Using sentiment analysis to review patient satisfaction data located on the internet.

Area of Science:

• Healthcare management
• Natural Language Processing
• Data analysis

Background:

• Online patient feedback is a growing source of information.
• Quantifying and organizing this text-based data presents challenges for healthcare administrators.
• Traditional methods may not fully capture the nuances of patient sentiment.

Purpose of the Study:

• To evaluate the effectiveness of sentiment analysis in quantifying online patient feedback.
• To demonstrate the utility of time-to-next-complaint methods for organizing sentiment data.
• To show how healthcare managers can use these techniques to derive actionable insights.

Main Methods:

• Sentiment analysis was applied to patient feedback for gynecologists.
• Time-to-next-complaint methods were used to structure the analyzed data.
• The study focused on transforming unstructured text into quantifiable information.

Main Results:

• Sentiment analysis and time-to-next-complaint techniques show promise as tools for healthcare managers.
• These methods can convert web-based text into meaningful and quantifiable data.
• The findings suggest a pathway for better utilization of online patient comments.

Conclusions:

• Healthcare administrators can leverage sentiment analysis and time-to-next-complaint methods to analyze patient comments.
• This approach offers a novel way to mine valuable information from the internet.
• The study highlights the potential for improving communication and decision-making in clinical settings.