Received medical services are increasingly discussed and recommended on physician rating websites (PRWs). The reviews and ratings on these platforms are valuable sources of information for patient opinion mining. In this paper, we have tackled three issues that come along with inconsistency analysis on PRWs: (1) Natural language processing of user-generated reviews, (2) the disagreement in polarity of review text and its corresponding numerical ratings (individual inconsistency) and (3) the differences in patients’ rating behavior for the same service category (e.g. ‘treatment’) expressed by varying grades on the entire data set (collective inconsistency). Thus, the basic idea is first to identify relevant opinion phrases that describe service categories and to determine their polarity. Subsequently, the particular phrase has to be assigned to its corresponding numerical rating category before checking the (dis-)agreement of polarity values. For this purpose, several local grammars for the pattern-based analysis as well as domain-specific dictionaries for the recognition of entities, aspects and polarity were applied on 593,633 physician reviews from both German PRWs and Furthermore, our research contributes to content quality improvement of PRWs because we provide a technique to detect inconsistent reviews that could be ignored for the computation of average ratings.