Broadening horizons: the case for capturing function and the role of health informatics in its use.

BACKGROUND: Human activity and the interaction between health conditions and activity is a critical part of understanding the overall function of individuals. The World Health Organization's International Classification of Functioning, Disability and Health (ICF) models function as all aspects of an individual's interaction with the world, including organismal concepts such as individual body structures, functions, and pathologies, as well as the outcomes of the individual's interaction with their environment, referred to as activity and participation. Function, particularly activity and participation outcomes, is an important indicator of health at both the level of an individual and the population level, as it is highly correlated with quality of life and a critical component of identifying resource needs. Since it reflects the cumulative impact of health conditions on individuals and is not disease specific, its use as a health indicator helps to address major barriers to holistic, patient-centered care that result from multiple, and often competing, disease specific interventions. While the need for better information on function has been widely endorsed, this has not translated into its routine incorporation into modern health systems. PURPOSE: We present the importance of capturing information on activity as a core component of modern health systems and identify specific steps and analytic methods that can be used to make it more available to utilize in improving patient care. We identify challenges in the use of activity and participation information, such as a lack of consistent documentation and diversity of data specificity and representation across providers, health systems, and national surveys. We describe how activity and participation information can be more effectively captured, and how health informatics methodologies, including natural language processing (NLP), can enable automatically locating, extracting, and organizing this information on a large scale, supporting standardization and utilization with minimal additional provider burden. We examine the analytic requirements and potential challenges of capturing this information with informatics, and describe how data-driven techniques can combine with common standards and documentation practices to make activity and participation information standardized and accessible for improving patient care. RECOMMENDATIONS: We recommend four specific actions to improve the capture and analysis of activity and participation information throughout the continuum of care: (1) make activity and participation annotation standards and datasets available to the broader research community; (2) define common research problems in automatically processing activity and participation information; (3) develop robust, machine-readable ontologies for function that describe the components of activity and participation information and their relationships; and (4) establish standards for how and when to document activity and participation status during clinical encounters. We further provide specific short-term goals to make significant progress in each of these areas within a reasonable time frame.

Gradient-flow adaptive importance sampling for Bayesian leave one out cross-validation for sigmoidal classification models.

We introduce a set of gradient-flow-guided adaptive importance sampling (IS) transformations to stabilize Monte-Carlo approximations of point-wise leave one out cross-validated (LOO) predictions for Bayesian classification models. One can leverage this methodology for assessing model generalizability by for instance computing a LOO analogue to the AIC or computing LOO ROC/PRC curves and derived metrics like the AUROC and AUPRC. By the calculus of variations and gradient flow, we derive two simple nonlinear single-step transformations that utilize gradient information to shift a model's pre-trained full-data posterior closer to the target LOO posterior predictive distributions. In doing so, the transformations stabilize importance weights. Because the transformations involve the gradient of the likelihood function, the resulting Monte Carlo integral depends on Jacobian determinants with respect to the model Hessian. We derive closed-form exact formulae for these Jacobian determinants in the cases of logistic regression and shallow ReLU-activated artificial neural networks, and provide a simple approximation that sidesteps the need to compute full Hessian matrices and their spectra. We test the methodology on an n≪p dataset that is known to produce unstable LOO IS weights.

Informatics Research on Mental Health Functioning: Decision Support for the Social Security Administration Disability Program.

The disability determination process of the Social Security Administration's (SSA's) disability program requires assessing work-related functioning for individual claimants alleging disability due to mental impairment. This task is particularly challenging because the determination process involves the review of a large file of information, including objective medical evidence and self-reports from claimants, families, and former employers. To improve this decision-making process, SSA entered an interagency agreement with the Rehabilitation Medicine Department, Epidemiology and Biostatistics Section, in the Clinical Center of the National Institutes of Health, intending to use data science and informatics to develop decision support tools. This collaborative effort over the past decade has led to the development of the Work Disability-Functional Assessment Battery and has initiated an approach to applying natural language processing to the review of claimants' files for information on mental health functioning. This informatics research collaboration holds promise for improving the process of disability determination for individuals with mental impairments who make claims at the SSA.

