Laura M. Cascella, MA, CPHRM

In healthcare, the concept of informed consent is generally straightforward. A patient is informed about a proposed test, treatment, or procedure; its benefits and risks; and any alternative options. With this knowledge, the patient decides to either consent or not consent to the recommended plan. In reality, though, informed consent is a more complex process that involves nondelegable duties and varies in scope based on the type of test, treatment, or procedure involved.

Laura M. Cascella, MA, CPHRM

When envisioning the future of healthcare, artificial intelligence (AI) is a preeminent part of the picture. Daily stories trend in the media related to AI applications and their widespread potential for revolutionizing medical practice and patient care. Yet, akin to the promises of electronic health records in the early 21st century, the excitement surrounding AI has sometimes led to an idealistic view of its capabilities while marginalizing technological and operational challenges as well as safety and ethical concerns.²

Laura M. Cascella, MA, CPHRM

One of the major red flags associated with artificial intelligence (AI) is the potential for bias. Bias can occur for various reasons. For example, the real-world data used to train AI applications (e.g., from medical studies and patient records) might be biased. Algorithms that rely on data from these sources will reflect that bias, perpetuating the problem and potentially leading to suboptimal recommendations and patient outcomes.¹ Likewise, bias can permeate the rules and assumptions used to develop AI algorithms, which “may unfairly privilege one particular group of patients over another.” ²

Laura M. Cascella, MA, CPHRM

Artificial intelligence (AI) systems and programs use data analytics and algorithms to perform functions that typically would require human intelligence and reasoning. Some types of AI are programmed to follow specific rules and logic to produce targeted outputs. In these cases, individuals can understand the reasoning behind a system’s conclusions or recommendations by examining its programming and coding.

Laura M. Cascella, MA, CPHRM

The concept of bias in relation to artificial intelligence (AI) usually is discussed in terms of biased data and algorithms, which pose significant ethical and safety issues. However, another type of bias also raises concern. Automation bias occurs when “clinicians accept the guidance of an automated system and cease searching for confirmatory evidence . . . perhaps transferring responsibility for decision-making onto the machine . . .”¹ Similarly, clinicians who use generally reliable technology systems might become complacent and miss potential errors, particularly if they are pressed for time or carrying a heavy workload.