Patient satisfaction is a central measure of high-performing healthcare systems, yet real-world evaluations at scale remain challenging. In this study, we analyzed over 120,000 de-identified patient reviews from 45 Ontario hospitals between 2015 and 2022. We applied natural language processing (NLP), including named entity recognition (NER), to extract insights on hospital wards, patient health outcomes, and medical conditions. We also examined regional demographic data to identify potential disparities emerging during the COVID-19 pandemic. Our findings show that nearly 80% of the hospitals studied had fewer than 50% positive reviews, exposing systemic gaps in meeting patient needs. In particular, negative reviews decreased during COVID-19, suggesting possible shifts in patient expectations or increased appreciation for strained healthcare workers; however, certain units, such as intensive care and cardiology, experienced fewer positive ratings, reflecting pandemic and related pressures on critical care services. 'Anxiety' emerged as a recurrent concern in negative reviews, pointing to the growing awareness of mental health needs. Furthermore, hospitals located in regions with higher percentages of visible minority and low-income populations initially saw higher positive review rates before COVID-19, but this trend reversed after 2020. Collectively, these results demonstrate how large-scale unstructured data can identify fundamental drivers of patient satisfaction, while underscoring the urgent need for adaptive strategies to address anxiety and combat systemic inequalities.

The authors have declared no competing interest.

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