ICD-10 Is Biggest Healthcare IT Story for 2014

Tony Berberabe, MPH @OncBiz_Wiz
Published: Monday, Mar 24, 2014

What’s the biggest healthcare IT story for 2014 so far? Well, it’s no surprise that it’s ICD-10, identified by nearly half (47%) of respondents at the 2014 Health Information and Management Systems Society (HIMSS) annual conference held recently in Orlando, Florida. Rounding out the top slots were big data (27%), EHR implementation (16%), and meaningful use (10%), according to survey results from Stoltenberg Consulting.

If hospitals and health systems think ICD-10 is a big deal, just imagine what the small solo oncology practices must be going through.

Small practices face all the difficulties of health IT implementation without the benefit of big budgets and numerous staff to diffuse the burdens, according to EHRIntelligence.com.

Five areas that practices should focus on include:
  • Budget
  • Coordination
  • Training
  • Productivity
  • Revenue
EHRIntelligence, which offers an online resource of the latest news and product information about electronic health records, ICD-10, and health information exchanges, provides a good overview here.

Shane Pilcher, vice president, Stoltenberg Consulting says most healthcare facilities don’t know what to do with all their data.

“Organizations need to understand and differentiate big data from the more appropriate 'smart healthcare data,'” Pilcher said. “Today, they are collecting data, which usually gets stored somewhere inside data warehouses in the hopes that it will eventually be used in the next five years or so.”

Pilcher warns that if organizations aren't monitoring what they're saving, the quality and quantity of the data will not be sufficient when they are ready to use it.

“These organizations might think they have several years of historical data to draw from when they begin their analytics but in reality, the data is often not the quality, quantity or type that they need," she said.

Pilcher emphasized that "smart healthcare data is the key – focusing on the type of data they have, the volume of data, and the validity of data. They must make sure that what they're collecting is what they're expecting."

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