Improving healthcare in the intelligent age requires cultural change and collaboration

It’s no secret that we are living in an age of unprecedented disruption. Innovation is moving at an astounding pace, impacting how we connect and communicate across industries globally.

Artificial Intelligence (AI) innovation is advancing far faster than other technological breakthroughs, with collaboration between pharma companies, biotech, academia and startups bringing new game-changing tools to the healthcare market.

From developing scientific progress to patient touchpoints, AI can optimize and elevate at all levels. Leadership is key to encouraging teams to restructure and adopt an open mindset regarding AI in their field.

BCG proposed the 10-20-70 rule for successful generative AI (GenAI) transformation, which involves focusing 10% on building new algorithms, 20% on infrastructure and 70% on people and processes.

Leaders must recognize that we can only harness AI innovation’s full capacity if we initiate cultural transformation, creating an openness to change among people.

AI tools can advance research and development by validating drug targets more quickly, optimizing clinical trial design structures and speeding up the new drug screening process by up to 50%.

Iambic Therapeutics, for example, recently announced a new AI drug discovery model called Enchant that streamlines research processes by predicting how a given drug will perform, even at the earliest stages of development, cutting years off time-consuming and expensive preclinical development processes.

Adopting AI means letting go of old ways of working. The technology will point to the most promising projects for investment and those less likely to succeed. Resources will be shifted, and some projects will be eliminated, requiring a culture and mindset shift that is not easy to achieve.

Leaders at all levels of the organization must encourage teams to expand their creative capacity and focus on the best areas of opportunity.

Recruitment proves continuously challenging, costing nearly $2 billion annually and resulting in 85% of clinical trials failing to recruit or retain a large enough sample size.

Leaders can utilize AI tools to streamline this challenging process by efficiently identifying individuals who are qualified matches to participate in trials. Companies such as Johnson & Johnson apply machine learning and AI algorithms to bring trials directly to patients.

Sanofi, Open AI, and Formation Bio recently collaborated to produce Muse, the first AI-powered tool to speed up clinical trial recruitment. Sanofi plans to implement Muse in its Phase 3 multiple sclerosis studies.

Utilizing these AI tools will address the common challenge of timely patient recruitment and improve accessibility factors and overall experience, encouraging patients to participate in clinical trial activities.

People expect and deserve the personalized solutions AI is already helping healthcare professionals develop, including individual treatment plans. These relationship mapping between drugs and factors can determine predicted patient outcomes and identify best-fit treatments.

Research from Genomics and Stanford University indicates that new genetic screening tests can identify those at risk for common premature diseases, including hypertension, breast cancer and diabetes.

These tools would identify genetic warning signs for these conditions, allowing health professionals to offer vulnerable groups earlier intervention and treatment, with the possibility of preventing premature deaths by 24.5%.

AI-powered technologies and treatments can help patients make more informed and active healthcare decisions. However, communications efforts must reach out to patients to build trust in medical advice and encourage them to proactively seek knowledge of their risk factors and stick to treatment plans.

It’s important to remember that generative models are only as good as the data they are fed. Patient outreach is needed to encourage clients to consent to their data being used to develop further research, as this helps to supplement data sets with actual use cases and expand the pools that researchers can pull from.

For example, Genentech has started using “lab in the loop” training for its AI based on data that it generated itself.

With the increased capabilities of pooled data, concerns about privacy are rising. To combat these hesitations, Owkin and others have utilized federated learning to ensure that stored data builds an atlas of data where key findings are analyzed to come out with gated solutions but the data remains protected at the academic centre.

There is also the potential to share innovations among healthcare organizations. Sanofi has adopted an “all boats rise” mentality by opening up its data sets for small AI initiatives to benefit from.

By shifting our perception and hope for the healthcare industry to a regenerative, circular model rather than a direct pipeline from singular company to consumer, we can expand our capacity to do good.

As with any new technology, change will receive resistance. Early adopters and leaders must translate how these tools can be used in real, tangible ways. At the heart of this is a need for a cultural shift to a learning mentality that rewards experimentation and innovation.

Satya Nadella stated that we have to move from an educational organization to a learning organization. Healthcare has traditionally been focused on providing education and advice. In today’s age, the shift to engagement will be critical to ensuring stakeholders’ buy-in.

Healthcare leaders need to recognize that innovations are not implemented on their own, no matter how great they are. Scientific breakthroughs need the support of a comprehensive communications strategy to build trust, enhance adoption and ultimately, realize the amazing breakthroughs that can impact every patient’s life.