HealthyData Blog
In healthcare, there are so many regulations and technologies to stay current on. In our HealthyData blog, you will read everything from CMS meaningful use to EMR news, clinical best practices and QAPI program initiatives.
There’s no question that users rely on the EMR In Basket for day-to-day workflow management. The “In Basket” or “Inbox,” depending on what EMR you’re using, provides a centralized location to receive notifications and important patient information, such as admission and discharge notifications, new lab results, refill requests, patient calls, appointment reminders, patient portal communication and much more.
Do you frequently find yourself searching for and routing documents, whether paper or electronic, to colleagues, care team members or departments that need them? Or, worse do you find yourself waiting for documents to be routed to you? In our work, helping hospitals to automate clinical data abstraction, we're struck by the hours of time lost each day to inefficient workflows involving "loose" records that we often find ourselves helping our customers extract data from.
If you've ever managed an EMR data conversion, you likely know how painful data conversions can be. They require someone with intimate knowledge of the old EMR to write complex queries to extract the data in the format that the new EMR requires it to be in. In addition, at some point in the process you have to transform the old values into the new system's values (assuming they can be mapped at all!). Even if you have experienced, intelligent people and excellent vendor support during this process it is expensive, time-consuming, risky, and can delay your go-live. So, what if you don't have experienced people and good vendor support for your healthcare data conversion? Believe me, it's gonna get ugly.
Critical results reporting or reporting lab results for priority patients from non-interfaced sources is no easy task. A delay in reporting can yield an unfortunate outcome for a patient whose condition is deteriorating. This is especially true for specialty departments that provide continued care for patients from far-flung locales, such as the transplant program. One transplant department receives thousands of these reports over a single patient's lifetime, and often hundreds of these documents for its patient population each day by fax.
A consultant who supports analytics for population health and quality of care recently told me that frequently, they can only access 80% or less of the total data needed for these initiatives.
If that data is truly random and characteristic of the whole body of data, than acquiring 80% of it is pretty good, perhaps even great. But what if that 80% comes largely from one population sub-group. What if it represents patients who are local - city-dwellers who live nearby and come directly to your facility for lab work and other tests - while the missing 20% is a completely different population. Perhaps this 20% is defined differently by lifestyle, geography or other variables because that population cannot easily come to your facility?
An ounce of prevention is worth a pound of cure. Or, reduce medical errors through better documentation. Which one of these expressions do we tend to remember? In healthcare we hear quite a bit of talk these days on reducing medical errors. Of course this is with good reason. When getting data into the EMR, errors such as inaccurate or delayed results, can negatively impact patient health and lead to extended hospital stays, unnecessary treatment or worse. As a matter of fact, many healthcare organizations are now striving to eliminate mistakes and streamline efficiency by adopting principles such as Six Sigma and other business practices which are designed to continuously evaluate and improve best practices.
The Centers for Medicare & Medicaid Services regulates laboratory testing performed on humans in the U.S. through the Clinical Laboratory Improvement Amendments (CLIA) to ensure quality laboratory testing. CLIA sets high standards for quality control, validation of data and tests, equipment calibration, proper training and certification of users and clear end result reporting that meets proper lab data requirements.
Health data challenges are a part of any healthcare organization. Frustration is a natural consequence. This is also a common topic in most healthcare press lately. It often feels like depending on which way the wind is blowing, the consensus is that EMR adoption by clinicians is either improving or getting worse. A recent KLAS report indicates adoption is improving across the globe. The decision to move to an enterprise EMR for most organizations often includes as a factor the goal of reducing the total number of applications supported. Initiatives to improve adoption then becomes a challenge if your department's prized application is removed for something "less than robust". Along with pain and frustration, keeping your clinicians happy is also a challenge
Tracking data in your transplant care software is a key component of QAPI programs, not only for CMS, but now also with UNOS debating the requirement of QAPI programs. One of the most challenging aspects of transplant program management is ensuring that your Quality Assessment and Process Improvement programs are measuring meaningful and actionable items that lead to program improvement.
In our last post in this series, we talked about the challenges of getting ready for the selection committee meeting. As a transplant coordinator, the selection committee meeting is your opportunity to advocate for your patient that you have been nurturing from the initial evaluation until this upcoming meeting. Now that your patient has completed the pre-transplant evaluation which I covered in the first post in this series, the decision of whether or not to add them to the waiting list must be made.
