CitiusTech Blog



    Priyank Chopra on Mar 16, 2015

    Every time clinical researchers start planning for a clinical trial they find a big elephant in the room. Patient recruitment and retention are, and always have been, a problem everyone knows exists, and the success rate of clinical studies often depends on the quality, speed, cost and efficacy of the recruitment process. We’ve already heard and read enough about the challenges and barriers around improving patient recruitment. 


    Let’s come to possible solutions, then. And one of the solutions is to leverage ‘Big Data’.


    To start with, every major clinical research organization has access to huge amounts of patient and investigator data. A few may also have access to external data sources which contain rich insights at both patient and population level (such as social media, disease registries, EHRs, claims systems, HIE databases, omics data etc.). Most times, all the data available is hardly ever used to improve patient identification and recruitment processes. Needless to say, what if we used this data? And how would we use it?


    Here are a few areas where the effective use of big data can make life easier for the researcher:


    Identifying and analyzing data points that make even the smallest difference.

    You already have the patient’s age, gender, and indication data. You may also have data that provides patient motivations as well. Say, what makes a patient participate in a clinical trial? Is it the long term treatment, the cost of therapy or the access to relief of symptoms? In case you have access to genomic data, can we target genetic subsets that increase the efficacy of the study?


    Such data points will help you reduce your in-trial dropout rates. This also applies to the investigator data (e.g., indication area, appropriate fee, etc.). If this kind of data helps you choose the right candidates, and design your marketing campaign with greater accuracy, it can help improve your success rate dramatically.


    Creating electronic platforms that can capture and share data with investigators and patients.

    Remember that the patient’s data is not only for researchers to use, but also a major motivational factor for patients to analyze their own progress. How have the patient’s bio-stats changed over the course of the study? Are there any clinical processes or interventions that the patient should be aware of?


    Again, there are various ways that data can be shared with patients and investigators for motivational impact. Researchers can customize notifications and have one-to-one communication, thereby creating highly focused patient engagements and also helping better categorize users, by parameters such as ‘interest area’,  ‘motivation’, ‘health habits’, ‘lifestyle’, etc.


    Getting rich analysis and insights from patient data

    Advanced analytics like as predictive algorithms, machine learning and pattern analysis can be used on your patient data to create meaningful insights for researchers, and help identify various problem areas such as study design, trial dropout rate, protocol adherence, etc. Analytics technologies can also be used to identify hidden patterns in patient data, assess risks and make intelligent forecasts on key success factors and probability of failure. For example, estimating the minimum number of patients required to meet the trial’s statistical end-points, or analyzing similar protocols used in earlier studies to predict future patient retention rates.


    Leveraging social media

    Researchers would agree that community discussions, healthcare-specific forums and patient boards provide great insight into patient behaviors and reactions to specific treatments or clinical processes. Listening is often a great way to gauge what the patient community is thinking of right now. A great way to get focused information is to use patient boards (often provided by sponsor organizations) where patients with similar profiles interact with each other. Researchers can use machine learning to identify patients who are at a greater risk of drop out and provide outreach and support as per regulatory guidelines.


    In conclusion, patient data, even if its ‘big’ data, cannot single handedly address all patient recruitment and retention challenges. Often, patients don’t even know if there is a relevant clinical trial in their vicinity, that they may be interested in, or do not participate due to a number social and behavioral reasons. Considering the environmental challenges around clinical studies, leveraging data for better patient recruitment is just one part of a successful clinical study. But, as they say, every drop counts.

  • CitiusTech Life Sciences Team

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