Categories
AI clinical Uncategorized

Comment: Amazon Transcribe Medical

The new machine learning-powered service, Amazon Transcribe Medical, will allow physicians to quickly dictate their clinical notes and speech into accurate text in real time, without any human intervention, Amazon claims.

Perez, S. (2019). Amazon debuts automatic speech recognition service, Amazon Transcribe Medical. Techcrunch.

I use voice recognition on my phone fairly often and am always impressed by the quality of the notes it makes. And with how quickly it improves. But when it comes to unusual words, like the ones we use in healthcare, natural language processing (NLP) on the phone is left wanting.

This demo from the AWS re:Invent conference goes some way to show how much easier things are going to get. Once the text is accurately recognised, a semantic system will then be able to “make sense” of the text and enter it into an EHR before making suggestions for appropriate follow up appointments, discharge notes, medical prescription, etc. We live in interesting times.

Categories
AI clinical

Comment: For a Longer, Healthier Life, Share Your Data

There are a number of overlapping reasons it is difficult to build large health data sets that are representative of our population. One is that the data is spread out across thousands of doctors’ offices and hospitals, many of which use different electronic health record systems. It’s hard to extract records from these systems, and that’s not an accident: The companies don’t want to make it easy for their customers to move their data to a competing provider.

Miner, L. (2019). For a Longer, Healthier Life, Share Your Data. The New York Times.

The author goes on to talk about problems with HIPAA, which he suggests are the bigger obstacle to the large-scale data analysis that is necessary for machine learning. While I agree that HIPAA makes it difficult for companies to enable the sharing of health data while also complying with regulations, I don’t think it’s the main problem.

The requirements around HIPAA could change overnight through legislation. This will be challenging politically and legally but it’s not hard to see how it could happen. There are well-understood frameworks through which legal frameworks can be changed and even though it’s a difficult process, it’s not conceptually difficult to understand. But the ability to share data between EHRs will, I think, be a much bigger hurdle to overcome. There are incentives for the government to review the regulations around patient data in order to push AI in healthcare initiatives; I can’t think of many incentives for companies to make it easier to port patient data between platforms. Unless companies responsible for storing patient data make data portability and exchange a priority, I think it’s going to be very difficult to create large patient data sets.

Categories
AI clinical

Doctors are burning out because electronic medical records are broken

For all the promise that digital records hold for making the system more efficient—and the very real benefit these records have already brought in areas like preventing medication errors—EMRs aren’t working on the whole. They’re time consuming, prioritize billing codes over patient care, and too often force physicians to focus on digital recordkeeping rather than the patient in front of them.

Source: Minor, L. (2017). Doctors are burning out because electronic medical records are broken.

I’ve read some physicians can spend up to 60% of their day capturing patient information in the EHR. And this isn’t because there’s a lot of information. It’s often down to confusing user interfaces, misguided approaches to security (e.g. having to enter multiple different passwords and a lack of off-site access), and poor design that results in physicians capturing more information than necessary.

There’s interest in using natural language processing to analyse recorded conversation between clinicians and colleagues/patients and while the technology is still unsuitable for mainstream use, it seems likely that it will continue improving until it is.

Also, consider reading: