The failure to retain recruited patients and collect outcome data can lead to additional problems and potentially biased outcome results. Consent information is unbalanced, focusing on patient’s rights to withdraw without accompanying information that promotes robust consent and sustained participation. Research to date has sought to influence patient behavior through the use of incentives, reminders and minimizing patient burden. Currently, external pilot trials are recommended for testing the feasibility of main or confirmatory trials. The average probability that a candidate emerging from lead optimisation will not make it to be a drug is above 99.8%. In drug discovery, companies need to be able to predict accurately what risks a candidate has as early & cost effectively as possible, and not rely on hugely expensive clinical trials as the sole or principal means of de-risking candidates.
AI will allow your organization to reduce costs in the clinical trial process by helping you reduce churn. Using AI’s classification features, your organization will be able to select candidates that are least likely to churn through the identification of factors that lead to a high probability of churn. Given that 86% of all clinical trials do not proceed due to failure to achieve enrollment deadlines, enhancing the candidate selection process will significantly improve your likelihood of success. Additionally, AI can help improve patient selection by reducing patient heterogeneity using AI based classification techniques. This will allow your clinical trial team to select a patient group that will be more inclined to respond to treatments. Leveraging AI in the clinical trial process will lead to a reduction of costs through improvement of patient retention and reduction of response failures.
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Healthcare companies are using machine learning and AI to increase top and bottom line through gaining competitive advantages, reducing expenses, and improving efficiencies. They are optimizing all areas of their business from readmission risk and occupancy rates to marketing, in order to make data-driven decisions that lead to increased profitability.