Elizabeth Lindemann

Representation of occupational information across resources and validation of the occupational data for health model

Reports by the National Academy of Medicine and leading public health organizations advocate including occupational information as part of an individuals social context. Given recent National Academy of Medicine recommendations on occupation-related data in the electronic health record, there is a critical need for improved representation. The National Institute for Occupational Safety and Health has developed an Occupational Data for Health (ODH) model, currently in draft format. This study aimed to validate the ODH model by mapping occupation-related elements from resources representing recommendations, standards, public health reports and surveys, and research measures, along with preliminary evaluation of associated value sets. All 247 occupation-related items across 20 resources mapped to the ODH model. Recommended value sets had high variability across the evaluated resources. This study demonstrates the ODH models value, the multifaceted nature of occupation information, and the critical need for occupation value sets to support clinical care, population health, and research.

Assessing the representation of occupation information in free-text clinical documents across multiple sources

There has been increasing recognition of the key role of social determinants like occupation on health. Given the relatively poor understanding of occupation information in electronic health records (EHRs), we sought to characterize occupation information within free-text clinical document sources. From six distinct clinical sources, 868 total occupation-related sentences were identified for the study corpus. Building off approaches from previous studies, refined annotation guidelines were created using the National Institute for Occupational Safety and Health Occupational Data for Health data model with elements added to increase granularity. Our corpus generated 2,005 total annotations representing 39 of 41 entity types from the enhanced data model. Highest frequency entities were: Occupation Description (17.7%); Employment Status - Not Specified (12.5%); Employer Name (11.0%); Subject (9.8%); Industry Description (6.2%). Our findings support the value of standardizing entry of EHR occupation information to improve data quality for improved patient care and secondary uses of this information.

Usability Evaluation of NLP-PIER: A Clinical Document Search Engine for Researchers.

NLP-PIER (Natural Language Processing – Patient Information Extraction for Research) is a self-service platform with a search engine for clinical researchers to perform natural language processing (NLP) queries using clinical notes. We conducted user-centered testing of NLP-PIER’s usability to inform future design decisions. Quantitative and qualitative data were analyzed. Our findings will be used to improve the usability of NLP-PIER.