Guest Column | February 6, 2024

3 Areas Where AI Could Revolutionize Patient Recruitment And Retention

By Ross Jackson, consultant

People portrait on world map-GettyImages-1727335145

Artificial intelligence (AI) is becoming an increasingly prevalent buzzword within the healthcare and life sciences industries.

There are obvious benefits for clinical trials, such as spotting patterns within enormous amounts of data, being able to develop “digital twins” for research purposes, analyzing, and summarizing complex protocols into layperson’s language, etc.

And I suggest the benefits of AI also will be clearly evident within the world of patient recruitment and retention. Here’s a look at three areas that should experience a positive impact.

1) Research Sites Searching Patient Databases

One of the most common methods for recruiting patients into trials is for research sites to utilize their existing database of contacts. Of course, with increasingly strict inclusion/exclusion eligibility criteria, this can be a laborious process — potentially taking a few hours to check through each patient record to find the relevant information.

The issue is exacerbated by the likely non-standardization of data that is present in patient records. This means relevant details may be recorded in multiple sections of the record and the same information may be expressed in multiple different ways. AI can offer a transformative solution to this challenge by leveraging its capabilities in natural language processing (NLP) and data analysis.

One of the most useful things about using AI for patient identification is its ability to process unstructured data. Patient records are often stored in various formats, including electronic health records (EHRs), handwritten notes, scanned documents, and voice recordings. Traditional software solutions struggle to interpret this diverse range of data, as they depend on structured and standardized formats — of the type that rarely exist in the real world. AI solutions can extract valuable information from these disparate sources by understanding the context and meaning of the data.

AI can recognize key medical terms, conditions, medications, and other relevant details, even when they are not presented in a standardized format. This capability significantly reduces the need for manual data preprocessing and formatting.

As well as this, AI will continuously learn and adapt to evolving medical knowledge and terminology that traditional software solutions will struggle to keep up with (requiring constant updates and manual adjustments).

Machine learning (ML) models also can be trained on historical patient data, including both successful and unsuccessful recruitment attempts. By analyzing patterns and correlations within this data, AI algorithms can identify “hidden” criteria that may be overlooked by manual screening. As a result, AI can identify potential patients who might have otherwise been missed, thus increasing the pool of potential trial participants.

And once the identification process is completed — in a much quicker time frame than through using other methods — AI can support personalized patient outreach and engagement strategies. With potential patients identified, AI-powered systems can generate tailored communication plans, taking into account patient preferences and availability. This personalized approach can enhance patient engagement and further increase the likelihood of successful recruitment.

Overall, the use of AI for this type of patient recruitment activity will not only accelerate the process but also reduce the administrative burden that often plagues staff at research sites, CROs, and sponsors.

2) Digital Outreach Through Social Media Advertising

A highly effective method for recruiting patients is to utilize social media advertising — most especially Facebook ads. Meta (Facebook) has already incorporated an element of AI, and I anticipate this continuing to develop into a more comprehensive AI-led advertising system.

Other social media platforms — for example, TikTok, Snapchat, Reddit, Pinterest, etc. — will also be incorporating elements of AI. In this context, Facebook represents all social media, as it is the leading platform for generating patient referrals.

Facebook has been moving ever closer toward a true self-service advertising platform, and the incorporation of more AI into its campaign interface will go a long way to achieving that aim. Its vast repository of data, encompassing successful ad campaigns and user behavior, should be able to be leveraged to streamline the ad creation process. One of the key aspects of this will be providing relevant prompts and utilizing AI-generated ad content. This should enable advertisers to simply input a set of criteria (e.g., demographic and location information) and have Facebook develop the target audience for the campaign.

For example, rather than selecting an age range from dropdown menus, then choosing a location and a radius, advertisers should simply be able to write these details in a text field input, with Facebook doing the rest.

It may be the process of ad creation that will be most enhanced through AI, though. Facebook possesses a wealth of information on user preferences, behaviors, and interactions with ads. This data could be utilized to generate prompts that are highly tailored to the specific needs of patient recruiters. For instance, Facebook could prompt advertisers for disease areas of interest, demographic details, location preferences, and other relevant criteria. By drawing on Facebook’s wealth of data, AI can guide advertisers in crafting ads that are more likely to resonate with the desired target audience.

Combining this capacity for interpreting useful prompts with Facebook’s access to an enormous library of successful (and unsuccessful) ads, should enable Facebook to present the advertiser with a range of options for imagery and text content within their ads. This not only accelerates the ad creation process but also ensures that ads are more likely to capture the attention of potential trial participants.

