ONA-Accredited Primary Health Care Quality through Google Review Analysis

Purpose – The quality of healthcare services is crucial, given the increasing demands of users and competition among institutions. This study analyzes user perception of primary healthcare services based on Google Reviews comments, using natural language processing.
Design/methodology/approach – This exploratory-descriptive study analyzed comments from 101 healthcare units in São Paulo, Brazil, certified by the Brazilian Accreditation Organization until February 2024. Python (googletrans, pandas) collected data, while matplotlib, nltk, WordCloud, and networkx were used for visualization and analysis.
Findings – A total of 7,689 comments were analyzed, showing users rated services more positively (51.4%) than negatively (40.7%), with 7.8% expressing neutral sentiments. Noteworthy for public services, indicating satisfaction. Eastern and Southern regions had the highest evaluations. The Northern region received more “strongly positive” comments. Negative aspects included personnel, time, and communication issues. Positive aspects highlighted service as “professional” and “attentive”.
Research limitations/implications – Limited to accredited primary healthcare units in São Paulo, Brazil, this study suggests opportunities for replication in various countries, healthcare institutions, and accreditation programs, offering insights into service quality assessment and improvement.
Practical implications – This study helps identify shortcomings and highlight positive aspects in primary healthcare services, contributing to continuous improvement in the quality of care.
Originality/value – This study stands out from others because it focuses on evaluating users’ perceptions of services offered by primary healthcare units in São Paulo, Brazil. It utilizes data mining techniques to analyze sentiments expressed in Google review comments.

Healthcare, Service Quality, Sentiment Analysis, Brazil