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Big Data for Big Pharma: The Power of Predictive Analytics

By   /  November 9, 2018  /  No Comments

Click to learn more about author Jasmine Morgan.

The pharmaceutical industry is a billion-dollar enterprise which sits on mountains of data. It could use a tool which takes these heaps of information and neatly classifies them, highlighting the relationship between different entities like doctors, patients, prescribed drugs, and diagnoses.

This industry faces countless problems related to selling, limitations dictated by privacy concerns, tighter marketing budgets and depending on the recommendation of the physician to make a sale. All these issues would be handled better if there was in place a way to anticipate future directions based on real-time data, not only historical recordings.

Predictive Analytics has the power to present elegant solutions to these by creating statistical models which can approximate the demand for certain drugs neatly, estimate the sales capacity of each doctor and follow each step of the patient journey.

Uses of Predictive Analytics in the Pharmaceutical Industry

Wherever there is data, you could potentially bring in the power of Analytics. It could be a recommendation engine for drugs, validating new substances or enrolling patients for clinical trials. Predictive Analytics can also be used in connected activities, such as marketing, logistics, and sales.

Recommendation Engine

Just think about how Amazon is able to come up with the right products based on your search and purchase history. What if medical software could be able to do the same regarding prevention, diagnosis, and treatment?

Predictive Analysis for the pharmaceutical industry could look at different data tables including patient’s characteristics, test results, family history, previous conditions and recommended drugs and come up with the best diagnosis as well as the prescribed medications. These personalized recommendations would be better than current generic textbook recommendations since they would combine allergies, past records and known reactions for an individual patient.

Select Patients for Clinical Trials

Currently, the selection criteria for taking part in clinical trials are not serving the patients’ best interests or that of research. It’s more of a bureaucratic approach or a first come, first served rule than actual science.

Predictive Analytics could identify those with the highest probability of benefiting from the treatment. Also, it could indicate specific underrepresented groups which could bring new insights into the mechanisms of the body’s reaction to the medication. Through this approach, a lot more factors could be involved in the final decision.

New Drug Development

Until now, developing and testing a new drug was a decades-long process. It involved a lot of trial and error through multiple hypothesis testing. Chemists had to work closely with doctors to eliminate side effects, dangerous interactions and still keep the desired effective dosage.

Predictive Analytics could help in this case by highlighting potential pitfalls in the early stages of development. This could even help identify those substances which have the highest chances of successful interaction with the disease. The next step is testing the interactions with other components or with the regular functioning of the human organism. Everything is in fact based on computing regression models and probabilities.

Predictive Analytics for Pharmaceutical-related Sectors

Not only could direct drug development or patient supervision benefit from Predictive Analytics, other related industries like marketing and sales are good candidates for this tool. After all, having accurate predictions related to demand, peaks, sales and logistics are competitive advantages for any business.

High Prescription Volumes

Like in any business, it’s about selling more or at least selling the more expensive item. The difference is that unlike the market for consumer products, you can’t just boost the demand for medicines with smart marketing. Some products are not even available without a prescription due to dangerous substances.

In the game of pharmaceuticals, the doctor is a key element to more sales. He decides to recommend one product or the other. Therefore, it becomes essential to identify those individuals who are great brand ambassadors, reward them, and motivate them to continue trusting a particular brand.

Until now it was a matter of sorting lists in a spreadsheet environment, usually at the end of the month or quarter and making the plan for the next period. Unfortunately, this approach is not efficient, since it assumes that getting a disease in a specific area is a linear, predictable process. It also disregards all social, economic and even weather patterns.

A good suggestion is also not to go after doctors who generate higher prescription volumes since they will be the target of more companies and it could boil down to a price war.

Predictive Analytics can pinpoint, much like Google Keyword planner does, those niches where there is low competition but a high chance of ROI. That is, in fact, a better solution, going off the beaten track and picking the hidden fruits.

Performance Evaluation

Like any tool, Predictive Analysis needs to be assessed for efficiency. Simply put, you need to evaluate how well the model fits the data and if it remains valid with a new set of observations. In the case of pharmaceutical applications, this step could mean the difference between life and death for a particular patient. That is why what would be generally considered a good fit (up to 99%) needs to be refined in the case of drugs, especially if these treat rare but deadly conditions, like some types of cancer.

What are the Next Steps?

The pharmaceutical industry could become inspired by the top results attained in other sectors. Possible applications include lifetime value optimization and dynamic pricing, next to the already mentioned recommendation systems.

To take full advantage of these applications, it is necessary to connect the pharmaceutical companies’ proprietary customer relationship management (CRM) systems to the core analyzing the situation on the market. To receive the full advantage of Predictive Analytics, you need to adjust the impulses generated by the market to your company’s strengths.

About the author

Jasmine Morgan is a technology consultant with a software engineering academic background and broad technical expertise gained through over a decade of experience in the IT industry. Jasmine has worked on high profile projects in both application and infrastructure domains as well as detailed consulting engagements assessing business capabilities. She has a keen interest in emerging technologies and enjoys learning about them through hands-on tutorials and exercises. She has a strong interest in IT architecture and understanding how technical solutions can be applied alongside business operating models to enable continuous improvement, including Microservice architectures and how business services can be delivered and managed through DevOps and Cloud capabilities.

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