Describing the patient experience from Yelp reviews of community pharmacies

To examine the characteristics of patient experience in community pharmacies through pattern exploration techniques of the unstructured free-text data from an online review website.

Retrospective observational study design using structural topic model (STM) and term frequency-inverse document frequency (tf-idf) to categorize free-text data. Tf-idf scores words in terms of importance, and STM extracts latent themes from free-text data based on the co-occurrence of words in a review. Human labels were assigned to STM output, with each topic's prevalence mapped to each level of the 1- to 5-star review ratings.

Data were obtained from the Yelp Academic data set from April 2006 through December 2017. These data were available for analysis from certain cities in the United States, Canada, and Europe. Included reviews were filtered based on the presence of pharmacy-specific character strings (e.g., "prescri").

Descriptive statistics of Yelp review characteristics, tf-idf scores, and topics produced from STM were used to characterize the content of Yelp reviews at each star-rating level.

The filtered data set contained 4463 reviews from 964 pharmacies in 8 U.S. states. The mean (±SD) review rating was 2.97 ± 0.91. The mean number of words in a review was 135 ± 116. STM revealed 9 topics that influenced patient experiences at community pharmacies, including waiting time, service attitude, and physical store characteristics. Friendly and helpful staff accounted for 28.3% of content in 5-star ratings, whereas waiting time accounted for 19.4% of 1-star ratings.

Yelp reviews provide a public look into patient experience at community pharmacies, and the reviews likely influence other patients' decisions to use the pharmacy. Pharmacies should focus their efforts on enabling pharmacy staff to provide high-quality care and minimizing unnecessary waiting times for patients.