EHRs and Pay for Performance

Echoing Paul Cerrato’s post on EHRs and Pay For Performance, which cites a study by Jonathan Weiner, et. al., at Johns Hopkins University titled New Paradigms for Measuring Clinical Performance Using Electronic Health Records, there are many shortcomings in current EHR support for a shift to Pay For Performance (P4P). Cerrato particularly calls out statistics showing very small fractions of ambulatory care encounters fully documented in EHRs and interoperable across providers – necessary to meaningfully impact quality of care across provider organizations.

There are two issues at work here: the capture and the use of data. When two provider EHRs cannot adequately share data for a single patient, this is an issue of the usage of the data. If the data cannot be effectively used across provider organizations, there is little chance of using the EHRs to drive improved quality, and thus succeeding at P4P.

3M and CodeRyte

Today, 3M HIS announced it has acquired CodeRyte. As division scientist, this is exciting news from a technology perspective. Here’s my take on what it means for NLP:

The distinctive feature of CodeRyte’s technology is its strong statistical NLP capability. As I discussed in the 3M white paper, Auto-coding and Natural Language Processing, statistical machine learning systems offer the possibility of significant accuracy improvements in data-rich environments, compared to traditional rules-based approaches. A good example is in the outpatient coding environment, where data volumes are large, and CodeRyte has a history of performing very well compared to other systems.

There are multiple ways to boost the accuracy of statistical NLP, and the combination of 3M and CodeRyte will allow us to pursue several paths to the direct benefit of end users. 

Talking to Caregivers Instead of Computers

Human language developed at least 100,000 years ago, and has evolved into an amazingly complex and subtle mechanism for communicating ideas.

Computers emerged on the scene a mere half-century ago. And yet, we often find ourselves trying to structure our clinical communications around “talking” to computers, rather than talking to other caregivers. The impact to quality of care is potentially large, and often ignored.

Data Standards, Natural Language Processing, and Healthcare IT

By: Richard Wolniewicz

With so many healthcare organizations evaluating applications that use natural language processing (NLP), I’m often asked if there is a specific standard that defines NLP best practice. Unstructured Information Management Architecture, or UIMA, is a technical platform that runs inside a computer process and serves to integrate a pipeline of software components, each of which executes a single NLP step (more on NLP processes and steps next time). The UIMA platform is used for NLP across many industries, not just Healthcare.

 

AMIA Retrospective

By: Richard Wolniewicz

AMIA 2011 has wrapped up, with a very strong NLP track throughout the conference. This reflects the growing prevalence of NLP applications in our lives, from IBM Watson and Apple’s Siri to emerging Healthcare applications such as CAC (computer-assisted coding). This trend is only likely to continue as the technology improves.

I was particularly impressed with Leonard D’Avolio’s presentation of the Automated Retrieval Console (ARC). ARC provides a quick setup for bootstrapping a NLP classifier based on cTAKES and machine learning from your specific data set.

Bringing NLP into Perspective: Part One

By: Richard Wolniewicz

Without change to current coding technology and processes, ICD-10 adoption will be very challenging for providers to absorb, due to ICD-10’s added complexity and coding overhead. Many of us are looking at automated Natural Language Processing (NLP) to reduce the coding burden. The U.S. healthcare industry is a late adopter of NLP, and I find there’s a lot of confusion about what NLP is and what it can and can’t do. Through this series of blog posts, I hope to demystify NLP; improve expectations for the technology’s use in the healthcare industry; and, provide examples to illustrate some of the best ways to leverage it.