Using Natural Language Processing to Code Patient Experience Narratives: Capabilities and Challenges

Format:

• Book

Author/Creator:

• Ish, Daniel

Contributor:

• Elliott, Marc N.
• Grob, Rachel
• Hays, Ron D.
• Martino, Steven C.
• Osoba, Osonde A.
• Parker, Andrew M.
• Schlesinger, Mark
• Shaller, Dale

Language:

English

Other Title:

Using Natural Language Processing to Code Patient Experience Narratives

Place of Publication:

Santa Monica, Calif. RAND Corporation 2020

Summary:

Patient narratives about experiences with health care contain a wealth of information about what is important to patients. These narratives are valuable for both identifying strengths and weaknesses in health care and developing strategies for improvement. However, rigorous qualitative analysis of the extensive data contained in these narratives is a resource-intensive process, and one that can exceed the capabilities of human analysts. One potential solution to these challenges is natural language processing (NLP), which uses computer algorithms to extract structured meaning from unstructured natural language. Because NLP is a relatively new undertaking in the field of health care, the authors set out to demonstrate its feasibility for organizing and classifying these data in a way that can generate actionable information. In doing so, the authors focused on two steps that must be performed by a machine learning (ML) system designed to classify narratives into such codes as those typically applied by human coders (e.g., positive or negative statements regarding care coordination). These steps are (1) numerically representing the text data (in this case, entire narratives as they are provided by patients) and (2) classifying the data by codes based on that representation. The authors also compared four related approaches to deploying ML algorithms, identified potential pitfalls in the processing of data, and showed how NLP can be used to supplement and support human coding.