Driving healthcare efficiencies through the power of AI and automation
2 mins read
Healthcare providers across the globe are facing a common challenge: how to deliver the best quality care with limited resources and in the face of fluctuating demands. And for clinical coding teams, this can be a real problem.
Effective clinical coding is essential for healthcare providers to understand how well they deliver services, perform and care for patients. It’s a specialised skill that requires knowledge of medical terminology, anatomy, physiology, disease processes and analytics. And the data that it produces is an integral part of managing health information, alongside clinical research, audits, health resource allocation, medical billing, clinical benchmarking, case management and health services planning.
The quality of the data that you collect and how well you manage your health information depends on a number of things, including having the right number of skilled coders, the ability to spot and manage data quality issues, the time to ensure that clinicians are engaged in data quality, and the capacity to engage with your clinical teams. Ultimately, if you’re not recording clinical coding timely and accurately, you can’t determine your performance and level of patient care, and that in turn clouds the view of performance management, responsive commissioning and, ultimately, the national healthcare data that’s supplied to NHS Digital.
Over the last year, we’ve seen very real stresses affecting clinical coding departments, from new coding rules to underpin Covid-19 recording and reporting to pressure on teams from sickness and different working environments.
Continuously moving forward with developments in clinical coding and helping people to perform at their best is important for the whole team. Bringing new technologies such as AI to clinical coding is at the heart of a strategy for transforming how hospitals deliver clinical coding services in the future. Allowing clinical coders to engage in a smaller amount of complex activity – and do it accurately – rather than on high-volume, low-complexity activity is a good way to make the most of people’s skills.
As AI develops, we should expect to see coders focusing on continuously improving data quality to underpin big data’s increasing use in health informatic systems.
Director of Clinical Coding and Benchmarking, CHKS
Bevin is the Director of Clinical Coding and Benchmarking for CHKS, part of Capita plc. Bevin has over 15 years’ experience of working with healthcare organisations on performance management and improvement, data quality, clinical coding and costing.