A potential cost savings analysis of a penicillin de-labeling program

IntroductionOver 95% of patients documented as penicillin allergic can tolerate a penicillin without a reaction. Inaccurate documentation of penicillin allergy leads to more expensive alternative antibiotic prescriptions and an increased incidence of resistant infections.ObjectiveTo understand the potential drug cost savings of a penicillin de-labeling program to a healthcare system.MethodsWe evaluated patient visits with documented penicillin allergy who presented to the pediatric Emergency Department (PED) and 22 associated primary care clinics. Patients were included if they were discharged home with a non-penicillin antibiotic when the first-line treatment for the diagnosis would have been a penicillin. The potential cost savings were the sum of all visit-level cost differences between the non-penicillin prescription(s) and a counterfactual penicillin prescription. To factor in a 95% successful patient de-labeling rate, we repeatedly sampled 95% from the patients with the eligible visits 10,000 times to produce an estimate of the potential cost savings.ResultsOver the 8-year period, 2,034 visits by 1,537 patients to the PED and 12,349 visits by 6,073 patients to primary care clinics satisfied eligibility criteria. If 95% of the patients could have been successfully de-labeled, it would have generated a cost saving of $618,653 (95$618,617—$618,689) for all the corresponding payers in the system.ConclusionsImplementing a penicillin de-labeling program across a healthcare system PED and its associated primary care clinics would bring significant cost savings. Healthcare systems should rigorously evaluate optimal methods to de-label patients with reported penicillin allergy

Effects of the 340B Drug Pricing Program on Hospitals’ Prescribing Behavior, Patient Mix, and Quality of Care

In 1992, Congress created the 340B Drug Pricing Program that requires drug manufacturers to provide outpatient drugs to participating hospitals with substantial discounts. Although the intent of the program is to allow covered entities to increase access to care for more vulnerable patients, hospitals are not required by law to pass on the discounts. Therefore, a concern is that hospitals might over-prescribe. This dissertation includes three chapters to study the effects of the 340B program on hospitals’ behavior changes: Chapter 1 uses state aggregate hospital service spending data from the Centers for Medicare and Medicaid Services (CMS) to study the nation-wide impact of state 340B hospital participation on state hospital service spending. Controlling for state fixed effects, time fixed effects and state specific time trends, I find, on average, a 1 percentage point increase in state 340B hospital share leads to a 12.8% increase in state hospital service spending per capita. With only hospital spending data, analysis in this chapter cannot distinguish between a scenario where hospitals increase their spending to improve quality of care, consistent with the intent of the 340B program, and a scenario where hospitals are simply increasing spending without improving quality to maximize profit. Chapter 2 complements the analysis in Chapter 1 by exploring the causal impact of the 340B program on hospitals’ medication cost, patient mix and quality of care. Working with 15 million ambulatory visits to Florida hospitals from 2005 to 2015, I use a series of difference-in-difference (DID) and synthetic control methods (SCM) based on the 2010 340B eligibility expansion, I find an average increase of $111.35 in medication cost per visit due to the 2010 expansion. Quantile regressions reveal that hospitals with the highest proportion of charity care and uninsured patients keep medication cost low and on the most expensive visits, they significantly reduce medication cost for patients. The remaining newly eligible hospitals significantly raise medication cost after the expansion. The increase becomes larger the more expensive the treatment is. Finally, I find some indications that newly eligible hospitals increased Medicaid patient mix and improved quality of care, but the evidence is not strong enough to be conclusive. Chapter 3 further extends the analysis by examining the impact of market power on 340B hospitals’ behavior changes. Using the CMS nation-wide state aggregate data, I find the positive relationship between the state’s 340B hospital share and state aggregate hospital service spending is stronger when hospitals’ market share is higher. Working with the Florida data, using a series difference-in-difference-indifference (DDD) regressions, complemented by DID and SCM estimations, I find the 340B hospitals with low market shares seem to fulfill the mission of the program by keeping medication cost low, treating more low-income patients covered by Medicaid and Medicaid managed care and provide more charity to the communities. Compared to them, hospitals with high market shares significantly raise additional medication cost, treat fewer low-income patients but substantially more commercially insured patients. There are some signs of post-expansion quality improvement among all the newly eligible hospitals, measured by the post-operative adverse reaction rates, but heterogeneity exists in hospitals’ length of stay and nonroutine discharge rates. Hospitals with high market shares seem to treat more patients in their own outpatient facilities with a shorter length of stay. While the ones with low market shares experience increased length of stay, possibly due to worse health conditions among the additional Medicaid and Medicaid managed care patients they treat. As a summary, this dissertation finds the average 340B hospital raise their medication cost upon participation in the program, but heterogeneity exists that some of them seem to fulfill the mission of the program. There are signs of quality improvement in the data, but future research could adopt more quality measures to study the cost-effectiveness on the price increase, as well as the welfare influence on the cost reduction

