Natural Language Processing to Extract Meaningful Information from Patient Experience Feedback

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.

Applying natural language processing and machine learning techniques to patient experience feedback: a systematic review

“…Five studies did not perform manual classification and employed existing software to perform the sentiment analysis, that is, TheySayLtd, 28 TextBlob, 52 SentiWordNet, 57 DICTION, 53 Keras. 50 We split the supervised approach based on sentiment analysis ( table 2A ) and text classification ( table 2B ), where we document the percentage of total comments manually classified into categories for sentiment and topics for text classification, the number of raters including the inter-rater agreement and the classifier(s) used for ML. In addition, where reported, we also highlight the configuration employed during the data processing steps.…”

“…This fails to capture the mixed sentiments or neutral sentiments which could provide useful insights into patient experience. Nawab et al 50 demonstrated that splitting the mixed sentiments by sentences revealed distinct sentiments. Therefore, although the percentage of mixed or neutral sentiment is low compared with the overall dataset, analysis of comments within these mixed and neutral sentiment can provide useful information and therefore should not be discarded.…”

“…For a limited amount of data, NLP may not be very expedient, and with a smaller number of comments the results may not be as fruitful and there may not be enough raw data to detect a specific pattern. 50 Furthermore, rating posted in social media reviews is not adjusted for user characteristics or medical risk, whereas structured survey scores are patient mix adjusted. 6 …”

Machine Learning for Detection of Correct Peripherally Inserted Central Catheter Tip Position from Radiology Reports in Infants

“…[9][10][11] The algorithms systematically split free text into individual segments (words and phrases), correct spelling mistakes, standardize abbreviations, and analyze the syntax and semantics of each individual segment to decide whether a concept or sentiment is present within the free text. 12,13 It is a powerful tool when there is a large set of unstructured data, and it allows us gain unique insights that may be difficult to appreciate otherwise, for example, analyzing patient feedback that is written in natural language. 13 A recent review, 9 examined 67 articles on NLP for radiology, reports that clinical support service was a major category of use.…”

“…12,13 It is a powerful tool when there is a large set of unstructured data, and it allows us gain unique insights that may be difficult to appreciate otherwise, for example, analyzing patient feedback that is written in natural language. 13 A recent review, 9 examined 67 articles on NLP for radiology, reports that clinical support service was a major category of use. NLP has also been used with progress notes to identify disease processes or to aid in billing and coding.…”