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

Abstract

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.

Operationalizing and digitizing person-centered daily functioning: a case for functionomics

“…The first one is investing in making functionomics data more structured, for example by creating new validated measurement tools and implementing these tools in standard clinical and research practice. However, as mentioned above, data on functioning is very context sensitive, using measurement tools we may lose this context and may not accurately present the patient’s perspective [ 38 ]. The second approach is to apply free text mining, like natural language processing (NLP), to extract meaningful concepts from the free text and convert them to structured formats.…”

A Systematic Review of Natural Language Processing Methods and Applications in Thyroidology

“… 7 , 21 , 34 , 43 In addition, data sources need to be representative of the population to avoid the incorporation of inequities and social bias into the models. 44 Finally, using NLP methods requires high optimization for the local environment and extensive domain knowledge, which can be expensive, and stakeholders’ lack of financial resources or prioritization could halt their implementation. Thus, efficacy and cost-effectiveness trials are important to demonstrate the value of the intervention and facilitate its adoption.…”