Technology and data are pivotal in determining clinical trial outcomes. At Clicebo, we capitalize on the latest tech solutions and clinical trial data to advance clinical research.

Decentralized and Hybrid Clinical Trial (DCTs)

With the advancements in technology, such as mHealth, wearables, sensors, IoT, big data analytics, AI, synthetic biology, telemedicine, and mobile communication, clinical trials are becoming simpler and more efficient, moving them directly to patient homes from the sites.

We provide a streamlined solution by designing and implementing DCTs that improve patient experiences. The integration of new technologies like electronic data acquisition, electronic informed consent, and electronic clinical outcome assessment helps attract more patients, gather more data, and offer remote patient monitoring. Additionally, big data analytics and artificial intelligence technologies are revolutionizing R&D and cutting costs.

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Artificial Intelligence in Clinical Trials

Artificial Intelligence’s (AI) ability to review large quantities of collected data to identify patterns, screen for adverse events, monitor patient participation, and assess progress and outcomes makes it a harbinger of change. It will soon alleviate the workload on principal investigators (PIs), clinical research coordinators (CRCs), and clinical research organizations (CROs).

Impact of AI on clinical trials

AI can cover a range of medical care from diagnosis, preventive medicine, and palliative medicine to drug design and development. AI has the potential to increase the success rate in drug development by enhancing R&D areas such as novel target identification, drug candidate selection, biometrics data analysis from wearable devices, and the prediction of the drug effects in patients with diseases.

AI is beneficial in conducting clinical trial by offering

Efficient patient selection from varied data sources including Electronic and Medical health records.

Identifying qualified investigators through automated data capture and sharing data across systems.

Interpreting diagnostic images like chest radiographs, mammograms, and MRIs.

Predicting the development of Cervical cancer using cervix images

Clicebo has adopted AI technologies in the following areas

Clinical trial design

Investigator and site selection

Appropriate patient recruitment via automatic eligibility evaluation, simplified trial description, and automatic trial recommendation from EMR database

Patient monitoring, medication adherence, and retention

Automated data quality assessment (data cleaning, medical coding, and query solving)

State-of-the-art AI-enabled medical image analysis (Eg. Cervigram for precision-based rapid screening and staging of cervical cancer)

Managing adverse events (AE) and protocol deviation by leveraging Risk: Benefit Analysis

Patient Event Monitoring – providing patient care through disease logs (disease bouts, medication administration, and health monitoring), and study logs (medication, administration, and record-keeping)

Patient Guidance – identifying patient discontinuation probability and taking appropriate measures through periodic patient counselling

Predictive Analytics – External Control (EC) data from completed clinical trials and electronic health records for early go/no-go decisions in randomized clinical trials (RCTs)

AI with Internet of Things (IoT) device – for remote patient monitoring