Nathaniel D. Bastian

Cost-effectiveness of sofosbuvir-based treatments for chronic hepatitis C in the US.

BackgroundThe standard care of treatment of interferon plus ribavirin (plus protease inhibitor for genotype 1) are effective in 50xa0% to 70xa0% of patients with CHC. Several new treatments including Harvoni, Olysiou2009+u2009Sovaldi, Viekira Pak, Sofosbuvir-based regimens characterized with potent inhibitors have been approved by the Food and Drug Administration (FDA) providing more options for CHC patients. Trials have shown that the new treatments increased the rate to 80xa0% to 95xa0%, though with a substantial increase in cost. In particular, current market pricing of a 12-week course of sofosbuvir is approximately US

Association of food environment and food retailers with obesity in US adults

84,000. We determine the cost-effectiveness of new treatments in comparison with the standard care of treatments.MethodsA Markov simulation model of CHC disease progression is used to evaluate the cost-effectiveness of different treatment strategies based on genotype. The model calculates the expected lifetime medical costs and quality adjusted life years (QALYs) of hypothetical cohorts of identical patients receiving certain treatments. For genotype 1, we compare: (1) peginterferonu2009+u2009ribavirinu2009+u2009telaprevir for 12xa0weeks, followed by 12 or 24xa0weeks treatment of peginterferonu2009+u2009ribavirin dependent on HCV RNA level at week 12; (2) Harvoni treatment, 12xa0weeks; (3) Olysiou2009+u2009Sovaldi, 12xa0weeks for patients without cirrhosis, 24xa0weeks for patients with cirrhosis; (4) Viekira Paku2009+u2009ribavirin, 12xa0weeks for patients without cirrhosis, 24xa0weeks for patients with cirrhosis; (5) sofosbuviru2009+u2009peginterferonu2009+u2009ribavirin, 12xa0weeks for patients with or without cirrhosis. For genotypes 2 and 3, treatment strategies include: (1) peginterferonu2009+u2009ribavirin, 24xa0weeks for treatment-naïve patients; (2) sofosbuviru2009+u2009ribavirin, 12xa0weeks for patients with genotype 2, 24xa0weeks for genotype 3; (3) peginterferonu2009+u2009ribavirin as initial treatment, 24xa0weeks for patients with genotype 2/3, follow-up treatment with sofosbuviru2009+u2009ribavirin for 12/16xa0weeks are performed on non-responders and relapsers.ResultsViekira Pak is cost-effective for genotype 1 patients without cirrhosis, whereas Harvoni is cost-effective for genotype 1 patients with cirrhosis. Sofosbuvir-based treatments for genotype 1 in general are not cost-effective due to its substantial high costs. Two-phase treatments with 12-week and 16-week follow-ups are cost-effective for genotype 3 patients and for genotype 2 patients with cirrhosis. The results were shown to be robust over a broad range of parameter values through sensitivity analysis.ConclusionsFor genotype 1, sofosbuvir-based treatments are not cost-effective compared to Viekira Pak and Harvoni, although a 30xa0% reduction in sofosbuvir price would change this result. Sofosbuviru2009+u2009ribavirin are cost-effective as second-phase treatments following peginterferonu2009+u2009ribavirin initial treatment for genotypes 2 and 3. However, there is limited data on sofosbuvir-involved treatment, and the results obtained in this study must be interpreted within the model assumptions.

A Robust, Multi-criteria Modeling Approach for Optimizing Aeromedical Evacuation Asset Emplacement:

The food environment has been shown to be a factor affecting the obesity rate. We studied the association of density of food retailer type with obesity rate in U.S. adults in local regions controlling for socioeconomic factors. Parametric nonlinear regression was used on publically available data (year=2009) at the county level. We used the results of this association to estimate the impact of the addition of a new food retailer type in a geographic region. Obesity rate increased in supercenters (0.25-0.28%) and convenience stores (0.05%) and decreased in grocery stores (0.08%) and specialized food stores (0.27-0.36%). The marginal measures estimated in this work could be useful in identifying regions where interventions based on food retailer type would be most effective.

