I helped pay for college in the early 90’s by being a “runner” or legal filer for a tax law firm who handled both litigation and corporate tax. The biggest technology innovation at the time was Wordperfect, a tool that changed the way law offices worked. It was heralded as the ultimate time saver that improved staff utilization. Even this technology advancement took time to get direct in the attorneys’ hands. It was a professional “typist” to translate long form briefs to a fileable document via Wordperfect.
The legal world is now ready for a much greater technology leap, Generative Text, an AI-based solution that could fundamentally change how legal offices approach everything from customer service to legal filings. It promises to create opportunities for greater efficiencies for lawyers and their back-office staff by assisting with legal drafts, speeding up detailed case research by shifting through mounds of documents without the need to hire a small army of people and could even help with translation and understanding of different country laws.
The use of generative text to summarize medical records could reduce the cost and take over the mundane task that can bog down a legal back-office.
The Importance of Medical Record Summarization
Medical records and other medical documents can be lengthy and complex, making it challenging for professionals to quickly and accurately understand and extract insights from them. In order to improve these processes, tools that can efficiently summarize this type of information in a clear and concise manner are required.
In the past, summarizing medical records was a time-consuming and labor-intensive task, typically performed by humans. This process was prone to errors and could be influenced by the subjective biases of the individuals summarizing the medical documents. However, advances in artificial intelligence have made it possible to automatically summarize medical records and documents.
What are Language Generation Models
Language generation models are a type of artificial intelligence (AI) that are specifically designed to generate written text. These models are trained on large datasets of text and able to generate a new text that is similar in style and content to the text they were trained on. These models are also able to use the patterns and structures they learned from the training data to generate text that is coherent and flows naturally, making it difficult for humans to distinguish the automatically generated text from one written by a person.
Language generation models are being increasingly adopted in various fields, such as journalism, customer service, marketing, finance, and education. These models offer the potential to streamline processes and reduce the workload of tasks that require large amounts of written communication.
The Advantages of Language Generation Models for Medical Summarization
Compared to Traditional AI Solutions
The difference between traditional AI solutions for medical summarization and the newer language generation models is significant and cannot be overstated. While the output of traditional AI solutions is lists of “medical concept entities”, language generation models generate actual text. This fundamental difference between a “list of items” and a narrative text that is easier to understand and digest is the major advantage of language generation models. These models are able to capture context, narrative and complex relations in a way that traditional AI cannot, making them a valuable tool for medical summarization.
For example, consider the following two summaries: An entity recognition-based summary:
Hypertension • Diabetes
Coronary artery bypass surgery
Blood pressure medications
A generative model-based summary: “The patient is a 55-year-old male with a history of hypertension and diabetes who was admitted to the hospital for a heart attack and underwent a coronary artery bypass surgery. He has made a good recovery and will be discharged with a prescription for blood pressure medication and a referral to a cardiologist for follow-up care. It is important to note that the patient has a tendency to skip his medications.”
While the traditional summary provides a list of conditions, procedures and referrals, the generative summary offers a cohesive narrative, providing context and an understanding of the patient’s medical history and treatment.
Impact on the Legal Space
The use of language generation models for medical summarization can greatly improve the legal analysis process in cases involving medical records and documents. These models can provide clear and concise summaries of medical information, helping lawyers and other legal professionals quickly understand and extract relevant insights. This will save time and effort in the legal analysis process as lawyers no longer need to spend as much time sifting through lengthy and complex medical documents.
The industry is poised to even take this further to help improve the strength of their legal case. It may not be ready for the full potential of generative text and its ability to assist the law office. But, similar to the transition to Wordperfect, which continues to be popular in parts of the industry, Generative Text will have a long run helping firms for years to come with even more new and innovative ways to assist the business of law firms.
*DigitalOwl uses proprietary technology for its NLP and Generative Text capabilities and does not rely on third-party services