     
     
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