Enhancing Disability Determination Decision Process Through Natural Language Processing

In this article, a new approach is introduced that makes use of the valuable information that can be extracted from a patient's electronic healthcare records (EHRs). The approach employs natural language processing and biomedical text mining to handle patient's data. The developed approach extracts relevant medical entities and builds relations between symptoms and other clinical signature modifiers. The extracted features are viewed as evaluation features. The approach utilizes such evaluation features to decide whether an applicant could gain disability benefits or not. Evaluations showed that the proposed approach accurately extracts symptoms and other laboratory marks with high F-measures (93.5-95.6%). Also, results showed an excellent deduction in assessments to approve or reject an applicant case to obtain a disability benefit.

An Information Extraction Framework for Disability Determination: A mental Functioning Use-Case (Preprint)

UNSTRUCTURED Natural language processing (NLP) in health care enables transformation of complex narrative information into high value products such as clinical decision support and adverse event monitoring in real time via the Electronic Health Record (EHR). However, information technologies for mental health have consistently lagged behind due to the complexity of measuring and modeling mental health and illness. The use of NLP to support management of mental health conditions is a viable topic that has not been explored in depth. This article provides a framework for advancing NLP methods to identify, extract and organize information on mental health and functioning in order to inform the decision-making process applied to assessing mental health. We present a use case related to work disability, guided by the disability determination process of the U.S. Social Security Administration (SSA). From this perspective the following questions must be addressed about each problem leading to a disability benefits claim: when did the problem occur and how long has it existed? How severe is it? Does it affect the person’s ability to work? What is the source of the evidence about the problem? Our framework includes four dimensions of medical information that are central to assessing disability — temporal sequence and duration, severity, context, and the information source. We describe key aspects of each dimension and promising approaches for application in mental functioning. For example, to address temporality, a complete functional timeline must be created with all relevant aspects of functioning such as intermittence, persistence and recurrence. Severity of mental health symptoms can be successfully identified and extracted on a four-level ordinal scale from absent to severe. Some NLP work has been reported on context for specific cases of wheelchair use in clinical settings. We discuss the links between the task of information source assessment and work on source attribution, coreference resolution, event extraction and rule-based methods. Gaps were identified in NLP applications that directly applied to the framework and in existing relevant annotated datasets. We highlighted NLP methods with potential to be applied to move the field forward in application to mental functioning. Findings from this work will inform development of instruments developed to support the SSA adjudicators in their disability determination process. The four dimensions of medical information may have relevance for a broad array of individuals and organizations responsible for assessing mental health function and ability. Further, our framework with four specific dimensions presents significant opportunity for application of NLP in the realm of mental health and functioning beyond the SSA setting, and it may support the development of robust tools and methods to support decision-making related to clinical care, program implementation, and other outcomes.

Current problems of medical and social expertise

This article analyses legal framework of medical and social expertise. We have assessed and identified negative aspects of the existing order of interaction between health facilities and bureaus of medical and social expertise during disability determination. We have established the key role of health facilities in determination of persons that need social protection and rehabilitation because of persistent impairments of health. The article reflects significant gaps in the Classifications and Criteria, that are used in practice of bureaus of medical and social expertise, during development and implementation of individual rehabilitation and habilitation plan of a person with disabilities.

The Role of Nonattorney Representation in the SSDI Determination Process: A Case Study of One Prominent Intermediary

Intermediary organizations that provide nonattorney representation services to people applying for Social Security Administration (SSA) disability benefits are a prominent but understudied part of the disability landscape. A better understanding of these intermediaries and their clients can help to inform policies that influence the extent to which intermediaries support or impede SSA’s disability determination processes. This article describes how one prominent nonattorney intermediary screens potential clients and supports actual clients throughout the application process for Social Security Disability Insurance (SSDI) benefits. We describe the intermediary’s operations and the characteristics of its clients and compare the characteristics and outcomes of the intermediary’s awardees with all SSDI awardees. Our findings point to one important avenue through which people enter SSDI and suggest some policy options that could improve the entry process and identify employment supports that might serve as alternatives to SSDI.

An exploration of SSA’s disability determination process based on efficiency analysis

Purpose This paper aims to estimate the performance of the social security administration (SSA) in dealing with disability benefits applications in American. Design/methodology/approach The authors propose a multi-stage data envelopment analysis (DEA) method to analyze the efficiency of 167 hearing offices (HOs) to find the best performed HOs and inefficient ones and detect total improvement of inefficient and weak efficient offices. Findings The results show that totally 299,711 applications were processed and more applications will be processed if all offices can work efficiently. To the best of the authors’ knowledge, this paper is the first one to analyze the performance of SSA HOs using the multi-stage DEA method. Originality/value To the best of the authors’ knowledge, this paper is the first one to analyze the performance of SSA HOs using the multi-stage DEA method.

Enhancing Disability Determination Decision Process Through Natural Language Processing

In this article, a new approach is introduced that makes use of the valuable information that can be extracted from a patient's electronic healthcare records (EHRs). The approach employs natural language processing and biomedical text mining to handle patient's data. The developed approach extracts relevant medical entities and builds relations between symptoms and other clinical signature modifiers. The extracted features are viewed as evaluation features. The approach utilizes such evaluation features to decide whether an applicant could gain disability benefits or not. Evaluations showed that the proposed approach accurately extracts symptoms and other laboratory marks with high F-measures (93.5-95.6%). Also, results showed an excellent deduction in assessments to approve or reject an applicant case to obtain a disability benefit.