• CRAs face growing workload demands across monitoring, documentation, and site‑level coordination
• Manual processes slow timelines, increase variability, and make it difficult to maintain efficiency as trial complexity grows

• Implemented four CRA-focused automations driven by citizen developers who are subject matter experts (SMEs)
• Created AI-enabled tools to reduce manual workload and support faster, more consistent operation execution
• These solutions – implemented on Parexel’s AI Assistant platform – target repetitive, labor‑intensive tasks, creating a more efficient workflow that improves monitoring cycles and operational quality.

There are four citizen developed CRA tools, early indicators point toward measurable value:

• Accelerated delivery timelines through automation‑driven efficiencies
• Improved labor efficiency, reducing manual effort and freeing CRAs for higher‑value oversight
• Quality improvement by 50% with filenotes
• Stronger differentiation by developing an AI culture at Parexel

• Medical writing teams face growing pressure to deliver high‑quality documents faster while managing complex templates, repetitive formatting, lengthy narrative creation, and time‑intensive quality control cycles
• These manual demands limit capacity, introduce variability, and slow down development timelines

Parexel deployed a broad, integrated set of AI‑based and automation‑driven solutions designed to accelerate authoring, reduce rework, and standardize output quality using:

• AI‑assisted lean authoring and summarization 
• Standardized clinical study report (CSR) authoring prompts
• Table results‑to‑text generator 
• Past‑tense converter and grammar, spelling, and style‑guide compliance assistants 
• AI‑based redaction tools for clinical trial transparency

• Up to 30% efficiency improvement with automated post‑to‑in‑text table conversion

• Safety teams must manually review large volumes of scientific literature to identify potential adverse events 
• They spend, on average, 30-45 minutes reading each article, collectively reviewing over one million articles per year
• The process is time‑intensive, inefficient, and complicated further by foreign‑language publications requiring translation before assessment

Parexel developed Parexel Reviewer, an AI‑powered literature review platform that uses natural language processing (NLP) and machine learning to dramatically streamline screening and case identification, using:

• AI‑assisted global and local screening 
• Automated ingestion and triage of citations from sources like PubMed and Embase 
• Adverse‑event‑focused NLP, highlighting relevant terms and prioritizing potential Individual Case Safety Reports (ICSRs)
• Built‑in translation capabilities for non‑English articles
• Integrated workflows, like article ordering, case book‑in, medical review, and centralized audit trails

As a literature case‑management system and an AI‑assisted processing tool, Parexel has gained:

• Up to 20% efficiency gain in global screening and up to 50% in local screening 
• Significant reduction in manual workload, especially for large‑volume safety portfolios 
• Improved timeliness and compliance, with accelerated identification and reporting of ICSRs
• Enhanced accuracy and quality through consistent highlighting and duplicate detection 
• Scalable global adoption, with over 1 million citations processed and integration into end‑to‑end pharmacovigilance workflows

• Pharmacovigilance teams face increasing volume, complexity, and velocity of safety data
• Manual and fragmented workflows slow literature case processing, periodic reviews, and expectedness assessments—delaying safety signal detection and placing pressure on quality, consistency, and regulatory performance

Parexel implemented an AI‑enabled case processing optimizer leveraging natural language processing (NLP) and machine learning (ML) to enhance pharmacovigilance (PV) workflows, including: 

• AI‑enabled literature case processing, periodic review, and literature review for signal detection 
• Expectedness assessment support, improving speed and consistency in evaluations
• An integrated PV workflow platform designed to improve both speed and quality while enabling human decision‑makers

The solution delivered measurable performance and quality improvements:

• 20%+ decrease in handle time for end‑to‑end literature case processing
• 25–50% reduction in handle time across key assessment components
• Expectedness assessments achieved a 20% efficiency gain and 60% reduction in listedness assessment errors
• Error rates reduced to 0.49%, compared with 1.29% prior to application usage
• Automated case intake has 50% productivity increase, 45% increase in data quality, and 25% improvement in case compliance