The International Classification of Functioning, Disability and Health (ICF) in Electronic Health Records

Maritz, Roxanne; Aronsky, Dominik; Prodinger, Birgit

doi:10.4338/aci2017050078

Cited by 22 publications

(2 citation statements)

References 36 publications

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“…For functioning to play a role in clinical practice-either in assessment, goal-planning, outcomes, and evaluation-work needs to be done on standardized clinical reporting of the functioning information (20,21). The case needs to be made, to the satisfaction of health policy-makers, that functioning information should be fully integrated into health information systems (22,23), potentially within electronic health records (24). Although the groundwork for using ICF as an international reference framework for functioning information has been laid, the challenge remains to break down the barriers to an increased demand for functioning information from clinical and a recognition on the part of policy makers of the need for routine collection of functioning information in order to strengthening rehabilitation within the health system.…”

Section: Icf As Reference Framework For Functioning Informationmentioning

confidence: 99%

Human Functioning: Developments and Grand Challenges

Bickenbach

2021

Front. Rehabilit. Sci.

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Section: Icf As Reference Framework For Functioning Informationmentioning

confidence: 99%

Human Functioning: Developments and Grand Challenges

Bickenbach

2021

Front. Rehabilit. Sci.

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“…Many studies have demonstrated the potential of automated and semi-automated ICD coding systems, using NLP methods, to streamline medical coding processes (35)(36)(37). Growing integration of the ICF into clinical and research settings, from primary care (38) and EHR implementation (39) to pediatric research (40), present similar opportunities to smooth the adoption and practical use of ICF codes with NLP-based coding systems. Vreeman and Richoz (41) describe potential benefits to both clinical care and research from integrating the ICF and other standardized vocabularies into EHRs, and Bettger et al (42) highlight the role of EHR data in providing key insights to advance quality measures, research, and policy for rehabilitation.…”

Section: Broader Implications Of Icf Coding With Nlpmentioning

confidence: 99%

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

Newman-Griffis

Maldonado

et al. 2021

Preprint

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BackgroundInvaluable 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.MethodsWe used NLP 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 ICF’s Activities and Participation domain 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 codes to mentions of functional activity.ResultsWe 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 codes. 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 codes. NLP systems for automated ICF coding achieved over 80% macro-averaged F-measure on both datasets, indicating strong performance across all ICF codes used.ConclusionsNLP 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.

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