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AI-Based Tech Could Speed Patient Documentation Process

Posted on August 27, 2018 I Written By

Anne Zieger is veteran healthcare consultant and analyst with 20 years of industry experience. Zieger formerly served as editor-in-chief of FierceHealthcare.com and her commentaries have appeared in dozens of international business publications, including Forbes, Business Week and Information Week. She has also contributed content to hundreds of healthcare and health IT organizations, including several Fortune 500 companies. Contact her at @ziegerhealth on Twitter or visit her site at Zieger Healthcare.

A researcher with a Google AI team, Google Brain, has published a paper describing how AI could help physicians complete patient documentation more quickly. The author, software engineer Peter Lui, contends that AI technology can speed up patient documentation considerably by predicting its content.

On my initial reading of the paper, it wasn’t clear to me what advantage this has over pre-filling templates or even allowing physicians to cut-and-paste text from previous patient encounters. Still, judge for yourself as I outline what author Liu has to say, and by all means, check out the write-up.

In its introduction, the paper notes that physicians spend a great deal of time and energy entering patient notes into EHRs, a process which is not only taxing but also demoralizing for many physicians. Choosing from just one of countless data points underscoring this conclusion, Liu cites a 2016 study noting that physicians spend almost 2 hours of administrative work for every hour of patient contact.

However, it might be possible to reduce the number of hours doctors spend on this dreary task. Google Brain has been working on technologies which can speed up the process of documentation, including a new medical language modeling approach. Liu and his colleagues are also looking at how to represent an EHR’s mix of structured and unstructured text data.

The net of all of this? Google Brain has been able to create a set of systems which, by drawing on previous patient records can predict most of the content a physician will use next time they see that patient.

The heart of this effort is the MIMIC-III dataset, which contains the de-identified electronic health records of 39,597 patients from the ICU of a large tertiary care hospital. The dataset includes patient demographic data, medications, lab results, and notes written by providers. The system includes AI capabilities which are “trained” to predict the text physicians will use in their latest patient note.

In addition to making predictions, the Google Brain AI seems to have been able to pick out some forms of errors in existing notes, including patient ages and drug names, as well as providing autocorrect options for corrupted words.

By way of caveats, the paper warns that the research used only data generated within 24 hours of the current note content. Liu points out that while this may be a wide enough range of information for ICU notes, as things happen fast there, it would be better to draw on data representing larger windows of time for non-ICU patients. In addition, Liu concedes that it won’t always be possible to predict the content of notes even if the system has absorbed all existing documentation.

However, none of these problems are insurmountable, and Liu understandably describes these results as “encouraging,” but that’s also a way of conceding that this is only an experimental conclusion. In other words, these predictive capabilities are not a done deal by any means. That being said, it seems likely that his approach could be valuable.

I am left with at least one question, though. If the Google Brain technology can predict physician notes with great fidelity, how does that differ than having the physician cut-and-paste previous notes on their own?  I may be missing something here, because I’m not a software engineer, but I’d still like to know how these predictions improve on existing workarounds.

Is Your Health Data Unstructured? – Enabling an AI Powered Healthcare Future

Posted on June 22, 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.

If you asked a hospital IT executive how much of their data is unstructured data, most of them would reasonably respond that a lot or most of their data was unstructured. If you asked a practice manager or doctor how much of health data is unstructured, they’d likely respond “What do you mean?”

The reality is that most doctors, nurses, practice managers, etc don’t really care if their data is structured data or not. However, they should care about it and more importantly they should care about how they’re going to extract value out of the structured and unstructured data in their organizations.

The reality in healthcare, as the above tweet and image point out, is that much of the data we have and are going to get is going to be unstructured data. Our systems and software need to handle unstructured data in order to facilitate the AI powered healthcare future. That’s right. An AI powered healthcare future is coming and it’s going to be built on the back of structured and unstructured healthcare data.

I think the reason so many healthcare providers are concerned with this AI powered future is that they know the data they currently have is not very good. That’s going to be a problem for many organizations. Bad data is going to produce bad AI powered support.

We shouldn’t expect technology to solve our problems of bad data but, technology will amplify the state of your organization. If your organization is doing an amazing job creating high quality health data, then the AI powered future will propel you in amazing ways to be an even better organization. However, the opposite is also true. If your health data is poor, then these new AI powered systems are going to highlight how poorly your organization is being run. I get why that’s scary for many people.

This should be one of the big lessons we take away from the EHR experience. Healthcare organizations with poor workflows hoped that implementation of an EHR would help them fix their workflows. Instead of EHR fixing the workflows it just highlighted the poor workflows. Technology accentuates and accelerates your current state. It doesn’t usually fix it. You have to fix your organization and workflows first and then use technology to accelerate your organization.

The next step after that is what Rasu Shrestha highlighted when he said, “How can we move from ‘doing digital’ to ‘being digital’. Let’s not replicate analog workflows. Let’s rethink!”