Linking job duties, functioning, and employment status using the Work-Disability Functional Assessment Battery (WD-FAB): An expert coding and quantitative analysis.

BACKGROUND: An understanding of the link between specific occupational demands and individual worker functioning is limited, although such information could permit an assessment of the fit between the two in a manner that would inform national and state disability programs such as vocational rehabilitation and Social Security disability programs. OBJECTIVE: Our goal was to examine the utility of assessing physical and mental functioning relative to self-reported job duties to identify the domains of worker functioning most likely to create barriers to fulfilling an occupation's specific requirements. METHODS: Through primary survey data collection, 1770 participants completed the Work-Disability Functional Assessment Battery (WD-FAB) instrument after reporting details on their occupations (or most recent occupation if not working). Expert coders evaluated the level of function expected to successfully carry out each self-reported job duty with respect to six scales of physical and mental function. Quantitative analysis is used to examine the relationship between functioning and job duties. RESULTS: Those not working due to disability were more likely to fall short of the threshold of the physical and mental functioning requirements of their last job's three main job duties compared to those currently employed. Mental function scales were most likely to be the area experiencing a shortfall. CONCLUSIONS: Functional difficulties impede the ability to continue working in particular jobs that require that ability. This points to a need for specific accommodations to be implemented to bridge the gap between job requirements and functional capacity so that workers may remain engaged in their current work.

Regularized Bayesian calibration and scoring of the WD-FAB IRT model improves predictive performance over marginal maximum likelihood.

Item response theory (IRT) is the statistical paradigm underlying a dominant family of generative probabilistic models for test responses, used to quantify traits in individuals relative to target populations. The graded response model (GRM) is a particular IRT model that is used for ordered polytomous test responses. Both the development and the application of the GRM and other IRT models require statistical decisions. For formulating these models (calibration), one needs to decide on methodologies for item selection, inference, and regularization. For applying these models (test scoring), one needs to make similar decisions, often prioritizing computational tractability and/or interpretability. In many applications, such as in the Work Disability Functional Assessment Battery (WD-FAB), tractability implies approximating an individual's score distribution using estimates of mean and variance, and obtaining that score conditional on only point estimates of the calibrated model. In this manuscript, we evaluate the calibration and scoring of models under this common use-case using Bayesian cross-validation. Applied to the WD-FAB responses collected for the National Institutes of Health, we assess the predictive power of implementations of the GRM based on their ability to yield, on validation sets of respondents, ability estimates that are most predictive of patterns of item responses. Our main finding indicates that regularized Bayesian calibration of the GRM outperforms the regularization-free empirical Bayesian procedure of marginal maximum likelihood. We also motivate the use of compactly supported priors in test scoring.

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

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.

Measuring Work Related Physical and Mental Health Function: Updating the Work Disability Functional Assessment Battery (WD-FAB) Using Item Response Theory.

OBJECTIVE: To further improve measurement of work-related physical and mental health by updating the Work Disability Assessment Battery (WD-FAB). METHODS: Cross-sectional study with 1024 disability claimants and 1000 working age (21 to 66 years) adults in the United States. Developed new items to replenish the WD-FAB and analyzed using factor analysis and item response theory (IRT). Computer adaptive testing (CAT) simulations evaluated the psychometric properties of the original versus updated WD-FAB. RESULTS: Analyses confirmed the structure of the WD-FAB. Twenty-three new items were added (basic mobility: 7, upper body function: 4, fine motor: 6, self-regulation: 1, resilience & sociability: 5 items). CONCLUSIONS: Findings support the WD-FAB as a robust, psychometrically sound assessment of work-related function. Extensive content coverage (331 items) represents eight physical and mental health domains. IRT/CAT methods allow administration in under 15 minutes. The WD-FAB may prove valuable for efficiently characterizing work-related function across work rehabilitation settings.