Technologies Transforming the Pharma Value Chain: Part 2

This is Part 2 of this article. Part 1 covers drug discovery, clinical trials, regulatory and supply chain and can be found here. This article will continue into the commercial aspects of the pharma value chain.

Technologies Used in Market Access

Price optimization

As a largely data-driven and analytical process, it’s unsurprising that we’re already seeing powerful AI solutions for price optimization.

Such modelling historically required many hours manually collecting data from clinical trials, real-world sales and event history, drug submissions, competitor data, and publications. Today, AI platforms can create value pricing and can predict optimal price with 90% accuracy. These types of AI platforms (see interview with one such company on my youtube channel here), all use white box AI which means it is explainable AI for transparency into decision making.

Time to reimbursement

You’ve heard the adage that time is money. But did you realize just how much money is at stake when launching a pharma product? According to Pharmaceutic Executive, a delay in launch can cost a company an average of $15 million per drug, per day. Delays also cut into the lifetime earning potential of the drug. In the competitive pharmaceutic industry, every day the launch is delayed is another day the competition has to catch up.

To ensure financial success of a drug, one needs to understand and incorporate the real payer drivers (for each payer influencing the decisions) in your strategy for each stage of development and commercialization.
Artificial Intelligence gives us the means to uncover and correct the inefficiencies and get the reimbursed faster. It is possible to gather insights based on past industry-wide experience and information on policy changes and process alterations. You likely already have much of the data needed to optimize your strategy, although it may be scattered throughout disparate internal sources. When combined with publicly available data on your competitors’ products and submissions and up-to-date information on policy change and process alterations, you can begin to uncover the factors that have the biggest impact on accelerating tie to reimbursement. These factors include everything from the type and tone of language used to the clinical trial data and pricing levels to local market trends and everything in between. When we analyze and compare the data using machine learning algorithms and natural language processing in the context of fast vs. slow reimbursement approval, it’s possible to understand which of these are important to the decision making process and outcomes of the reimbursement authorities.

In addition to the factors selected, an AI-powered approach can also uncover previously unknown or disregarded factors that would likely lead to more effective strategies. And these insights could be used to guide future submission strategies as well. It can even enable time-to-market estimates and success probabilities for planning and budgeting purposes so you can prioritize your best chances of success.
The benefits of AI in this area aren’t limited to just gaining insights. It can also streamline the execution of those plans and procedures. Using sophisticated techniques, you can develop the submission documents utilizing both the drivers for that formulary as well as the optimal language shown in previous submissions to work best for that formulary committee.

The challenges associated with reimbursements and market access are only going to get more complex as payers and regulatory bodies align with trends such as value-based care, precision medicine, and consumerism. Pharma companies that don’t want to fall behind the competition need to start putting plans in place now to realize the full potential of the products currently in their pipeline.

AI in Strategic Planning

The purpose of strategic planning is to prepare an organization for the future by setting goals and a process for achieving them. This requires capturing data and insights and then synthesizing that into a strategy. The data typically fall into four key areas:
1. What the company/organization offers (resources & competencies)
2. Who the company/organization serves (markets & customers)
3. Who competes with the company/organization (competitors)
4. The market environment the company/organization operates in (laws & regulations, economics, technologies, demographics)

Collating all relevant available data is the first step in an effective strategic plan. For healthcare strategic planning, this includes a wide range of disparate data sources. The sheer quantity of data available for each of the areas above is more than any normal strategic planning team can analyse and synthesise. And moreover, traditional strategic planning tools are incapable of integrating and synthesizing this amount of data.
So in most cases, strategic plans are based on too little data to enable good strategic decisions.

Enter AI.

Imagine if you had an ongoing strategic planning process that was automated (so it doesn’t need significant amounts of human time), highly intelligent and highly accurate in terms of growing the company results.
A system spearheaded by a Professor from Harvard, and a team of data scientists from MIT, are planning a game changer in strategic planning – for those who can afford it. Here are some of the features that make this AI system so exciting:
• Full automation, operating 24/7 without human intervention
• Instant big data import, data analysis and strategic conclusions so that instant response to market events or competitor strategies can be implemented
• Constant automated data search (the types of data will be specified by humans)
• Constant automated data currency review and update from real data, so no data is out of date
• Automatic data validity check and data cleansing
• Automatic review of data relationships
• Automated review of reasoning and conclusions
• Constant automated review of assumptions underlying strategic decisions
• Automatically prompted creation of strategic implementation plans
• Constant automated reviews of strategy implementation for results analysis and adjustment for more rapid progress
To read the full detailed article I previously wrote about this, click here.

Technologies Used in Market Research and Insights

Market research in healthcare has already greatly benefited from the digital revolution, and artificial intelligence (AI) is already being used to reshape market research, from recruitment and questionnaire design to data organization and analysis. In addition, AI-powered disruptors are dismantling all areas of the value chain in market research, taking revenue away from incumbent healthcare market research agencies.

AI is being used for recruitment, conducting primary market research, analyzing and collating insights from research data (primary and secondary). To read more about this, read my more in-depth article on this here.
https://eularis.com/how-to-embed-ai-into-healthcare-market-research-for-greater-results/

Technologies Used in Pharma Sales and Marketing

Sales Force

Artificial intelligence (AI) is revolutionizing sales processes in industries around the world, allowing for a most customer-centric and data-driven approach. Pharmaceutical companies that are able to fully leverage AI, machine learning (ML), and other digital tools will benefit greatly, reversing the deleterious effects of COVID-19 and market disruption and coming out the other side stronger than ever.

AI is being used in numerous ways in sales teams from predicting behaviour (physician behaviour in this case such as switching), precision physician targeting, customised sales messaging by individual physician, scheduling and automation, filling in forms, and much more.

Imagine you could quantify and reproduce the expertise and wisdom of your most experienced sales reps, those whose years of experience have allowed them to develop the intuition and insights to know the next best course of action to take with any lead.

AI is now able to do just this—and make that knowledge available to every sales representative on your team. This approach leverages many of the same AI tools as those used to improve the customer journey, but on a far more granular level, providing representatives with the right content for the right channel delivered at the right time and to the right customer, for thousands of customers. Top-line sales has been shown to increase as much as 30% as a result of successfully implementing next best-action programs.

Building an AI-powered sales pipeline may seem like a daunting task, but the benefits are both proven and considerable.

For more use cases in pharma sales, please read my linkedin article that can be accessed here.

Marketing

I’ve written about many of these topics before but as a top line AI can be used to do things such as identifying rare disease patients, predict and modify patient adherence, put your content marketing on steroids, segment your customers by their dynamic behaviour and personalize the marketing by individual, create true omnichannel personalized marketing, map your customer journey, conduct analysis of social listening, enhancing customer call centre output through intelligent NLP and automation, KOL/TL mapping, to name but a few.

More detailed information about how Artificial Intelligence technologies can assist the pharma sales and marketing process can be found in my linkedin article on this topic here.=