10 Key Technologies and Process Transformation Opportunities to Enable Decentralized Clinical Trials

By Richard Clements, Chief Marketing Officer, ThoughtSphere

ThoughtSphere recently had the opportunity to participate with SCDM India in a virtual panel discussion where the topic was focused on “Patient Centricity” and “Decentralized Clinical Trials”. I thought I would share and expand on our view of this topic as it relates to technology.

Whether or not you’re talking about traditional trials or decentralized or virtual trials, the fundamentals of clinical research will remain the same and we will still have the same stakeholders – sponsors, CROs, sites, investigators, and, most importantly, patients. The difference is decentralized trials will digitalize or bring the entire clinical process online with patients at the center. Enabling this will require sophisticated orchestration of multiple interoperable elements. Technology will be the key common theme along with process remodeling. But, before we get to the technology, let me describe what a decentralized trial might look like from the patient’s point of view.

As a patient, I am going to find a clinical trial through social media. Sponsors and sites are going to use social media to recruit me and other patients to their clinical trials. I am going to use my device of choice to consent to participate (eConsent). Any drugs or kits or other products that I need to participate in the trial are going to be shipped directly to me.

I will interact with my physician primarily using telemedicine. That doesn’t mean I’ll never visit my physician, but it does mean my physical interactions will be more limited. I may need or choose to have an at-home nurse and any lab work I need to have done could potentially be collected by me at home, depending on the type of lab work, or I could leverage my local lab.

I will choose to use my mobile device and laptop to collect data. I’ll record a daily diary to capture information on outcomes. Wearable sensors will automatically record and transmit my health metrics (heart rate, blood pressure, etc). The resulting benefit for sites and investigators is they’ll get all of this data in real-time and it will be of higher quality. This process of collecting data will be good for patients as well because they can also receive real-time feedback and reminders on their mobile devices.

So, what does this mean from a technology perspective, and how does technology enable the patient to be at the center of the universe in this model?

  1. Social Media: Patient recruitment is a major obstacle to the success of clinical trials. Social media offers a cost-effective way to reach a more broad and diverse set of patients. Social media can also be used for listening to patients, giving caregivers and site staff a clear picture of the patient burdens and how to best care for patients through recruitment and retention.
  2. Tele/Video Conferencing: Tele/video conferencing is absolutely essential for connecting patients virtually from the comfort of their homes with clinical investigative teams at the sites. This will reduce the patient burden of traveling to sites for multiple visits and tests, removing geographic and logistical constraints to participate. The evolution of the internet and the available bandwidth across the world will be a further underpinning to facilitate this.
  3. Smart Phones for mHealth Platforms/ Applications: Smartphone devices will be used to input data directly and serve as robust technology platforms to receive and house study documents electronically, serve up eConsent forms, communicate with other stakeholders, host tele-visits and even coordinate with stakeholders for study workflows. Additionally, we will see other solutions such as eSource (EMR/EHR), video-based dosing compliance, ePatient diary, direct to patient drug/supplies shipment, and samples collection automation.
  4. IoT/Sensor Devices: There will be an advancement of clinically acceptable sensor IOT devices/wearables allowing for continuous data capture remotely, transmitting, and storing data in a secure platform. Apple watches come to mind, or similar devices monitoring heart ECG and alerting a caregiver when the signals indicate things are not right. Regulators have started accepting these data for evaluation.The above technologies are all important and visible and patients will interact with them. But, let’s look at this through the lens of data management and how technology will enable this transition behind the scenes.
  5. Protocol Simplification: It all starts with the protocol design. With virtual or decentralized trials and with patients at the center a fundamental problem for study designers is simplifying protocols, removing duplicate work and inconsistencies for patients, and ensuring all activities are tied to the end goal with the patient in mind. The design should take into account that decentralized trials mean real-time access to data through mHealth and wearables, and it means an environment that fosters more accurate data.
  6. Study Build Automation: An area ripe for innovation is the study build process. The process to create case report forms and data check specs, for example, is manually intensive and can take 400-500 man-hours. Machine learning and natural language processing can help automate this process and reduce timelines by up to 80%.
  7. Data Processing with Data Lakes: As SCDM points out, data processing should be focused on data consolidation from many diverse technologies and sources rather than data cleansing. That means data collection must be source system and data-agnostic to collect data from wearables, patient apps, electronic health records, labs and eCOA accommodating large volumes of structured and unstructured data from any source. Data lakes offer the ability to quickly ingest, aggregate and standardize diverse sets of data with minimal manual effort on the part of the user. With immediate access to this data, the focus for sponsors will be on analyzing clinical or operational data of interest in real-time.
  8. Statistical Analysis: The emergence of machine learning points to a future in which automated systems rapidly analyze large datasets to extract benefits, value and insights. For example, we can use statistics to investigate the natural differences among patients in how they respond or categorize pain. Immediate access to data can help sponsors identify anomalies in the data and investigate why they have occurred – carelessness in data capture for example, or whether data are exceeding some relevant threshold, or whether or not fraud is occurring. All of this can be quickly captured using central monitoring and remote monitoring capabilities. This information can be used in communication with patients to make any adjustments needed as the study progresses.
  9. Cloud Computing: All of this has implications for cloud computing. There will be a continuing evolution of affordable cloud infrastructure over the internet, moving computing power to the cloud. This is important because it allows us to process vast amounts of data involved in clinical research allowing the researcher to make timely decisions using the data.
  10. Data Sharing, Security and Privacy: Our industry is fragmented from a solutions perspective. We don’t share data well and data sharing should be a best practice. We really need to take a hard look and understand how to protect data in a more virtualized world and manage data privacy and data security. We can’t get privacy and security wrong, especially when we’re talking about patients giving us their health information via connected devices.

Decentralized trials are where we’re headed and this will result in a great number of benefits in the areas of patient recruitment, data quality, compliance and patient retention. At ThoughtSphere we are confident that industry can and will make decentralized clinical trials a reality and greatly improve the experience of patients and the success of clinical trials and move our industry forward.

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