Artificial intelligence (AI) is a burgeoning field in health information technology and a key element in envisioning the future of healthcare. 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 a sensationalized view of its capabilities while marginalizing technological and operational challenges as well as safety and ethical concerns.2
Read more 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 data used to train AI applications might be biased; research has shown racial, gender, socioeconomic, and age-related disparities in medical studies. Algorithms that rely on data from these studies 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
Read more 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.
Read more Biased data and algorithms have been identified as significant ethical and safety concerns with artificial intelligence (AI); however, another type of bias also raises concern — automation bias. Humans, by nature, are vulnerable to cognitive errors resulting from knowledge deficits, faulty heuristics, and affective influences/situativity. In healthcare, these cognitive missteps are known to contribute to medical errors and patient harm, particularly in relation to delayed and incorrect diagnoses.
Read more Artificial intelligence (AI), much like other types of health information technology, raises concerns about data privacy and security — particularly in an era where cyberattacks are rampant and patients’ protected health information (PHI) is highly valuable to identity thieves and cyber criminals.
Read more At the heart of many innovations in healthcare are patients and finding ways to improve the quality of their care and experience. This is perhaps no more true than in the case of artificial intelligence (AI), which offers vast potential for improving patient outcomes through advances in population health management, risk identification and stratification, diagnosis, and treatment. Yet even with this promise, questions arise about how patients will interact with and react to these new technologies as well as how these advances will change the provider–patient relationship.
Read more Training and education are imperative in many facets of healthcare — from understanding clinical systems, to improving technical skills, to understanding regulations and professional standards. Technology often presents unique training challenges because of the ways in which it disrupts existing workflow patterns, alters clinical practice, and creates both predictable and unforeseen challenges.
Read more Healthcare practices, like hospitals, need to be ready for the tragic reality of an active shooter at their location. However, unlike hospitals, they have fewer people to protect and cover less square feet. Despite the physical environment of a healthcare practice, having an emergency preparedness plan in place that addresses an active shooter situation is critical.
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