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Companies who successfully harness the AI potential of the growing amount of medical data available will gain a competitive advantage by fast generating insights.
FREMONT, CA: Artificial intelligence (AI) has been causing a stir in the pharmaceutical industry for a long time. Several pharmaceutical companies are attempting to model their business operations by estimating future needs. The application of AI can aid in developing new and current product strategies, with the next step being the use of AI in market access decision-making.
Why AI has to be part of pharma pricing and market access debates
To accomplish the required return on investment and market share, pharma makers must rethink worldwide launch tactics and local market access strategies like interactions with payers, prescribers, and regulators. As 'conventional' global product launch methods no longer generate the required outcomes, industry trends necessitate a more comprehensive approach and way of thinking to enhance market share through Big Data and AI analytics.
Role of AI in Decision-Making
Manufacturers will need to demonstrate innovation and distinction to build a case for new medication prices that will be evaluated by payers, providers, and regulators in light of these trends. The purpose isn't only to set pricing to recuperate research costs and meet profit targets, as with conventional systems like International Reference Pricing (IRP) and Launch Sequence.
AI can help speed up the gathering and processing of medical evidence, market access data, and clinical trial findings that support the case, which would otherwise take a long time if done manually (i.e., human-intensive analysis).
Market Access-related applications of AI
AI in Pricing and Reimbursement and Health Technology Assessment (HTA)
When it comes to HTA evaluation, pricing, and negotiations, AI offers many benefits by assisting companies in developing value propositions, differentiating products, and responding quickly to concerns from HTA organizations.
AI can include evaluating enormous volumes of data, for example, utilizing AI, a GAP analysis of three prominent ulcerative colitis medicines was conducted, focusing on real-world trials. The system correctly linked together with different results for every medicine depending on paper reporting results in under 20 minutes, which would have been difficult without machine learning.
AI to predict roadblocks for Field Reimbursement Managers (FRMs)
Organizations are utilizing AI and machine learning to create risk score models that will enable Field Reimbursement Managers (FRMs) and other similar services to intervene proactively on behalf of patients who are probably to encounter bottlenecks in their path before the roadblocks happen. More information like insurance benefit details, channel, diagnosis code, and out-of-pocket payment can be included in patients. It can even connect patient status data to other relevant information like dispensing, price, and demographic data to create new predictive model elements.