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Execs Say Silicon Valley Has The Jump On Healthcare Innovation

Posted on September 12, 2018 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.

Lately, it’s begun to look as though the leading lights of Silicon Valley might bring the next wave of transformation to healthcare. But can they work big changes in the industry on their own, or are they more likely to succeed by throwing their extremely considerable muscle behind existing healthcare players? That’s one of the many questions at issue as companies like Google, Amazon (Yes, I know they’re in Seattle), and Facebook shoulder their way into the business.

According to a new survey by Reaction Data, many healthcare execs think Amazon, in particular, has the potential to change the game.  When asked which outside entrants were most likely to disrupt the healthcare industry, two-thirds of respondents said the that the online retailing giant topped the list. “Amazon is ahead of the game in many ways compared to the other companies,” a chief nursing officer told Reaction Data.

There’s little doubt that there’s an opening for a company like Amazon to solve some pressing problems. As an industry outsider – unless you count its recent big-ticket acquisition of PillPack, which happened about a minute ago – Amazon may be able to bring fresh eyes to some of healthcare’s biggest problems. For example, what health exec wouldn’t kill to benefit from the e-retailer’s immense logistics capabilities? The mind boggles.

Facebook and Google aren’t making as many healthcare headlines, but they too are moving carefully into the business. For example, consider Google’s partnership with Stanford aimed at creating digital scribes. The digital scribe initiative may not seem like much, but I wouldn’t underestimate what Google can learn from the effort and how effectively it can operationalize this knowledge. It isn’t 2010 anymore, and I think the search giant has come a long way since its Google Health PHR effort collapsed.

Facebook, too, has made some tentative steps toward building a healthcare business, such as its recent agreement to collaborate with the NYU School of Medicine on speeding up MRI scanning using AI. The social networking giant hasn’t shown itself capable of much diversification to date, but I wouldn’t count it out, if for no other reasons than the massive profits to be made. Even for Facebook, we’re talking about serious money here.

If you’re wondering what these companies hope to accomplish, it’s not surprising. There are so many possibilities. One place to start is rethinking the EHR. Maybe I’m a starry-eyed dreamer, but I agree with observers like Dale Sanders, an executive with HealthCatalyst, who argues that Silicon Valley disrupters might be poised to bring something new to the table. “I keep hoping that the Googles, Facebooks and Amazons of the world will quietly build a new generation EMR,” Sanders writes in a recent column.

EMR transformation is just one of many potential targets of opportunity for the Silicon Valley gang, though. There’s obviously a raft of other goals healthcare leaders might like to see realized, The truth is, though, that it matters less what the Silicon Valley giants do than the competitive scramble they kick off within the industry. Even if these behemoths never succeed in leading the charge, they’re likely to spur others to do so.

Does NLP Deserve To Be The New Hotness In Healthcare?

Posted on August 30, 2018 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.

Lately, I’ve been seeing a lot more talk about the benefits of using natural language processing technology in healthcare. In fact, when I Googled the topic, I turned up a number of articles on the subject published over the last several weeks. Clearly, something is afoot here.

What’s driving the happy talk? One case in point is a new report from health IT industry analyst firm Chilmark Research laying out 12 possible use cases for NLP in healthcare.

According to Chilmark, some of the most compelling options include speech recognition, clinical documentation improvement, data mining research, computer-assisted coding and automated registry reporting. Its researchers also seem to be fans of clinical trial matching, prior authorization, clinical decision support and risk adjustment and hierarchical condition categories, approaches it labels “emerging.”

From what I can see, the highest profile application of NLP in healthcare is using it to dig through unstructured data and text. For example, a recent article describes how Intermountain Healthcare has begun identifying heart failure patients by reading data from 25 different free text documents stored in the EHR. Clearly, exercises like these can have an immediate impact on patient health.

However, stories like the above are actually pretty unusual. Yes, healthcare organizations have been working to use NLP to mine text for some time, and it seems like a very logical way to filter out critical information. But is there a reason that NLP use even for this purpose isn’t as widespread as one might think? According to one critic, the answer is yes.

