Advanced Predictive Analytics: Driving Operational Efficiency across Pharma Processes

Clinical Trials:
The goal of these data analytical models is to improve the operational efficiency of all phases of the clinical trial. Their focus is to increase the probability of high-validity clinical findings that can support product development and help define therapeutic indications with the right patient populations to target and then optimize the protocols to improve the odds of success of these trials. Advanced analytics also help ensure that clinical trials produce higher-validity, regulatory quality data by differentiating and sorting data-entry errors, inconsistencies and outliers with misreported adverse events that can undermine clinical development findings.

Drug Discovery:
These technologies are helping biotech companies and their research partners identify and select promising therapeutic molecules with a potential to treat certain illnesses. To do this, predictive analytical models in drug discovery process consider varied data points about individual targets. This involves collating historical information on how a particular target behaves while interacting with other proteins and data about the behavior of the target in previous experiments and what type of drug did not work before.

Drug Safety and Pharmacovigilance:
In drug safety processes and pharmacovigilance, analytics are also playing a pivotal role. Drug safety remains a touchy topic in a pharma company and involves a lot of cost, especially if it involves recalling batches of medication. Companies also must consider adverse drug reaction or ADR that could lead to severe legal implications. Predictive analytics can reduce this risk and identify patient populations who might not tolerate these drugs and those who are at a greater risk of experiencing an ADR.

One of the biggest challenges in pharma are the time and cost of patient recruitment in clinical trials. Predictive analytics makes this process more operationally efficient by identifying which patient populations have specific characteristics and would fit in certain clinical trials. Predictive models can thereby reduce the number of test subjects needed for research while ensuring that only those who fit stay engaged till the end of trial. This results in more effective results with less wasted resources and shorter market time to develop a drug.

Drug Supply Chain:
Next key area of impact is in the drug supply chain. Predictive analytical models help pharma companies understand how demand is impacted by variables such as regional demographics and economic conditions – thereby allowing drug manufacturers to make use of efficient resources and improving patient access to critical medicines thereby streamlining the supply chain management.

Using advanced machine learning algorithms and predictive analytics models, pharma companies can also forecast drug sales correctly. This leads to a more efficient drug distribution process through improved inventory management that reduces risks of over stocking and having too few drugs to meet demand. These technologies also are used alongside historical data points about past sales and trends in regions by customer segments (pharmacies and hospitals) with other variables that could contribute to future sales forecasts.

These analytical models also help pharma companies identify and prioritize new sales opportunities. Pharma companies can identify profitable customer segments for a drug or medication and can focus on those with better potential for a return on investment.

Drug Marketing and Advertising:
Another area of operational efficiency is drug marketing and advertising. Companies rely heavily on predictive analytical models during early stages of product development – from identifying those patient populations most likely to benefit from a new drug to its long-life cycle. Customer Relationship Management or CRM is also an essential aspect of pharma process. These companies can better understand how customers react and interact with aspects of business operations through each phase or touchpoint within an overall sales process. Predictive modelling and advanced analytics can track this information throughout the entire customer experience life cycle via numerous channels such as email campaigns, call center interactions, online ads, and more, giving companies valuable insights needed to market their products well.

Although predictive analytics can help pharma companies take note of the abundant data generated in the sales marketing processes and the entire drug lifecycle, there are growing challenges in terms of organizational preparedness and strategy to implement data analytics solutions.