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.

Representation of Social History Factors Across Age Groups: A Topic Analysis of Free-Text Social Documentation.

As individuals age, there is potential for dramatic changes in the social and behavioral determinants that affect health status and outcomes. The importance of these determinants has been increasingly recognized in clinical decision-making. We sought to characterize how social and behavioral health determinants vary in different demographic groups using a previously established schema of 28 social history types through both manual analysis and automated topic analysis of social documentation in the electronic health record across the population of an entire integrated healthcare system. Our manual analysis generated 8,335 annotations over 1,400 documents, representing 24 (86%) social history types. In contrast, automated topic analysis generated 22 (79%) social history types. A comparative evaluation demonstrated both similarities and differences in coverage between the manual and topic analyses. Our findings validate the widespread nature of social and behavioral determinants that affect health status over populations of individuals over their lifespan.