AI helps predicting the future of pharma
Our expert's opinion
"Prescriptive Analytics means using Artificial Intelligence (or AI) to analyse huge amounts of data automatically. It can be used in various sectors and it can be particularly interesting in pharma. Indeed, robots with powerful AI can analyse and interpret data much faster (and cheaper) than humans, defining trends and isolating irregularities. All departments could benefit from it: better results in R&D, tailored Marketing, perfect stock use in Supply Chain, etc ... I'm sure that, in the coming years, Prescriptive Analytics will help pharma companies to offer a better service and that, in the end, patients will directly be positively affected by it."
- Antoine Desprez, Associate Consultant
Prescriptive Analytics: Pharma’s Fortune Teller?
In any industry, the power to predict the future is the holy grail. Thanks to artificial intelligence, we may be closer than you think...
Companies funnel vast resources into research in a bid to peer into the murky future and discern the next frontier. If the gamble pays off, countless lives can be enriched, and entire economies buoyed. If the gamble backfires, markets can tumble and lives destroyed.
The pharmaceutical industry can ill-afford such a high-stakes roll of the dice, but what if there was a way to eliminate uncertainty by predicting the future?
Enter the world of prescriptive analytics. In layman’s terms, it refers to the process of employing artificial intelligence to analyze enormous amounts of data. The insights gleaned are then used to evaluate the impact of future business decisions by rigorously testing all possible scenarios to determine the best outcome.
These insights could drive decision-making throughout an organization, from salesforce through to R&D, enabling everything from real-time tailoring of marketing messages to perfectly matching pharma stock levels to demand.
A fully automated analytical system will identify problem areas, changes in a physician’s prescribing habits, and what the competition is doing, allowing you to adapt the frequency of HCP visits and tailor messaging to highlight a specific feature.
“This next-best-action recommendation boosts the effectiveness of the reps in the field; it’s a vastly different approach and it really supports dynamic decision making across the organization says Doron Aspitz, CEO of Verix, a platform that specializes in data analytics for the life sciences.
“It ensures reps are able to take immediate advantage of the most relevant information such as a newly achieved formulary status within a tight timeframe,” he adds.
A graduated scale
Aspitz gauges a company’s level of maturity by breaking it down into a series of steps.
If the data journey begins with entry-level sorting and cleaning for simple analysis, he says the next step is the provision of more advanced reports. For sales reps, this allows them to compare and benchmark their performance against their peers and competition.
Prescriptive analytics is not the final stop, however – prescriptive intelligence is one step further. This is the ability to show insights at a more granular level, emboldening a rep with information on exactly what’s driving the decision-making at an HCP level.
The apex is then competing on analytics, where sophisticated insights, based on AI and machine learning, are implemented at an organizational level, enabling you to predict future trends, pinpointing important, actionable insights that are relevant specifically to you.
How mature is pharma today?
“Most companies in life sciences are at the ‘advanced report’ stage, but no further,” says Aspitz. “Sometimes a simple report about performance will suffice – for example, when a vice-president of sales is having a cup of coffee and wants to check progress. But, if a sales rep manager really wants to understand what’s happening to their HCPs – and where to go next to ensure they scale up performance – you need something a lot more sophisticated, which goes much deeper.”
Equally, development is held back when integrated datasets are not available organization-wide. From deciding what drugs to deliver through to where to position a field force, decisions are being made at both a strategic and tactical level by people who need to be empowered with the right materials. “You start to get a lot of ad-hoc solutions and become very siloed in your decision-making, because there are snippets of applications or dashboards scattered all over the organization, leveraging different datasets and different business roles. That stands in the way of optimization.”
Aspitz concludes: “One of the advantages of today’s advanced analytic techniques is that once you start to embed more sophisticated forms of statistical analysis and machine learning into a business process, and so automate those processes, you’re able to make business decisions in the most optimal manner rather than based on six months old data.”
Source: Eye For Pharma