DO-BO® – the summarization platform

An intuitive AI-enhanced summarization solution for automated creation of drug dossiers. Designed to accurately transform lengthy clinical trial reports and data packages from systematic literature searches into concise drug reports for public institutions.

About the Project

Our client is MArS – leading consulting agency for healthcare companies in Central and Western Europe. One of its jobs is supporting pharmaceuticals in their reimbursement submissions. To this end, the agency distills elaborate reports, often over 400 pages long each, into summarized drug dossiers for health care systems. Unicsoft supported the agency in the development of a e time and costs saving dossier creation based on an AI-powered platform for text summarization — DO-BO®.

Problem Statement
A pharmaceutical company usually creates up to 10 dossiers annually. Due to the sheer volume of raw data and the number of tables the reporters must analyze, a single dossier requires many man-months and hundreds of thousands of dollars of investment.
Managers must sift through vast amounts of data to identify relevant information spread across clinical study reports and further data packages e.g. from systematic literature searches.
Writers must have expertise in different jurisditcions of health systems – and every system needs a country specific submission (including the local language).
Completing a drug dossier can take up to a year in some cases.
A drug reimbursement dossier consists generally of four to five modules tailored to specific requirements. The manager’s key challenge is finding, screening, and summarizing complex medical data dispersed across lengthy reports. The limitations of existing AI tools add even more complexity to this challenge. These tools have trouble capturing the clinical report content, including embedded images and tables, often leading to factual errors and information omissions – and with most of these platforms confidential information must not be uploaded.
healthcare data collection
  • Data complexity. Report summarization requires manual extraction and review due to vast amounts of unstructured digital texts.
  • Complex terminology. Medical jargon must be simplified and correctly translated so regulatory agencies can understand the report.
  • Format compatibility. Managers must verify all information imported from PDFs, images, and TLFs (tables, listings, and figures).
  • Requirement compliance. Reimbursement dossiers have to align with varying international jurisdictions and specific template structures.
  • Factual accuracy. AI summaries might have hidden inaccuracies, making the drug reimbursement dossier ineligible for reimbursement approval.
  • Contextual misinterpretation. Given the intricacy of clinical reports, untrained AI might misread the trial goals or methodology.
  • Data omission. Most AI tools struggle to prioritize information and often ignore important facts and details in text summaries.
The Solution
We have started the project as a Proof of Concept (PoC) for an intuitive platform that integrates AI technologies to speed up the creation of drug reimbursement dossiers. The model simplifies search, extraction, summarization, and formatting. It also incorporates a machine learning model to improve the report quality as more and more documents are processed.
Dossier template. The platform allows identifying sections of the dossier that summarize, reword, or duplicate information.
Self-learning AI module. Our machine learning model will re-train every 24 hours based on user searches, edits, and manual input.
Intelligent search. Allows users to locate information in text, graphs, images, and tables based on various search criteria.
Table formatting. Organizes data into structured tables by aligning column names in source documents based on search queries.
Image extraction. OCR (optical character recognition) organizes information extracted from documents into tables and graphs.
Contextual understanding. Semantic analysis and natural language processing help simplify complex terms into plain language.
Accurate referencing. The extracted information is cross-referenced with the source document, which helps verify its accuracy.
Collaboration tools. Multiple users can review the summarization results and easily adjust the search criteria for fine-tuning.
The Result – DO-BO®
The prototype streamlines time-consuming aspects of dossier creation, like searching, extracting, rewording, and formatting. DO-BO®, the AI-powered platform, helps generating accurate documents, which align with international standards and payer requirements, much faster. The current focus is now to shift expanding the platform’s capabilities with each new version, with the ultimate goal of fully automating the process of creating drug reimbursement dossiers.
  • The initial versions of DO-BO® can accelerate data scanning, screening, and analysis in large clinical and literature reports by up to 60%.
  • The AI automation tool greatly cuts the dossier creation costs.
  • Semantic analysis tools transform jargon-heavy text into plain, straightforward summaries translated into multiple languages.
  • The platform improves document quality by eliminating human error, including typos, incorrect terminology, and misformatting.
  • Robust authorization mechanisms, role-based access, and encrypted communications reliably protect confidential information.
  • The platform’s intuitive interface and customizable structure make dossier creation easy for users with diverse technical skills.
Elevate Your Clinical Studies: AI Summarizing Tool for Effortless Portfolio Mastery