Bringing a drug to market is a long and onerous process. Studies estimate that the clinical trial process — where new drugs are tested on patients before they are approved — lasts nine years and costs $1.3B on average.
The Covid-19 pandemic has sparked the adoption of technologies that can greatly improve the efficiency and cost of the traditional clinical trial process. To keep people healthy and safe into the future, we will become increasingly dependent on quick moving and highly effective trials.
When Investment Rhymes with Canada
Canada has a proud history of achievement in the areas of science and technology, and the field of biomanufacturing and life sciences is no exception.
Artificial Intelligence (AI) will play a major role in transforming the trial process – and ultimately keeping us healthier and better protected from disease. The healthcare industry leads in AI adoption, experimenting with applications ranging from machine learning-assisted diagnostics to extracting information from electronic health records.
However, AI adoption in the actual clinical trial process is still in its early stages. Compared to other areas of healthcare, fewer startups are directly targeting clients in the clinical trials space. And in many aspects of clinical trials, the need for digitization precedes the need for AI.
Many clinical studies use rudimentary data collection and verification methods — which often put the onus on the patient, such as: sending patient medical records via fax, manually counting leftover pills in bottles, and relying on patients’ diary entries to determine medication adherence. Needless to say, this process is ripe for disruption.
The time for change is NOW
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Anthony Lange Anthony Lange is SVP & Head of Healthcare and Life Sciences, Virtusa. Prior to his position, Mr. Lange served as head of Global New Business Development for Healthcare, Insurance and Life Science, where he spent time building and cultivating the organization’s new business development skills and extending the know-how into a broader geographic […]
We cannot simply accept that testing new drugs will continue to be a slow and expensive process. AI has the potential to disrupt the current approach to clinical trials — from patient recruitment to adherence monitoring and data collection – and it is time to seize these opportunities.
Patients often only enroll in a drug trial when existing forms of treatments have already failed. In addition, not all diagnosed patients are eligible for trial participation as determining eligibility alone can be a Herculean task.
For those that are eligible, participating in a trial is often costly and time-intensive. The process is inefficient for other stakeholders too: as mentioned above, drug trials average nearly a decade, costing over $1B on average.
Current trials also lack the analytical power, flexibility and speed required to develop complex new therapies that target smaller and often heterogeneous patient populations.
Furthermore, suboptimal patient selection, recruitment, and retention, together with difficulties managing and monitoring patients effectively are contributing to high trial failure rates and raising the costs of research and development.
The use of AI-enabled digital health technologies and patient support platforms can revolutionize clinical trials with improved success in attracting, engaging, and retaining committed patients throughout study duration and after study termination.
In summary, applying artificial intelligence can reduce clinical trial cycle times while improving the costs of productivity and outcomes of clinical development.
AI algorithms, combined with an effective digital infrastructure, could enable the continuous stream of clinical trial data to be cleaned, aggregated, coded, stored, and managed.
AI-powered technology has the potential to change every stage of the clinical trials process, from finding a trial to enrollment to medication adherence.
The adoption of AI has the potential to transform clinical trials in four key areas:
- Clinical trial design: Biopharma companies are adopting a range of strategies to innovate trial design. Increasing amounts of scientific and research data, such as current and past clinical trials, patient support programs and post-market surveillance, have energized trial design. AI-enabled technologies, having unparalleled potential to collect, organize and analyze the increasing body of data generated by clinical trials, including failed ones, can extract meaningful patterns of information to help with design.
- Patient enrichment, recruitment and enrollment: Matching the right trial with the right patient is a time-consuming and challenging process for both the clinical study team and the patient. In fact, only 3 percent of cancer patients today are enrolled in clinical trials. AI-enabled digital transformation can improve patient selection and increase clinical trial effectiveness, through mining, analysis and interpretation of multiple data sources, including electronic health records (EHRs), medical imaging and ‘omics’ data.