Effects of the 340B Drug Pricing Program on Hospitals’ Prescribing Behavior, Patient Mix, and Quality of Care

In 1992, Congress created the 340B Drug Pricing Program that requires drug manufacturers to provide outpatient drugs to participating hospitals with substantial discounts. Although the intent of the program is to allow covered entities to increase access to care for more vulnerable patients, hospitals are not required by law to pass on the discounts. Therefore, a concern is that hospitals might over-prescribe. This dissertation includes three chapters to study the effects of the 340B program on hospitals’ behavior changes: Chapter 1 uses state aggregate hospital service spending data from the Centers for Medicare and Medicaid Services (CMS) to study the nation-wide impact of state 340B hospital participation on state hospital service spending. Controlling for state fixed effects, time fixed effects and state specific time trends, I find, on average, a 1 percentage point increase in state 340B hospital share leads to a 12.8% increase in state hospital service spending per capita. With only hospital spending data, analysis in this chapter cannot distinguish between a scenario where hospitals increase their spending to improve quality of care, consistent with the intent of the 340B program, and a scenario where hospitals are simply increasing spending without improving quality to maximize profit. Chapter 2 complements the analysis in Chapter 1 by exploring the causal impact of the 340B program on hospitals’ medication cost, patient mix and quality of care. Working with 15 million ambulatory visits to Florida hospitals from 2005 to 2015, I use a series of difference-in-difference (DID) and synthetic control methods (SCM) based on the 2010 340B eligibility expansion, I find an average increase of $111.35 in medication cost per visit due to the 2010 expansion. Quantile regressions reveal that hospitals with the highest proportion of charity care and uninsured patients keep medication cost low and on the most expensive visits, they significantly reduce medication cost for patients. The remaining newly eligible hospitals significantly raise medication cost after the expansion. The increase becomes larger the more expensive the treatment is. Finally, I find some indications that newly eligible hospitals increased Medicaid patient mix and improved quality of care, but the evidence is not strong enough to be conclusive. Chapter 3 further extends the analysis by examining the impact of market power on 340B hospitals’ behavior changes. Using the CMS nation-wide state aggregate data, I find the positive relationship between the state’s 340B hospital share and state aggregate hospital service spending is stronger when hospitals’ market share is higher. Working with the Florida data, using a series difference-in-difference-indifference (DDD) regressions, complemented by DID and SCM estimations, I find the 340B hospitals with low market shares seem to fulfill the mission of the program by keeping medication cost low, treating more low-income patients covered by Medicaid and Medicaid managed care and provide more charity to the communities. Compared to them, hospitals with high market shares significantly raise additional medication cost, treat fewer low-income patients but substantially more commercially insured patients. There are some signs of post-expansion quality improvement among all the newly eligible hospitals, measured by the post-operative adverse reaction rates, but heterogeneity exists in hospitals’ length of stay and nonroutine discharge rates. Hospitals with high market shares seem to treat more patients in their own outpatient facilities with a shorter length of stay. While the ones with low market shares experience increased length of stay, possibly due to worse health conditions among the additional Medicaid and Medicaid managed care patients they treat. As a summary, this dissertation finds the average 340B hospital raise their medication cost upon participation in the program, but heterogeneity exists that some of them seem to fulfill the mission of the program. There are signs of quality improvement in the data, but future research could adopt more quality measures to study the cost-effectiveness on the price increase, as well as the welfare influence on the cost reduction

An overview of the American trauma system

The American trauma system is designed to provide an organized response to injury. It draws its foundations from lessons learned from America's involvement in the wars of the 20th century as well as principles developed in urban community hospitals. Although run at the local and state government level, it is guided by national societies and has become a world class example. It also currently faces challenges with declining reimbursement and providing equal access to care for all Americans. Professional societies and legislative bodies are continuing to work together for fair and equitable solutions to these issues. Keywords: Trauma system, Trauma center, United State

AI-Based Faster-Than-Real-Time Stability Assessment of Large Power Systems with Applications on WECC System

