What are the real costs of risk and errors in Radiology?
[London, 7 September 2022] – Shiraz Austin, Augnito Co-Founder and expert in AI-driven speech recognition addresses the current challenges and risks associated in radiology reporting.
Radiologists are facing increased pressure to manage NHS resource shortages. With unprecedented backlogs, delays in training newly qualified consultants and demand increasing with every passing day, Trusts and healthcare providers need new ways to reduce discrepancies in diagnostics and radiology reporting errors to ultimately drive the cost of risk down.
Shiraz Austin, Co-Founder of Augnito, stated: “There is a financial cost and a very real human cost to risk and errors in radiology. Issues with radiology reporting – often due to workloads, reporting accuracy, or radiologist cognitive overload expose Trusts to litigation, expose radiologists to burnout and withdrawal from the profession at a time when they are most needed, and expose patients to inferior care/health standards, or worst still, life limiting outcomes.”
According to NHS statistics ¹, 12,629 clinical claims were made against the NHS in 2020/21 at a cost of around £2 billion. Perhaps more alarming is that this number of claims was 133% higher than nine years ago demonstrating a significant increase year on year.
Austin continued: “These numbers are unacceptable, the human cost is unacceptable. While radiologists are addressing diagnostic risks by taking proactive steps to reduce errors at every stage, ensuring reporting accuracy, backlogs, and checking accuracy standards in the solutions they use, remain vital components of their service, there are digital solutions that can help support them and radiology as a whole. Solutions using speech recognition (SR) technology can ease the workload backlog and mitigate risks.”
“Advances in today’s SR solutions help to streamline workflow, improve transparency and promote clinician efficiency. Augnito’s voice-AI-driven technology is highly accurate straight out of the box and works with radiologists however and wherever they need to work. It assists radiologists to report faster without errors, whilst embracing reporting standards through consistently structured formats and a natural learning processing engine with natural language understanding.
The cost of errors and delays to the NHS – through litigation, or in time lost as each day passes without accurate solutions being implemented – and in patient care, placing a risk to life, cannot continue in the current upward trend. In its first year of launching, Augnito’s technology is already deployed across a large percentage of the UK’s radiology reporting and teleradiology platforms ̶ accurately supporting radiologists every day in their key patient-care role. Our mission to democratize speech recognition across the whole continuum of care has only just started” concluded Austin.
ENDS
Read the full blog here https://www.scribetech.co.uk/2022/08/30/the-costs-of-risk-and-errors-in-radiology-reporting/
For further information, interview opportunities or accompanying graphics please contact: Georgina Pavelin, Mixed Media – [email protected]
About Augnito
Augnito is a secure, cloud-based, AI-driven clinical speech recognition product suite. It offers fast, easy ways to capture live clinical data on any device with 99% accuracy, support for multiple medical specialties, and no need for voice profile training. Augnito brings seamless speech recognition to daily workflows and third-party clinical systems, turning medical information into clinical documentation and making healthcare intelligence securely accessible everywhere. Augnito was developed by its parent company Scribetech, a clinical voice solutions innovator, fusing 20 years of transcription and digital dictation services to the NHS, speech-to-text and clinical coding solutions for the healthcare sector, and its own speech recognition engine with advanced voice AI technology.
www.scribetech.co.uk/augnito-speechrecognition
¹ https://resolution.nhs.uk/resources/annual-report-statistics/