By Tina Witte
Sr. Associate Director, Life Sciences
Fletcher
Given the sheer volume and variety of news stories, coupled with the growing body of AI-powered platforms and solutions in healthcare, the utility of AI (and Machine Learning, ML) made this subject area among the most discussed this year in market and competitive intelligence circles. This was further demonstrated by the prevalence of AI-associated sessions at this year’s US Pharma CI conference. In fact, the Director and I delivered a presentation at that same venue on how tools such as AI can and should be incorporated as part of the new ‘WoWs’ (Ways of Working).
Contributions of AI to the medical and healthcare industry are notable and bold. Challenging paradigms and revolutionizing care, AI is no longer being viewed as disruptive. Here is a glimpse of how AI in the healthcare industry has already started to prove its worth:
For pharmaceutical manufacturers
Drug discovery: In a fraction of the time that it takes humans, AI analyzes exceptionally large data sets, from patient demographics to potential candidate compounds. In doing so, it can more efficiently make predictions on the most promising molecular entities to advance to clinical studies. AI in the drug discovery realm has started to significantly reduce time and expenditures in this notoriously lengthy and laborious initial step.
Clinical trials: Once promising developmental entities are identified, AI platforms are now hosting a multitude of capabilities across entire clinical study timelines. From planning to execution, AI’s efficiencies, for example, include securing requirements such as meeting patient diversity conditions, accelerating enrollments, and transforming granular data from real-time analytics for actionable insights, to ensure overall program success. Collectively, these also shave off time and costs for manufacturers and developmental sponsors, and more innovative treatments will enter the market faster.
For clinicians
Clinical decision support: Evidence-based healthcare is progressively being directed by data derived from EHRs, claims, and/or even from the patients themselves. The use of AI and ML solutions in harmonizing data from a multitude of sources (including remote or ‘telemedicine monitoring’ channels) is now being increasingly relied upon to predict and assess a patient’s risk for developing certain conditions, and to detect early signs and signals of abnormalities leading to deleterious diseases and pathologies. This will allow for earlier interventions, prevent further complications, and reduce hospitalizations and deaths, to improve patient outcomes.
For Payers:
Personalized and individualized medicine: The surge in cell, gene, and targeted therapies in development is setting the stage for how payers view and handle precision medicine moving forward, as personalized treatments will be the next frontier in addressing the most complex and costliest of medical conditions. AI’s big data processing capabilities analyze substantial amounts of patient and payer claims data, including genetic information, medical histories/comorbidities, and lifestyle considerations. Patterns identified make predictions on which targets have the highest efficacy potential specific to patients’ individual medical conditions. These analyses become critical inputs to the economics of payer policies and reimbursement frameworks in this category. Treatments with the highest likelihood of success will be prioritized to ensure the best patient outcomes and reduce the likelihood of adverse events while establishing cost-control measures and improving access to patients in need of these sophisticated therapies.
For Administrators:
Administrative tasks: The automation of ongoing and certain ‘mundane’ administrative tasks alone, positions AI as an indispensable asset. From appointment scheduling to processing insurance claims — this benefit is one of the most circulated and prominent examples of efficiency gains and associated cost reductions, to ultimately free up staff to focus on the delivery of high-quality, patient care.
The opportunities from AI may also lead to more variability observed in developmental initiatives (both in the advancing, as well as the abandoning, of clinical studies). With pipelines, future outlooks, modeling, and forecasts in flux, manufacturers will need to reassess and adjust their commercial strategies more frequently to position optimally and competitively. Critical insights into payer dynamics garnered by AI will also pave the way for future market access approaches, especially in areas such as oncology and rare diseases. Finally, at the practitioner level, AI platforms may serve as an avenue to consolidate all disparate parts of an otherwise disconnected patient journey, to better streamline prevention measures, enhance results from detection and diagnostic platforms, accelerate evaluation methods, and optimize treatment selection.
Though the jury is still out on the true implications, ethical considerations, and extent of autonomous and real-world functionality, one thing is for sure, the concepts of AI and machine learning utilization will only go up (exponentially) from here.
Market and competitive intelligence (MI & CI) partnerships will be essential in assisting organizations to become even more competitive and agile in the backdrop of AI. While leveraging AI as a complementary asset, MI and CI will be increasingly entrusted to help navigate the nuances associated with adopting and incorporating AI approaches across all areas of the global healthcare value chain.
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