‘Artificial Intelligence (AI)’ – the science of simulation of intelligent behavior in computers, has the potential to leave a transformational impact on virtually everything that we see and feel around us. As many will know, the modern definition of AI is “the study and design of intelligent agents where an intelligent agent is a system that perceives its environment and takes actions which maximizes its chances of success.”
Let me begin with a couple of exciting examples on the application of AI for general use. One such is Siri the voice-activated computer in the iPhone that one can interact with as a personal assistant, every day. The other is the self-driving features with the predictive capabilities of Tesla cars; or even the well-hyped Google driver-less car. Alongside, Google is also in pursuit of creating AI with ‘imagination’ through its ‘DeepMind’. It develops algorithms that simulate the human ability to construct plans.
Pharma’s emergence in the AI space:
The unfathomable potential of AI is being slowly recognized in the healthcare arena, as well, including pharma industry. It’s gradual emergence in the space of ‘intelligent learning’, often called ‘machine learning’, ushers in a new paradigm of learning from a vast pool of highly credible real-time data. Innovative applications of this process can fetch a game changing business performance. Its scope spans right across the pharma value chain – from Drug Discovery, including Precision Medicine; Clinical Trials; Pharmacovigilance; Supply Chain Management, and right up-to Sales and Marketing.
Pharma’s emergence in the AI space is quite evident from Reuters report of July 3, 2017. It wrote that GlaxoSmithKline (GSK) has inked a new USD 43 million deal with Exscientia to help streamline the company’s drug discovery process by leveraging AI. With this deal in place, Exscientia will allow GSK to search for drug candidates for up to 10 disease-related targets. GSK will provide research funding and make this payment, if pre-clinical milestones are met.
Again, on July 27, 2017, Insilico Medicine – a Baltimore-based leader in AI, focusing on drug discovery and biomarker development, announced a similar agreement with the biotechnology player Juvenescence AI Limited. According to this agreement, Juvenescence AI will develop the first compounds generated by Insilico’s AI techniques, such as Generative Adversarial Networks in order to generate novel compounds with desired pharmacokinetic and pharmacodynamic properties.
Several other pharmaceutical giants, including Merck & Co, Johnson & Johnson and Sanofi are also exploring the potential of AI for streamlining the drug discovery process. It would help them to significantly improving upon the hit-and-miss business of finding new medicines, as Reuters highlighted. Eventually, these applications of AI may be placed right at the front-line of pharma business – in search of new drugs.
I have already discussed in this blog – the ‘Relevance of AI in creative pharma marketing’ on October 31, 2016. In this article, I shall mainly focus on leveraging AI in health care for greater patient-centricity, which is emerging as one of the prime requirements for excellence in the pharma business.
Imbibing patient-centricity is no longer an option:
In an article published in this blog on the above subject, I wrote that: ‘providing adequate knowledge, skills and related services to people effectively, making them understand various disease management and alternative treatment measures, thereby facilitating them to be an integral part of their health care related interventions, for better health outcomes, are no longer options for pharma companies.’
The craft of being ‘patient-centric’, therefore, assumes the importance of a cutting-edge of pharma business for sustainable performance.
What exactly is ‘patient-centricity’?
BMJ Innovations – a peer reviewed online journal, in an article titled, ‘Defining patient centricity with patients for patients and caregivers: a collaborative endeavor’, published on March 24, 2017, defines ‘patient-centricity’ as: “Putting the patient first in an open and sustained engagement of patient to respectfully and compassionately achieve the best experience and outcome for that person and their family.”
Thus, to deliver the best experience, and treatment outcomes to patients, their participation and engagement, especially with the doctors, hospitals and the drug companies assume significant importance.
The June 2017 ‘Discussion Paper’ of McKinsey Global Institute, titled ‘Artificial Intelligence the Next Digital Frontier’ also captured this emerging scenario, succinctly. Recognizing that health care is a promising market for AI, the paper highlighted the enormous potential in its ability. The power of which can draw inferences by recognizing patterns in large volumes of patient histories, medical images, epidemiological statistics, and other data.
Thus, AI has the potential to help doctors improve their diagnoses, forecast the spread of diseases, and customize treatments. Combined with health care digitization, AI can also allow providers to monitor or diagnose patients remotely, as well as transform the way we treat the chronic diseases that account for a large share of health care budgets, the paper underscored. This poses the obvious question: what exactly AI can possibly do in the space of health care?
What can AI do for health care?
In a nutshell, the application of AI or ‘machine learning’ system in health care generally uses algorithms and software to approximate human cognition in the analysis of relevant, yet complex scientific and medical data. In-depth study and interpretation of these in a holistic way would be of immense use in many areas. For example, to understand the relationships between prevention or treatment processes and outcomes, or various debilitating conditions affecting people with the advancement of age, to name just a few.
This necessitates the generation of a huge pool of relevant and credible data from multiple sources, storing and analyzing them meaningfully, and then garnering the capabilities of ‘machine learning’ with the application of AI. Such a process helps in zeroing-in to a series of complex, interdependent strategic actions to go for the gold, in terms of business results. Using conventional methods, as exist today, other than imbibing AI or ‘machine learning’, may indeed be a Herculean task, as it were, to achieve the same.
Invaluable business insights thus acquired need to be shared, across the various different functions of a company, for greater patient-centricity within the organization.
Moving from ‘patient-engagement’ to ‘patient-centricity’:
While making a significant move from just ‘patient engagement’ to being ‘patient centric’, one-size-fits-all strategy is unlikely to yield the desired results. The process of gathering adequate knowledge and understanding of any individual’s disease management skills, which mostly depend on complex multi-factorial, interrelated and combinatorial algorithms, will be a challenging task, otherwise.
Thereafter, comes the need to deliver such knowledge-based value offerings to target patients for better health outcomes, which won’t be easy, either, in the prevailing environment.
Considering these, AI seems to have an immense potential in this area. Some global pharma players are also realizing it. For example, GSK is reportedly engaged with IBM’s Watson in the development of AI-enabled interactive digital Apps for its cold and flu medication to provide relevant information to patients.
Patient-centricity would soon be the name of the game for pharma business excellence. However, to be truly patient-centric, especially in the sales and marketing operations, pharma players would require to source, process and analyze a huge volume of relevant data in several important areas. These include, target patients, target doctors, environmental dynamics, demographic variations, regulatory requirements, current practices, competitive activities, to name a few.
In this strategic business process, AI or ‘machine learning’ will help accurately mapping the ongoing dynamics and trends in virtually all critical areas. It will help ferret out the nuances of turning around the competitive tide, if any, and that too with immaculate precision. In that sense, AI is likely to emerge as a game changer in imbibing patient-centricity, in the real sense. Consequently, it carries a promise of delivering significantly better outcomes, yielding higher financial returns, alongside.
Although, some concerns on AI are being expressed by several eminent experts, it is generally believed that on the balance of probability, it’s crucial potential benefits far outweigh the anticipated risks. In my view, this holds good even for the pharma industry, especially while leveraging AI for greater patient-centricity, better disease prevention, and more desirable treatment outcomes – improving the quality of life of many, significantly.
Disclaimer: The views/opinions expressed in this article are entirely my own, written in my individual and personal capacity. I do not represent any other person or organization for this opinion.