Abstract
Background Due to reimbursement tied in part to patients' perception of their care, hospitals
continue to stress obtaining patient feedback and understanding it to plan interventions
to improve patients' experience. We demonstrate the use of natural language processing
(NLP) to extract meaningful information from patient feedback obtained through Press
Ganey surveys.
Methods The first step was to standardize textual data programmatically using NLP libraries.
This included correcting spelling mistakes, converting text to lowercase, and removing
words that most likely did not carry useful information. Next, we converted numeric
data pertaining to each category based on sentiment and care aspect into charts. We
selected care aspect categories where there were more negative comments for more in-depth
study. Using NLP, we made tables of most frequently appearing words, adjectives, and
bigrams. Comments with frequent words/combinations underwent further study manually
to understand factors contributing to negative patient feedback. We then used the
positive and negative comments as the training dataset for a neural network to perform
sentiment analysis on sentences obtained by splitting mixed reviews.
Results We found that most of the comments were about doctors and nurses, confirming the
important role patients ascribed to these two in patient care. “Room,” “discharge”
and “tests and treatments” were the three categories that had more negative than positive
comments. We then tabulated commonly appearing words, adjectives, and two-word combinations.
We found that climate control, housekeeping and noise levels in the room, time delays
in discharge paperwork, conflicting information about discharge plan, frequent blood
draws, and needle sticks were major contributors to negative patient feedback. None
of this information was available from numeric data alone.
Conclusion NLP is an effective tool to gain insight from raw textual patient feedback to extract
meaningful information, making it a powerful tool in processing large amounts of patient
feedback efficiently.
Keywords natural language processing - knowledge modeling and representation - patient satisfaction
- patient engagement - patient - consumer health
