ARTICLE | doi:10.20944/preprints201806.0363.v2
Subject: Business, Economics And Management, Finance Keywords: health policy, VHA, VA, CMS TRICARE, Scandal
Online: 18 July 2018 (11:28:15 CEST)
In 2014, a whistleblower reported that many U.S. veterans died while waiting for care at the Phoenix VHA. Problems with veteran’s care through 2018 reveal ongoing and systematic problem. In March 2018, the VA Inspector General identified critical deficiencies at the Washington, DC VA Medical Center including failures to track patient safety events accurately, ineffective sterile processing, and more than 10 thousand open or pending prosthetic / sensory aid consults. The VHA clearly has problems with access and quality in a budget-constrained environment. In this policy analysis, four separate interventions that address the gap between the magnitude as well as the use of the VHA’s fixed budget versus access and cost expectations are explored. These policy interventions include maintaining the status quo, returning to a “VHA-only” option, transitioning to a CMS central payer system, and consolidating care under the DoD TRICARE insurance plans. An objective evaluation suggests that extending TRICARE to veterans while phasing out the VHA’s care responsibilities, while politically unpalatable, would likely provide the best of four possible solutions under various criterion weighting schemes. A central payer solution under the CMS would also be viable consideration. A Friedman’s test with Wilcoxon rank sum post-hoc tests suggests that TRICARE patient perceptions of quality are superior to VHA and non-VHA / non-DoD (p<.001), that access provided by the TRICARE program is ranked second in terms of venue acceptance only to the CMS solution set based on primary provider acceptance, and that the cost per beneficiary of a TRICARE solution ($6.5K / beneficiary) is far better than a VHA-only solution ($14.0 K / beneficiary), the CMS central payer solution ($12.2K / beneficiary), or the status quo (between $12.2K and $14.0K / beneficiary). The intent of this paper is to provoke thoughtful consideration of solutions for providing access to high-quality healthcare for veterans within our outside of the VHA. In this policy analysis, separate interventions that address the gaps between cost, quality, and access are explored. These policy interventions include maintaining the status quo, returning to a VHA-only option, transitioning to a CMS central payer system, and consolidating care under TRICARE.
ARTICLE | doi:10.20944/preprints201907.0345.v1
Subject: Medicine And Pharmacology, Neuroscience And Neurology Keywords: Alzheimer’s Disease; Extreme Gradient Boosting; Deep Residual Learning; conolutional neural networks; machine learning; dementia
Online: 31 July 2019 (04:33:43 CEST)
Alzheimer's is a disease for which there is no cure. Diagnosing Alzheimer's Disease (AD) early facilitates family planning and cost control. The purpose of this study is to predict the presence of AD using socio-demographic, clinical, and Magnetic Resonance Imaging (MRI) data. Early detection of AD enables family planning and may reduce costs by delaying long-term care. Accurate, non-imagery methods also reduce patient costs. The Open Access Series of Imaging Studies (OASIS-1) cross-sectional MRI data were analyzed. A gradient boosted machine (GBM) predicted the presence of AD as a function of gender, age, education, socioeconomic status (SES), and Mini-Mental State Exam (MMSE). A Residual Network with 50 layers (ResNet-50) predicted CDR presence and severity from MRI's (multi-class classification). The GBM achieved a mean 91.3% prediction accuracy (10-fold stratified cross validation) for dichotomous CDR using socio-demographic and MMSE variables. MMSE was the most important feature. ResNet-50 using image generation techniques based on an 80% training set resulted in 98.99% three class prediction accuracy on 4,139 images (20% validation set) at Epoch 133 and nearly perfect multi-class predication accuracy on the training set (99.34%). Machine Learning methods classify AD with high accuracy. GBM models may help provide initial detection based on non-imagery analysis, while ResNet-50 network models might help identify AD patients automatically prior to provider review.
ARTICLE | doi:10.20944/preprints202306.0812.v1
Subject: Public Health And Healthcare, Health Policy And Services Keywords: U.S. Veteran health; Comorbidities; Risk-factors; Military readiness; COVID
Online: 12 June 2023 (09:32:26 CEST)
Chronic diseases affect a disproportionate number of United States (U.S.) Veterans, causing significant long-term health issues and affecting entitlement spending. This longitudinal study examined the health status of U.S. Veterans as compared to non-Veterans pre- and post-COVID utilizing the annual Center for Disease Control and Prevention (CDC) Behavioral Risk Factor Surveillance System (BRFSS) survey data. Age-adjusted descriptive point estimates were generated independently for 2003 through 2021, while complex weighted panel data were generated from 2011 and onward. General linear modeling revealed that the average U.S. Veteran reports a higher prevalence of disease conditions except for mental health disorders when compared to the non-Veteran. These findings were consistent with both pre- and post-COVID, however, both groups reported a higher prevalence of mental health issues during the pandemic years. The findings suggest that there have been no improvements in reducing Veteran comorbidities to non-Veteran levels and that COVID adversely affected the mental health of both populations.