By Sameer Dhanrajani
AI is transforming Healthcare and how clinical operations occurs, delivering significant time and cost efficiencies while providing better faster insights to inform decision making. Advances in AI coupled with the availability and integration of vast amounts of healthcare data have already helped automate processes and improve data quality across dozens of clinical operations efforts.
As AI evolve, new opportunities will continue to emerge that drive further benefits to the clinical operations landscape. The emergence of artificial intelligence (AI) and machine learning (ML) in healthcare in the past few years has led to a dramatic unfolding with a rapid rise in tangible use cases and increased efficiencies in clinical operations. That said, what is emerging now is the fast-paced development of AI- and ML-based algorithms, being applied to patient care and to clinical operations, if far more cautiously than anticipated, and through deliberative processes involving physicians and other clinicians, and lots and lots of testing of hypotheses, all of that activity evolving forward over
For clinical applications, what patient care organization leaders are finding universally is that there are literally no shortcuts involved; put specifically, teams of clinicians, data scientists, and informaticists in hospitals, medical groups, and health systems, are developing algorithms, testing them, and beginning to deploy them, all one use case at a time, while engaging physicians and nurses from the start, and developing specific algorithms based on explicitly called-for use-case needs. As many are putting it, the idea of the equivalent of “stopping off at Target to pick up algorithms off the shelf” is simply not happening.
Clinical operations, an enormous area in hospitals in particular, is finally now seeing the leveraging of AI to its benefit. Here are the pertinent areas AI has worked on increasing efficiencies & minimizing risks in clinical operations:
· NLP offering relief for staffing issues and increasing patient engagement: Natural language processing (NLP) and conversational AI have become sophisticated enough to be leveraged on the front line, as the first point of patient contact. Voice and chat bots are rolling out on patient phone lines, clinic apps and websites, helping lift some of the burden of staff. Healthcare organizations that have implemented the technology have seen a 21% reduction in average handle time, and containment rate, which refers to whether a bot is able to handle a call or if it needs to be passed to a human, have gone as high as 60%.
· Improving billing and coding efficiency : Whether it’s creating codes, predicting the claims or speeding up denials processing, AI and robotic process automation (RPA), are offering tremendous gains; reports suggest that up to 10% of claims get denied on first submission, and 65% of those never get reworked. AI models can predict the probability of a denial before the claim is submitted and organizations have reduced errors by up to 50% in claims before they’re submitted, improving the chances of appeal.
· Reducing case manager burdens : Administration is a significant part of the behind-the-scenes work in a healthcare organization. AI is designed to handle those kinds of repetitive jobs that require accuracy and speed; organizations have realized a reduction in medical necessity review by 75% for case managers, when an AI solution is deployed.
Many healthcare organizations, agnostic of size, have already been implementing AI solutions in their processes, particularly when it comes to increasing efficiencies in clinical operations. But scaling that investment is challenging and they need to look at traversing the full roadmap of AI journey to recognize the benefits, and understand that it’s time to leap into the fray.
(The author is a President, 3AI. Views expressed are personal and do not reflect the official position or policy of the FinancialExpress.com.)