Financial performance monitoring of the technical efficiency of critical access hospitals: a data envelopment analysis and logistic regression modeling approach.

According to current force health protection policy, the U.S. Army’s Health Service Support system is designed to maintain a healthy force and to conserve the combat strength of deployed soldiers. Specifically, this system remains particularly effective by employing standardized aeromedical evacuation assets and providing a responsive field-sited medical treatment facility for the wounded soldiers evacuated from the battlefield. Since the beginning of Operation Enduring Freedom, military commanders have faced a significant combinatorial challenge integrating these life-saving yet limited air evacuation assets into a fully-functional, comprehensive system for the entire theatre, which deserves thorough analysis for decision-making. This work describes a robust, multi-criteria decision analysis methodology using a scenario-based, stochastic optimization goal-programming model that U.S. Army medical planners can use as a strategic and tactical aeromedical evacuation asset-planning tool to help bolster and improve the current air evacuation system in Afghanistan. Specifically, this model optimizes over a set of expected scenarios with stochastically-determined casualty locations to emplace the minimum number of helicopters at each medical treatment facility necessary to maximize the coverage of the theatre-wide casualty demand and the probability of meeting that demand, while minimizing the maximal medical treatment facility evacuation site total vulnerability to enemy attack.

A Mixed-Methods Research Framework for Healthcare Process Improvement

EXECUTIVE SUMMARY From 1980 to 1999, rural designated hospitals closed at a disproportionally high rate. In response to this emergent threat to healthcare access in rural settings, the Balanced Budget Act of 1997 made provisions for the creation of a new rural hospital— the critical access hospital (CAH). The conversion to CAH and the associated cost‐based reimbursement scheme significantly slowed the closure rate of rural hospitals. This work investigates which methods can ensure the long‐term viability of small hospitals. This article uses a two‐step design to focus on a hypothesized relationship between technical efficiency of CAHs and a recently developed set of financial monitors for these entities. The goal is to identify the financial performance measures associated with efficiency. The first step uses data envelopment analysis (DEA) to differentiate efficient from inefficient facilities within a data set of 183 CAHs. Determining DEA efficiency is an a priori categorization of hospitals in the data set as efficient or inefficient. In the second step, DEA efficiency is the categorical dependent variable (efficient = 0, inefficient = 1) in the subsequent binary logistic regression (LR) model. A set of six financial monitors selected from the array of 20 measures were the LR independent variables. We use a binary LR to test the null hypothesis that recently developed CAH financial indicators had no predictive value for categorizing a CAH as efficient or inefficient, (i.e., there is no relationship between DEA efficiency and fiscal performance).

A hybrid recommender system using artificial neural networks

UNLABELLEDnThe healthcare system in the United States is spiraling out of control due to ever-increasing costs without significant improvements in quality, access to care, satisfaction, and efficiency. Efficient workflow is paramount to improving healthcare value while maintaining the utmost standards of patient care and provider satisfaction in high stress environments. This article provides healthcare managers and quality engineers with a practical healthcare process improvement framework to assess, measure and improve clinical workflow processes.nnnDESIGN AND METHODSnThe proposed mixed-methods research framework integrates qualitative and quantitative tools to foster the improvement of processes and workflow in a systematic way. The framework consists of three distinct phases: 1) stakeholder analysis, 2a) survey design, 2b) time-motion study, and 3) process improvement.nnnRESULTSnThe proposed framework is applied to the pediatric intensive care unit of the Penn State Hershey Childrens Hospital. The implementation of this methodology led to identification and categorization of different workflow tasks and activities into both value-added and non-value added in an effort to provide more valuable and higher quality patient care.nnnCONCLUSIONSnBased upon the lessons learned from the case study, the three-phase methodology provides a better, broader, leaner, and holistic assessment of clinical workflow. The proposed framework can be implemented in various healthcare settings to support continuous improvement efforts in which complexity is a daily element that impacts workflow.nnnPRACTICAL IMPLICATIONSnWe proffer a general methodology for process improvement in a healthcare setting, providing decision makers and stakeholders with a useful framework to help their organizations improve efficiency.

