Data Analytics: Enhancing Clinical Trial Recruitment & Retention

Joshua Kaycè-Ogbonna
8 min readJun 8


The outcome of clinical trials is pivotal in determining whether to adopt or discard a procedure and if it should be approved for widespread use. The success(es) of evidence-based medicine and guide for healthcare professionals are hinged on clinical trials as they provide valuable information about patient care.

Clinical trials are an essential component of the research and development process in healthcare. To evaluate the safety efficacy and accuracy of any medical procedure or intervention, it must go through clinical trials to be certified fit for human usage. To advance this, subjects, patients, or any other name by which they are called are required to volunteer with the primary goal being to gather sufficient evidence as per safety and efficacy in these medical devices, investigational drugs diagnostic tests, or even remotely the behavioral analysis test.

The trials are subjected to established protocols and consensual methodologies established by the relevant regulatory authorities and ethical guidelines, to ensure the reliability, reproducibility, and validity of the results.

Phases of Clinical Trials

All clinical trials do not have typically rigid phase descriptions. They however progress through different phases, each serving a specific purpose:

Phase 1: At this stage, clinical trials are conducted by a select number of active volunteers to primarily access dosage, safety, and the potential side effects of the investigation. In the first phase, since safety profile is the metric at test, the volunteers are most often healthy individuals, rather than patients suffering from a disease condition.

In this stage, dose calculation (in drug trials) to ascertain the appropriate dosage range for further investigation and to isolate incidences of potential adverse effects or side effects associated with the treatment are of primary interest. Signs of toxicity or unexpected reactions to the drug are also vital components of this phase. This is where rigorous monitoring in the form of regular medical assessments, equipment tests and calibration, and close observation, is carried out to ensure participant well-being.

Concerning jurisdictional application, this stage goes on with informed consent, Mens rea, Ethical considerations, and regulatory guidelines strictly followed to ensure the rights and safety of the volunteers are covered.

Phase 2: In the second phase, the sample size is increased. This will avail the researchers with sufficient data to support the entire decision-making process for advancing the trial to the next phase, in most cases, Phase 3. Phase 2 trials provide valuable insights into the potential benefits of the investigated product and inform the design of subsequent studies.

Phase 3 This phase sees the mainstreaming of placebo into the clinical trials and then involves a much larger sample size than the previous phase(s). Here we can compare the investigational treatment with existing standard treatments or placebos. In phase 3, the researcher finds additional evidence of the modality’s effectiveness, safety, and optimal usage.

Phase 4: Phase 4 involves public health safety and is known as post-marketing surveillance. This phase is conducted when the modality has been approved by regulatory authorities and is available to the general population. In phase 4, trials monitor long-term clinical or environmental safety and evaluate the modality’s use in different patient populations and its impact on public health.

Clinical trials are not standalone activities by the originating body or institute. They are conducted by researchers, often in collaboration with medical institutions, academic centers, companies, or contracted research organizations. The trials are protocoled strictly rather than randomized control trials, in compliance with ethical considerations, patient rights, environmental safety, and well-being.

To get participants involved, the issue of informed consent is taken seriously as it must be obtained from all trial participants. The trial participants get detailed information on the procedures, the potential risks, the purpose and objectives, the inclusion and exclusion criteria as well as the regularity and nature of follow-up visits or monitoring before deciding to participate.

The outcome of clinical trials is pivotal in determining whether to adopt or discard a procedure and if it should be approved for widespread use. The success(es) of evidence-based medicine and guide for healthcare professionals are hinged on clinical trials as they provide valuable information about patient care.

Overall, clinical trials are vital in the progression experienced in medical knowledge, disease prognostication, developing innovative treatments, improving patient care, and ultimately shaping the future of healthcare.

Can data analytics decentralize clinical trials, especially the potential to enhance recruitment and retention efforts?

The challenges associated with clinical trials require proactive strategies, effective communication, human-centered trial design, and a labyrinth of connections between researchers, healthcare providers, rights advocacy groups, and the community. However, employing data-driven recruitment strategies can improve recruitment and retention rates in clinical trials.

Data analytics has come to stay, but the potential to decentralize clinical trials and significantly optimize recruitment and retention is uncapped. From inception, clinical trials have been conducted in centralized locations such as academic medical centers or dedicated research facilities, which is always a strong ask for patients. However, data analytics can reduce this barrier by bringing clinical research trials closer to the subjects.

In response to the variegated challenges it has faced in trialing drugs, Novartis created an in-house clinical research organization, AI in Novartis, to improve drug testing and trial by transforming how to create innovative medicines and facilitate more engagement with patients and healthcare providers, with the overall aim being to improve operational efficiency. What are the reproducible features of this approach?

