The advent of artificial intelligence, machine learning, and various other disruptive innovations has help pharma companies to deal with data analytics.
Fremont, CA: The pharmaceutical data analytics has been a manual and tedious task conducted by commercial research, health outcomes, R&D, and Clinical Study groups at Pharma companies, small and large. With the introduction of machine learning, artificial intelligence, and other disruptive innovations, Pharma, similar to other industries, has also started its slow but sure transition to a more agile, data-driven model – one where in-house research is supplemented by intelligence gathered by applying algorithms across terabytes of Physician Rx, Patient Claims, and other related datasets.
Here are some use cases of machine learning and artificial intelligence in Pharma.
Patient Finder using Claims Databases
Finding patients in claims databases can be accomplished by identifying patients who show similar characteristics to other patients with the same diagnosis codes. For instance, using a cohort of patients who have been confirmed to have diabetes, companies can create ML models, which can then be applied to other patients in order to identify potential undiagnosed cases or patients.
A standard patient identification approach can also be utilized to find patients with rare diseases.
The latter is challenging, but still a prevalent topic. Rare disease patients sometimes remain undiagnosed until it is too late. With ML’s help, it might be possible to detect the disease very early in the progression. It is also economically advantageous for pharma companies as rare disease drugs are usually costly, and the per-patient revenues could be very significant.
Searching Physician Trends for Commercial Market Research
Leveraging Associative Rules Mining, or “apriori,” data scientists can create models with the outcome variable becoming a quantitative value related to Rx records. For example, given a dataset of physicians’ Rx records whose prescriptions of a particular medicine are marked as increasing or decreasing on a quarterly or monthly basis, pharma companies can use Associative Rules to find previously unknown patterns.
Market Mix Modeling
It is a common practice among the pharmaceutical companies to apply promotion response modeling to find the optimal sequence of mix multi-channel marketing and other activities like detail (P1, P2) and call frequency. Typically, such activities have been performed using negative exponential. In more recent days, alternative support vector machines have been successfully used to find an optimal mix. The preliminary results have been promising and should prompt further research to assess the viability of new approaches.
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