Digitizing the clinical protocol: Small steps for seismic change

We are in an era of clinical trial modernization. Decentralization and direct data capture contribute to the proliferation of platform systems, software, devices, and data sources used in a clinical trial. A “city of systems” usually requires bespoke integrations to exchange data which drives up cost, complexity, and cycle time. At the same time, efforts to further streamline development through platform trial designs, pragmatic or point-of-care trials, and data sharing or reuse are gaining traction to accelerate trials and harness the power of data. This carries some risk of introducing or worsening a so-called “digital divide” and places an increased technology burden on all stakeholders in the clinical research ecosystem.

These changes intersect with a unique challenge: reducing the friction for data exchange. This data exchange problem is notably present with information in the protocol, which forms the backbone of any clinical trial. Protocol information is needed by virtually every system used to plan, run, analyze, and report a clinical trial. Still, it is often trapped in a document that is not machine-readable (essentially, electronic forms of paper), requiring repeated human interpretation and data entry. Manual approaches like these stifle speed and limit innovation, making realizing the industry’s ambitions to modernize clinical trials more difficult.

In recent years, TransCelerate BioPharma, in collaboration with CDISC, has made progress toward tackling this problem with the Digital Data Flow (DDF) Initiative, a program of work aimed at automating the exchange of protocol information across the study life cycle. The initiative has delivered foundational standards that create a common digital language for protocols that may be applied consistently across organizations. CDISC’s Unified Study Definitions Model (USDM) offers far greater machine-readable structure for protocol information than has ever existed in an industry-standard format. Alongside study and study design concepts, the model captures what patient data is to be collected, how, and on what schedule, and can accommodate a range of complex designs and study types.

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