Identification of Eligible Participants: With due recourse to patients’ rights and privacy considerations, data analytics can help researchers to leverage various sources of data like; electronic health records (EHRs), patient registries, medical imaging data, biobanks and biorepositories, social media communities, insurance claims databases, to identify potential trial participants. By analyzing large datasets, researchers can utilize optimized data-driven parameters to isolate a list of potential participants who meet the specific eligibility criteria for a clinical trial.

Patient Outreach and Engagement: With tools like demographic and behavioral data, data researchers can identify the most effective patient engagement strategies and communication channels to custom-create messages to specific target populations. This personalized approach will increase patient awareness and willingness to participate in clinical trials.

Dynamic Recruitment Monitoring: With real-time clinical trial analytics, researchers can monitor recruitment metrics. This is helpful when dealing with complex data sets other than the number of participants enrolled, demographic profiles, and recruitment sources. This monitoring will help identify effective strategies that will make room for timely adjustments of recruitment goals not met. This measure can upscale the overall trial efficiency.

Retention Strategies: The inherent trial factors such as the complexity of the process, length of the study, and excessive burden on participants can greatly increase the chance of attrition. This is why data-driven retention and overall clinical trial enrollment efforts must identify patterns and predictors of retention. With this, the implementation of targeted retention strategies, such as personalized patient support, reminders to participants, and other forms of interventions, can improve participant retention throughout the trial.

ClinicalConnection connects over 850,000 members with clinical research trials and has successfully harnessed project-centered designs and novel retention strategies like video consultations, periodic evaluation, and electronic data capture. As suggested in the past, participant support programs like online forums and educational materials, gamification techniques like the play-to-earn model, remote monitoring options where practicable, and secure online platforms to provide study updates and reminders.

Flexible Trial Designs: The improvement in trial design will come faster with data analytics. Recently, the Dana-Farber Cancer Institute has carried out phases of clinical trials with artificial intelligence with successful outcomes. At its 2nd Transatlantic Exchange program in Paris, France, a session focused on artificial intelligence (AI) and data science in oncology studies discussed a model for this implementation. This framework for supportive and adaptive trial designs, where trial parameters can be modified based on interim data analysis, can be flexible. This flexibility improves the efficiency of operation and creates more patient-centric trial designs, resulting in quicker identification of effective treatment modalities and ultimately reducing the overall trial time and associated costs.

Identifying potential trial participants with predictive modeling and machine learning.

In medical trials, predictive modeling and machine learning offer great potential in identifying potential trial participants. Through advanced algorithms and large datasets, valuable insights and predictions that facilitate participant identification and recruitment can be beneficial.

Through these algorithms, specific data such as age, gender, medical conditions, genetic markers, available electronic health records, patient demographics, medical histories, and genetic profiles that are relevant to the trial’s inclusion and exclusion criteria can be streamlined to identify patterns and associations that may indicate potential trial participants. By systematically analyzing this information, researchers can identify individuals most likely to meet the study requirements, improving the efficiency of participant recruitment.

Machine learning grants researchers access to a wider pool of potential participants and facilitates the recruitment process for trials focusing on rare or niche diseases. By integrating patient-specific data, such as genetic information, biomarkers, and treatment response data, participant selection can be enhanced by identifying those likely to respond positively to the intervention, reducing unnecessary exposure to interventional procedures.

The emerging trends and technologies in data analytics for clinical trial recruitment strategies and retaining participants in clinical trials will provide a developmental framework for worldwide clinical trials. They will also raise the bar for best practices in the lab. The overall aim, which is to harness the power of data analytics, will see researchers optimize participant recruitment processes, identify potential trial candidates more accurately, streamline trial enrollment, and most importantly, enhance the quality and reliability of trial data.

In conclusion, researchers, sponsors, and regulators can leverage data analytics effectively in clinical trials. This leverage must be compelling and holistic. It should acknowledge the far-reaching implications of malpractices. All stakeholders can unlock the full benefits of data analytics while safeguarding participant rights. This can be done by;

  1. Fostering a culture of continuous learning by Increasing public confidence by conducting post-trial analyses,
  2. Providing clear and updated guidelines on the use of data analytics in clinical trials ( particularly in the areas of data handling, privacy protection, and regulatory requirements),
  3. Advanced analytics training and workshops for researchers such as the periodic training by Helix Research Center,
  4. Establish robust data governance models to ensure the security, privacy, and integrity of clinical trial data,
  5. Promote standardized and interoperable datasets across different clinical trial systems and platforms.

Author’s Note: This blog post was AI-guided. However, it was the product of a delightful collaboration with my trusty AI assistant. The human touch added creativity and context, while the AI contributed its algorithmic prowess.

For all inquiries, collaborations, and engaging discussions, connect by sending an email to Don’t miss out on this chance to be part of the AI healthcare revolution! Act now and open the door to endless possibilities.



Joshua Kaycè-Ogbonna

Academic| Data| AI/ML in Healthcare