The following is a paid blog post sponsored by Intel.
Healthcare analytics is all the talk in healthcare right now. It’s really no surprise since many have invested millions and even billions of dollars in digitizing their health data. Now they want to extract value from that data. No doubt, the promise of healthcare analytics is powerful. I like to break this promise out into two categories: Patient Analysis and Patient Influence.
On the one side of healthcare analytics is analyzing your patient population to pull reports on patients who need extra attention. In some cases, these patients are the most at risk portions of your population with easy to identify disease states. In other cases, they’re the most expensive portion of your population. Both of these are extremely powerful analytics as your healthcare organization works to improve patient care and lower costs.
An even higher level of patient analysis is using healthcare analytics to identify patients who don’t seem to be at risk, but whose health is in danger. These predictive analytics are much more difficult to create because by their very nature they’re imperfect. However, this is where the next generation of patient analysis is going very quickly.
On the other side of healthcare analytics is using patient data to influence patients. Patient influence analytics can tell you simple things like what type of communication modality is preferred by a patient. This can be used on an individual level to understand whether you should send an email, text, or make a phone call or it can be used on the macro level to drive the type of technologies you buy and content you create.
Higher level patient influence analytics take it one step further as they analyze a patient’s unique preferences and what influences the patient’s healthcare decision making. This often includes pulling in outside consumer data that helps you understand and build a relationship with the patient. This analytic might tell you that the patient is a huge sports fan and which is their favorite team. It might also tell you that this person has a type A personality. Together these analytics can inform you on the most appropriate ways and methods to interact and influence the patient.
What’s Holding Healthcare Analytics Back?
Both of these healthcare analytics approaches have tremendous promise, but many of them are being held back by a healthcare organization’s current analytics infrastructure.
The first problem many organizations have is where they are storing their data. I’d describe their data as being stored in virtual prisons. We need to unlock this data and free it so that it can be used in healthcare analytics. If you can’t get at the data within your own organization, how can we even start talking about all the health data being stored outside the four walls of your organization? Plus, we need to invest in the right storage that can support the growth of this data. If you don’t solve these data access and storage pieces, you’ll miss out on a lot of the benefits of healthcare analytics.
Second, do you trust your data? Most hospital CIOs I talk to usually respond, “Mostly.” If you can’t trust your data, you can’t trust your analytics. A fundamental building block of successful analytics is building trust in your data. This starts by implementing effective workflows that capture the data properly on the front end.
Next, do you have the processing power required to process all these analytics and data? Healthcare analytics in many healthcare organizations reminds me of the old days when graphic designers and video producers would have to wait hours for graphics programs to load or videos to render. Eventually we learned not to skimp on processing power for these tasks. We need to learn this same lesson with healthcare analytics. Certainly cloud makes this easier, but far too often we under fund the processing power needed for these projects.
Finally, all the processing power in the world won’t help if you don’t have your most important piece of analytics infrastructure: people. No doubt, finding experienced people in healthcare data analytics is a challenge. It is the hardest thing to do on this list since it is very competitive and very expensive. The good news is that if you solve the other problems above, then you become an attractive place for these experts to work.
In your search for a healthcare analytics expert, you can likely find a data expert. You can find a clinical expert. You can find an EHR expert. Finding someone who can work across all three is the Holy Grail and nearly impossible to find. This is why in most organizations healthcare analytics is a team sport. Make sure that as you build your infrastructure of healthcare analytics people, you make sure they are solid team players.
It’s time we start getting more value out of our EHR and health IT systems. Analytics is one of those tools that will get us there. Just be sure that your current infrastructure isn’t holding you back from achieving those goals.
If this topic interests you and you’ll be at HIMSS 2017, join us at the Intel Health Booth #2661 on Tuesday, 2/21 from 2:00-2:45 PM where we’ll be holding a special meetup to discuss Getting Ready for Precision Health. This meetup will also be available virtually via Periscope on the @IntelHealth Twitter account.