Accelerating the Medical Writing Process with Automation From research to publication

Regulatory medical writing is a highly complex task and requires expertise. There are various challenges that medical writers face every day while creating documents required for submissions to competent health authorities (HAs). One of the major challenges here is the time required for the entire medical writing process, from the first draft to the final submission-ready draft. If some of the steps in this process are automated, it will save medical writers a lot of time and reduce the turnaround time. Medical Writing Automation (MWA) will help ease the task of medical writers. However, the lack of awareness of how automation and medical writers’ expertise go hand in hand to make medical writing tasks more productive is a huge challenge that must be overcome. This article decodes how automation is a boon for the medical writing industry and how various companies are proactively trying to adopt automation in medical writing.

The pharmaceutical and life sciences industries have seen constant growth in the need for medical writing. The global medical writing market was valued at US$3.6 billion in 2021 and is expected to reach US$8.4 billion in 2030, growing at a Compound Annual Growth Rate (CAGR) of 10.41 per cent. Patents have been expiring, Regulatory standards have been rapidly changing, and Research and Development (R&D) expenditure has been steadily rising. Because of this, the necessity to continuously adapt, produce, maintain, and update medical materials has evolved.

Medical writing is a highly specialised field that involves the art and science of content creation and clinical research. It entails creating well-structured scientific resources, such as clinical research papers, web content for the Healthcare industry, periodicals, journals, etc. With the increasing need for medical writing, writers are under constant pressure to deliver high-quality content within limited timeframes. It would be easy for medical writers if some steps of the process were cut down with automation tools.

The Medical Writing industry has been trying to stay at par with the revolution in the industry and the shift towards automation by adopting innovative and intelligent solutions that will ease the process of Regulatory writing, thereby ensuring a quick turnaround time.

Complexities and Challenges in Medical Writing

Medical writing is a highly technical and operationally difficult process involving a broad range of stakeholders and deep subject-matter expertise. The creation of several crucial papers, such as clinical trial protocols, investigators’ brochures, clinical and non-clinical study reports, summary documents, and labeling documents, is a crucial component of medical writing during clinical or R&D phases. The following are some of the challenges that medical writing professionals face:

1. Depending on the nature of the papers, there are numerous ways to represent data-heavy documents, and no standardised format exists.
2. Knowing what to focus on while completing quality checks in a limited amount of time is difficult.
3. It is necessary to have in place error-free processes due to the emergence of strict data redaction and anonymization guidelines in various countries, for instance, the European Medicines Agency's (EMA) Policy 70.
4. Because the requirements are different for each document, consistency and time allocation are difficult, which can be a problem, especially when they must be delivered as soon as possible.
5. The quality control process must be documented, and findings must be addressed and justified, which can be daunting.
6. Due to redundant content and the efforts required, information stored in different silos, and persistent reliance on human knowledge throughout the entire process, the efficiency and productivity of the medical writing process are negatively impacted, which increases the Time to Market (TTM).

Medical Writing Automation (MWA)

MWA is a framework that generates content using Natural Language Processing (NLP) and Natural Language Generation (NLG) techniques and algorithms. Artificial Intelligence (AI) has made significant advances in text generation, processing, and data mining. AI-powered engines used in these services may comprehend the context and recommend the relevant terminology. The technology is also useful when developing user-friendly content. A computer, when correctly programmed, shows no bias. It makes forecasts and suggestions based on its training. Medical writers can benefit from computer innovation and the advent of AI in the domains of NLP and NLG when creating medical documentation.

The MWA Process

NLP is a five-step process that begins with identifying and analysing the structure of words, checking the grammar, arranging words meaningfully, drawing the exact dictionary meaning of words, comparing the meaning of a sentence to the sentence preceding it, and finally, reinterpreting the actual meaning of a sentence. NLG produces meaningful phrases and sentences in natural language from some internal interactions between human language and computers. It involves text planning, sentence planning, and text realisation.

AI, combined with NLP and NLG, automatically extracts information from a wide range of data sets, whether structured or unstructured. The extracted data is subsequently analysed to interpret and categorise the substance and context of the information, and the content and context data are stored in a dynamic semantic model. The diagram below (Figure 1) depicts how NLP and NLG aid the medical writing process, making it more efficient.

Source: Created by Author

The process described above modifies the information and context of the material when it needs to be reused or repurposed to suit the needs of diverse stakeholders in the life sciences ecosystem. The solution maintains a data set that is easily searchable using Natural Language queries. Additionally, impact analysis is performed to improve change management whenever any new or updated content is made public.

MWA is especially useful for repetitive activities with a high level of redundancy. Most of the time and effort devoted to developing these documents is spent acquiring data from pre-existing sources (such as study procedures, figures, tables, and statistical analyses) and organising them under appropriate section headings. The figure below (Figure 2) explains how the AI/NLP solution can reduce 50–80 per cent of the time, as compared to the traditional approach.

Read more: https://www.pharmafocusasia.com/articles/accelerating-the-medical-writing-process-with-automation-from-research-to-publication

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