Streamlining Clinical Study Reporting with AI Automation

Clinical study reporting is a critical and complex process in the development of new medical treatments. It involves compiling and analyzing data, generating reports, and ensuring compliance with regulatory standards. AI automation is revolutionizing this process by increasing efficiency, accuracy, and consistency, thereby accelerating the timeline from study completion to regulatory submission and publication.

1. Streamlining Clinical Study Report with AI
Automation
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Welcome
ChoppariSrivalli
B. Pharmacy
CSRPL_STD_IND_HYD_ONL
/CLS _071/062024

2. Index
What is CSR
Introduction
Guidelines of CSR
Types of clinical study report
Automation ?
CSR with AI
Process Summary
Use of AI in CSR
Protocol deviations
Regulatory submissions of CSR with AI
Conclusion
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3. WHAT IS CSR
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Clinical study report (CSR) on a clinical trial is a document, typically very long,
providing much detail about the methods and results of a trial.
A CSR is a scientific document addressing efficacy and safety, not a sales or
marketing tool.
Its content is similar to that of a peer-reviewed academic paper. Results of trials
are usually reported in a briefer academic journal paper, but methodological flaws
are often glossed over in the briefer paper.
CSRs answer questions such as: Why was the trial done? What were the important
questions asked in the trial? What were the results?
CSRs also include extensive details on the course of treatment for patients, the
medical information collected from the patients as part of the research, and
demographic data, as well as other kinds of information to explain how the trial
was conducted and results were analyzed.

4. Introduction
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Creating Clinical Study Report (CSR) highly manual and time consuming,
which require other medical writer to perform the quality control of numbers
on the Table, safety narratives and information from other documents.
Significant amount of information to the CSR comes from other sources
such as Protocol, SAP, Safety Narratives, In-text tables etc. The appendices
section is constructed from CRF, TLF etc.
Automating the CSR creation by utilizing emerging technology such as
machine learning, Natural language processing and Natural language
generation (ML/NLP/NLG) will effectively reduce the manual efforts. This
paper will discuss about one such Algorithm of Machine learning which was
implemented in a tool.
The tool is designed to produce pre-filled CSR with information from
Protocol, SAP and other sources and interpretation of in-text tables in the
respective sections of the template that follows ICH-E3 guideline.
CSRs answer questions such as: Why was the trial done? What were the
important questions asked in the trial? What were the results? CSRs also
include extensive details on the course of treatment for patients, the medical
information collected from the patients as part research.

5. Guidelines of CSR
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E3
CTD
Canadian
USFDA Requirement
TGA
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6. Summary of the guidelines
This guideline has been developed by the appropriate ICH Expert working group
and has been subject to consultation by the regulatory parties ,in accordance with
ICH process.
NOTE :
Altoughthis guideline is mainly aimed at efficacy and safety trails , the basic
principles and structure described can be applied to other kinds of trials , such as
clinical pharmacology studies .Depending on the nature and importance of such
studies , a less detailed might be appropriate
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7. Types of clinical study reports
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8. What is Automation
Content automation is the use of technology to create content of any kind, be it trend
reporting, an article or Clinical Study Reports (CSRs). Artificial intelligence is a great ally
to content automation, specifically thegeneration of natural language.
Natural language generation is a subfield of AI. It is capable of processing large amounts
of data and then sharing the information using human-sounding language. That
is,technology can write a report just as a person would. Incredible right!
FOUR benefits of automating clinical study reports are :
1.Save time and money
2.Error –free process
3.Improvement in study quality to AI
4.Differentiation point
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10. Steps inovledin CSR with AI
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11. Process Summary
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12. UseofAI
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Prediction Accuracy: The text from PDF document such as Protocol/SAP are extracted
using ML/NLP and the texts are understood by the system engine.
Identifying Individual Sections: o ML model is used to predict the best matching
content from various source documents (Protocol, SAP, In-text etc.,) for all the sections
in a CSR.
Named Entity Recognition (NER) which is a subprocess of NLP is used to identify the
drug names, dosages, duration of drug, sponsor name and protocol number.
Title Page & Synopsis: Customized ML Algorithm used in title page and study
synopsis.
Deep Learning Model usage:The section 10 (study population), section 11 (results)
and section 12(safety evaluation) are more of in-text tables that utilizes Deep Learning
Model to find the best matching In-text tables.

13. Work flow of CSR
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14. Protocol Deviations
To Study inclusion or exclusion criteria , conduct of the trial ,patient management
or patient assessment should be described .
In the body of the text , protocol deviations should approxmiatlysummarized by
centreand grouped into different categories such as :
i.Those who enterdthe study even though they did not satisfy the entry criteria
ii.Those who developed withdrawal criteria during the study but were not
withdrawn
iii.Those who received the wrong treatment or incorrect dose
iv.Those who received an excluded concomitant treatment.
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15. Regulatory submissions of CSR with AI
NLP, ML, and predictive analytics can improve efficiency, compliance and transparency
for research sites and sponsors, by helping researchers monitor performance and risk
indicators for audits, change controls, non-conformance and complaints.
AI can automate identification of operational lapses, FDA findings, root causes, risks,
reminders, schedulers, actions and validation, automated review and approval, reporting
and detection of trends and patterns.
This can significantly lower trial cost, time and risk to deliver milestones on time.
AI can help bring products faster to the market and reduce cost of development by
leveraging multiple evolving datasets for better insights and decision-making, and to
enhance speed, operational efficiency, accuracy and compliance.
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16. Conclusion
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17. Thank You!
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