Last week, IĀ attended the Colorado Healthcare Strategy and Management conference on predictive analytics. Ultimately, the panelists all agreed that data analytics are vital to the success of and improvement of healthcare, both on an individual and macro level.

Right now, there are many problems with data in the medical industry. One of the biggest issues they are having is getting actionable information in front of the right people. But in order to achieve this, they must firstĀ overcome their data sharing (or lack of it) problem. You would think that we would all want to share all the data we can in medicine, to get the complete picture, so we are able to more and greater medical discoveries. But ultimately, each of these hospitals, clinics and doctors offices are independent businesses, and act in their own best interest.

Furthermore, data sharing, even between different silos under one roof is difficult. This is the case for any business, as we have seen time and time again. From what I gathered in the conference, this is even a bigger dilemma for the healthcare industry; it sounds like doctors are “very separate” from admin staff, the executives and even other medical professionals such as nurses. This results in a lack of “interest” in sharing this data and information between all the different employees.

And, just as we see in most businesses, because each clinic, and each employee is acting in their own best interest, data manipulation is common. Often times the data is manipulated for silo-based justifications – to explain why something happened, or to falsely represent good behavior. This can be the case from the input of data, to the consolidation and analysis of the data. This is one of the most common problems businesses have with hosting an in-house data team. More often than not, it is best to have an outside, unbiased data team analyzing your data; a biased analyst means biased data.

In other cases, the data may not be biased, but rather incomplete. Because of a lack of data sharing, and because the use of data analytics in this industry is relatively new, this is a common problem. One of the panelists gave a great example of this. When she evaluated her own heart health risk data, she was in a very low percentile (under 1%). However when she took a consumer assessment of heart health (based more on behaviors, eating habits exercise habits etc.), she was in the upper percentile (60%). This goes to show that the data the clinic had access to was so incomplete, that it was wrong.

This represents the number one problem we see in analytics: disparate sources. It’s not just hospitals and clinics that have problems with this, but nearly all organizations. It is difficult to combine all the data from so many disparate sources to get a complete picture.

A final problem, that stumped even the panelists is the lingering question of…how do you actually get people to do it? How do you get your frontline employees to embrace data input, and get them to do it correctly and accurately? How can you train them to do this? Ultimately, we are unable to solve any of the above problems if the frontline employees are not on board. Very few data services and consultants address this, probably because it is such a difficult problem to solve. But, if we use the data correctly, can it tell us how to do this?

Our team at Cliintel says yes. Unlike many in the industry, we have successfully developed training programs that train all employees, from the C-level to the frontline employees, how to input data correctly, so it can be fully utilized. We also help develop standardization across all levels and all silos to ensure the data is not biased. Learn more about our data solutions.




Each of the panelists made some strong points, but seemed stumped when asked one question: how do you ensure your front-line employees are using and inputting the data correctly.


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