Strategic Considerations for Selecting Artificial Intelligence Solutions for Institutional Integration: A Single-Center Experience

Abstract

Artificial intelligence (AI) promises to revolutionize health care. Early identification of disease, appropriate test selection, and automation of repetitive tasks are expected to optimize cost-effective care delivery. However, pragmatic selection and integration of AI algorithms to enable this transformation remain challenging. Health care leaders must navigate complex decisions regarding AI deployment, considering factors such as cost of implementation, benefits to patients and providers, and institutional readiness for adoption. A successful strategy needs to align AI adoption with institutional priorities, select appropriate algorithms to be purchased or internally developed, and ensure adequate support and infrastructure. Further, successful deployment requires algorithm validation and workflow integration to ensure efficacy and usability. User-centric design principles and usability testing are critical for AI adoption, ensuring seamless integration into clinical workflows. Once deployed, continuous improvement processes and ongoing algorithm support ensure continuous benefits to the clinical practice. Vigilant planning and execution are necessary to navigate the complexities of AI implementation in the health care environment. By applying the framework outlined in this article, institutions can navigate the ever-evolving and complex environment of AI in health care to maximize the benefits of these innovative technologies.

Figure 1

Process chart to show artificial intelligence (AI) integration tasks that lead to institutional decisions. The process from left to right shows the major decisions and thinking for institutions to take in their journey of integrating AI into clinical workflow. Not all solutions will spend the same time in each box. EHR, electronic health record.

Figure 2

Illustrations of various integration patterns of artificial intelligence (AI) algorithms in clinical workflows. (A) Typical clinical consultant workflow without AI. (B) Integration of an AI point solution into a clinical consultant workflow. The information produced by the AI algorithm is incorporated into the consultant workflow, but not directly communicated outside the work unit. (C) Integration of an AI cross-domain solution. The information produced by the AI algorithm is communicated directly to clinical areas beyond the work unit to facilitate medical decision-making. This process can occur with or without a human-in-the-loop validation step.

Figure 3

Clinical workflow including integration of cross-domain artificial intelligence (AI) algorithm output with information from the patient medical record and embedded calculator functionality. This contextualization of AI results requires deeper integration with clinical systems but can increase the impact of the solution by driving more personalized clinical recommendations. Additionally, aggregation of information from multiple clinical systems in combination with AI outputs can decrease clerical burden of providers and facilitate clinical decision-making.