What strategies can pharma companies use to accelerate drug development?
Clinical trial efficiency may be enhanced by artificial intelligence (AI) enabled systems that enhance patient and location acquisition. Three Strategies Artificial Intelligence Can Enhance Clinical Research Advancements.
AI can increase clinical trial diversity.
The population’s lack of access to basic medical treatment is alarming. The patient population encounters noteworthy obstacles when it comes to engaging in clinical trials, primarily transportation, time, and financial constraints to trial locations.
Clinical trials can also result in a partial or skewed understanding of the safety and effectiveness of drugs in certain populations, which could have detrimental effects on the general public’s health.
To solve these problems, significant efforts must be made to guarantee that clinical trials are inclusive and representative of the diverse patient population, as well as to expand access to healthcare for marginalized communities.
Researchers and physicians must consider the influence of genetic variation on medication metabolism and treatment results. This is because varying patient populations may require customized doses and treatment regimens. A high-quality healthcare system for all patients is reflected in clinical research.
Numerous clinical trials study initiations were postponed, and remote monitoring became more prevalent. Many industry stakeholders are pushing for the continued use of digital technology to reach a wider range of patients.
Clinical trials are becoming more diverse and decentralized with the help of AI-enabled technology.
Listed below are some instances.
- AI-powered patient IDs based on clinical characteristics quickly find possible subjects for clinical and observational research.
- Remote patient monitoring using AI capabilities decreases the frequency of visits to the trial site, which increases participant retention while gathering objective, real-world health data.
- Collaboration powered by AI improves clinical data exchange and reduces the time to diagnosis by gathering and analyzing vast amounts of data from hospital hubs and spoke networks.
AI can boost monitoring accuracy and speed.
- Most research teams employ manual techniques in the screening process for clinical trial volunteers, which increases the likelihood of human error and delays the process.
- The challenge of reaching this goal is exacerbated by the tremendous problems sponsors have been having persuading their usual research sites to take part in clinical trials. This is partially explained by the growing need for clinical trials.
- Efficiency in recruiting participants for clinical trials can be increased by using AI to perform real-time automated eligibility checks.
AI has the potential to bridge the gaps between clinical care and trial research.
- Most people rely on their doctors to inform them of relevant clinical trials and research.
- Artificial Intelligence is expediting the per-screening of clinical trial candidates by automating the analysis of hospital imaging at sites and referral facilities.
Once a candidate has been found, AI software can use a communication system to spread their information. Subsequently, the enrollment procedure can be optimized and made more efficient.
