Analyzing the SSA Disability Evaluation Process

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Abstract

Analytics (Objective 1) 1) Expanding SSAs Use of Electronic Medical Evidence Initial SSA decisions are made by examiners who have very little time to review evidence for a case, usually in the form of lengthy medical records from various healthcare providers. The objective of this focus area is to develop, adapt, and apply methods that can process and leverage electronic medical records with a focus on identifying and characterizing language related to whole-person function. The ability to automatically review the electronic medical records aids SSA disability adjudicators to reach accurate, consistent, and timely decisions in accordance with SSA regulations and available medical evidence. In FY2022, the NIHs research focused on developing natural language processing (NLP) models for identifying function information related to criteria included in SSAs adult listing of impairments, and developing an ontology for mental functioning that can be used to compare language across information sources including SSA policy and the medical evidence. NLP Models for Functioning Information: The aim of this subproject is to extract functioning information from clinical documentation, which is an underdeveloped area of machine learning and NLP. In FY2022, we made progress on methods for extracting functioning information from samples of medical documents, especially in the domain of communication & cognition. This is the fourth and final domain for which we will develop NLP models, which is in addition to domains for mobility, self-care and domestic life, and interpersonal interactions and relationships (social functioning). To advance this work, we continued to develop annotation resources informed by content experts to serve as a gold standard for machine learning methods, models to automatically identify functional information, and resources to support the development and implementation of these models. Representing functioning information is supported by additional NLP methods and research. In FY2022, we have continued developing models that can extract temporal information for descriptions of function in medical records, which can be used to build timelines that SSA adjudicators could then use to evaluate a claimants evidence related to function over time. Mental Functioning Ontology: We have also continued to develop an ontology for mental functioning along with a corresponding terminology. The ontology and terminology can support NLP models in the relevant domains of functioning. In FY2022, we have worked to refine the ontology, improve the definitions for concepts within the ontology, and develop NLP models that can apply these concepts to medical evidence and other records to identify mental functioning information. Because an ontology also includes information on the relationships among terms and concepts, we can use the ontology to help SSA adjudicators understand when criteria related to mental functioning in SSA policy is documented in the medical records, even if there are differences in the specific language used. 2) Strengthening SSAs Employment Support Programs In addition to the disability benefits programs that SSA administers, SSA also oversees a program called Ticket to Work, which provides career development to beneficiaries and supports beneficiaries pursuing opportunities to return to work. In FY2022, we began a new research project with SSA to develop new ways to characterize individuals and occupations to inform and support return to work efforts. In particular, we are leveraging data from multiple SSA sources to identify characteristics of beneficiaries who are most likely to participate in the Ticket to Work program and most likely return to work. WD-FAB development (Objective 2) In collaboration with the SSA, the NIH and Boston University developed a comprehensive and efficient assessment instrument called the Work Disability Functional Assessment Battery (WD-FAB). Contemporary models of disability indicate that in order to assess work disability, what individuals can do and what they are expected to do for work must both be assessed. The WD-FAB is intended to assess what individuals can do. The WD-FAB is a 15-20-minute individualized assessment of functional activity that uses Item Response Theory (IRT), along with computer adaptive technology (CAT), to select the most relevant test items from a large pool of items to measure self-reported functional ability. Item-based scoring means respondents do not need to answer all items or the same items to obtain comparative scores and scores are obtained in a highly efficient manner. 3) Functional Assessment The objective of this focus area is to develop new ways to collect, structure, and interpret functional data for use by SSA. This work will include development of the WD-FAB and methods to assist in interpreting WD-FAB results. WD-FAB instrument development: The aim of this subproject is to finalize the development of the WD-FAB so that it is ready for real-world, applied testing. The instrument now includes over 300 items across eight domains, four of which represent physical function (basic mobility, upper body function, fine motor function, and community mobility) and four of which represent mental health function (communication & cognition, resilience & sociability, self-regulation, and mood & emotions). Functional stages (e.g., low, moderate, high functioning) were developed by content experts to aid score interpretation. To date, the reliability and validity of the WD-FAB have been supported by a variety of evidence from a continuum of studies. Differentiating sub-populations: In FY2022, we have continued to analyze WD-FAB data collected through various research efforts. These research efforts include collaborations with the University of New Hampshire to study the alignment of WD-FAB scores with job demands and SSAs ongoing Supported Employment Demonstration (SED) where the WD-FAB is one of several measures collected as part of an effort to understand what supports help individuals with mental health disorders return to work and/or prevent application for disability benefits. We are using longitudinal WD-FAB data from the SED to explore changes in scores over time, which will then support future work with SSA that looks at the potential role of the WD-FAB in their continuing disability review process, which assesses whether beneficiaries continue to meet SSAs definition of disability. During this fiscal year, we are also starting to analyze WD-FAB scores across the study samples to understand how the WD-FAB differentiates sub-populations of adults with work disability as an additional step in validating the WD-FAB. WD-FAB Research Study: SSA is currently conducting a pilot study to evaluate the feasibility of incorporating the WD-FAB into their continuing disability review (CDR) process, where SSA periodically reviews current beneficiaries to ensure they continue to meet SSAs definition of disability. This is a longitudinal study that collects WD-FAB data for a sample of beneficiaries currently undergoing a CDR twice over a six month period. These data will be available for NIHs analysis in FY2023. In FY2022, we prepared for these analyses by conducting a focus group with SSA employees to understand the CDR process and its challenges and to introduce the WD-FAB to solicit feedback on how such an assessment could inform the CDR process.