Clinical Trials: Top Ways to Make Patient Recruitment Process More Efficient

Clinical trials can now be conducted remotely using technology tools such as custom-created mobile apps for enrolling and engaging patients, clinically certified wearables and sensors for collecting patient data, and digital communication platforms.

FREMONT, CA: Patient recruitment and retention are two of the most pressing issues confronting the clinical trial business today. Clinical trials account for about 60 percent of the entire cost of medication development. Furthermore, patient recruiting and retention account for 32 percent of the total cost of a clinical trial. The global clinical trial market is steadily increasing. Annually, more than 2 billion dollars is spent on patient recruitment for clinical trials. The time has come for a paradigm shift in the way patients are recruited for clinical trials and the use of technology to accomplish this.

Here are the top ways that technology can make the patient recruitment process in clinical trials more efficient.

Virtual Clinical Drug Trials

Virtual clinical trials, often known as siteless trials, are a new trend in clinical research. It moves medication studies out of clinics and into the homes of patients. Clinical trials can now be conducted remotely using technology tools such as custom-created mobile apps for enrolling and engaging patients, clinically certified wearables and sensors for collecting patient data, and digital communication platforms.

 When comparing virtual trials to traditional on-site trials, the per-participant expenditures of drug studies are cut in half. Furthermore, the ease of involvement it provides enables a more diversified engagement that transcends physical, economic, and geographic barriers.

Use of Artificial Intelligence (AI)/ Machine Learning (ML) for Patient Recruitment

Patient recruitment is reaching new heights due to software-driven initiatives. Clinical drug trials are seeing unprecedented advancements thanks to artificial intelligence, natural language processing, and machine learning. ML algorithms can go through various data types and medical records to make the best choice in terms of fit and patient engagement.

AI algorithms can also aid with processes like electronic phenotyping, which helps to reduce population heterogeneity. AI and ML algorithms can aid improve patient selection by drawing on many data sources, including EHRs, physician notes, images, and patient scans, among others. They have the ability to enhance variety, reduce population heterogeneity, identify a cohort of patients who are more capable of responding to treatment, and choose patients who are more likely to have a measurable clinical objective.

AI can also be used to create therapeutic protocols. Large data sets from past experiments can be examined to find parallels and areas of concern. Researchers and Contract Research Organizations (CROs) can use this information to improve the protocol design of a future trial and devise effective patient recruiting techniques.

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