A roadmap to reduce information inequities in disability with digital health and natural language processing.

People with disabilities disproportionately experience negative health outcomes. Purposeful analysis of information on all aspects of the experience of disability across individuals and populations can guide interventions to reduce health inequities in care and outcomes. Such an analysis requires more holistic information on individual function, precursors and predictors, and environmental and personal factors than is systematically collected in current practice. We identify 3 key information barriers to more equitable information: (1) a lack of information on contextual factors that affect a person's experience of function; (2) underemphasis of the patient's voice, perspective, and goals in the electronic health record; and (3) a lack of standardized locations in the electronic health record to record observations of function and context. Through analysis of rehabilitation data, we have identified ways to mitigate these barriers through the development of digital health technologies to better capture and analyze information about the experience of function. We propose 3 directions for future research on using digital health technologies, particularly natural language processing (NLP), to facilitate capturing a more holistic picture of a patient's unique experience: (1) analyzing existing information on function in free text documentation; (2) developing new NLP-driven methods to collect information on contextual factors; and (3) collecting and analyzing patient-reported descriptions of personal perceptions and goals. Multidisciplinary collaboration between rehabilitation experts and data scientists to advance these research directions will yield practical technologies to help reduce inequities and improve care for all populations.

Digital Scarlet Letters: Sexually Transmitted Infections in the Electronic Medical Record.

ABSTRACT: The harms of implicit bias in clinical settings are acknowledged but poorly understood and difficult to overcome. We discuss how structural components of electronic medical record (EMR) user interfaces may contribute to sex and gender-based discrimination against patients via constant, duplicative presentation of stigmatizing sexually transmitted infection (STI) data irrespective of clinical significance. Via comparison with symbolism and representative quotes in Hawthorne's 1850 novel The Scarlet Letter, we propose a metaphor to examine how EMRs function as a platform for moral judgment, which may display an indelible "scarlet letter" for pregnant patients with STI history. We consider whether current depictions of STIs in EMRs are structurally unjust and may contribute to biased treatment by directing attention to violations of hegemonic sex/gender norms regarding sexual behavior and thus triggering moral judgments of maternal fitness. We conclude with recommendations for how to address these challenges to improve ethical stewardship of sensitive sexual/reproductive health data.