Industry
Life Sciences
Patient-centered Care Models Taking Center Stage in Life Sciences
Highlights
- How do you overcome the cost of running an end-to-end drug R&D process?
- Can AI help shrink lengthy trial timelines?
- Discover the potential AI offers in the clinical studies space.
In this article
Premise
One of the major challenges faced by pharma companies in clinical trials deals with the cost of running an end-to-end drug R&D process as well as the sunk cost incurred due to long trial timelines.
By design, traditional clinical trials take around ten to fifteen years to develop a drug and bring it to market . Right from recruiting the correct patient cohort to screening clinical data for observing the effects of drugs and obtaining all regulatory approvals, the entire process involves human interference. Consequently, artificial Intelligence (AI) and other new-age technologies have been mooted as impactful and effective solutions to cut through the challenges and shrink the long trial timelines.
Pharmaceutical organizations are gradually adopting AI in their entire clinical R&D processes. Of late, the industry has witnessed many organizations collaborating with technology providers to leverage AI/ML and other advanced technologies to decrease the costs associated with clinical trials and improve decision-making in drug design and manufacturing.
In clinical trials, AI has immense potential to improve trial efficiency and save time and cost by implementing it in:
- Patient recruitment and enrollment
- Site identification
- Remote patient monitoring
- Clinical and operational data capture
- Data testing to mark safety issues
Patient recruitment and enrolment
AI can be used to identify and recruit the right patients for a clinical trial in terms of ethnicity, disease type, and genetic factors. In a traditional clinical trial cycle, patient recruitment can take anywhere between weeks to months.
Usually, patients are randomly enrolled by a health system or provider, and often, there’s no guarantee that one gets the best match for their studies. Moreover, the waiting time to receive a match itself is pretty stretched. AI can reduce this time to a matter of days by using data-driven approaches for patient recruitment and enrollment. Using various machine-run algorithms that administer a patient’s symptoms, past medical history, convenience, and provider capacity, an AI platform can come up with the best patient match through a process called predictive modeling. This is also achieved by running the machine through sets of data from various electronic health records (EHRs) and provider data sets. The AI application analyzes structured data such as the international classification of diseases (ICD) codes and unstructured data such as prescriptions, diagnostic reports, and other medical data that is difficult to search manually. The application subsequently leverages processes that convert unstructured data into structured ones in a rapid manner, for example, a patient graphic that includes all the information needed to match complex trial parameters.
Site Identification
Improper site identification often results in poor patient recruitment.
Implementing the inclusion and exclusion criteria of a study using machine learning by running through the Emergency Health Records (EHRs) helps the investigator to assess the capacity of a site and provide an accurate number of patients for the study. This, consequently, decreases situations of false availability of patients on the site. The direct benefit and impact lie in the reduction of expenses involved during the early phase of trials and meeting stringent deadlines.
Remote patient monitoring (RPM) and clinical data capturing
AI-enabled wearables and medical devices send notifications and reminders when patients deviate from their dosing schedules or trial protocols.
Biometric data can be transmitted to providers who, in turn, access the information through clinical decision support systems and view the patient status in real time. AI-enabled machines can provide alerts by dynamically adapting questions based on patient responses. Effectively, AI in RPM supplements the early detection of patient behavioral traits, adverse events, and interventions. AI and data analytics solutions can help in identifying novel biomarkers precisely. This, in turn, helps various stakeholders like pharma, biotech, and contract research organizations (CROs) in gaining in-depth insights into the prediction of biomarker response in a disease model or along pathways.
Testing data to mark safety issues
AI has immense potential to mitigate safety issues that arise due to data entry errors or missing data points.
It not only helps in the automatic analysis of numerous datasets and directs clean data to the trial master file but also plays a key role in detecting diagnostic errors or out-of-range lab results.
Conclusion
AI, though in its infancy at present, has the potential to become indispensable for clinical trials in the long run.
Ultimately, the rapid adoption of AI will help in better patient profiling in a short time and accelerating the patient recruitment process. It can help bring novel, breakthrough drugs to patients while reducing costs and ensuring quality. Eventually, AI envisages bringing life-saving drugs faster to patients in need.
The direct benefit and impact of AI lies in the reduction of expenses involved during the early phase of trials and in meeting stringent deadlines.
