Can Digital Drug Delivery Help Eliminate Healthcare Disparities and Close Research Gaps?

Machine learning and AI tools are already driving precision medicine and improving diagnostic, treatment and prevention strategies for individual patients.  However, we can celebrate these achievements only so much. Because, at the same time, significant gaps persist in research about underserved populations, particularly Black, Indigenous, and other people of color (BIPOC) or Black, Asian and minority ethnic (BAME), LGBTQIA2+, children and women, or those with rare diseases. When developing new drugs and technologies, researchers have historically neglected the needs of these groups, or have been unable to gather enough data to support research into new therapies. As a result, underserved individuals have shorter life spans, experience limits to healthcare access, and are most likely to be misdiagnosed and suffer from untreated chronic conditions.

COVID-19 is just one example of the debilitating effects of the long-standing neglect these communities continue to experience. Individuals in neglected communities are among those most reluctant to receive the vaccine due to lack of understanding and research of the effects of new medicines on them.  

Without ongoing clinical trials, research, data collection, and deep learning to better understand their unique healthcare challenges, underserved communities will continue to receive inferior care.  Researchers, clinicians, and drug and device developers have ever-increasing access to detailed patient data and tools to mine and analyze it. As industry stakeholders and innovators, what will we do to close the research gaps that could provide invaluable, even life-saving insights for underserved communities? To close these gaps, we need to invest in understanding all of these populations.

Data sets that drive current clinical research are skewed by the individuals and demographic groups prioritized, targeted, surveyed, recruited – as well as by those who choose to participate in clinical trials and share their medical history. Simply put, machine learning and AI are tools that will help us to glean insights from the data we can secure and aggregate, as well as leverage to the best of our capability.  

Recent innovations present tremendous opportunities for Molex and our customers to work together to close these research gaps. A survey conducted by Molex with third-party research firm, Dimensional Research, shows that nearly 90% of pharmaceutical companies believe that digital drug delivery is “very” or “extremely” important and can help improve patient outcomes.  The digitization of drug delivery could help close the research gaps making it possible, for their data to be collected and ultimately used to fuel drug development with patient consent.  Indeed, through digital drug delivery, researchers could potentially reach out to and collect data from many more patient populations and correlate the data for meaningful insights, with a sharper focus on underserved communities. The ability to gather data to understand and adapt or develop new drugs for rare diseases also has the power to be life changing, providing insight into groups that have previously been difficult to run clinical studies on. With these new insights, pharmaceutical companies could potentially develop new drugs for previously untreated poorly understood diseases.  

While the industry will continue to rely on established approaches to research, such as clinical trials, Molex is also committed to collaborating with our customers on digital drug delivery as a new way to improve patient outcomes. Using AI and machine learning, clinicians, researchers, and other stakeholders can perpetually achieve more precise insights into how drugs, delivery systems, and other healthcare devices and technologies need to be adapted to best treat each individual.

Data and insights captured with consent via digital drug delivery, can advance innovation and accelerate the simultaneous development of multiple alternative medicines.  With deep learning and more precise patient insights, pharmaceutical companies can simulate different scenarios and patients’ reactions to new drugs. Each of these simulations generates a new data set from which researchers can perpetually build their understanding of specific patient populations and individual patients.  Such simulations can help create new targeted efficiencies in clinical drug trials, speeding up the time to market for new medicines. With access to wider and more inclusive data sets for a wide range of common or rare diseases, pharmaceutical companies will deepen their understanding of and sharpen their ability to meet the healthcare needs of previously underserved populations.  

Molex and Phillips-Medisize are working closely with our customers to eliminate health care disparities. We share their mission to develop life-saving diagnostics and therapies and address the needs of all patients, including those who have been underserved for too long. We urge our customers to make the best uses of AI and machine learning to ensure understanding of the precise healthcare needs of each patient. Together, we can make real a future where healthcare is truly equitable for all communities and for every individual.

Marketing Director, Phillips-Medisize