Talking Medicines, which launched in 2013, has received £1.1 million in a syndicated funding round. Tern, an investment company specialising in the internet of things, is the lead investor alongside the Scottish Investment Bank, the investment arm of Scottish Enterprise.
The firm will use the funds to support the launch and roll-out of a new artificial intelligence (AI) data platform, which will translate what patients are saying into “actionable pharma-grade intelligence”. As part of these plans, the business intends to take on nine additional employees.
Talking Medicines, which is led by chief executive Jo Halliday alongside co-founders Dr Elizabeth Fairley and Dr Scott Crae, uses a combination of AI, machine learning and natural language processing tech tools to capture and analyse the conversations and behaviours of patients at home, with the aim of transforming big pharma’s understanding of patient sentiment.
Through mapping the patient voice from social media and connected devices to regulated medicine information, the firm is able to build “data points” to determine trends and patterns of patient sentiment. This intelligence enables pharmaceutical companies to make “patient-centric marketing decisions”.
Halliday said: “We are delighted that Tern is joining our investor group, and [Tern chief executive] Al Sisto will be bringing his wealth of experience to the board.
“This investment will scale our team and the development of our tech tools to translate what patients are saying into actionable pharma grade intelligence through our global patient confidence score by medicine.”
The firm has raised £2.5m to date, including three previous seed funding rounds with previous investors including SIS Ventures and the Scottish Investment Bank.
Sisto added: “We are truly excited to add Talking Medicines to our portfolio. It is a company that brings expertise in artificial intelligence and machine learning to our portfolio with its platform and is solving a critical problem for an industry that spends around $30 billion on marketing annually, whilst lacking systematic data tools that can structure patient sentiment from social channels.”