In a recent piece, Dale Sanders, president of technology at HealthCatalyst, goes after the use of comparative data, predictive analytics and NLP in healthcare, arguing that their benefits to healthcare organizations have been oversold.

Sanders, who says he came to healthcare with a deep understanding of NLP and predictive analytics, contends that NLP has had ”essentially no impact” on healthcare. ”We’ve made incremental progress, but there are fundamental gaps in our industry’s data ecosystem– missing pieces of the data puzzle– that inherently limit what we can achieve with NLP,” Sanders argues.

He doesn’t seem to see this changing in the near future either. Given how much money has already been sunk in the existing generation of EMRs, vendors have no incentive to improve their capacity for indexing information, Sanders says.

“In today’s EMRs, we have little more than expensive word processors,” he writes. “I keep hoping that the Googles, Facebooks and Amazons of the world will quietly build a new generation EMR.” He’s not the only one, though that’s a topic for another article.

I wish I could say that I side with researchers like Chilmark that see a bright near-term future for NLP in healthcare. After all, part of why I love doing what I do is exploring and getting excited about emerging technologies with high potential for improving healthcare, and I’d be happy to wave the NLP flag too.

Unfortunately, my guess is that Sanders is right about the obstacles that stand in the way of widespread NLP use in our industry. Until we have a more robust way of categorizing healthcare data and text, searching through it for value can only go so far. In other words, it may be a little too soon to pitch NLP’s benefits to providers.

Tips on Implementing Text Analytics in Healthcare

Posted on July 6, 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.

Most of us would agree that extracting clinical data from unstructured physician notes would be great. At present, few organizations have deployed such tools, nor have EMR vendors come to the rescue en masse, and the conventional wisdom holds that text analytics would be crazy expensive. I’ve always suspected that digging out and analyzing this data may be worth the trouble, however.

That’s why I really dug a recent article from HealthCatalyst’s Eric Just, which seemed to offer some worthwhile ideas on how to use text analytics effectively. Just, who is senior vice president of product development, made a good case for giving this approach a try. (Note: HealthCatalyst and partner Regenstrief Institute offer solutions in this area.)

The article includes an interesting case study explaining how healthcare text analytics performed head-to-head against traditional research methods.

It tells the story of a team of analysts in Indiana that set out to identify peripheral artery disease (PAD) patients across two health systems. At first gasp, things weren’t going well. When researchers looked at EMR and claims data, they found that failed to identify over 75% of patients with this condition, but text analytics improved their results dramatically.

Using ICD and CPT codes for PAD, and standard EMR data searches, team members had identified less than 10,000 patients with the disorder. However, once they developed a natural language processing tool designed to sift through text-based data, they discovered that there were at least 41,000 PAD patients in the population they were studying.

To get this kind of results, Just says, there are three key features a medical text analytics tool should have:

  • The medical text analytics software should tailor results to a given user’s needs. For example, he notes that if the user doesn’t have permission to view PHI, the analytics tool should display only nonprivate data.
  • Medical text analytics tools should integrate medical terminology to improve the scope of searches. For example, when a user does a search on the term “diabetes” the search tool should automatically be capable of displaying results for “NIDDM,” as this broadens the search to include more relevant content.
  • Text analytics algorithms should do more than just find relevant terms — they should provide context as well as content. For example, a search for patients with “pneumonia,” done with considering context, would also bring up phrases like “no history of pneumonia.” A better tool would be able to rule out phrases like “no history of pneumonia,” or “family history of pneumonia” from a search for patients who have been treated for this illness.

The piece goes into far more detail than I can summarize here, so I recommend you read it in full if you’re interested in leveraging text analytics for your organization.

But for what it’s worth, I came away from the piece with the sense that analyzing your clinical textual information is well worth the trouble — particularly if EMR vendors being to add such tools to their systems. After all, when it comes to improving outcomes, we need all the help we can get.