Healthcare Disparities in Dysvascular Lower Extremity Amputations

Hurwitz, Max; Fuentes, Molly

doi:10.1007/s40141-020-00281-5

Cited by 2 publications

(2 citation statements)

References 81 publications

(184 reference statements)

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“…This multidimensional lens presents two key questions for reducing disparities for people with disabilities: understanding where the disparities come from (i.e., contributions of environmental and personal factors in addition to health condition and impairment) and how they are realized in peoples' lived experiences. Many factors affect disparities in outcomes, from bias in provider interactions 12,13 and quality of services received 14,15 to structural disparities in access to healthcare 16,17 and rates of poverty 18,19 . By knowing what to look for, we can identify people at risk early and tailor the services they receive to support their underlying needs 8,20 .…”

Section: Introductionmentioning

confidence: 99%

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

Newman-Griffis¹,

Hurwitz²,

McKernan³

et al. 2022

Preprint

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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 three key information barriers to more equitable information: (1) a lack of information on contextual factors that affect a person’s experience of function; (2) under-emphasis 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 three 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.

show abstract

Section: Introductionmentioning

confidence: 99%

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

Newman-Griffis¹,

Hurwitz²,

McKernan³

et al. 2022

Preprint

View full text Add to dashboard Cite

show abstract

“…This multidimensional lens presents 2 key questions for reducing disparities for people with disabilities: understanding where the disparities come from (i.e., contributions of environmental and personal factors in addition to health condition and impairment) and how they are realized in peoples’ lived experiences. Many factors affect disparities in outcomes, from bias in provider interactions [ 12 , 13 ] and quality of services received [ 14 , 15 ] to structural disparities in access to healthcare [ 16 , 17 ] and rates of poverty [ 18 , 19 ]. By knowing what to look for, we can identify people at risk early and tailor the services they receive to support their underlying needs [ 8 , 20 ].…”

Section: Introductionmentioning

confidence: 99%

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

Newman-Griffis

Hurwitz²,

McKernan³

et al. 2022

PLOS Digit Health

Self Cite

View full text Add to dashboard Cite

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.

show abstract

Healthcare Disparities in Dysvascular Lower Extremity Amputations

Cited by 2 publications

References 81 publications

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

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

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

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