Narrativa Pathway for Safety Narratives Automation
How Blood Cancer United accelerated patient narratives with AI—Saving time, costs, and lives
Blood Cancer United (formerly The Leukemia & Lymphoma Society) is a non-profit organization dedicated to finding cures and ensuring access to treatments for blood cancer patients. It was founded in 1949 and has since become one of the largest voluntary health organizations in the United States.
Blood Cancer United funds research, educates and supports patients and their families, and advocates for policies that benefit blood cancer patients. BCU required Narrativa’s solutions because they wished to further enable their stated mission of bringing support to blood cancer patients sooner and more effectively. Furthermore, the quality and consistency of human-authored patient narratives fell below the organizational standards due to sheer volume and the inability of the medical writing teams to address them in a timely manner.
Through the power of the Narrativa Navigator platform, BCU is now fully automating patient safety narrative reports, large portions of CSRs (clinical study reports), and TLFs (tables, listings and figures) to fast-track the clinical trial regulatory documentation process of their drug discovery helping to bring more life-saving cures to patients around the globe.
Patient safety narratives are intended to communicate adverse events that could be associated with the therapeutic agent under investigation. The process of authoring them requires medical writers to analyze the provided data, identify the safety signals, and then write concise narratives that clearly and effectively communicate the information collected while the study was conducted. As the volume of data increases (i.e. increased number of patients, the complex nature of the therapeutic area, and many others), the process of drafting correct and consistent narratives becomes challenging. Narrativa, a leading provider of agentic AI automation solutions, supports medical writing teams by automating the authoring process of these narratives.
KPIs
- Time Savings: The first measured KPI was the time savings achieved when utilizing Narrativa’s agentic AI automation solutions. This KPI involved tracking the time spent on data analysis, narrative authoring, and the reviewing/editing processes before and after implementing the solutions.
- Cost Savings: The cost savings KPI tracked the cost spent on resources.
- Quality and Accuracy: The quality and data accuracy of the AI-generated patient safety narratives was compared to those drafted manually. A scoring system was put in place to assess the clarity and accuracy of the generated narratives.
- Consistency: The next KPI assessed was consistency. This was a critical KPI since the safety information needs to be presented uniformly and adhere to a pre-specified template. For this domain, the consistency of the AI-generated narratives was validated against manually-created ones through independent audits and using a scoring system.
The impact
- 72% Time Savings: The use of Narrativa’s agentic AI solutions to automate the authoring of patient narratives saved medical writers an average of 72% of the time spent on data analysis and narrative creation compared to manual processes.
- 68% Cost Savings: The cost per project dropped 54-68% on average; as less time was spent working on the same project, more projects were able to be handled. This led to a tremendous increase in cost savings.
- 100% Data Accuracy: The solution produced 100% data accuracy. An average of 83% improvement in the quality of the initial draft was also observed. This resulted in a significantly lower internal draft rejection rate.
- 99% Consistency: Consistency was measured at a rate of 99% across all narratives produced by Narrativa, compared to the consistency rate of 68% with the manual approach.
Outcome
Using Narrativa’s AI agents, patient narrative solutions improved the overall efficiency, quality, and consistency of the safety narratives. With Narrativa, data analysis and narrative creation processes reduced the time spent by medical writers and improved the style and consistency of the documents. User satisfaction was high. Regarding areas where further benefits could be realized, Blood Cancer United cited that having begun implementation before the database was locked produced further successes. Risk was mitigated, data discrepancies decreased, and medical writers were afforded more time to execute deeper quality control exercises. BCU also mentioned that the potential scalability of the solution is unparalleled in today’s marketplace.
About the Beat AML® Master Clinical Trial
The Beat AML® Master Clinical Trial is the first collaborative precision medicine clinical trial in a blood cancer. Launched in 2016 and focused on newly diagnosed patients aged 60 or older, the trial uses advanced genomic technology to match patients to the most promising targeted treatment based on their unique genetic mutations. The trial tests multiple therapies in multiple study arms simultaneously under a “Master Trial” protocol that not only has the power to bring new therapies to acute myeloid leukemia patients faster, but also has the potential to stand as a model for future clinical trials. The trial has already generated strong results, showing superior survival rates and better quality of life when genomic analysis is used to match patients to targeted therapies.
As the head of clinical operations at Beat AML, Len Rosenberg, shared with the Applied Clinical Trials publication at the 2023 SCOPE Summit in Orlando, Florida, about the ways that he and his Beat AML team are tackling the operational obstacles that come with clinical trials, including background treatment dosages. With the support of The Leukemia & Lymphoma Society, BAML is now developing a less toxic regiment for a more promising treatment that will allow the patient to receive treatment in the privacy, safety, and comfort of their own home while breakthrough technologies allow for precision monitoring during the treatment process.
In addition to providing a more precise and adaptable treatment process for the patients, BAML is now partnering with Narrativa to utilize artificial intelligence (AI) machine learning (ML) algorithms to take safety datasets and automate them relative to how the trials are done. The Narrativa AI SaaS platform helps automate large portions of regulatory documentation, specifically Patient Safety Narratives, TLFs (Tables, Listings, and Figures) and Cross-Reporting, so that the BAML team improves the time and cost efficiency of the reporting process. This ultimately allocates more funding toward actual trials and finding more cures for patients.
Watch the full interview:
