Medical vs. Organizational Complaints: A Machine Learning Analysis Reveals Divergent Patterns in Patient Reviews Across Russian Cities
Irina Evgenievna Kalabikhina, Anton Vasilyevich Kolotusha, Vadim Sergeevich Moshkin
TL;DR
This study uses machine learning to classify patient complaints in Russian cities, revealing that medical complaints are rising in Moscow but not in other cities.
Contribution
A novel machine learning algorithm is developed to distinguish between medical and organizational complaints in patient reviews.
Findings
Moscow shows a shift toward more medical complaints since 2021, unlike other cities.
St. Petersburg has a higher proportion of medical complaints in key specialties compared to other cities.
Gender differences in complaint types are most notable in St. Petersburg, with women more likely to write medical complaints.
Abstract
Background: The growth of digital patient feedback presents a new opportunity for healthcare quality monitoring. This study addresses the need to automatically classify the content of patient reviews to identify primary sources of dissatisfaction. Objective: The purpose of this study is to develop a machine learning algorithm for classifying negative patient reviews into two core categories: medical content (M—pertaining to diagnosis, treatment, and outcomes) and organizational support (O—pertaining to logistics, cost, and communication). We aim to identify which type of concern prevails and to analyze variations across cities, patient gender, and medical specialties. Methods: A database of 18,680 negative patient reviews (rated 1 star) was compiled from the Russian aggregator infodoctor.ru for the period from July 2012 to August 2023. A training set was created using an independent…
Genes, proteins, chemicals, diseases, species, mutations and cell lines named across the full text — each resolved to its canonical identifier and authoritative record.
Click any figure to enlarge with its caption.
Figure 1
Figure 2
Figure 3
Figure 4
Figure 5
Figure 6
Figure 7
Figure 8
Figure 9
Figure 10
Figure 11
Figure 12
Figure 13
Figure 14
Figure 15
Figure 16
Figure 17
Figure 18
Figure 19Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
Taxonomy
TopicsPatient Satisfaction in Healthcare · Medical Malpractice and Liability Issues · Pharmaceutical industry and healthcare