- Patient monitoring, medication adherence and retention: AI algorithms can help monitor and manage patients by automating data capture, digitalizing standard clinical assessments, and sharing data across systems. AI algorithms, in combination with wearable technology, can enable continuous patient monitoring and real-time insights into the safety and effectiveness of treatment while predicting the risk of dropouts, thereby enhancing engagement and retention.
- Using operational data to drive AI-enabled clinical trial analytics: Trials generate immense operational data, but functional data silos and disparate systems can hinder companies from having a comprehensive view of their clinical trials portfolio over multiple global sites. Consolidating all data – whatever the source – on a shared analytics platform, supported by open data standards, can foster collaboration and integration, and provide insights across vital metrics. Incorporating a self-learning system, designed to improve predictions and prescriptions over time, together with data visualization tools can proactively deliver reliable analytics insights to users.
Setting our sights on the future
AI is already being used to transform the clinical trial process and experience, but there are some challenges. In many clinical trials, researchers still fax requests for patient records to hospitals, who often send the data back as PDFs or images (including pictures of handwritten notes).
Structured data can also become unstructured due to these transmission methods. For example, a spreadsheet that is faxed or turned into a read-only document (such as PDF) loses much of its structure. This dated, manual system makes it difficult for clinical trial researchers to collect accurate data needed to determine a patient’s eligibility.
AI solutions apply Natural Language Processing (NLP) to extract clinical data — such as symptoms, diagnoses, and treatments — from patient records. Its software can even identify patients with conditions not explicitly mentioned in EHR data, improving the match rate between patients and clinical trials.
Non-adherence is another challenge and can have adverse effects on a patient’s health, incur costs if a study has to recruit new patients, and interfere with the accuracy of study outcomes. Generally, adherence rates of 80% or more are required for therapeutic efficacy. However, up to 50% of medications prescribed in the US are taken incorrectly. In response, clinical study sponsors are investing in emerging technology to minimize non-adherence.
Some startups are providing visual confirmation of medication administration. For example, some platforms use an interactive medical assistant (IMA) to identify patients at risk of non-adherence based on visual data collection. Patients use their phones to take a video of themselves swallowing a pill allowing the platform to confirm the right person took the right pill.
Embracing change
The healthcare and life sciences industries are on the brink of large-scale disruption driven by interoperable data, open and secure platforms, consumer-driven care, and fundamental shifts in how we manage our health.
Together, big tech players and startups are setting the course for faster and more effective clinical trials in the future. The ultimate goal is to drive innovation and better healthcare by embracing AI across the clinical trial journey.
The next decade will see continued empowerment of patients as individuals rather than as a cohort characterized by a medical diagnosis or set of symptoms. Those who participate in clinical trials will expect a more holistic and immersive experience, providing opportunities for continuous learning while receiving quality care. Personalized health reports, data visualizations, and portable raw data provided at the conclusion of a trial will provide participants the opportunity to carry forward their experience and power greater ownership over their health and care.
2021 continues to be a complex and uncertain year for healthcare. But despite the challenges, dedicated leaders across the globe are using the power of healthcare IT to create a more efficient and effective clinical trials that empower patients and clinicians, literally, life changing for the future.
Anthony Lange is SVP & Head of Healthcare and Life Sciences, Virtusa. Prior to his position, Mr. Lange served as head of Global New Business Development for Healthcare, Insurance and Life Science, where he spent time building and cultivating the organization’s new business development skills and extending the know-how into a broader geographic footprint. Before that, he led Virtusa's Healthcare new business development in North America, helping to build the foundation for Virtusa’s Healthcare business.
Mr. Lange has more than 30 years of technology and management experience including extensive experience with global delivery models and Healthcare industry. Prior to Virtusa, Mr. Lange served as a leader at Unisys Global Outsourcing business unit and multiple start-ups. Mr. Lange has an BS degree in Computer Science and Finance from LeMoyne College.
