[News release] As diagnosis codes change, data lost in translation – both ways

From the 16 March 2015 UIC news release

Changes in how medical diagnoses are coded under the latest international disease classification system – known as the ICD-10 codes – may complicate financial analysis, research projects and training programs that depend on look-back comparisons of health care data, report researchers at the University of Illinois at Chicago.

The report, a collaboration of researchers at UIC and at the University of Arizona, is online in the Journal of the American Medical Informatics Association (JAMIA).

Codes for diagnoses – used to justify payments, among other things – may not translate from ICD-10 back to ICD-9 in a simple way, says Andrew Boyd, assistant professor of biomedical and health information sciences at UIC and first author of the paper.

Boyd and his colleagues have been looking at issues that could come up as physicians and hospitals change from one system to the other. Previously they found that some ICD-9 codes map well to ICD-10, but many more have highly convoluted mappings, and some don’t map at all. This forward-mapping is needed for continuing payments of ongoing medical conditions.

“Now, we are taking the same methodology and looking backward,” Boyd said. Reverse-mapping from ICD-10 back to ICD-9 will be important for all sorts of retrospective analyses, he said, “because we have 30 years of data that we want. We don’t want to lose all this information.”

Clinical researchers and analysts conducting studies across datasets – and hospital administrators who manage growth and watch trends for strategic planning – will need to pull data under both the new and the old codes. Mapping back from ICD-10- to ICD-9 is just as complex as mapping from ICD-9 to ICD-10.

The researchers created a web portal tool and translation tables designed to provide guidance on ambiguous and complex translations and to reveal where analyses may be challenging or impossible. The tool lists all ICD-9-CM diagnosis codes related to the input of ICD-10-CM codes and classifies their level of complexity, which can be a one-to-one “identity,” or reciprocal (the simplest); class-to-subclass; subclass-to-class; “convoluted”; or “no mapping.”

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Better medicine, brought to you by big data through new types of data analysis

 

A good overview of how improved data analysis and presentation is improving health care delivery.

I especially liked the slideshare presentation found below in Related Articles.
The 42 slides in Big data – a brief overview outlines what big data is, its sources and processes, how it is analyzed, current “players”,examples, market analysis, future, and opportunities.

From the 15 July 2012 blog post at Gigaom

Slowly but surely, health care is becoming a killer app for big data. Whether it’s Hadoop, machine learning, natural-language processing or some other technique, folks in the worlds of medicine and hospital administration understand that new types of data analysis are the key to helping them take their fields to the next level.

Here are some of the interesting use cases we’ve written about over the past year or so, and a few others I’ve just come across recently. If you have a cool one — or a suggestion for a new use of big data within the healthcare space — share it in the comments:

Genomics. This is the epitomic case for big data and health care. Genome sequencing isgetting cheaper by the day and produces mountains of data. Companies such asDNAnexus, Bina Technologies, Appistry and NextBio want to make analyzing that data to discover cures for diseases faster, easier and cheaper than ever using lots cutting-edge algorithms and lots of cloud computing cores.

BI[definition of business intelligence] for doctors. Doctors and staff at Seattle Children’s Hospital are using Tableau to analyze and visualize terabytes of data dispersed across the institution’s servers and databases. Not only does visualizing the data help reduce medical errors and help the hospital plan trials but, as of this time last year, its focus on data had saved the hospital $3 million on supply chain costs….

..Semantic search. Imagine you’re a doctor trying to learn about a new patient or figure out who among your patients might benenfit from a new technique. But patient records have been scattered throughout departments, vary in format and, perhaps worst of all, all use the ontologies of the department that created the record. A startup called Apixio is trying to fix this by centralizing records in the cloud and applying semantic analysis to uncover everything doctors need, regardless who wrote it…

..Getting ahead of disease. It’s always good if you figure out how to diagnose diseases early without expensive tests, and that’s just what Seton Healthcare was able to dothanks to its big data efforts…

and more!

Related articles

• Better medicine, brought to you by big data [GigaOM] (gigaom.com)
• Intel and NextBio seek Big Data upgrades in genomics (fiercebiotechit.com)
• Big Changes Are Ahead For The Health Care Industry, Courtesy Of Big Data (fastcompany.com)

• Big data – a brief overview (slideshare.net) [a slide presentation, 42 slides]

• Oracle adopts the popular R language for the enterprise and big data. (oracle.com)
• Presentation: Scalability Challenges in Big Data Science (architects.dzone.com)
• Salesforce intros Radian6 Insights for social big data (zdnet.com)
• Big Data Modeling – Part I – Defining “Big Data” and “Data Modeling” (infocus.emc.com)
• NextBio and Intel Announce Collaboration to Optimize Use of Hadoop Stack And Move Forward With Big Data Technologies in Genomics (ducknetweb.blogspot.com)
• A Beautiful Friendship: Big Data and Social Media (blogs.sap.com)
• Stanford rides Big Data wave in medical research (fiercebiotechit.com)
• Big Data? Big Deal! (clean-clouds.com)