When you watch a Texas Hold’em poker tournament on TV, it’s easy to get a false sense of how good you are. There’s a simple reason behind that: It’s a lot easier to make good decisions when you can see all the other players’ hands.

Fortunately, this same principle also applies to healthcare. If you’re a provider and the only data you can see regarding the medications and devices that have been prescribed for your patients is your own (or your organization’s), it’s like only being able to see your hand. But what if you could gain that TV view, (i.e., see everything that has been prescribed by every provider who cares for that patient)? Your odds of delivering better outcomes would increase exponentially.

Actually, there is a way to obtain that information virtually on-demand. It just requires a change in thinking.

Rather than viewing life sciences organizations as merely purveyors of products, providers should be building collaborative relationships with them—especially around data sharing. Consider that life sciences organizations work with a broad range of healthcare organizations, giving them a wider span of data than any healthcare provider alone can accumulate.

When life sciences organizations feed the data from multiple providers into next-generation predictive analytics, they can develop insights that help providers recognize trends and issues before they become costly problems. How valuable is that in a value-based care world? They can also identify which treatments have proven most effective for patients with isolated or co-morbid conditions, helping drive better outcomes while reducing waste, duplication, and the overall cost of care.

Here are some of the ways this provider/life sciences collaboration can be an “ace in the hole” for delivering measurable results.

Identify Areas of Higher Risk

Providers necessarily tend to be focused on the patient in front of them. That is the key to delivering quality, personalized care. The downside is they may not recognize that a particular patient’s symptoms or illness may be indicative of a larger pattern. Even if they suspect it, they often don’t have enough data, or in some cases the tools, to follow up on these suspicions.

This is where a data-sharing relationship with a life sciences organization can help. For example, a life sciences organization can analyze its diabetes medication sales, as well as prescription and claims data from multiple, disparate providers and overlay the results on a geographical map to determine areas where diabetes “hot spots” exist. By adding Zip+4, demographic, and socioeconomic data into the analytics, they can go even further, creating personas that represent segments of the overall population who share similar attributes.

With this information in hand, providers can go back through their own patient panels to determine which ones fit the highest-risk personas. They can then make diabetes detection a priority for those patients, and perhaps even start them on preventive treatment to avoid, or at least, delay development of that costly chronic condition.

How does that play out? Suppose a provider works with a large Hispanic population. Statistically, Hispanics tend to exhibit a higher instance of diabetes than other ethnic groups. By targeting this high-risk group, providers can use their population health management technologies and protocols to encourage those who fit the persona but who have not been diagnosed yet, as well as those already on the “watch list,” to have their HbA1c levels checked on a more regular basis.

Using the data and analytics in this way can help reduce costs while delivering a better long-term quality of life to patients—and help providers meet value-based care outcomes targets.

Prescribe with Confidence

Most data about the effectiveness of a particular life sciences product comes from clinical trials. The problem with that is clinical trials typically only represent a small percentage of the overall population. For example, the American Cancer Society says only 5% of all adult cancer patients participate in a clinical trial.

Additionally, most minorities are severely underrepresented in the clinical trials that do take place. Given all the variables we humans have, that leaves a lot of questions as to whether a particular medication or device is truly the best for a given patient or population.

Life sciences organizations that are using data and analytics can demonstrate the value of their products with much greater precision. After drawing usage and outcomes data from providers, they can use their analytics to detail how effective their products have been in managing or mitigating a particular condition, both within a specific population and in the face of defined co-morbidities. With this information, providers can make care decisions that have the highest likelihood of yielding the best results.

See What Might Otherwise be Obscured

Sometimes the severity of an issue is difficult to discern up-close. It’s only when you step back a level or two that the full scope can be realized.

The opioid epidemic is often one of those issues. On an individual basis it can be difficult to identify. Taken in the aggregate, however, the need to address the issue becomes apparent.

Life sciences organizations have a higher-level view of where opioids are being prescribed, in what volume, and at what frequency. By matching their data to provider data, and overlaying it with geographical and other data, they can identify opioid “hot spots” and work together to develop programs and prescribe medications that will help wean patients off opioids. They can also be more aware of the likelihood that opioid dependence will be created so they can address issues before individual patients become addicted or are otherwise harmed.

Holding All the Aces

Whether you’re playing Texas Hold’em or working with patients, the more information you have about the big picture, the better outcomes you’ll be able to create.

By sharing data with life sciences organizations that are using next-generation predictive analytics, providers can gain much deeper insights into the overall care of their patients and what will be most effective for them. At which point everybody wins.

  • John Pagliuca

    John Pagliuca is Vice President, Life Sciences at SCIO Health Analytics, where he leads the global commercialization efforts of SCIO’s advanced analytics and SaaS solution suite within the Life Science market. He has more than 18 years of sales, marketing, and technology experience in quantitative analytics and SaaS solutions for Life Sciences.

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