Linking Free Text Documentation of Functioning and Disability to the ICF With Natural Language Processing

Abstract

Background: Invaluable information on patient functioning and the complex interactions that define it is recorded in free text portions of the Electronic Health Record (EHR). Leveraging this information to improve clinical decision-making and conduct research requires natural language processing (NLP) technologies to identify and organize the information recorded in clinical documentation.Methods: We used natural language processing methods to analyze information about patient functioning recorded in two collections of clinical documents pertaining to claims for federal disability benefits from the U.S. Social Security Administration (SSA). We grounded our analysis in the International Classification of Functioning, Disability, and Health (ICF), and used the Activities and Participation domain of the ICF to classify information about functioning in three key areas: mobility, self-care, and domestic life. After annotating functional status information in our datasets through expert clinical review, we trained machine learning-based NLP models to automatically assign ICF categories to mentions of functional activity.Results: We found that rich and diverse information on patient functioning was documented in the free text records. Annotation of 289 documents for Mobility information yielded 2,455 mentions of Mobility activities and 3,176 specific actions corresponding to 13 ICF-based categories. Annotation of 329 documents for Self-Care and Domestic Life information yielded 3,990 activity mentions and 4,665 specific actions corresponding to 16 ICF-based categories. NLP systems for automated ICF coding achieved over 80% macro-averaged F-measure on both datasets, indicating strong performance across all ICF categories used.Conclusions: Natural language processing can help to navigate the tradeoff between flexible and expressive clinical documentation of functioning and standardizable data for comparability and learning. The ICF has practical limitations for classifying functional status information in clinical documentation but presents a valuable framework for organizing the information recorded in health records about patient functioning. This study advances the development of robust, ICF-based NLP technologies to analyze information on patient functioning and has significant implications for NLP-powered analysis of functional status information in disability benefits management, clinical care, and research.

Capturing and Operationalizing Participation in Pediatric Re/Habilitation Research Using Artificial Intelligence: A Scoping Review

“…For example, the Participation and Environment Measures (PEM) (63)(64)(65) assess for how often a child, youth or young adult participates in home, (pre-)school/daycare/work and community activities, their level of involvement in those activities, the desire for participation to change, applied participation-focused strategies, and the perceived impact of the environment on child, youth or young adult participation. Applications of AI such as recommender algorithms (e.g., constraint satisfaction and optimization) or NLP might provide simplified, more practical and lowcost ways for self-or proxy-reported data collection and interpretation for individual goal setting such as by systematically integrating responses into the individual child or youth participation profile, their participation goal, and intervention planning (12,16,66,67).…”

“…Regardless of the type of AI method employed [e.g., machine learning (ML), natural language processing (NLP)] ( 12 ), AI is commonly used to simplify processes and to customize information to individuals’ preferences and needs, which could benefit the healthcare industry ( 13 ). In pediatric re/habilitation, AI may help to consolidate and analyze information in ways that afford for providers to more efficiently enact the evaluation and goal-setting, intervention, and reevaluation phases of the therapeutic process ( 14 ) to deliver client-centered and participation-focused re/habilitation interventions ( 15 , 16 ). In the last decade there has been a vast increase in research on the use of AI in participation-focused pediatric re/habilitation warranting need for summarizing the body of literature in this area of work ( 17 ).…”

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

“…Newman-Griffis et al developed methods for extracting information about limitations in daily living activities from EHR text, including linkage to the ICF. [61][62][63] . Functioning information has also been investigated in targeted contexts such as geriatric syndrome 64,65 and frailty.…”

“…Structured coding systems such as the ICD classifications or CPT have transformed automated clinical decision support systems and data-driven quality assessment; NLP-driven alignment of clinical observations with the ICF can integrate functional outcomes and facilitators into these processes as well. 63 Indexing information on function and context in EHR can not only support clinician-directed chart review to understand functional trajectories over time, but also enable automated visualization of functional measurements and support remote monitoring in patient care. Further, retrieval and display of patient experiences and priorities alongside clinical measures of function can help guide patient-centered interventions and interactions to improve health outcomes.…”

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

“…Agaronnik and colleagues [50,60] used NLP to extract data about wheelchair use from EHR narratives and to identify the frequency with which functioning was documented in oncology notes. Newman- Griffis and colleagues developed methods for extracting information about limitations in daily living activities from EHR text, including linkage to the ICF [61][62][63]. Functioning information has also been investigated in targeted contexts such as geriatric syndrome [64,65] and frailty [66].…”