Three Reasons You Can’t Get the Data You Need from Your EMR
If your specialty practice is looking for valuable patient-focused insight from your existing EMR system, it’s likely that you’ll have a hard time getting what you – and your patient – need. It’s not necessarily that the data isn’t there— most EMR systems are strong at collecting data. The challenge is that there are not a lot of good options for aggregating and analyzing EMR data in immediately useful ways for you, your practice and your patients.
There are three primary obstacles most practices face in seeking actionable EMR insight.
Reason #1 -
Reporting tools are difficult to use, require specialized skills and can’t handle unstructured data
>Any non-specialty EMR with a broad focus is going to have a difficult time providing effective reporting tools for a specialty practice area. Plus, since most practices feature large amounts of data in unstructured narrative text – forms, pdfs, long form text – any off-the-shelf analytical application, or even a report writer will find gleaning meaningful analytics a very challenging problem to solve.
Disease-specific knowledge to be able to successfully mine the narrowly focused data for relevant insight you can use
Dedicated and skilled analytics staff to work with data, and quickly deliver the type of patient care information you need
The framework and infrastructure to be able to handle such requests
EMR vendors may not have specialty analytics skills and technology
If you seek patient-focused data from your EMR vendor, they may not have the resources to meet your needs. That’s because the vast majority of EMR vendors are currently experiencing extreme pressure to meet deadlines mandated by the federal government. Funding constraints, inconsistent ROI and other market forces have inhibited the growth of the EMR marketplace, making more advanced service offerings, such as analytics, a lower priority for vendors.
In addition, executing EMR analytics successfully requires:
Reporting tools can’t easily gain insight from clinically-rich unstructured data in EMR systems because of the highly unique nature of this data, which is very hard to capture using templates, and difficult to store in a structured format.
- If vital patient information – problems, encounter notes, lab results and more – is collected across the continuum of care within an EMR system as unstructured data, it becomes trapped within separate electronic silos.
This disparate data makes analytics reporting difficult, and poses the potential for missed therapy opportunities for the patient, possibly impacting the outcome of care.
Manual processes – whether in house or outsourced – aren’t realistic.
Some practices resort to manual processes to mine EMR data, but this approach is very time consuming and prone to human error. Without a fairly sizable staff with specialty knowledge in analytics, such an approach is a formula for missed opportunities and cost overruns.
We’ve all heard of big data, but it’s not necessarily the volume of data that makes patient-focused analytics difficult to pull off with manual processes. It’s more the nature of EMR data:
- In aggregate, EMR data is a relatively small amount of data, but what you need is focused on smaller elements about a specific patient.
This focus around one patient can include a significant amount of work to search the data and process it.
Once identified, gaining meaningful insight from this valuable, patient-focused data is very time-consuming.
PPS Analytics makes patient-focused analytics easy and fast
If your practice had all the time in the world and unlimited staff, the path to patient insights from your EMR system begins with segmenting your patients into appropriate “buckets,” or categories. Then your army of healthcare specialty statisticians would apply methodical techniques and specialized skills to gain the valuable knowledge you need to impact patient care.
But you don’t have a data mining army and endless amounts of time. The good news is that The PPS Analytics Platform features powerful technologies that jumpstart the process with highly sophisticated patient categorization and identification algorithms. When tailored to your practice’s specific care protocols, these algorithms lead to meaningful data you can use to better care for your patients.