The widespread adoption of wearable devices, combined with improved network connectivity and data analytics, is fundamentally transforming the clinical trial landscape. The industry is moving towards decentralized clinical trials (DCTs) where patients participate remotely using wearables, unlike the traditional model where they needed to be physically present at a lab or healthcare facility. While this helps tackle the issue of limited patient diversity and patient dropouts, enterprises now need to contend with a new set of challenges. The biggest among these is ensuring data security and identity management, with patient safety being at the heart of it.

Decentralized clinical trials work on a complex web of interconnected devices and applications spread across several different locations. This includes the wearable devices and mobile apps used by the patients, telehealth tools, apps for the clinicians and analysts, and the servers and networks connecting these components. This opens up several new attack surfaces, many of which are physically outside the security perimeter of the lab. Hence, having stringent cybersecurity measures in place is critical to ensure the success of DCTs.

At the heart of this shift lies cloud-native data fabric, a flexible, scalable architecture that connects data from wearables, mobile apps, electronic health records (EHRs), and lab systems. As the clinical research landscape moves towards DCTs, enterprises need to embed safety and security measures from the very beginning to ensure the integrity of their clinical trials and data. Identity and Access Management (IAM) then becomes the new focal point for cybersecurity for health tech enterprises that want to build a practice in DCTs.

Cloud-native data fabrics provide a smoother way to analyze and process this data in real time. In the absence of a robust IAM, the whole system can be vulnerable, jeopardizing the entire trial.

Some of the critical challenges include:

• Federated Identity: The patients, clinicians, and lab researchers are at different locations, and often from different organizations. For IAM to be effective, there must be a safe and secure digital identity linked across all authorized systems.
• Granular Access Control: This is critical given the number of different stakeholders involved. Creating role and attribute-based access control (RBAC/ABAC) to the system is important to ensure that data safety and integrity are maintained.
• Authentication: A simple, but secure, authentication process that works for the patients at home, and enterprise-grade authentication systems for researchers and clinicians are a must. Different authentication processes have to work in sync across the same system, but have different attributes depending on who is accessing it.
• Auditability: Every step of the process has to be logged in order to be compliant with HIPAA, GDPR, and FA regulations. These parameters need to be built into the system from the very beginning.

IAM goes beyond simple access control. There are elements of identity verification, access, and compliance depending on the role, along with monitoring the system, that comprise a strong IAM policy. At the same time, the security measures cannot come at the risk of usability. Patient engagement is critical to the success of any clinical trial. If the access or verification process is too cumbersome, there is a higher risk of patients dropping out of the trial. Some of the participants might be older patients, managing chronic conditions or not adept with technology – all these situations have to be considered while designing a secure, but user-friendly IAM.

Cloud-native data fabrics come with inherent safeguards built in that address the risks associated with DCTs.

• Unified data sets: Cloud-native data fabrics create a seamless layer that connects all the data that has been collated from multiple sources like EHRs, wearables, apps and existing databases. This gives researchers a unified and integrated data set which makes it easier to analyze the data and make real-time changes, if needed.
• Enhanced data security and governance: Cloud-native data fabrics are built to provide stringent security features like access control, multi-factor authentication, end-to-end encryption and detailed audit logging. This is useful in maintaining data integrity as well as complying with regulatory requirements.
• Boosting research efficacy and scalability: Having a cloud-native data fabric streamlines operations, giving easy access to all information in a secure manner. Additionally, systems can be scaled as per the enterprise needs which saves on fixed IT costs. The integration of artificial intelligence-based tools into the system can further boost research efficacy and speed up the analysis and uncover patterns faster.

Best practices

Given how crucial identity and access management is to the success of decentralized trials, certain standards are non-negotiable. This includes following federated identity standards like SAML, OAuth2 and OpenID Connect, and having Single Sign-On (SSO) and Multi-factor authentication (MFA) for all users. Additionally, using AI-enabled monitoring tools to detect anomalies and potential breaches in the system can further strengthen IAM, while improving the user experience.

Some best practices include:

• Risk-based Authentication: Building in authentication practices that dynamically adjust depending on the user. The system customizes this based on a combination of factors like whether the user is accessing it from within a secure lab, if it’s the first login attempt, device integrity and IP address. A clinician accessing sensitive trial data from a secure hospital network will face fewer checks than a first-time user at a lab in a different country.
• User-Centric Design (or Human-Centered Design): Security safeguards can be built into the IAM without making it cumbersome. Features like multi-factor authentication or biometric logins can be integrated into mobile apps in a seamless and intuitive manner to improve the overall user experience.
• Progressive Access Control: The user profile of an individual determines what parts of the system they have access to, further enhancing security measures. While researchers and sponsors would be able to access clinical trial data and analysis, patients logging onto the same system would only see what they require, like surveys, etc.

The way forward

Clinical trials will slowly move to a decentralized model given the inherent advantages this offers. Cybersecurity has to be at the heart of how healthtech firms and service providers design their IAM and trials. Enabling features like blockchain-based consent tracking, edge-based identity management for remote sensors and wearables, and implementing Agentic AI for threat response will help build a secure and efficient IAM.

In this new environment, IAM is more than a security and procedural requirement. It can be a strategic enabler by ensuring greater patient participation, scalable operations across geographies, faster trial execution, and increased collaboration through secure data sharing, and needs to be central to any clinical trial.

This article is published on Express Computer.