Regulatory and clinical research bodies are calling for systematic patient input, however patient experience is not usually available via traditional data sources such as electronic medical records or claims data.
Other sources of data such as health-related social media forums and patient surveys can provide valuable insights in that regard. These unstructured textual data sources can reveal hidden insights that may be unknown to treating physicians such as unexpected adverse events or patients’ preferences.
Natural language processing (NLP) can be used to support traditional methods such as qualitative analyses in analysing data from these sources.
We present in this abstract two examples of using NLP to explore patients’ emotional burden, one from a large survey on infertility, and another from breast cancer patient/caregiver forums. Additionally, we also used NLP to identify from social media posts information about adverse events, patients’ unmet needs, treatment preferences, and the impact of health technology interventions.
In these examples, a range of NLP techniques was used to extract information from textual data, from word frequencies, ngrams, and word correlations, to topic modellings and sentiment analysis.
These methods showed great flexibility and efficiency, however, human manual qualitative analyses on a subset are usually required to ensure the reliability of the results. Methods such as sentiment analysis can be very efficient in summarizing emotion polarity, however, it can sometimes misinterpret the information if multiple subjects are mentioned. Some of the advantages of NLP include its power to analyse large textual datasets in real-time, and its ability to identify hidden insights that could be missed by a human due to unconscious bias. In conclusion, when used in combination with traditional methods and with human interpretation, NLP can be a powerful tool to extract valuable patient insights from social media and patient surveys.

Using Natural Language Processing to Extract Patient Insights From Social Media and Patient Surveys

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