Assessing Artificial Intelligence Readiness of a Healthcare Organization: Part 1

Introduction

With the advent of more advanced artificial intelligence (AI) technology such as ChatGPT and other large language models in this era of generative AI, there is an escalating interest amongst executives to initiate efforts in adopting these AI tools as part of an AI strategy. A number of “AI Maturity” models for organizations has been published, including a few such models for hospitals and healthcare organizations. These AI maturity models, however, are not well suited for the dynamic nature of healthcare systems and are excessively tedious to deploy and maintain in the healthcare milieu. In addition, these AI maturity models lack an efficient and objective measurement of the AI “readiness” of healthcare organizations, the all-too-important initial step of a healthcare organization’s AI journey. We prefer a different approach to the AI maturity model and designed an “AI Readiness” model; we term this objective and efficient data-based assessment tool the “Medical Intelligence Quotient (MIQTM)” of the healthcare organization. Just like the human intelligence quotient (IQ), this MIQTM is the initial baseline assessment of the inherent capabilities of the institution (vs individual for IQ). In short, AI readiness assessment in the form of MIQTM is precisely where the healthcare organization is as it begins this AI journey. In addition, we established a data science approach to this MIQTM assessment process not only for each individual hospital but also for a small cohort of diverse healthcare organizations for insightful comparative purposes. This MIQTM is designed by actively practicing clinicians who are leading the AI efforts at their hospitals rather than consulting technologists or administrators who are not actively engaged in AI efforts in hospitals or health systems.

Anthony Chang, MD, MBA, MS, MPH is the CEO at www.MI10.ai