Agentic AI-Powered Claims Intelligence: A Deep Learning Framework for Automating WorkersCompensation Claim Processing Using GenerativeAI

If individuals decide whether to file an insurance claim based in part on the expected value of the benefits they will receive, then changes in the probability that a claim will be denied by the insurer will influence the probability that a claim is filed. Increasing claim-denial rates to reduce claim rates and cut costs would be a questionable strategy for an insurance company that marketed policies to the individuals who will file claims. But in the workers compensation insurance market, insurers sell policies to employers, while it is workers who file claims that may be denied. This article examines evidence from a period in the state of Oregon during which workers whose employers were covered by different workers compensation carriers faced different claim-denial rates at the same point in time, and during which the state's largest carrier changed its claim-denial rate dramatically over time. Using this evidence, the author finds that higher denial rates were associated with lower rates of claim filing. F urthermore, this relationship was strong and significant for claims arising from back injuries but insignificant for claims arising from traumatic injuries. Possible reasons for this latter finding are explored. INTRODUCTION Insurance carriers do not accept all the claims made by those they insure. Some claims are denied, whether because the insurer believes that the insured party misunderstood the terms of the coverage, or because the insurer suspects exaggeration of losses or some other intentional fraud on the part of the insured. Standard economic reasoning suggests that in deciding whether to file a claim, the insured individual takes into account this possibility of denial, along with any costs in terms of time and trouble imposed on him or her by the screening process through which the insurer makes the denial decision. Thus, an increase in the probability that all or part of a claim will be denied, or in the delays and requirements imposed on the claimant while that decision is reached, lowers the number of claims filed, Of course, an insurance company competing in a private market would likely be wary of a strategy designed to reduce the number of claims filed by increasing denial rates and/or the rigorousness of claims- screening procedures, as such behavior would potentially lead to a loss of customers to other carriers. Designers and administrators of social insurance programs such as Unemployment Insurance or Social Security Disability Insurance are in a different position, as they do not need to solicit customers for their insurance in a competitive marketplace. When faced with rising costs or concerns with moral hazard on the part of potential recipients, they can raise denial rates by tightening eligibility requirements or enforcing eligibility requirements more stringently, subject only to political pressures from potential recipients and those who represent them. Such increases lower program costs directly by lowering the number of filed claims that are paid. But, as Parsons (1991) has pointed out, an increase in claim-denial rates also acts as a self-screening mechanism, discouraging some potential recipients from even attempting to file a claim. Parsons offers empirical evidence of the claim discouraging effect of rising denial rates in the Social Security Disability Insurance program, as do Helpern and Hausman (1986 ) and Gruber and Kubik (1997). Likewise, Blank and Card (1991) show that the proportion of eligible unemployed workers filing for Unemployment Insurance benefits is lower, other things equal, in states that disqualify a greater percentage of those who actually do file claims. This study looks at another social insurance program, workers compensation insurance, for evidence that claiming is discouraged by higher claim-denial rates. The author examines data on workers compensation claims from the state of Oregon, covering a period during which, because of changes in state law and policy changes by the state's largest workers compensation insurer, there was considerable variation in claim-denial rates experienced by claimants over time, and by different groups of claimants at the same point in time. …

P137. Preliminary analysis on the influence of workers’ compensation status on outcomes of anterior lumbar interbody fusion at L5-S1

Background: The workers’ compensation (WC) status has been associated with inferior outcomes in orthopedic procedures. Therefore, it is usually excluded from clinical outcome studies. Purpose: To determine the impact WC status has on outcomes for patients undergoing hip arthroscopy for femoroacetabular impingement (FAI) and labral tears at a minimum 10-year follow-up, and to compare these findings with a propensity score–matched 1:3 benchmark control group without WC claims. Method: Data was retrospectively analyzed for patients who underwent hip arthroscopy as treatment for FAI and labral tears between 2008 and 2013 with a WC claims. Included patients had complete pre- and postoperative questionnaires at minimum 10-years follow-up for mHHS, NAHS, HOS-SSS, patient satisfaction, and VAS or documented an endpoint during the study time frame. Patients were propensity matched in a 1:3 ratio to a benchmark control group without WC claims based on age at surgery, sex, body mass index (BMI), acetabular outerbridge grade and capsular treatment. Clinically significant thresholds for hip arthroscopy, complications, revision hip arthroscopy, conversion total hip arthroplasty (THA) rates, type of work and return to work rates were included in the analysis. Results: A total of 280 patients were included in the study. WC patients displayed significant improvements across all PROs and reported high patient satisfaction. When compared to the benchmark control group, the WC group started with significantly lower baseline preoperative scores for all PROs. The study group displayed a significantly higher magnitude of improvement for mHHS, NAHS, HOS-SSS, and VAS. Both groups reached similar postoperative scores across all PROs, reaching the MCID and PASS for mHHS, NAHS, HOS-SSS, and VAS at similar rates at the latest follow-up. Importantly, 74.3% of patients were able to return to work at an average of 8.04 ± 7.40 months. However, the WC group showed a higher revision hip arthroscopy rate (p < 0.05). Conclusion: Hip arthroscopy for the treatment of FAI and labral tear in patients with WC claims showed favorable outcomes and a high return-to-work rate at a minimum 10-year follow-up. These results were comparable to a benchmark control group with no WC claims. However, the WC group had a significantly higher rate of revision hip arthroscopy with a 3-fold relative risk.(p < 0.01).