Evaluating the Impact of Hospital Efficiency on Wellness in the Military Health System

Neural network based hybrid recommender system utilizing review metadata is proposed.The system optimizes model hyper-parameters to minimize log-loss.Validate predictive capability of model against heterogeneous business categories. In the context of recommendation systems, metadata information from reviews written for businesses has rarely been considered in traditional systems developed using content-based and collaborative filtering approaches. Collaborative filtering and content-based filtering are popular memory-based methods for recommending new products to the users but suffer from some limitations and fail to provide effective recommendations in many situations. In this paper, we present a deep learning neural network framework that utilizes reviews in addition to content-based features to generate model based predictions for the business-user combinations. We show that a set of content and collaborative features allows for the development of a neural network model with the goal of minimizing logloss and rating misclassification error using stochastic gradient descent optimization algorithm. We empirically show that the hybrid approach is a very promising solution when compared to standalone memory-based collaborative filtering method.

The AMEDD Uses Goal Programming to Optimize Workforce Planning Decisions

Like all health care delivery systems, the U.S. Department of Defense Military Health System (MHS) strives to achieve top preventative care and population health outcomes for its members while operating at an efficient level and containing costs. The objective of this study is to understand the overall efficiency performance of military hospitals and investigate the relationship between efficiency and wellness. This study uses data envelopment analysis and stochastic frontier analysis to compare the efficiency of 128 military treatment facilities from the Army, Navy, and Air Force during the period of 2011 to 2013. Fixed effects panel regression is used to determine the association between the hospital efficiency and wellness scores. The results indicate that data envelopment analysis and stochastic frontier analysis efficiency scores are congruent in direction. Both results indicate that the majority of the MHS hospitals and clinics can potentially improve their productive efficiency by managing their input resources better. When comparing the performance of the three military branches of service, Army hospitals as a group outperformed their Navy and Air Force counterparts; thus, best practices from the Army should be shared across service components. The findings also suggest no statistically significant, positive association between efficiency and wellness over time in the MHS.

Resource allocation decision making in the military health system

The mission of the Army Medical Department AMEDD is to provide medical and healthcare delivery for the U.S. Army. Given the large number of medical specialties in the AMEDD, determining the appropriate number of hires and promotions for each medical specialty is a complex task. The AMEDD Personnel Proponency Directorate APPD previously used a manual approach to project the number of hires, promotions, and personnel inventory for each medical specialty across the AMEDD to support a 30-year life cycle. As a means of decision support to APPD, we proffer the objective force model OFM to optimize AMEDD workforce planning. We also employ a discrete-event simulation model to verify and validate the results. n nIn this paper, we describe the OFM applied to the Medical Specialist Corps, one of the six officer corps in the AMEDD. The OFM permits better transparency of personnel for senior AMEDD decision makers, whereas effectively projecting the optimal number of officers to meet the demands of the current workforce structure. The OFM provides tremendous value to APPD in terms of time, requiring only seconds to solve rather than months; this enables APPD to conduct quick what-if analyses for decision support, which was impossible to do manually.

Measuring the effect of pay-for-performance financial incentives on hospital efficiency in the military health system

The necessity to efficiently balance and re-allocate system resources among hospitals in a hospital network is paramount, especially as health systems experience increasing demand and costs for health services. In this paper, we proffer a resource allocation-based optimization model that adjusts resources (system inputs) automatically, which provides decision makers (such as health care managers and policy-makers) with a decision-support tool for re-allocating resources in large health systems that are centrally controlled and funded, such as the Military Health System. In these systems, inputs are fixed at certain levels and may only be adjusted within medical treatment facilities, while outputs must be maintained. We provide a mathematical formulation and example solutions from a case study using real-world data from sixteen U.S. Army hospitals. We also find utility in the use of multi-start evolutionary algorithms to store multiple optimal solutions for consideration by decision makers.