How Do Data Loopholes Slow Down the Treatment of Breast Cancer?
Updated: Feb 19, 2022
Considering it is Breast Cancer Awareness Month, the timing of this post is hopefully helping a very important cause. For reasons I won’t go into here, I’ve recently become more familiar with breast cancer than I would have otherwise. When confronted with a new topic of interest, it’s my nature to dig in and learn everything I can about it.
The National Cancer Institute provides a wealth of information on breast cancer but being a “software guy” … the way a mammogram results combined with a clinical breast exam can detect early signs of cancer stood out to me as an important information issue.
I began to wonder where that information was captured and stored (after the test and examination) … and how it was ultimately used in follow-up care with the patient. I didn’t expect to learn what I did.
The American College of Radiology (ACR) has established a uniform way for radiologists to describe mammogram findings. The system is called BI-RADS and includes standardized structured codes or values. Each BI-RADS code has a follow-up plan associated with it to help radiologists and other physicians manage a patient’s care. These values are often used to trigger notifications of the findings or other follow-up steps. This makes perfect sense to me except there is a problem.
Big Data Loophole
The BI-RAD findings (or values) are typically found on a text-based report … or determined by the examining physician. They are then captured or manually transcribed in the EMR as free text notes that are added to the medical record as text … unstructured data living in a structured data environment. This is the loophole! It’s technically there but not able to be used.
Sometimes this step can be missed completely and the results are not put into the EMR system at all (human error) … or, more likely, the BI-RAD value is not transcribed in the right place as a structured data field. There are just two of the reasons this loophole can be caused.
Electronic Medical Records
You may not be aware, but an Electronic Medical Records (EMR) system is generally optimized for structured data. Most EMRs don’t leverage text-based unstructured data (test results, physician notes, observations, findings, etc.) in ways that they could. It’s a known weakness of many of today’s EMR systems.
To net this out … it’s entirely possible that cancer is detected using the BI-RADS value but the information does not find its way into the right place in the EMR system because it’s text-based and the EMR cannot recognize it. This EMR system limitation has no way of determining what the text-based information is, or how to use it.
The impact of this is staggering. Let’s think about this in terms of timely follow-up on cancer detection. A system that is not able to use the BI-RAD value could mean patients are not being followed up on properly (or at all) – even though they are diagnosed with breast cancer. Yes, this can actually happen if the value is buried in the text and not being used by the EMR. The unstructured data loophole is a big deal!
Don’t take my word for it. The University of North Carolina Health Care (UNCH) has announced new findings from mining clinical data to improve the accuracy of its 2012 Physician Quality Reporting System (PQRS) measures, achieving double-digit quality improvements in the areas of mammogram, colon cancer and pneumonia screenings. They are taking steps to close data loopholes.
The new findings indicate mammogram values are present in structured data 52% of the time … and present in unstructured data 48% of the time. Almost half the time the unstructured data is not presented with the rest of the structured data. Ouch, that’s a big data loophole.
The new findings also indicate CRC screening (colon cancer) values are present in structured data just 17% of the time … and present in unstructured data 83% of the time. As a man of a certain age, this scares me in words that can’t be published. Another big data loophole.
Thankfully leading organizations like UNCH are closing these data loopholes today with solutions that understand unstructured data and can “structure it” for use in EMR systems … pasted from an IBM press release...
Timely Follow-up of Abnormal Cancer Screening Results: Follow-up care for patients with abnormal tests is often delayed because the results are buried in electronic medical records.
Using IBM Content Analytics, UNCHC can extract abnormal results from cancer screening reports such as mammograms and colonoscopies and store the results as structured data. The structured results are used to generate alerts immediately for physicians to proactively follow-up with patients that have abnormal cancer screening results.
This is an example of what IBM calls Smarter Care … where advanced analytics and cognitive computing can enable a more holistic approach to individuals’ care and can lead to an evolution in care delivery, with the potential for more effective outcomes and lower costs. If an ounce of prevention is worth a pound of cure, an ounce of perspective extracted from a ton of data is priceless in potential savings. IBM Content Analytics is part of the IBM Patient Care and Insights solution suite.
I’ve written several previous blogs on related topics that you might find interesting:
Healthcare Data is the New Oil: Delivering Smarter Care with Advanced Analytics
Moving Beyond One-Size-Fits-All Medicine to Data-Driven Insights with Similarity Analytics
As always, leave me your feedback, questions and suggestions.
#NLP #textanalytics #ECM #SmarterCare #contentanalytics #IBMWatson #cancer #analytics #Watson #IBMECM