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Searching EMR For Risk-Related Words Can Improve Care Coordination

Posted on September 18, 2017 I Written By

Anne Zieger is a healthcare journalist who has written about the industry for 30 years. Her work has appeared in all of the leading healthcare industry publications, and she's served as editor in chief of several healthcare B2B sites.

Though healthcare organizations are working on the problem, they’re still not as good at care coordination as they should be. It’s already an issue and will only get worse under value-based care schemes, in which the ability to coordinate care effectively could be a critical issue for providers.

Admittedly, there’s no easy way to solve care coordination problems, but new research suggests that basic health IT tools might be able to help. The researchers found that digging out important words from EMRs can help providers target patients needing extra care management and coordination.

The article, which appears in JMIR Medical Informatics, notes that most care coordination programs have a blind spot when it comes to identifying cases demanding extra coordination. “Care coordination programs have traditionally focused on medically complex patients, identifying patients that qualify by analyzing formatted clinical data and claims data,” the authors wrote. “However, not all clinically relevant data reside in claims and formatted data.”

For example, they say, relying on formatted records may cause providers to miss psychosocial risk factors such as social determinants of health, mental health disorder, and substance abuse disorders. “[This data is] less amenable to rapid and systematic data analyses, as these data are often not collected or stored as formatted data,” the authors note.

To address this issue, the researchers set out to identify psychosocial risk factors buried within a patient’s EHR using word recognition software. They used a tool known as the Queriable Patient Inference Dossier (QPID) to scan EHRs for terms describing high-risk conditions in patients already in care coordination programs.

After going through the review process, the researchers found 22 EHR-available search terms related to psychosocial high-risk status. When they were able to find nine or more of these terms in the patient’s EHR, it predicted that a patient would meet criteria for participation in a care coordination program. Presumably, this approach allowed care managers and clinicians to find patients who hadn’t been identified by existing care coordination outreach efforts.

I think this article is valuable, as it outlines a way to improve care coordination programs without leaping over tall buildings. Obviously, we’re going to see a lot more emphasis on harvesting information from structured data, tools like artificial intelligence, and natural language processing. That makes sense. After all, these technologies allow healthcare organizations to enjoy both the clear organization of structured data and analytical options available when examining pure data sets. You can have your cake and eat it too.

Obviously, we’re going to see a lot more emphasis on harvesting information from structured data, tools like artificial intelligence and natural language processing. That makes sense. After all, these technologies allow healthcare organizations to enjoy both the clear organization of structured data and analytical options available when examining pure data sets. You can have your cake and eat it too.

Still, it’s good to know that you can get meaningful information from EHRs using a comparatively simple tool. In this case, parsing patient medical records for a couple dozen keywords helped the authors find patients that might have otherwise been missed. This can only be good news.

Yes, there’s no doubt we’ll keep on pushing the limits of predictive analytics, healthcare AI, machine learning and other techniques for taming wild databases. In the meantime, it’s good to know that we can make incremental progress in improving care using simpler tools.

We Don’t Use the Context We Have in Healthcare

Posted on June 1, 2017 I Written By

John Lynn is the Founder of the HealthcareScene.com blog network which currently consists of 10 blogs containing over 8000 articles with John having written over 4000 of the articles himself. These EMR and Healthcare IT related articles have been viewed over 16 million times. John also manages Healthcare IT Central and Healthcare IT Today, the leading career Health IT job board and blog. John is co-founder of InfluentialNetworks.com and Physia.com. John is highly involved in social media, and in addition to his blogs can also be found on Twitter: @techguy and @ehrandhit and LinkedIn.

I was recently looking at all the ways consumer technology has been using the context of our lives to make things better. Some obvious examples are things like Netflix which knows what shows we watch and recommends other shows that we might enjoy. Amazon knows what we’ve bought before and what we’re searching for and can use those contexts to recommend other things that we might want to consider. I know I’ve used that feature a lot to evaluate which item was the best for me to purchase on Amazon.

Everywhere we turn in our consumer lives, our context is being used to provide a better experience. Sometimes this shows up in creepy ways like the time a certain cleaning product was mentioned in my kitchen and then I saw an ad for it on a website I was visiting. Was it just coincidence or did Alexa hear me talking about it and then make the recommendation to buy based on that data? Yes, some of this stuff can bit a little creepy and even concerning. However, I personally love the era of personalization which generally makes our lives better.

While this is happening everywhere in our personal lives, healthcare has been slow to adopt similar technologies. Far too often we’re treated in healthcare without taking into account the context of our needs. Sometimes this is as simple as a healthcare provider not taking time to look at the chart. Other times we deny patients request that we add their medical record to our own record or we store it in a place where no one will ever actually access it.

Those are just the basic ways we don’t use context to help us better serve patients. More advanced ways are when we deny patients the opportunity to share their patient generated health data or we don’t use the health data they’re providing. Many people are working on pushing out social data which can provide a lot of context into why a patient is experience health issues or how we could better treat them. This is only going to grow larger, but we’re doing a poor job finding ways to seamlessly incorporate this data into the care that’s being provided.

One of the big challenges of AI is that it has a hard time understanding context. However, humans have a unique ability to include context in the decisions they make. Our interfaces should take this into account so that humans have the information they need to be able to make the proper contextual decisions. At least until the robots get smart enough to do it themselves.

Have you seen other places where healthcare didn’t use the context of the situation and should have used it? How about examples where we use context very effectively?