March 14th, 2018

AI, mHealth Adoption in Clinical Trials Remains Slow


Patient recruitment and retention for clinical trials have always been difficult, but AI and mHealth can help – if sponsors and CROs are willing to invest in them.

Drug development has never been quick; it can take up to 15 years to bring a new medication from initial design to widespread use. Unfortunately, most of that time is spent on clinical trials, tying up vital resources for months – if not years. With a single clinical trial costing upwards of $100 million, trial spending can quickly and dramatically skyrocket.

Thanks to artificial intelligence (AI) and mobile health (mHealth), however, it may be possible to cut down the time needed to carry out successful trials. By leveraging emerging technology to recruit and retain the right patients, trials can cut down on drug development timelines and reduce costs. That is, if stakeholders can be convinced that AI and mHealth are worth the investment in the first place.

Leveraging AI and mHealth to Design Better Trials

AI and mHealth capabilities offer numerous avenues to improve recruitment and the patient experience. AI can streamline recruitment and compliance processes, while mHealth helps patients stay engaged over the course of the trial.

Clinical trials that recruit via social media or with the help of AI can yield more a more interactive and diverse subset of patients. Ultimately, this results in trial reports that are more informative on a greater percentage of the overall population. AI capabilities such as machine learning and natural language processing can even aid in onboarding patients, tracking updated consent forms, and monitoring data for irregularities.

mHealth infrastructure, on the other hand, leverages social media, e-consent, telemedicine, mobile apps, and biosensors to streamline communication and provide for more accurate data collection, increasing patient retention. Clinical trials that employ text messaging and apps to stay in touch with patients can unobtrusively remind them when it’s time to take medication, offer simple ways to log health data, and field inquiries that patients may have as they come up.

Eventually, mHealth may enable completely remote clinical trials. 70 percent of potential patients in the US live two hours or more from the nearest possible investigator site. This means that an ideal patient may have to decline simply because of distance. But with virtual trials, patients can receive medication by mail while using mHealth platforms and devices to send consistent data to their site.

Understanding Obstacles to Widespread Adoption

While the potential benefits of AI and mHealth technologies are clear, it’s difficult to translate that potential into industry-wide adoption. Understandably, there are financial and regulatory hurdles to overcome before this technology can be used in primary research. Instead, trials are piloting isolated aspects of both AI and mHealth in secondary research. This siloed approach to innovation is more in the service of enhancing and improving current processes and platforms rather than remaking them.

Sponsors cite concerns about the dependability of AI, limited IT capacity, and basic data analytics capacity as roadblocks to a wider adoption of these technologies. Though many sponsors and CROs are calling for more streamlined technology, the dozens of data platforms and clinical systems used in one trial make it difficult to untangle the data flow.

Whether it’s the unfettered sharing of internal data across multiple platforms, the maintenance of consent and privacy records, or the revamped patient recruitment process, digitally modernized clinical trials will improve performance and make life easier for patients, CROs, and sponsors alike. And although we have some way to go before we hit widespread adoption, it’s clear that this tech has the potential to revolutionize the way we conduct clinical research.