By Joachim Roski, Principal, Booz Allen Hamilton and Kevin Vigilante, Executive Vice President and Chief Medical Officer, Booz Allen Hamilton.
As artificial intelligence (AI) continues to transform our world, the healthcare sector stands to substantially gain from AI. The ability to compute a massive amount of information quickly has promising implications for delivering better health outcomes, improved healthcare operations, or expanded research capabilities. However, AI is not immune to the risks that accompany the adoption of any new technological advancement – risks that may cause healthcare professionals to doubt its reliability or trustworthiness.
Fortunately, there are steps available that can help build a strong AI foundation, improving the quality of life for patients and healthcare professionals alike. If you’re looking to implement AI in your healthcare organization, here are a few things to keep in mind:
Understand your Data
If a data is not of high quality or is not representative of the intended group of study, the conclusions based on that data will likely be flawed. To accurately anticipate how a flu outbreak will impact hospital visits among a community population, it’s essential to account for variables that can affect all or part of the relevant population. For example, if a significant subset of the population is more susceptible to serious flu symptoms (e.g., based on pre-existing conditions), this needs to be taken into account by the hospital(s) in question. Given that algorithmic models are often designed to grow and expand with compounding data sets, any previous mistakes can snowball and lead to significant long-term bias or inaccuracy in the results.
As such, it’s critically important to build an AI foundation on a robust and comprehensive data acquisition and management operating system, complete with careful and consistent oversight of AI algorithms. Such a system should be supported by compliance and monitoring protocols to ensure that data is securely flowing. Having a clear understanding of where relevant data comes from and how it was collected is critical to ensure AI algorithms create meaningful output.