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Digitalization of Medical Writing: Balancing AI and Rule-based algorithms with Human Supervision in Medical Writing QC 

Introduction

The digitalization of medical writing refers to using digital technologies and tools to create, edit, and review medical content. This includes various aspects of medical communication.  

In recent years, emergence of digitalization has led to a significant transformation int the field of medical writing. The integration of digital technologies and tools has revolutionized how medical content is created, managed, and published. This digital revolution has not only streamlined the workflow of medical writers but has also improved accessibility to accurate healthcare information for healthcare professionals and patients worldwide. The emergence of digitalization has significantly transformed the field of medical writing, offering numerous benefits and opportunities for both medical writers and healthcare professionals.  

Digitalization has also paved the way for improved accessibility to any individual on relevant medical information. Online content and the use of interactive educational materials, such as videos and graphics, has proven to be effective in enhancing patient comprehension and empowering them to make informed healthcare choices. 

Future of Medical Writing: 

As digitalization continues to evolve, the future of medical writing holds even more promise. AI and rule-based algorithms have made significant contributions to medical writing. Natural language processing (NLP) algorithms can analyse vast amounts of medical literature, extract relevant information, and assist in literature reviews, saving considerable time for medical writers. NLP can also assist in generating summaries and abstracts. Artificial intelligence (AI) and machine learning algorithms have the potential to automate certain aspects of medical writing, such as literature searching and reference formatting. AI-powered tools can aid in grammar and spell-checking, style consistency, and reference formatting, reducing errors and enhancing the overall quality of written content. An example of such tool is DocQC, supported by AI and ML algorithms and developed by technological and subject matter experts to automate complex quality control checks. 

Advantages of AI and Rule-based algorithms: 

  • Accuracy and Consistency: AI algorithms can contribute to the accuracy and consistency of medical writing. They can identify potential errors, inconsistencies, or deviations from established guidelines, ensuring that medical documents adhere to the highest standards. AI-powered tools can also assist in cross-referencing information, identifying redundant content, and maintaining consistency throughout the writing.  
    DocQC ensures the accuracy and consistency of the data by automating the quality control checks and minimizing human errors and ambiguity. DocQC validates the data in the medical documents internally and externally and checks for the accurate facts. 
  • Efficiency and Speed: The integration of AI and rule-based algorithms in medical writing has improved efficiency and speed. Automated processes can handle routine tasks, allowing medical writers to focus on higher-level tasks, such as data interpretation and critical analysis. This accelerates the writing process and enables faster broadcasting of medical information.  
    Using DocQC, medical writers can reduce the manual efforts on multiple, time-consuming cycles of manual review and focus towards other high priority tasks involving critical thinking. DocQC provide the QC results within few minutes, enabling the medical writers to quickly evaluate the document quality. 

Intervention of Human Supervision: 

While AI and rule-based algorithms bring efficiency and accuracy, human supervision still remains a crucial element in medical writing. Human medical writers possess the expertise, clinical knowledge, and detailed understanding required to interpret medical data, contextual information, and ensure the quality of scientific content. They can critically analyse research findings, assess the clinical relevance, and provide valuable insights that AI algorithms may overlook.  

Medical writing involves ethical considerations and requires a deep understanding of the medical context. Human supervision is essential to navigate these details. Medical writers are responsible for ensuring the accuracy and appropriate interpretation of research findings, adhering to ethical guidelines, and presenting information in a way that is accessible, unbiased, and understandable to the target audience. Human supervision is crucial to maintain the ethical integrity of medical writing and mitigate potential biases. 

As AI continues to evolve, it is essential for medical writers to adapt and acquire new skills. Medical writers should familiarize themselves with AI technologies, understand their capabilities and limitations, and leverage them effectively in their work. There should be a healthy balance between the digital technologies with Medical Writer’s human supervision. They should also stay updated with the latest advancements in AI and rule-based algorithms to make informed decisions about when and how to incorporate them into their writing process. 

Conclusion: 

The optimal approach to balancing AI and rule-based algorithms with human supervision lies in collaboration. While these technologies enhance efficiency, accuracy, and productivity, human supervision remains essential to ensure the integrity, quality, and ethical standards of medical writing. By striking the right balance, medical writers can harness the power of AI while leveraging their expertise to produce high-quality, accurate, and contextually relevant medical content that meets the diverse needs of healthcare professionals and patients, empowering patients to actively engage in their healthcare journey. Medical writers and AI technologies can work collectively, with AI-powered tools assisting in routine tasks and human writers providing critical thinking, context, and expertise. Continuous learnings and developments are key to staying level to AI advancements and leveraging them effectively and efficiently. 

Hargun Kaur Sethi 
Software Development and Business Growth, GENINVO 

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