As Pharma executives seek commercial solutions for the disruption of “business as usual,” many are looking to Artificial Intelligence (AI) to provide insight and strategy. Like the Financial sector, Pharma’s reputation has been tarnished in recent years. Product safety issues, adverse media exposure, increased fines and revenue/market share losses are all affecting the industry negatively.
Pharma industry executives are realizing that the current model for manufacturing and distributing medicines isn’t fit for Pharma’s future needs. The high margins that made it feasible to tie up capital in large stocks of raw materials and finished goods are ending. Most companies also have asset bases that are ill-suited to produce the sort of therapies currently in the pipeline or to cope with new environmental regulations, so they’ll have to sell or re-engineer much of their existing plants. Not only is the current model for R&D no longer working, the entire way medicine is delivered is changing. Customer expectations have a greater emphasis on outcomes, plus the digitization and personalization of everything affects healthcare delivery.
It is expected that the Pharma of the future will be managing a vast network of service providers in addition to manufacturing and distributing their own products. They will also have to acquire a much deeper understanding of patients. It’s a fundamental shift from the way Pharmaceuticals are currently developed and sold to a new approach that is heavily patient and outcome focused. As you know, healthcare is being digitized, from electronic health records to e-prescribing and remote monitoring. Pharma has the opportunity to provide a more personalized healthcare experience to patients and help them take a more active role in their healthcare.
Patients are already playing a bigger part in the process. Indeed, they are even helping to decide which products should reach, or remain on, the market. For example, patient power was a critical factor in the decision to approve Gleevec for use by the FDA, as well as Herceptin for use in the British NHS. The influence of patients will only increase with the growth in access to reliable healthcare information, as well as with the use of co-payments and the increasing trend towards self-medication. These are not small challenges. It’s a disruption of the entire Pharma industry and yet, with great change comes great opportunity. To this end, many Pharma companies are finding success with AI (Artificial Intelligence).
AI can assist Pharma with the following market changes:
1. Changing customer landscape
The primary decision-maker was the physician when I began working in Pharma. This is no longer the case. The merging of various provider systems, the influence of the payers and patient empowerment has dramatically changed the balance of power in the decision-making in Pharma. Each player has different, and complex, decision-making processes. Pharma now needs to be able to address a wide range of needs when planning and marketing their drugs.
How AI can help
This would become an insurmountable task if it were not for the power of AI. AI can sift through trillions of variables and come up with the core that impact specific segments. The power of AI is that it can use Big Data from numerous sources to develop highly impactful and specific drugs designed and marketed for targeted groups of people…and it can do it quickly and effectively.
2. The perceived outcome and value of drugs
In the old days, products had to meet standards of efficacy, safety and minimal side effects. Whilst products still need to meet the traditional hygiene factors of safety and efficacy, they also need to include a focus on outcomes and value. In terms of patient outcomes, patients want to know how much better they’ll feel and how soon. The physicians want to know if the drug will improve their patients’ quality of life, and the payers want to know if it will enable them to keep working or reduce the cost of care. These are qualitative aspects that are as important as the clinical outcomes in today’s world. Governments are outcome and value focused too. In both developed and emerging markets, governments seek to minimize Pharmaceutical spending growth by enacting pricing and reimbursement legislation. While reference pricing systems have already brought prices down in many countries, governments are still pushing for more reductions. The Pharma pricing systems now are moving towards a pay-for-performance model that began in some hospitals in the US insisting on this kind of model. The trend for this is growing.
How AI can help
The power of AI is that it can be used to analyze Big Data from numerous sources to gain a better understanding of what components of drugs, and price, will meet the stakeholder perceptions of outcomes and value. Eularis now link real-world patient data with payer claims data to data from clinical trials, and use machine learning techniques to know what price meets the stakeholder perception of value as well as delivering the highest profit to the manufacturer to ensure optimal pricing at launch.
3. Marketing approaches
In the past, the industry launched new products with big budget campaigns. However, by 2020, new medicines will be launched with live licenses. They will have rapidly evolving labels as the indications for which they can be prescribed are extended, new dosing schedules will be developed and their side effects will become more obvious. “Big bang” launches will be replaced by a process in which information is continuously disseminated in a series of much smaller waves. Most companies will have to change their marketing and sales functions quite substantially as their focus switches to specialist medicines. The armies of sales reps of the past will be gone, and a small group of specialists who can speak with top level physicians on an equal footing will take their place. These specialists are also likely to be able to negotiate with the payers about the value of their brands also.
How AI can help
The power of AI comes to its own in marketing. With more data available than ever before – including chip-in-a-pill data, sensor data, IOT data, HER data, claims data and more – the environment is ready. This combination of data can be integrated to understand all stakeholders much better than ever before. This can then be used to provide transformative insights to be used in marketing as well as allowing us to now utilize omni-channel real-time marketing to serve up the right content to the right person in the right sequence in the right channel at the right time, enabling real power within marketing.
