Information overload (IO) refers to a state of overwhelming information that may result in adverse unintended consequences such as inaccurate information processing, cognitive fatigue, and decision inertia among information consumers. IO is becoming a growing challenge in the information age, where individuals, groups, and organizations are frequently exposed to a large body of information that is challenging to organize, manage, and utilize for effective decision-making. In healthcare settings, patients and providers are presented with a wide range of information within a brief period of clinical encounters. From an information systems (IS) perspective, IO presents an intersection of critical IS artifacts, such as problems associated with information management, strategic use of information, and value creation by leveraging information through effective and efficient processes. Also, optimal use of information technology in healthcare is necessary for the digital transformation of healthcare (Edmunds & Morris, 2000), which can be impacted by IO within healthcare ecosystems. Despite a growing scholarly interest in IO in healthcare, there is a paucity of synthesized knowledge that can inform the state-of-the-art of IO and facilitate future research and practice addressing this prevalent problem in digital healthcare. This study uses a text mining-based systematic review of the literature to evaluate research themes on IO in the context of healthcare. Moreover, this study deployed a six-step Wilsonian content analysis emphasizing the factors associated with IO in healthcare providers, patients, and caregivers (Wilson, 1970). Critical challenges such as information filtering failure and poor fit-for-purpose information processes affected the availability of credible information for decision-making, whereas the lack of ready-to-use information created further demand for information, resulting in inefficient information management practices. Healthcare providers and organizations leverage clinical decision support systems, which often lack interoperability with other information sources, which affects information congruity among patients and providers. Notably, an overabundance of health-related information in digital media can affect the selection and use of information in patients and caregivers, where misinformation and disinformation can result in suboptimal decision-making, health behaviors, and practices leading to adverse health outcomes. Further, adopting a grounded theory methodology, this study demonstrates how IO affects healthcare decision-making and results in adverse outcomes for patients, providers, and healthcare organizations. This study suggests that different socioeconomic groups with varying power and capacities may have disproportionate IO as well as information poverty in healthcare ecosystems, which necessitates critical IS research on human-computer interactions, adoption and diffusion of IT, neurocognitive assessments, and IT innovations in health information management using blockchain and artificial intelligence. This study contributes to IS theories and applications emphasizing information management mechanisms in healthcare and offers a research agenda on IO advancing IS scholarship.