Diagnostic data is transformative yet often underutilized. The volume of diagnostic claims and their related financial data is ripe for harvesting insights that can be a catalyst for health system decision making. Identifying leading indicators such as payor behavior and reimbursement trends can assist outreach programs and laboratories in optimizing their processes, and better managing their bottom lines. However, gaining those insights without inadvertently applying another person’s interpretation or subconscious bias is critical.
Mining insights from diagnostic data with artificial intelligence (AI) and data science can be compared with the process historians go through to understand our past. In each case, we are looking back at something that has happened and making interpretations about it and how it might influence our future. In doing so, both data scientists and historians rely on primary data sources and sometimes secondary data sources as well. The challenge of using secondary sources is that it has already had someone else’s interpretation applied. In other words, it is derived data, which is different than source data.
Another key to improving decisions with diagnostic data and AI is to make sure those insights are shared back into the larger health system. This is important because these insights can serve as a catalyst for health system business decisions as well. They can be used to drive or influence payor negotiations, services, test menu expansion, and patient and physician engagement initiatives. That said, most health systems lack the data management infrastructure to easily create this feedback loop.
To learn more about the practical applications on diagnostic data and AI to improve health system decision making, Xtelligent Healthcare Media and XIFIN recently hosted a complimentary 1-hour webinar. In this on-demand webinar you can expect to learn:
In addition, executives Kyle Fetter and Jeff Carmichael from XIFIN will discuss how they manage, organize, and analyze more than $40 billion of diagnostic claims annually.