Look at any list of hot technologies in healthcare and machine learning/artificial intelligence (AI) is sure to be at or near the top. That makes sense, because unlike so many technologies that seem to be more hype than substance, machine learning already has a practical application: helping organizations actually use the mountains of data they’ve been accumulating over the past decade by uncovering patterns and trends that might not be seen otherwise.
It is also one of the key components of digital intelligence, which combines data and domain knowledge to create context that enables organizations to outperform the market and their competitors.
Here are five ways life sciences organizations can apply machine learning to drive success.
Demonstrating real-world drug outcomes
As the business model in healthcare changes from fee-for-service to value-based, outcomes-oriented care, success in controlled clinical trials prior to FDA approval isn’t enough to add a drug to a formulary or get a device approved. Instead, payers want to see value in a real-world setting.
Machine learning offers that proof by combining medical and pharmacy data to show how outcomes, such as the total cost of care, or rate of inpatient admissions and emergency department visits, differs between one organization’s drug and a competitor’s over a two-to-three-year period. With that data in-hand, sales enablement teams can show payers and providers how the organization’s offering improves outcomes and reduces risks for different populations, easing the way to increased sales.
Seizing limited-time opportunities
The period right after a drug is launched, and the roughly six months between the time it comes off-patent and generics flood the pharmacies, are critical to life sciences organizations that want to maximize their sales/ROI. Machine learning can help them find the optimal targets for these efforts, such as neighborhoods or areas with a high probable concentration of undiagnosed hypertension when the organization is introducing a new blood pressure medication.
When a drug is coming off-patent, life sciences organizations can use machine learning to understand which providers or patients are least likely to move to a generic based on previous patterns. The organization can then focus its efforts on persuading others to stay with the drug to boost their sales (and protect the brand).
Finding off-label use
While physicians commonly experiment with a drug developed for one issue to see if it can solve another, they may not document their success. The result is a new application remains hidden from others who could benefit from it.
By comparing HCC and NDC codes, plus biometric data, machine learning can find those otherwise hidden relationships between unusual uses and outcomes. Life sciences organizations can then look into them more deeply to determine whether an existing drug can be sold in new markets.
Focus on rare conditions
It is unlikely that there will be many blockbuster drugs that affect large populations anymore. Instead, the future is in specialty drugs for rare diseases and conditions.
Unfortunately, the typical analytics approach of feeding data about rare conditions into regression models won’t work because the occurrence of those conditions is too small for traditional analytics to pick up correlations. The low prevalence isn’t a problem for machine learning, however.
It can uncover even the smallest relationships between seemingly unrelated data, creating new opportunities for life sciences organizations to address these rare conditions with treatments that are proving effective. This ability to narrow the focus will become even more valuable as medicine becomes more personalized.
More effective physician outreach
Another area where machine learning can help life sciences organizations is with their sales and marketing efforts. It can ensure those organizations are reaching out to the right physicians with the right message at the right time. It can also ensure the message is delivered in the way it can best be consumed by each physician.
Here’s an example. Life sciences organizations can target physicians with messages about diabetes screening and treatment in areas with a growing Hispanic or Asian population since both of those ethnic groups tend to have a higher-than-average propensity for diabetes.
Through machine learning, life sciences organizations can uncover which messages to deliver to whom, and when, so they can act as a true, value-added partner rather than being perceived simply as a seller of products.
The synergy of man and machine
While machine learning offers many benefits to life sciences organizations, having the technology alone isn’t enough. Organizations must ensure they are able to incorporate all of the required data into their models. They must also have the human expertise available to determine which machine learning discoveries are worthy of attention and which should be largely ignored.
Life sciences organizations that don’t already possess these capabilities may want to consider working with a partner that has expertise in this area rather than attempting to build it in-house. This approach will ultimately cost less while enabling the organization to take advantage of machine learning faster, shortening the time-to-value.
No matter which route they take, however, there is no doubt that machine learning is one technology solution that’s definitely worth pursuing. It’s an investment that will continue to pay dividends for years to come.
Lalithya Yerramilli is vice president of analytics at SCIO Health Analytics.
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