ICD-10 is Coming Our Way! Hooray! Hooray!

With all the talk of and worry about the impending shift to ICD-10 medical coding classifications in 2013, some people are looking for outlets in humor. This interesting and amusing topic was found on Government Health IT in their article, “What Dr. Seuss might have said about ICD-10.”

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Compliance Challenges and Semantic Solutions

One of the common challenges for data and tool integration is how complex biological data can be due to its inter-connectivity. With no standards or processes widely adopted, this left little hope for improvement in the situation. However, recent attempts have focused on the use of semantics to assist integration, and Semantic Web technologies are being welcomed by this community.

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Governance is Key

There are a lot of issues with implementing taxonomies – designing, building, and of course the technical swarm of terms. These challenges are typically what get all the attention and focus. What is quite often forgotten and of equal importance, if not more, is governance.

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Healthcare is Key to Rallies

With all the talk of heathcare costs in the current form of healthcare, the talk of universal healthcare makes many fear it will rise to levels beyond our imagination. Those involved in the rally for Occupy Wall Street scare the same people for similar reasons.

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Avoid the Chaos

Many healthcare professionals are worried that reimbursements will decline drastically after the ICD-10 implementation in 2013. Some are even comparing it to the mortage crisis.

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Project Plans Are A Must

We have talked before about the deadline most healthcare providers are counting down to – October 1, 2013. This is the day that ICD-10 will be the new standard for coding and billing. Providers must be compliant or else. Else equals a dramatic impact on their cash flow and bottom line.

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Clearing the Air

Few would argue about the growing volumes of data our digital lives have created. Many look to semantic technologies as a solution, or at least enhance processing and analyzing data. The constant challenge is the data was mostly published for human consumption and software finds it a challenge to format that which it doesn’t understand.

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