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 mo...
| Main Authors: | , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Public Library of Science (PLoS)
2022-11-01
|
| Series: | PLOS Digital Health |
| Online Access: | https://doi.org/10.1371/journal.pdig.0000135 |
| _version_ | 1797698287048851456 |
|---|---|
| author | Denis R Newman-Griffis Max B Hurwitz Gina P McKernan Amy J Houtrow Brad E Dicianno |
| author_facet | Denis R Newman-Griffis Max B Hurwitz Gina P McKernan Amy J Houtrow Brad E Dicianno |
| author_sort | Denis R Newman-Griffis |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-03-12T03:51:58Z |
| format | Article |
| id | doaj.art-db766a64eedc42db9b09464bb7fa6174 |
| institution | Directory Open Access Journal |
| issn | 2767-3170 |
| language | English |
| last_indexed | 2024-03-12T03:51:58Z |
| publishDate | 2022-11-01 |
| publisher | Public Library of Science (PLoS) |
| record_format | Article |
| series | PLOS Digital Health |
| spelling | doaj.art-db766a64eedc42db9b09464bb7fa61742023-09-03T12:20:02ZengPublic Library of Science (PLoS)PLOS Digital Health2767-31702022-11-01111e000013510.1371/journal.pdig.0000135A roadmap to reduce information inequities in disability with digital health and natural language processing.Denis R Newman-GriffisMax B HurwitzGina P McKernanAmy J HoutrowBrad E DiciannoPeople 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.https://doi.org/10.1371/journal.pdig.0000135 |
| spellingShingle | Denis R Newman-Griffis Max B Hurwitz Gina P McKernan Amy J Houtrow Brad E Dicianno A roadmap to reduce information inequities in disability with digital health and natural language processing. PLOS Digital Health |
| title | A roadmap to reduce information inequities in disability with digital health and natural language processing. |
| title_full | A roadmap to reduce information inequities in disability with digital health and natural language processing. |
| title_fullStr | A roadmap to reduce information inequities in disability with digital health and natural language processing. |
| title_full_unstemmed | A roadmap to reduce information inequities in disability with digital health and natural language processing. |
| title_short | A roadmap to reduce information inequities in disability with digital health and natural language processing. |
| title_sort | roadmap to reduce information inequities in disability with digital health and natural language processing |
| url | https://doi.org/10.1371/journal.pdig.0000135 |
| work_keys_str_mv | AT denisrnewmangriffis aroadmaptoreduceinformationinequitiesindisabilitywithdigitalhealthandnaturallanguageprocessing AT maxbhurwitz aroadmaptoreduceinformationinequitiesindisabilitywithdigitalhealthandnaturallanguageprocessing AT ginapmckernan aroadmaptoreduceinformationinequitiesindisabilitywithdigitalhealthandnaturallanguageprocessing AT amyjhoutrow aroadmaptoreduceinformationinequitiesindisabilitywithdigitalhealthandnaturallanguageprocessing AT bradedicianno aroadmaptoreduceinformationinequitiesindisabilitywithdigitalhealthandnaturallanguageprocessing AT denisrnewmangriffis roadmaptoreduceinformationinequitiesindisabilitywithdigitalhealthandnaturallanguageprocessing AT maxbhurwitz roadmaptoreduceinformationinequitiesindisabilitywithdigitalhealthandnaturallanguageprocessing AT ginapmckernan roadmaptoreduceinformationinequitiesindisabilitywithdigitalhealthandnaturallanguageprocessing AT amyjhoutrow roadmaptoreduceinformationinequitiesindisabilitywithdigitalhealthandnaturallanguageprocessing AT bradedicianno roadmaptoreduceinformationinequitiesindisabilitywithdigitalhealthandnaturallanguageprocessing |