Newman-Griffis Denis R, Hurwitz Max B, McKernan Gina P, Houtrow Amy J, Dicianno Brad E
Department of Biomedical Informatics, University of Pittsburgh, Pittsburgh, Pennsylvania, United States of America.
Center for Health Equity Research and Promotion, VA Pittsburgh Healthcare System, Pittsburgh, Pennsylvania, United States of America.
PLOS Digit Health. 2022 Nov 17;1(11):e0000135. doi: 10.1371/journal.pdig.0000135. eCollection 2022 Nov.
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.
残疾人出现负面健康结果的比例过高。对个体和人群中残疾经历各方面信息进行有针对性的分析，可以指导采取干预措施，减少医疗保健和结果方面的健康不平等现象。与当前实践中系统收集的信息相比，这种分析需要更全面的关于个体功能、先兆因素和预测因素以及环境和个人因素的信息。我们确定了实现更公平信息存在的3个关键信息障碍：（1）缺乏关于影响个人功能体验的背景因素的信息；（2）电子健康记录中对患者声音、观点和目标的重视不足；（3）电子健康记录中缺乏记录功能和背景观察结果的标准化位置。通过对康复数据的分析，我们确定了通过开发数字健康技术来减轻这些障碍的方法，以便更好地获取和分析有关功能体验的信息。我们提出了未来利用数字健康技术，特别是自然语言处理（NLP）促进更全面了解患者独特体验的3个研究方向：（1）分析自由文本记录中有关功能的现有信息；（2）开发新的由NLP驱动的方法来收集有关背景因素的信息；（3）收集和分析患者报告的个人认知和目标描述。康复专家和数据科学家之间的多学科合作，以推进这些研究方向，将产生实用技术，帮助减少不平等现象，改善所有人群的医疗保健。