Achieving clean energy goals will require significant advances in regard to addressing the computational needs for next-generation renewable-dominated power grids. One critical obstacle that lies in the way of transitioning today’s power grid to a renewable-dominated power grid is the lack of a faster-than-real-time stability assessment technology for operating a fast-changing power grid. This paper proposes an artificial intelligence (AI) -based method that predicts the system’s stability margin information (e.g., the frequency nadir in the frequency stability assessment and the critical clearing time (CCT) value in the transient stability assessment) directly from the system operating conditions without performing the conventional time-consuming time-domain simulations over detailed dynamic models. Since the AI method shifts the majority of the computational burden to offline training, the online evaluation is extremely fast. This paper has tested the AI-based stability assessment method using multiple dispatch cases that are converted and tuned from actual dispatch cases of the Western Electricity Coordinating Council (WECC) system model with more than 20,000 buses. The results show that the AI-based method could accurately predict the stability margin of such a large power system in less than 0.2 milliseconds using the offline-trained AI agent. Therefore, the proposed method has great potential to achieve faster-than-real-time stability assessment for practical large power systems while preserving sufficient accuracy

Routing Recovery for UAV Networks with Deliberate Attacks: A Reinforcement Learning based Approach

The unmanned aerial vehicle (UAV) network is popular these years due to its various applications. In the UAV network, routing is significantly affected by the distributed network topology, leading to the issue that UAVs are vulnerable to deliberate damage. Hence, this paper focuses on the routing plan and recovery for UAV networks with attacks. In detail, a deliberate attack model based on the importance of nodes is designed to represent enemy attacks. Then, a node importance ranking mechanism is presented, considering the degree of nodes and link importance. However, it is intractable to handle the routing problem by traditional methods for UAV networks, since link connections change with the UAV availability. Hence, an intelligent algorithm based on reinforcement learning is proposed to recover the routing path when UAVs are attacked. Simulations are conducted and numerical results verify the proposed mechanism performs better than other referred methods.Comment: IEEE GLOBECOM 2023, 6 pages, 4 figure

Integrating PointNet-Based Model and Machine Learning Algorithms for Classification of Rupture Status of IAs

Background: The rupture of intracranial aneurysms (IAs) would result in subarachnoid hemorrhage with high mortality and disability. Predicting the risk of IAs rupture remains a challenge. Methods: This paper proposed an effective method for classifying IAs rupture status by integrating a PointNet-based model and machine learning algorithms. First, medical image segmentation and reconstruction algorithms were applied to 3D Digital Subtraction Angiography (DSA) imaging data to construct three-dimensional IAs geometric models. Geometrical parameters of IAs were then acquired using Geomagic, followed by the computation of hemodynamic clouds and hemodynamic parameters using Computational Fluid Dynamics (CFD). A PointNet-based model was developed to extract different dimensional hemodynamic cloud features. Finally, five types of machine learning algorithms were applied on geometrical parameters, hemodynamic parameters, and hemodynamic cloud features to classify and recognize IAs rupture status. The classification performance of different dimensional hemodynamic cloud features was also compared. Results: The 16-, 32-, 64-, and 1024-dimensional hemodynamic cloud features were extracted with the PointNet-based model, respectively, and the four types of cloud features in combination with the geometrical parameters and hemodynamic parameters were respectively applied to classify the rupture status of IAs. The best classification outcomes were achieved in the case of 16-dimensional hemodynamic cloud features, the accuracy of XGBoost, CatBoost, SVM, LightGBM, and LR algorithms was 0.887, 0.857, 0.854, 0.857, and 0.908, respectively, and the AUCs were 0.917, 0.934, 0.946, 0.920, and 0.944. In contrast, when only utilizing geometrical parameters and hemodynamic parameters, the accuracies were 0.836, 0.816, 0.826, 0.832, and 0.885, respectively, with AUC values of 0.908, 0.922, 0.930, 0.884, and 0.921. Conclusion: In this paper, classification models for IAs rupture status were constructed by integrating a PointNet-based model and machine learning algorithms. Experiments demonstrated that hemodynamic cloud features had a certain contribution weight to the classification of IAs rupture status. When 16-dimensional hemodynamic cloud features were added to the morphological and hemodynamic features, the models achieved the highest classification accuracies and AUCs. Our models and algorithms would provide valuable insights for the clinical diagnosis and treatment of IAs

A Review of Clean Electricity Policies—From Countries to Utilities

Due to the heavy stress on environmental deterioration and the excessive consumption of fossil resources, the transition of global energy from fossil fuel energy to clean energy has significantly accelerated in recent years. The power industry and policymakers in almost all countries are focusing on clean energy development. Thanks to progressive clean energy policies, significant progress in clean energy integration and greenhouse gas reduction has been achieved around the world. However, due to the differences in economic structures, clean energy distributions, and development models, clean energy policy scope, focus, and coverage vary between different countries, states, and utilities. This paper aims at providing a policy review for readers to easily obtain clean energy policy information on various clean energies in the U.S. and some other countries. Firstly, this paper reviews and compares some countries’ clean energy policies on electricity. Then, taking the U.S. as an example, this paper introduces the clean energy policies of some representative states and utilities in the U.S in perspectives of renewable energies, electric vehicles, and energy storage