Efecto sobre la rentabilidad que tiene para el afiliado la comisión cobrada por las administradoras de fondos de pensiones

Worker's compensation insurance for the oral surgeon

BackgroundBayesian methods show promise for classifying injury narratives from large administrative datasets into cause groups. This study examined a combined approach where two Bayesian models (Fuzzy and Naïve) were used...

Our client and partner, a public accident-insurance company (equivalent to the Worker's Compensation Insurance in the U.S.), has shifted their focus towards the consultation in the designing and redesigning of workplaces. Therefore, after conducting a series of careful analyses, we ascertained that a computer-based system would greatly assist these experts with their analyzing and consulting projects. This viable system, called “ErgoKnowMe”, was successfully tested both in the laboratory and in the field.

Long-term follow-up after thoracic outlet decompression: An analysis of factors determining outcome

Intelligent transportation systems are vital for modern traffic management and optimization, greatly improving traffic efficiency and safety. With the rapid development of generative artificial intelligence (Generative AI) technologies in areas like image generation and natural language processing, generative AI has also played a crucial role in addressing key issues in intelligent transportation systems (ITS), such as data sparsity, difficulty in observing abnormal scenarios, and in modeling data uncertainty. In this review, we systematically investigate the relevant literature on generative AI techniques in addressing key issues in different types of tasks in ITS tailored specifically for road transportation. First, we introduce the principles of different generative AI techniques. Then, we classify tasks in ITS into four types: traffic perception, traffic prediction, traffic simulation, and traffic decision-making. We systematically illustrate how generative AI techniques addresses key issues in these four different types of tasks. Finally, we summarize the challenges faced in applying generative AI to ITS, and discuss future research directions based on different application scenarios.

The central goal of linguistics-the science of human language-is understanding the following: why linguistic expressions are structured the way they are, how these structures represent meaning and externalized signal (sound for spoken languages, manual gestures for signed languages), how humans comprehend and produce these structures, and how humans acquire the mental capacities that make all of this possible (see, among many others, Chomsky, 1965 and Baker, 2002).By discovering the core underlying principles governing language, linguistics aims to 'carve nature at its joints.'Some of these principles may ostensibly be surface false in that they may not capture superficial surface patterns but instead reveal and explain deeper systematic phenomena.Consider, for example, the principle that allows language users to add relative clauses as modifiers of nouns, for example, the bracketed relative clause in the boy [the girl met].The principle, which appears to hold in almost every language, allows us-when stated properly-to replace the girl with any other expression of the same syntactic category, any other noun phrase, for example, the woman.However, this demand, in turn, is not surface true, as observed when considering the psychological difficulties associated with certain so-called center-embedding constructions.So, for example, replacing the girl with a more complex noun phrase such as the girl the woman likes yields a result that a speaker will never use and an addressee will find very difficult to understand: the boy [the girl the woman likes met]. 2 So, we have a fairly general principle with a host of considerations arguing that it is correctly stated, yet it yields results that appear to be false on the surface.The consensus has been that, despite this surface falsity, the principle is, nevertheless, correct in its idealized form (as part of the grammar internalized by a speaker of the language, speaker's competence).The principle appears to be sometimes false, according to this consensus, due to interfering factors (performance factors) pertaining to how speakers make use of the principles of grammar (of their competence) in producing and comprehending sentences in real time.This distinction between competence and performance is none other than the distinction between idealized theory and noise, familiar from high school physics (e.g., the distinction between the theory of motion and friction).And a consequence of this distinction is that there is a notion of 'being correct' for natural languages (being grammatical) that is importantly distinct from the notion of being 'probable' or 'expected.'No such competence-performance distinction (and no distinction between grammaticality and expectedness) is designed into current generative AI systems for language (see Yngve, 1960; Miller & Chomsky, 1963; and Chomsky, 1965; and in the context of LLMs, see Fox & Katzir, 2023; Katzir, 2023; and further in section 2.1).In contrast to some of the earlier works on generative modeling, which were driven by scientific understanding (Rumelhart & McClelland, 1986; Elman, 1993; Hinton, 2007), the development of modern generative AI technologies is largely use or performance driven: generative models of molecules can help scientists discover new drugs, image generation models allow nonexperts to create high-quality graphics at low cost, and LLMs enable computers to perform useful tasks involving language-such as answering a question given a user prompt.These models are generally trained on 'raw' data (e.g., pixels, words) and prioritize prediction over scientific understanding of the data domain.As an example, LLMs make use of enormous neural networks-

