Written in September 2023, by Vegar Andreas Bergum - Head of AI, Norlab.ai
In an era where data is considered the new oil, the efficiency with which organizations manage and harness insights from their document repositories is critical for success. A key player in this arena is Artificial Intelligence (AI) – and natural language processing as a whole – whose capabilities have been exposed to a wider audience through OpenAI’s ChatGPT demo. When looking beyond ‘conversing with your data’ in a chat interface you’ll soon realize a language model’s immense promise in codifying and managing documents. By automating processes and drawing insights from vast swathes of text, AI significantly augments organizational efficiency and decision-making, if applied correctly. This post explores how language models, and AI in general, can be employed in codifying documents and the benefits that accrue thereof – with examples from the healthcare industry.
|
Codifying documents involves the organization and categorization of information to facilitate easy retrieval and analysis. The process entails converting tacit knowledge within documents to explicit, structured information that is easy to access and manipulate. |
In the fast-paced world of healthcare, efficient document management can make a world of difference. Take, for instance, medical General Practitioner (GP) letters, which are crucial in conveying patient information between healthcare professionals. These are letters containing mostly text and natural language. Codifying these documents using standardized coding systems and taxonomies like the Unified Medical Language System (UMLS) standard, such as SNOMED-CT, can revolutionize healthcare data management.
Imagine an AI-driven system that reads GP letters and automatically extracts critical information, such as diagnoses, procedures, medications, and patient demographics. Through the power of UMLS and SNOMED codes, this system can then translate the often verbose and unstructured text of these letters into a structured format. For example, it can convert "Patient has a history of hypertension and is currently taking amlodipine" into precise codes that healthcare professionals across the globe can understand, reducing ambiguity and the potential for misinterpretation.
Firstly, it streamlines the information retrieval process, allowing doctors to quickly access relevant patient data. Secondly, it enhances data analysis capabilities, enabling healthcare institutions to identify trends, monitor patient health over time, and even contribute to medical research more effectively.
|
Document classification and tagging |
Language models can automate the categorization of documents based on their content, context, or other criteria. Natural language processing (NLP) techniques help in understanding the text, while machine learning models assist in assigning categories or tags to documents, making them easily searchable. |
|
Information Extraction |
AI can extract crucial information like names, dates, and other entities, from unstructured data. This also applies to industry specific terminology and entities, like medical diagnosis as in our GP letter example. It can also identify and extract specific data points or sections of text based on predefined criteria. |
|
Anomaly Detection |
By understanding what constitutes 'normal' in a set of documents, AI can flag anomalies or unusual patterns in document data which could indicate errors or important events. |
|
Knowledge Graph Creation |
Through the relationship mapping between different pieces of information, AI can create knowledge graphs which help in visualizing how different documents or data points relate to one another. |
|
Automated Summarisation |
AI can generate summaries of lengthy documents, making it easier to quickly grasp the content without going through the entire text. |
|
Semantic Search |
Beyond simple keyword-based searches, AI enables semantic search which understands the intent and contextual meaning of terms to deliver more accurate results. |
Document codification, exemplified by the application of standardized coding systems like SNOMED in healthcare, yields a myriad of benefits across diverse industries. In healthcare, it streamlines information exchange, enhancing patient care and catalyzing medical research breakthroughs. Beyond the medical realm, insurance processes become more accurate, while pharmaceutical companies expedite drug development, legal professionals navigate complex cases efficiently, and public health agencies detect outbreaks promptly. Moreover, it empowers policymakers, aids medical device manufacturers in ensuring safety, optimizes education and training, drives IT innovation, and advances data-driven decision-making, ultimately fostering efficiency, cost reduction, and improved services throughout the interconnected landscape of global industries.
|
Efficiency and Time-Savings |
AI significantly reduces the time and effort required to organize, search, and extract information from documents. |
|
Enhanced Decision-Making |
By structuring data and extracting insights, AI supports better informed and timely decision-making. |
|
Reduced Operational Costs |
Automating document management processes reduces the need for manual intervention, subsequently lowering operational costs. |
|
Improved Compliance and Risk Management |
AI can ensure documents adhere to regulatory requirements and help identify potential risks in the early stages. |
|
Scalability |
AI systems can scale with the growing data and document management needs of an organization. |
In conclusion, embracing AI for document codification is a smart move for modern organizations. Not only does it promise streamlined operations, but it also positions companies to be more data-driven and informed in their decision-making processes. As AI continues to evolve, its capabilities in document management are only likely to broaden, making now an opportune time to invest in this technology.
This article was authored in cooperation with generative AI.