Clinical Intelligence Is the Next Challenge for Healthcare CIOs

David Wagner, Managing Editor | 3/7/2012 | 5 comments

David Wagner
In the past couple of years, healthcare CIOs have put so much of their attention on electronic health records (EHRs) and meaningful use. The deadlines and penalties established by the federal government have worked well, and now most medical centers have their plans in place, if not the actual EHRs up and running. Because of this, most of the talk at the recent HIMSS conference was on what happens next. One of the obvious ways to go is improving clinical intelligence (CI). In many ways, that might be a bigger challenge for healthcare CIOs than EHRs.

For those unfamiliar with the term, CI is essentially the healthcare equivalent of business intelligence. The goal is to collect and analyze data to support better clinical decisions. A longer definition can be found here. It is the obvious next step after EHR adoption.

EHRs have rationalized and digitized information that was previously difficult to access in large scale. Combining them with CI allows you to slice and dice your long-term data about clinical outcomes by gender, age, vital signs, symptoms, diagnoses, or anything else. The benefits are so numerous that it is hard to list them all, but let’s highlight a few.

  • Research: Some of the most powerful yet most difficult research projects are longitudinal and cross-sectional studies, which track a population over a certain period. (Longitudinal studies often follow a person's entire life.) These studies have existed for a long time and have gotten easier with computer-generated data, but until recently, they have relied too much on very routine data (gender and age). As EHR use expands, so does the amount of easily searchable data -- bringing the advantages of parsing out more complete populations with more specific symptoms.

    There are also benefits to determining which dosages and medicines work best for certain types of diseases. Do people with certain genetic characteristics respond differently to certain types of cancer drugs? Up to this point, that data has been difficult to store and access.

  • Clinical outcomes: Do patients with similar symptoms recover equally well with all of your doctors? In all of your rooms? Are infection rates higher where certain employees work? If you monitor certain vital signs remotely 24/7, does it allow you to intervene quicker for certain complications? The potential is endless. What we’ll be measuring will go up exponentially in the next few years.
  • Insurance rates: We all know about doctors and unnecessary tests, but with better clinical outcomes, you can expect total costs to go down -- and hopefully, lower insurance costs will follow. In addition, CI is likely to reduce medical fraud.
  • Resource and supply planning: Many hospitals are accustomed to using BI to order supplies and stock the pharmacy. However, with the expansion of the CI database, you can expect improved planning tools and superior information, which can save hospitals time and money.

Some serious challenges come with the sudden surge in data. Hospitals are used to storing data, but using this data will require some new skill sets. The staff is trained in dealing with individual files, but working with larger groups of data will be new territory. Looking at the numbers and knowing how to respond may take new organizational capabilities. Nurses, since they are most responsible for responding to changes in patient status, will have to get used to processing much more automated data.

IT departments will likely have to create and service “dashboards” that track the ongoing automated data for clinical staff, as well as creating ways for researchers to access data in large scale while remaining compliant with privacy laws and regulations. Data warehouses will grow exponentially and create a big data problem that not only affects costs, but also takes a different kind of expertise to manage.

Clinical intelligence provides an opportunity to revolutionize medical care at every level. But it seems that, like most medical revolutions today, the CI revolution will be led by the CIO, rather than the clinical staff. Good CIOs will make this a priority and a competitive differentiator for their institutions now.

View Comments: Newest First | Oldest First | Threaded View
Sara Peters   Clinical Intelligence Is the Next Challenge for Healthcare CIOs   3/8/2012 4:11:50 PM
Re: bring on the intelligence
whoops! You're right dave. I got it backwards.
David Wagner   Clinical Intelligence Is the Next Challenge for Healthcare CIOs   3/8/2012 3:56:53 PM
Re: bring on the intelligence
Also, I have to agree with you Dave that the potential threats outweigh the potential benefits.

I think you mean the other way around.

But yes, i agree that CI should be the goal of EHR, but I think the way that the federal incentives have worked (mainly pointed at medicare payments as penalties) too many people have looked at it as a way to simplify billing and coding, and just to replace all that paper floating around.

CI is now taking the forefront again because the basics are in place (or at least a plan for the basics in some cases).

Sara Peters   Clinical Intelligence Is the Next Challenge for Healthcare CIOs   3/8/2012 3:29:30 PM
bring on the intelligence
Y'know, I think that the best reason for EHRs in the first place is to generate better clinical intelligence. If clinicians aren't getting better insight, then what's the point? Also, I have to agree with you Dave that the potential threats outweigh the potential benefits. I've already decided to give my body to science when I die, so that they can rip the whole thing apart and figure out why I really have so many health problems. It would be nice if they could find a way to get that info before I'm dead. Maybe CI can help?
David Wagner   Clinical Intelligence Is the Next Challenge for Healthcare CIOs   3/8/2012 11:21:06 AM
Re: EHR concerns
@Gigi- I guess it depends on how they accessed it. Personally, i think that is exactly the best thing about EHR. Ir i were a drug company, i could access all of the information without any names or other identifying information. I could then analyze that information and find out that, for instance, women didn't respond to my drug as well as men, or that the dosages that the company originally recommended were wrong. Eventually, as genotyping becomes standard practice, the company could start seeing patterns in genetic types that needed a slightly different formulation of the same drug for it to be effective.

The ultimate long term goal of data in the healthcare industry is individualized medicine-- the right drug(s), at the right dose, at the right time for you. Not a one size fits all drug.

There is a lot of belief that drugs have been made that might work for certain genetic types but not for others, but that those drugs have been abandoned because in the large scale testing, they seemed only occasionally effective. In the future, we might find those abandoned drugs can be revived and used for people with the right genes. And other therapies can spring for that as you learn what is blocking or enhancing what is happening from the drug.

Of course, you can abuse this, too. A drug company could find out a certian doctor like to prescribe a competitior drug, and they could target the doctor for kickbacks to switch to their drug.

they could also use it for targeted advertising or worse.

But i think the potential is bigger than the risks as long as we attempt to manage the risks as they come.
Gigi   Clinical Intelligence Is the Next Challenge for Healthcare CIOs   3/8/2012 1:46:57 AM
EHR concerns
David, am very much favor for the EHR because of its convenience and portability. At the same time am very much concerned about the security aspects. Recently I had read that some of the medical/drug manufacturing companies accessed this EHR data with the help of a hospital staff for analysis purposes. This shows that drug companies can access the whole data bank for their own research and promotions.

I don't think any automation or best judgment using software is hundred percent accurate. At a particular stage human interventions are required for best analysis.

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