Texas' unique elective system of workers' compensation (WC) coverage is being discussed widely in the United States as a possible model to be adopted by other states. Texas is the only state that does not mandate that employers provide state-certified WC insurance. Oklahoma passed legislation for a similar system in 2013, but it was declared unconstitutional by the Oklahoma Supreme Court in 2016. This study examined 9523 work-related hospitalizations that occurred in Texas in 2012 using Texas Department of State Health Services data. We sought to examine work-related injury characteristics by insurance source. An unexpected finding was that among those with WC, 44.6% of the hospitalizations were not recorded as work related by hospital staff. These unrecorded cases had 1.9 (1.6-2.2) times higher prevalence of a severe risk of mortality compared to WC cases that were recorded as work related. Uninsured and publicly insured workers also had a higher prevalence of severe mortality risk. The hospital charges for one year were $615.2 million, including at least $102.8 million paid by sources other than WC, and with $29.6 million that was paid for by injured workers or by taxpayers. There is an urgent need for more research to examine how the Texas WC system affects injured workers.

PGI25 Long- and Short-Term Disability and Workers' Compensation Trends for Employees with Digestive System (Gastrointestinal) Conditions in the United States

We present a comparative analysis of patterns of exposure to job stressors and stress-related workers' compensation (WC) claims to provide an evaluation of the adequacy of claims-driven policy and practice. We assessed job strain prevalence in a 2003 population-based survey of Victorian [Australia] workers and compared these results with stress-related WC statistics for the same year. Job strain prevalence was higher among females than males, and elevated among lower vs. higher occupational skill levels. In comparison, claims were higher among females than males, but primarily among higher skill-level workers. There was some congruence between exposure and WC claims patterns. Highly exposed groups in lower socio-economic positions were underrepresented in claims statistics, suggesting that the WC insurance perspective substantially underestimates the job stress problems for these groups. Thus to provide a sufficient evidence base for equitable policy and practice responses to this growing public health problem, exposure or health outcome data are needed as an essential complement to claims statistics.

Utilizing Worker's Compensation Claims to Assess sars-cov-2 Transmission in Healthcare Workers at a Large Health System

Objectives: Employers increasingly focus on absence benefits and connections with employee health. United States absence benefits include Sick Leave (SL), Short- and Long-Term Disability (STD and LTD, respectively) for non-work-related injuries/illnesses, and Workers’ Compensation (WC) for work-related injuries/illnesses. This research explores all-cause absence (SL, STD, LTD, and WC) utilization and changes from baseline for eligible employees with rheumatoid arthritis to determine if the use a constant payment factor is appropriate for models. Study Design: Retrospective multi-year database analysis. Methods: The Workpartners database (1/1/2001-12/31/2019) was used to identify employees with rheumatoid arthritis with adjudicated medical claims. Annual prevalence, benefit utilization, mean days of leave, and median payments (as % of salary) were analyzed. Annual outcomes were calculated as a percent of baseline (2001). Results: Rheumatoid arthritis prevalence averaged 0.5% between 2001 and 2019. At baseline, the percent of eligible employees using STD = 15.5%, LTD = 0.7%, WC = 1.7%, SL = 61.7%. Mean absence days were 48.5, 367.5, 43.8 for STD, LTD, WC, respectively and median payments were 70.5%, 22.2%, 65.7% of salary for STD, LTD, WC, respectively. From 2002-2019: 11.7%-16.9% of eligible employees filed STD claims for 82.1%-995.9% of baseline days and 80.4%-125.9% median payments; 0.6%-2.9% of eligible employees filed LTD claims for 66.6%-114.7% of baseline days and 63.2%-254.8% median payments; 0.3%-1.6% of eligible employees filed WC claims for 44.0%-472.8% of baseline days and 70.4%-271.5% median payments. Median payments were highest in 2012, 2019, 2003 for STD, LTD, WC, respectively and the most absence days were used in 2017 for SL and LTD, 2008 for STD, and 2005 WC. Conclusion: Employees with rheumatoid arthritis used absence benefits at differing rates over time with varying leave-lengths and payments. Using a constant cost or salary replacement factor for absence costs over time and across benefits is not accurate.