Relief in Sight? Chatbots, In-baskets, and the Overwhelmed Primary Care Clinician

Abstract

The recent emergence of publically facing artificial intelligence (AI) chatbots has generated vigorous discussion in the lay public around the possibilities, liabilities, and uncertainties of the integration of such technology into everyday life. As primary care clinicians continue to struggle against ever-increasing loads of asynchronous, electronic work, the potential for AI to improve the quality and efficiency of this work looms large. In this essay, we discuss the basic premise of open-access AI chatbots such as CHATGPT, review prior applications of AI in healthcare, and preview some possible AI chatbot-assisted in-basket assistance including scenarios of communicating test results with patients, providing patient education, and clinical decision support in history taking, review of prior diagnostic test characteristics, and common management scenarios. We discuss important concerns related to the future adoption of this technology including the transparency of the training data used in developing these models, the level of oversight and trustworthiness of the information generated, and possible impacts on equity, bias, and patient privacy. A stepwise and balanced approach to simultaneously understand the capabilities and address the concerns associated with these tools will be needed before these tools can improve patient care.

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