JMIR Publications

ABSTRACT

Background:

Measuring patient satisfaction is a crucial aspect of medical care. Advanced natural language-processing techniques enable the extraction and analysis of high-level insights from textual data; nonetheless, data obtained from patients are often limited.

Objective:

To create a model that quantifies patient satisfaction based on diverse patient-written textual data.

Methods:

In this cross-sectional study, a neural network-based natural language processing (NLP) model was constructed using the textual content from disease blogs written in Japanese on the Internet between 1994 and 2020. We extracted approximately 20 million sentences from 56,357 patient-authored disease blogs and constructed a model to predict the patient satisfaction index (PSI) using a regression approach. After evaluating the model’s effectiveness, PSI was predicted before and after cancer notification to examine the emotional impact of cancer diagnoses on patients.

Results:

We assessed the correlation between the predicted and actual PSI values, labeled by humans, using the test set of 169 sentences. The model successfully quantified patient satisfaction by detecting nuances in sentences with excellent effectiveness (Spearman’s correlation coefficient: 0.832; root mean squared error: 0.166; P<.001). Furthermore, the PSI was significantly lower in the cancer notification period compared to the preceding control period (−0.057 and −0.012, respectively; t= 5.392, P<.001), indicating that the model quantifies the psychological and emotional changes associated with the cancer diagnosis notification.

Conclusions:

Our model demonstrates the ability to quantify patient dissatisfaction and identify significant emotional changes during the disease course. This approach may also help detect issues in routine medical practice.