Near Wave: From virtual to reality
Our research indicates that in the near future, fully virtual trials could become standard.
In this wave, doctors, patients, and their families will be directed to suitable, available trials, checked for eligibility, and fully informed and onboarded using intelligent digital agents. Decentralized data repositories will manage data, largely in response to heightened standards and awareness on security and ownership, which will improve the overall management of clinical data. In this scenario, patients will actively own their data and may be able to provide it to researchers and clinics to further the research agenda, on their terms.
The ideas for this wave that we have collated focus on patient experience and the rapid influx of vast volumes of data. In our research, one Accenture managing director commented that this future is not “that far away” but further noted that “the future can be quite overwhelming” for what is traditionally a conservative industry. A cautious approach from the industry would of course slow down the trajectory of these developments.
- Intelligent trial onboarding
- In data we trust
- Seamless data management
- Stay-at-home trials
05 An intelligent approach to onboarding
Before a patient can be enrolled on a trial, they must be able to find it. And 86% of clinical trials do not meet enrolment timelines due to issues with recruitment.
In the Near wave, to create an improved patient experience from the beginning of the trial, we predict that doctors, patients, and their families will have access to intelligent digital agents to direct them to existing, appropriate trials based on their data.
The agent will then check whether the patient is eligible for any of the suggested trials and provide supplementary information.
Once a patient gives their informed consent, the agent can complete the administrative onboarding process.
For this to work, all parties (e.g. trial sponsors, pharma, clinical research organizations, and hospitals) need to be using paperless management systems.
These support the accurate collection of trial information, inclusion/exclusion criteria, and collecting the patient’s EHR data. For such a system to apply across all known clinical trials, all administrative data must be collected and represented in a consistent, standardized, unified form.
Artificial intelligence and machine learning will power the intelligent digital agent. As these technologies mature, agents will be trained to conduct complex cognitive tasks, such as determining the eligibility of patients for a trial in the context of the various clinical trials’ requirements and the individual health status of each patient.
By facilitating fast, efficient trial discovery and on-boarding, these digital agents should be able to recruit patients quickly and easily.
Eighty percent of trials are delayed due to recruitment problems, according to a study by the Center for Information and Study on Clinical Research Participation.
From the patient’s perspective, Intelligent Trial Onboarding provides a more fully supported experience by guiding them directly to suitable trials and education materials.
However, the efficiency of the system may have a deleterious effect on the patient’s confidence in the system. Consider the social responsibilities and ethics related to such a system. The move towards technology introduces inherent bias and risk due to its focus on a specific population. Low- and middle-income countries, or factors relating to social class, could generate bias in these systems.
In order to have access to these systems, patients probably need broadband access or an accessible EHR. Even that can unfairly skew the eligible pool towards a specific demographic.
However, pharmaceutical clients in our innovation sessions recognized that there is already a “market in this” that is being captured in the start-up space, by companies such as Medable, Deep 6 AI, and Antidote. The rest of the industry will have to catch up.