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Poll: Providers Struggle To Roll Out Big Data Analytics

Posted on April 10, 2017 I Written By

Anne Zieger is veteran healthcare editor and analyst with 25 years of industry experience. Zieger formerly served as editor-in-chief of FierceHealthcare.com and her commentaries have appeared in dozens of international business publications, including Forbes, Business Week and Information Week. She has also contributed content to hundreds of healthcare and health IT organizations, including several Fortune 500 companies. She can be reached at @ziegerhealth or www.ziegerhealthcare.com.

A new poll by a health IT publication has concluded that while healthcare organizations would like to roll out big data analytics projects, they lack many of the resources they need to proceed.

The online poll, conducted by HealthITAnalytics.com, found that half of respondents are hoping to recruit data science experts to serve as the backbone of their big analytics efforts. However, many are finding it very difficult to find the right staffers.

What’s more, such hires don’t come cheaply. In fact, one study found that data scientist salaries will range from $116,000 to $163,500 in 2017, a 6.4 percent increase over last year’s levels. (Other research concludes that a data scientist in management leading a team of 10 or more can draw up to $250,000 per year.) And even if the pricetag isn’t an issue, providers are competing for data science talent in a seller’s market, not only against other healthcare providers but also hungry employers in other industries.

Without having the right talent in place, many of providers’ efforts have been stalled, the publication reports. Roughly 31 percent of poll respondents said that without a data science team in place, they didn’t know how to begin implementing data analytics initiatives.

Meanwhile, 57 percent of respondents are still struggling with a range of predictable health IT challenges, including EMR optimization and workflow issues, interoperability issues and siloed data. Not only that, for some getting buy-in is proving difficult, with 34 percent reporting that their clinical end users aren’t convinced that creating analytics tools will pay off.

Interestingly, these results suggest that providers face bigger challenges in implementing health data than last year. In last year’s study by HealthITAnalytics.com, 47 percent said interoperability was a key challenge. What’s more, just 42 percent were having trouble finding analytics staffers for their team.

But at the same time, it seems like provider executives are throwing their weight behind these initiatives. The survey found that just 17 percent faced problems with getting executive buy-in and budget constraints this year, while more than half faced these issues in last year’s survey.

This squares with research released a few months ago by IT staffing firm TEKSystems, which found that 63 percent of respondents expected to see their 2017 budgets increase this year, a big change from the 41 percent who expected to see bigger budgets last year.

Meanwhile, despite their concerns, providers are coping well with at least some health IT challenges, the survey noted. In particular, almost 90 percent of respondents reported that they are live on an EMR and 65 percent are using a business intelligence or analytics solution.

And they’re also looking at the future. Three-quarters said they were already using or expect to enhance clinical decision making, along with more than 50 percent also focusing laboratory data, data gathered from partners and socioeconomic or community data. Also, using pharmacy data, patient safety data and post-acute care records were on the horizon for about 20 percent of respondents. In addition, 62 percent said that they were interested in patient-generated health data.

Taken together, this data suggests that as providers have shifted their focus to big data analytics– and supporting population health efforts – they’ve hit more speed bumps than expected. That being said, over the next few years, I predict that the supply of data scientists and demand for their talents should fall into alignment. For providers’ sake, we’d better hope so!

How Much Time Do You Spend Cleaning Your Data?

Posted on June 29, 2015 I Written By

John Lynn is the Founder of the HealthcareScene.com blog network which currently consists of 10 blogs containing over 8000 articles with John having written over 4000 of the articles himself. These EMR and Healthcare IT related articles have been viewed over 16 million times. John also manages Healthcare IT Central and Healthcare IT Today, the leading career Health IT job board and blog. John is co-founder of InfluentialNetworks.com and Physia.com. John is highly involved in social media, and in addition to his blogs can also be found on Twitter: @techguy and @ehrandhit and LinkedIn.

I recently came across this really great blog post talking about data scientists wasting their time. Here’s a quote from the article (which quotes the NYT):

“Data scientists, according to interviews and expert estimates, spend 50 percent to 80 percent of their time mired in [the] mundane labor of collecting and preparing unruly digital data, before it can be explored for useful nuggets.”
– Steve Lohr, NYT

Then, they have this extraordinary quote from Monica Rogati, VP for Data Science at Jawbone:

“Data scientists are forced to act more like data janitors than actual scientists.”

Every data scientist will tell you this is a problem. They spend far too much time cleaning up the data and they all wish they could spend more time actually looking at the data to find insights. I’ve seen this all over health care. In fact, I’d say we have more data janitors than data scientists in healthcare. Sadly, many healthcare data projects clean up the data and then don’t have any budget left to actually do something with the data.

The solution to this problem is easy to write and much harder to do. The solution is to create an expectation and a culture of clean data in your organization.

I predict that over the next 5-10 years, healthcare data is going to become the backbone of healthcare data decision making. Those organizations that houses are a mess are going to be torn down and sold off to the hospital that’s kept a clean house. Is your hospital data clean or dirty?