Clinical trials are imperative for testing drugs and therapies, advancing the science of care, and coming up with an effective treatment approach to enhance outcomes for patients. A major challenge that organisations engaged in clinical trials is patient enrolment and retention. According to Forte Research, a meagre 7 out of 100 known patients complete a trial successfully, while 18per cent of patients end up dropping out of the trial at various stages. There are several barriers that contribute to the persistently low rates of trial participation and dropouts and these include refusal to comply, financial, logistical concerns, lack of resources for both clinicians and patients.
Digital disruption and artificial intelligence (AI) have powered the industry through every stage of drug development. AI has the potential to bring down clinical trial cycle time while enhancing the costs of productivity and improving outcomes. Leveraging predictive analytics and AI models can help increase the pace of understanding diseases, identifying patients and investigators for a new clinical study. A well-established digital infrastructure powered with AI algorithms paves way for continuous streaming of clinical data. This data can be aggregated, stored and managed for future requirements. When data is captured electronically, it minimises human errors and helps the investigators integrate with their databases seamlessly.
A Deloitte report on the impact of AI on the biopharma value chain indicates that traditional approach to clinical development offers a 10 per cent success rate and rightly observes that it’s time for a transformation in clinical trials for increasing productivity. Biopharma companies have notably been able to access to real-world data, but with not much fruition due to lack of Editorskills and technologies for effective utilisation of the data. The Randomised Controlled Trials (RCT) approach was primarily designed for test mass-market drugs and continues to be the standard to validate the efficacy and safety of new drugs.
As pharmaceutical companies continue their efforts to gather real-world data and evidence for providing value-based outcomes, patient engagement becomes a critical. Patient-centricity helps obtain real-time data. In order to move in this direction, all the key stakeholders will have to begin focusing on the patients’ needs and preferences. Also, the sponsors ought to develop open communication channels to share information with patient and seek perspectives, which would help engage patients and retain them throughout study. Digital has the potential to transform clinical development by reducing clinical cycle times, making clinical trials more cost-effective and drug development more productive. Biopharma companies would do well to partner with technology firms in deploying effective digital strategies for clinical development.
The cover story of this issue talks about challenges around patient enrolment and retention for clinical trials. In this article, the author throws light on how technology can help bridge the digital world with physical to provide unique interactive experience for patients