Pharmaceutical research is being driven toward change through many channels, but the impetus seems to be focused on economic objectives rather than on improving the patient experience. Through effective collaboration and innovation, we can accomplish both.
Advancements in mobile health (mHealth) wearables are changing paradigms in pharmaceutical research by offering significant improvements in subject recruitment, engagement, and data management. As these mobile health technologies mature and as artificial intelligence and machine learning can be applied, the possibilities for their use in driving operational efficiencies may be endless. These possibilities need to be balanced with pragmatic use within clinical trials, and integration with electronic Clinical Outcome Assessment (eCOA) data.
By integrating varied mHealth data streams with eCOA data, researchers may gain improved visibility into the patient experience, which could fuel patient-centric development strategies and deliver real value to patients and their caregivers. Each new data stream must be evaluated on its own merit by asking the goal and value of the collected data. Once the proof-of-concept is confirmed and the data stream designed, developed, and tested for clinical-data quality, these data streams have the potential to enable patients and their physicians to access and interpret their data for better health management.
For many pharmaceutical researchers, the consideration and practice of incorporating mHealth technologies into clinical development can be considered risky. Early adoption bears the advantage of first-use, and carries the risk of the yet-unregulated domain.
So with all of this in mind, the questions become: how can pharma determine which technology solutions to explore amidst a seemingly endless amount of technological invention? How can industry reduce development costs? How are pharma to evaluate and optimize their patient-centric data capture strategies during clinical development?
Historically the answers to similar questions have come from effective collaboration as seen through consortiums and work groups. Pharmaceutical researchers can reduce risk while leveraging the expertise of data collection experts and technology leaders. Collaboration between data collection experts and pharmaceutical teams leads to new approaches that will make drug discovery safer and more efficient. Collaboration between technology leaders and data collection experts opens new options for solutions that pharma can use effectively. And collaboration with regulatory experts balances new ideas with a path to meet regulations and patient safeguards.
By effectively collaborating to innovate new data collection processes, pharma researchers can benefit from better data and increased trial efficiencies, and ultimately, deliver improved patient healthcare experiences.
Karin Beckstrom is a Senior Product Manager at ERT.