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Innovation in Medical Writing

With Innovation and AI (artificial intelligence) creeping into clinical research, it took time to establish a place in the medical writing domain. The process began with an attempt to automate the generation of documents that reuse clinical information. However, medical writers were not comfortable with the use of such cumbersome software versus the quality of output generated. With advancements in technology, there are now multiple options available in the industry that provide truly meaningful alternatives to speed up writing process.

There were few challenges in bringing automation and gaining efficiency in the medical writing process.

So far, the venture to automate the document quality check (QC) or review process yielded only partial success until now. Tools are available to check grammar, punctuation, language consistency etc. Due to the subjectivity involved in writing data-heavy documents, there is no one correct way of representation. Knowing what to focus on while performing QCs in limited time is equally challenging. Also, consistency and time allocation are difficult since requirements for each document are different. They must also be deliverable early, despite QC occurring at the end. The QC process needs to be documented, and findings need to be addressed and annotated with a justification as well. These challenges have existed for so long because the amount of data gathered during clinical trials is huge and needs to be reportable in concise manner. There are also other factors, such as lack of sufficient time, changing regulatory requirements, and human dependency that impact the quality of QC.

Current Scenario

With Innovation and AI (Artificial Intelligence) creeping into clinical research, it took time to establish a place in the Medical Writing domain. The process began with an attempt to automate the generation of documents that reuse clinical information however, medical writers were not comfortable with the use of such cumbersome software versus the quality of output it generated. With advancements in technology, there are now multiple options available in the industry that provide truly meaningful alternatives to speed up writing process.

There were few Challenges in bringing automation and gaining efficiency in Medical writing process.

So far, venture to automate the document quality check (QC) or review process yielded partial success until now. Tools are available to check grammar, punctuation, language consistency etc. Due to the subjectivity involved in writing data-heavy documents there is no one correct way of representation. Knowing what to focus on while performing QCs in limited time is equally challenging. Also, consistency and time allocation are tough since requirements to be met for each document are different, but they must be delivered at the earliest despite QC being performed at the end. The QC process needs to be documented and findings need to be addressed and annotated with a justification as well.

These challenges have existed for so long because the amount of data gathered during clinical trials is huge and needs to be reported in concise manner. There are also other factors such as lack of sufficient time, changing regulatory requirements and human dependency that impact the quality of QC.

How we bring in the GENINVO Difference

As solutiondue to advances in data capture, there is real-time availability of “clean data,” which leads to better time management. With enhanced communication systems and automation opportunities, the said challenges are addressable in today’s world. To be able to fully overcome the problems, organizations require a dedicated software. That’s the purpose of DocQC. Checks are programmable and runnable in the background so the scope of QC is defined, and writers can focus on other tasks at hand. It can run complex checks within minutes, to verify consistency of data against source documents as well as across documents representing the same data. Since the checks are programmable, human error is negated. Users train and test the system in advance. For the future, simplified sequential autorun checks and the ability to read and verify data in text against respective sources should be a priority. The user should also be able to configure specific requirements on their own, as per individual project requirements.

Observing the effects of change in Innovation, whether it results in failure or success, is one of the keys to improvements and developments in healthcare. When the change is something new or involves the process of introducing something new, the results should benefit healthcare. The criteria for innovation in health is then achieved.

Beyond satisfying these criteria, newly introduced ideas, methods, products, and/or the process of introducing something new in healthcare face the additional burden of adoption in the field. In addition to these hurdles, external demands of stakeholders, funders, regulators, competitors, consumers, and general accountability must be met.

By understanding what is innovative and what is not, as well as the barriers to adoption and implementation, we are better able to conceptualize what is necessary in the field to bring about long-lasting and large-scale developments for increased efficiency and effectiveness in various areas of healthcare.

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