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How natural language processing impacts professions

Language represents the most fundamental form of intelligence we use, and companies are increasingly teaching technology how to make it more efficient.


The history of intelligent machines is one that has offered varying theses, from the humanoid robot emulating emotions, to robots that endow us with superhuman traits. In 2020, it is predicted as many as a quarter of all organizations will have integrated a virtual customer assistant, or chatbot – a robot that imitates human conversation, according to Gartner. Today, chatbots are being deployed to fight the coronavirus outbreak by letting people track their symptoms and, to address the rising need for crisis communications during the 2020 COVID-19 pandemic. This brand of assisted artificial intelligence relies on automated technology that can decipher language and is called natural language processing (NLP). NLP makes sense of the human languages by using machine learning. Its ultimate objective is to process written and spoken information to derive meaning to improve our personal and professional lives.

In our COVID-19 Resources Center you can read more on our COVID-19 related expert solutions we are making available for clinicians, hospitals, governments, legal customers, and other institutions to help them with the latest information available as the coronavirus situation evolves.

At Wolters Kluwer, NLP-based products and services use this technology to provide value to professionals when it comes to unlocking insights from their data and saving time. NLP technology can be leveraged for powerful results that impact how professionals shape the future of their industries, making us more intelligent and intuitive in the work we perform on a daily basis.

We spoke to Quentin Reul, Director of Product Software Engineering, to learn about how NLP impacts different industries, whether it will affect the future of the workforce, and its role in the professions we serve at Wolters Kluwer.

What does NLP do?

Quentin Reul: All the information that needs to be researched and reviewed every day can be overwhelming for professionals. NLP uses advanced technology to process and augment the knowledge of any subject-matter expert (SME), knowledge that until now has had to remain quite literally in their mind. Traditionally, companies would have processed the information manually, but NLP now lets them apply pre-determined algorithms and rules to any of their experts’ work, which means they can find anything they need at the drop of a hat. This lets companies handle their content and workflows more effectively.

NLP also decreases the margin of error because professionals no longer have to rely on subjective experience. NLP-based solutions can for example highlight relevant pieces of text, extract key pieces of information, and cluster similar documents together, which improves employee productivity and access to insights.

Read: Transforming workflows with predictive analytics

What are some examples of best practice when it comes to NLP?

Quentin Reul: The best-practice case for NLP depends on the type of activity you’re engaged in as an end-user, and your preferred approach to that activity. With CCH Axcess iQ and CCH iQ, two of our products for tax and accounting professionals in the US and Australia, Wolters Kluwer uses NLP to identify new tax regulations that have been introduced. Our teams are then able to identify new tax and regulatory changes and match that against the respective firm’s client data, alerting the clients. NLP, in effect, identifies which of the firm’s clients are impacted by the change and enables staff to proactively engage them.

At Wolters Kluwer we are focused on solving our customers’ problems and NLP is one of the tools that lets us address these problems.

NLP in healthcare

With the outbreak of the COVID-19 virus, the world is hyper-focused, as it should be, on the caregivers and the population of patients contracting the virus. However, we must also keep in mind the need for clear and concise documentation regarding the diagnosis, testing, and tracking of the spread of the virus.

There are hundreds of medical terminologies that document every step of the health care process through admission, testing, treatment, discharge, and billing. So, when there is a large health event or worldwide pandemic, like COVID-19, it requires quick changes and updates to those terminology sets. This is required to ensure health care providers have the ability to accurately document, track, and treat their patients in a timely fashion.

Wolters Kluwer’s Clinical Natural Language Processing (cNLP) solution optimizes and accelerates manual medical records review. For healthcare organizations, this information extraction supports Quality Measure reporting, risk stratification, medical necessity review and predictive analytics.

“Our data science team is already applying machine learning and natural language processing to mine through unstructured text, like clinician notes, to extract valuable information. Often there is embedded information such as a patient’s allergies or interactions and we’re able to apply NLP, extract it, codify it and map it to industry standards to make it actionable and analyzable, which is what people need,” Jean-Claude Saghbini, Chief Technology Officer, Wolters Kluwer Health.

How do decision-makers start introducing NLP-based products?

Quentin Reul: Before introducing NLP products, it’s important decision-makers understand their team’s needs as the team understands and can frame the business problem that needs to be solved. Decision-makers must start with setting up a clear strategy and priorities. Once the foundation is created, they should prioritize developing NLP-based products in the areas where their company has amassed unique content.

At Wolters Kluwer, we have introduced an advanced user experience, which involves designing solutions based on contextual inquiries. Contextual inquiry is when teams partner with customers to engage, inquire, contextualize, and interpret their problem, helping us understand their pain points. Our user experience and engagement teams work directly with customers to understand their activities and identify where NLP can complement their existing workflows. Our teams then custom-create tools to extract insights from the content.

Our strategy involves “failing fast to pivot” through a continuous engagement and validation process. We iterate by digging deeper to understand the problem better or by optimizing the technical aspects of the function.

How is NLP slated to impact the future of the workforce and workflows?

Quentin Reul: When professionals make critical decisions that impact their clients, for example a doctor giving a patient a diagnosis, NLP can provide suggestions that allows the doctor to make more informed decisions. This is what we often refer to as human-in-the loop solutions.

Another example is what we at Wolters Kluwer recently did with Cortana, Microsoft’s voice assistant. We let tax professionals ask Cortana anything they wanted, entirely circumventing a web search. Companies that invest in their human expertise by integrating it with automated solutions drive better outcomes for their customers and employees. And at the end of the day, whether you’re a doctor or lawyer or tax assistant, you will always want to understand and sign off on the decision AI presents because you are responsible.

NLP opens the door for higher productivity, faster turnaround times and overall improved quality in the professionals’ work. As for the robot-replaces-human trope – NLP will not replace the workforce, but it will require us to change how we do things.

NLP across industries

Enablon Juno

Enablon Juno is a smart, context-aware assistant which leverages NLP to help improve user experience and performance, compliance, and safety in the workplace. Juno can also be used for compliance with new regulations and for GDPR purposes.


OneSumX is an NLP solution for the financial industry. It enables institutions to meet their regulatory obligations by extracting relevant clauses, tag and cluster requirements from regulatory citations.


monKEY uses NLP to classify snippets of text according to different topics and to give semantical meaning to the snippets. This is an example where users can be guided by the concepts and classification to retrieve relevant answers.


Navigator uses NLP to classify documents against the Kluwerbrede thesaurus. This is another example whereby users can leverage concepts to retrieve content.

Kluwer Competition Law

Kluwer Competition Law uses NLP to classify documents against domain-specific topics. In this case, the NLP is used to group documents to facilitate post-search filtering within different policy area.

Augmenting professional knowledge

NLP is a kind of artificial intelligence which has already made headway in augmenting our professional and personal lives. Before introducing NLP products decision-makers must understand the foundation of its need by assessing their team’s needs and framing the business problem. NLP is most valuable when human expertise is integrated with its automated solutions, resulting in higher productivity and turnaround times as well as quality improvement through a decrease in the margin of error. When applied to traditional professions, from healthcare to the legal and financial industries, NLP delivers actionable insights and impactful decision-making in everyday activities which enables our customers to deliver better outcomes.

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