Robotic Process Automation (RPA)
Robotic Process Automation (RPA) is a term used to describe the process of replicating what a computer user does when completing certain tasks by using software to mimic the way they interact with their mouse, keyboard and computer screen, otherwise known as the User Interface (UI). By capturing all the actions they carry out, RPA software is able to automate these otherwise manual processes, drastically enhancing how organisations are able to handle data and manage their workflows.
RPA utilises Artificial Intelligence (AI) and Machine Learning (ML) to handle high-volume, repeatable tasks that previously required humans to perform, such as system queries, calculations and maintenance of records and transactions.
Up until now, RPA has typically been deployed to drive efficiency into “back office” processes, but pioneering organisations are beginning to use automated processes to enhance the “front office” Customer Experience (CX). By combining this with AI solutions and with intelligent human design input, very high standards of CX can be achieved, whilst maintaining efficiency across the organisation.
Natural Language Processing (NLP)
Natural language processing is the component of artificial intelligence that allows computers to process and analyse human language. It is the bridge between human communication and computer understanding that has evolved from the study of computational linguistics and computer science.
Machine code or machine language is the way that computers comprehend information and is made up of millions of zeros and ones in a sequence. NLP is important because it allows computers to translate English, French, Afrikaans or any other language into machine code and to communicate with humans in their own language, without relying on a computer programmer. It can take staggering amounts of unstructured data, analyse it, structure it, and interpret it.
The Benefits of NLP
Some everyday tasks that benefit from high-level NLP capacities include:
- Classifying content, detecting duplicates, search and indexing of information
- Identifying the theme and meaning of text collections and applying analytics to interpret the data, for example, forecasting
- Extracting and understanding the context from text-based sources
- Opinion mining and sentiment analysis
- Transcribing speech-to-text and converting text-to-speech
- Summarising large quantities of data
- Translation from one language to another
The above tasks take large quantities of data and interpret them in a way to deliver a higher level of value.
Workplace Applications of NLP
Practical applications of this technology include:
- Automating the entry of new patient information from the form into the computer system
- Monitoring electronic medical records for high-risk profiles of dangerous illnesses
- Extracting data from physicians notes to assign billing codes or to pre-authorise scripts
- Summarising large bodies of text to efficiently collate and compare medical policy and clinical guidance to ensure best practice care delivery
- Develop human to machine language instructions for procedures such as robot-assisted surgery