For ad copy, AI could suggest compelling and medically accurate language that communicates the benefits of participating in a clinical trial, while at the same time addressing potential concerns or misconceptions. AI could also recommend visuals that are known to perform well in engaging users and conveying a message effectively.

Additionally, AI could generate ads that it anticipates will resonate with the unique attributes of the audience being targeted. And with Facebook’s algorithm learning about reactions in real time, AI functionality could suggest revisions to ad content, based on what it can see is working best at a particular moment.

Utilizing Facebook’s data in this way — to create the type of ads that are known to perform well, then show them to an audience of people who are more likely to be receptive to the ads’ messaging — will be a very powerful way to benefit from the power of AI.

In the longer term — though there would certainly be some privacy issues to overcome — it may also be possible to utilize AI within Facebook’s ad system to increase the diversity of the trial participant population. With the trend already in place for people from underrepresented backgrounds to be recruited for trials, having a relevant AI prompt within Facebook’s ad system could help with showing ads to a relevant audience, as well as helping to engage a diverse audience through use of audience-specific wording (including generating ads in languages other than English), imagery, etc.

3) Retaining Trial Participants Using Chatbots

AI-powered chatbots could play a crucial role in maintaining continuous communication with trial participants, as well as providing support and responding to various events and milestones during the trial journey. (And not just trial-related events. For example, NLP of a patient’s records should enable AI to identify a patient’s birthday and subsequently create and send a relevant greeting.)

The kind of personalization available for this engagement activity can help foster a positive patient experience and build trust and rapport between participants and the clinical trial team. This conveys the impression — hopefully based on what’s really happening on the ground — that the people behind the trial value and care about each participant's well-being, which should have a significant impact on patient retention.

In this manner, AI-powered chatbots can maintain ongoing communication with patients throughout the duration of the trial. This can include such simple, yet effective, actions as sending reminders about scheduled site visits, encouraging participants to record their feelings using electronic patient-reported outcomes (ePRO), and being available to pass on important information about the trial, such as medication schedules or dietary guidelines. These timely reminders and prompts can help participants stay engaged with the trial, as well as ensure they adhere to the trial protocol — which is obviously critical for the study's success.

AI-powered chatbots actively engaging with patients to collect such things as ePRO can make it easier for patients to provide essential data. This not only reduces the burden on patients but also ensures that the trial's data collection is consistent and accurate — again, something that is vital for the validity of the research being undertaken.

AI chatbots can attempt to proactively address the all-too-frequent events of missed appointments or participants failing to fill in diaries by sending friendly and encouraging messages to remind participants ahead of time. These automated reminders should be tailored to the individual's circumstances and preferences, helping to reengage patients who may have temporarily disengaged from the trial for whatever reason. This helps free up the time of research site staff. Plus, AI could detect trends or patterns in missed appointments or noncompliance and provide insights to trial coordinators for further intervention if necessary.

AI-driven chatbots could also provide immediate responses to participant inquiries or concerns, offering valuable support 24/7 in a way that is usually unavailable. This could be particularly important for participants who have questions or experience unexpected side effects outside of regular site opening hours. In this situation, the chatbot could offer guidance, escalate urgent matters to human staff, or provide reassurance that helps to alleviate anxiety,  thus further ensuring participants feel supported throughout their journey in the trial.

For a more customer experience-led approach — which I wholeheartedly recommend — chatbots could also gather feedback from participants regarding their trial experience. This could include asking about satisfaction levels, any challenges faced, or suggestions for improvement. This type of feedback can be instrumental in refining the trial process, enhancing patient satisfaction, and ultimately improving retention rates for current and future trials, as participants who feel they are being appreciated and listened to are more likely to remain engaged.


As things stand, it does appear as though AI is the buzzword du jour — perhaps having inherited that mantle from DCT. But it’s also no surprise that in the coming years AI is almost certain to have a significant effect on healthcare in general and clinical trials in particular. The three areas I’ve covered above are some of those in which I thoroughly anticipate AI having a large and beneficial impact.

The AI genie is out of the bottle. It only makes sense, then, to take advantage of what it can offer — delivering better outcomes all around in the field of patient recruitment and retention.

About The Author:

Ross Jackson is a patient recruitment specialist and author of the books “The Patient Recruitment Conundrum” and “Patient Recruitment for Clinical Trials using Facebook Ads.”

Having started out with digital marketing in 1998, Ross quickly developed a specialty in the healthcare niche, evolving into a focus on clinical trials and the problems of patient recruitment and retention.

Over the years Ross branched out from the purely digital and now operates in an advisory capacity helping sponsors, CROs, sites, solutions providers, and others in the industry to improve their patient recruitment and retention capabilities — having advised and consulted on over 100 successful projects.