With the overwhelming amount of data accumulated by healthcare providers across the country, the application of true smart healthcare data analytics can seem challenging. Last month at HIMSS15, we saw HIT leaders express confusion toward data analytics in our third annual Health IT Industry Outlook Survey. Within the survey, 84 percent of participants, representing CIOs, CMIOs, IT project managers, IT directors and consultants had questions around type, quantity, and ways in which to use their healthcare data. On top of this, 62 percent stated the biggest barrier to IT initiatives around MU and data analytics was a lack of organizational buy-in or financial resources. So, how do we transition these concerns into smart actions?
As our blog series comes to an end, we arrive at another topic dominating many discussions in the health IT world: big data. In our HIMSS14 survey, we asked: Why is big data viewed as something in the distant future, especially if it’s considered the next big thing?
Responses noted that big data is too intimidating (7%), the tools and strategies to address big data aren’t available yet (22%) and other HIT initiatives such as ICD-10 or Stage 3 MU are taking precedence over big data (31%), while the majority of respondents said most organizations don’t know what to do with all their data (40%). Because these responses are all interconnected in various ways, let’s address each one.
“Most organizations don’t know what to do with all their data.”
The initial phase of data analytics is data collection. What most organizations have unfortunately lost sight of are the keywords here: initial phase. Organizations can’t collect data for the sake of collecting data, they must collect data as part of a greater, long-term plan, which must always remain in perspective. So, it comes as no surprise that many organizations may have plenty of data but zero direction as to how to use it all. For data collection to be effective, it must be performed with the future applicability of that information in mind.
Organizations are just now arriving at the fact that they are not able to utilize much of the data they have been collecting for the few years since EHR implementations began. In an effort to ease the transition into EHRs for clinicians and expedite adoption without resistance, the data entry methods were adjusted to offer a free text option on top of existing answer choices. Unfortunately, enabling clinicians to input their own answers provided a convenience that wound up compromising the applicability of the information being collected. The result? The vast majority of current healthcare data is unstructured and unusable.
To prevent similar scenarios 5-10 years from now, we have to better establish the data collection process. For big data success, organizations must first work towards obtaining smart healthcare data. Smart healthcare data is the intermediate phase on the path to big data and organizations can successfully make their data ‘smart’ by focusing on the type of data that they are collecting, the volume of the data and its validity. The results from any analysis of data will not be meaningful if the data originally collected was not of the right type, right amount or even trustable.
By establishing such best practices in the data collection process, organizations won’t be left with large amounts of data that don’t lend themselves to being analyzed well and have a much better idea of how to utilize their information.
“The right tools and strategies to address big data aren’t available yet.”
It’s true – the processing capacity of today’s systems cannot handle the size or complexity of the data sets that are represented by big data. But if we lost all momentum for innovation whenever the right tools were unavailable, advances in healthcare, or any industry for that matter, would never occur. Pushing forward our technological evolution comes from fully understanding our needs first – both the needs we have now and the needs we will have in the future. Organizations have to create demands for products and solutions that not only answer their present questions but also have the capacity to answer the questions they anticipate having to address years from now.
The future is forever uncertain, but rather than treating the uncertainty as a barrier, organizations should treat it as a basis to develop creative and imaginative roadmaps that are prepared for unexpected scenarios. Working backwards from there will offer the necessary guidance to refine the data collection process for future success.
Although the right tools and strategies for big data are not yet available, developing an idea of our long term objectives with analytics will help make them a reality sooner. Then, with appropriate plans and processes already in place, we can hit the ground running once they have arrived.
“Other HIT initiatives are taking precedence over big data.”
It’s understandable that other initiatives have led to healthcare organizations putting big data on the back burner. They are often being pulled in several directions by multiple projects while having to work with increasingly limited financial and staff resources. So naturally, anything that appears to be in the distant future receives little to no time or attention, let alone resources.
The mistake here is that organizations believe big data is in the distant future. I’ve said it in the past: big data is much like looking in your side view mirror – it’s much closer than we think.
Additionally, the healthcare industry is aware that its business practices have to evolve. After years of treating the business aspect and the care aspect of healthcare separately, we’ve arrived at a time of payment reform, ACOs and value-based purchasing, which demand that healthcare organizations know the cost of the care they provide and how patient outcomes are affected. Data analytics can be utilized by organizations to identify areas where costs can be decreased while delivering better care for patients.
Overall, big data is an initiative that can help organizations drop operational costs, grow profits and maintain or improve patient outcomes. The extra budget such savings create can be applied towards bolstering data analytics programs or helping fund other HIT initiatives. And any initiative that creates avenues to assist other initiatives should receive equal, if not more, attention!
“Big data is too intimidating.”
Read above. Still think so?
Big data isn’t intimidating – it’s an unbelievable opportunity. Through it, we can leverage amazing masses of information and find insights that allow us to do things better than ever in healthcare and achieve organizational success while we’re at it.