Machine learning modeling practices to support the principles of AI and ethics in nutrition research.

Background: Nutrition research is relying more on artificial intelligence and machine learning models to understand, diagnose, predict, and explain data. While artificial intelligence and machine learning models provide powerful modeling tools, failure to use careful and well-thought-out modeling processes can lead to misleading conclusions and concerns surrounding ethics and bias.

Methods: Based on our experience as reviewers and journal editors in nutrition and obesity, we identified the most frequently omitted best practices from statistical modeling and how these same practices extend to machine learning models.

Deep Vision for Breast Cancer Classification and Segmentation.

(1) Background: Female breast cancer diagnoses odds have increased from 11:1 in 1975 to 8:1 today. Mammography false positive rates (FPR) are associated with overdiagnoses and overtreatment, while false negative rates (FNR) increase morbidity and mortality. (2) Methods: Deep vision supervised learning classifies 299 × 299 pixel de-noised mammography images as negative or non-negative using models built on 55,890 pre-processed training images and applied to 15,364 unseen test images.

Healthcare market segmentation and data mining: A systematic review.

Providing insight into healthcare consumers' behaviors and attitudes is critical information in an environment where healthcare delivery is moving rapidly towards patient-centered care that is premised upon individuals becoming more active participants in managing their health. A systematic review of the literature concerning healthcare market segmentation and data mining identified several areas for future health marketing research. Common themes included: (a) reliance on survey data, (b) clustering methods, (c) limited classification modeling after clustering, and (d) detailed analysis of clusters by demographic data.

Reducing cost drivers in total joint arthroplasty: understanding patient readmission risk and supply cost.

Understanding and planning for the factors that impact supply cost and unplanned readmission risk for total joint arthroplasty (TJA) patients is helpful for hospitals at financial risk under bundled payments. Readmission and operating room supply costs are two of the biggest expenses. Logistic and linear regressions are used to measure the impacts of TJA patient attributes on readmission risk and supply costs, respectively.

Evaluating the Relationship between Productivity and Quality in Emergency Departments.

Background: In the United States, emergency departments (EDs) are constantly pressured to improve operational efficiency and quality in order to gain financial benefits and maintain a positive reputation.

Objectives: The first objective is to evaluate how efficiently EDs transform their input resources into quality outputs. The second objective is to investigate the relationship between the efficiency and quality performance of EDs and the factors affecting this relationship.

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

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 Impact of a Pay-for-Performance Program on Central Line-Associated Blood Stream Infections in Pennsylvania.

Healthcare associated infections have significantly contributed to the rising cost of hospital care in the United States. The implementation of pay-for-performance (P4P) programs has been one approach to improve quality at a reduced cost. We quantify the impact of Highmark's Quality Blue (QB) hospital P4P program on central line-associated blood stream infections (CLABSI) in Pennsylvania.

Stochastic multi-objective auto-optimization for resource allocation decision-making in fixed-input health systems.

The management of hospitals within fixed-input health systems such as the U.S. Military Health System (MHS) can be challenging due to the large number of hospitals, as well as the uncertainty in input resources and achievable outputs.

A Mixed-Methods Research Framework for Healthcare Process Improvement.

Unlabelled: The 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.

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

Background: The standard care of treatment of interferon plus ribavirin (plus protease inhibitor for genotype 1) are effective in 50 % to 70 % of patients with CHC. Several new treatments including Harvoni, Olysio + Sovaldi, 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 80% to 95%, though with a substantial increase in cost.

Evaluating MEDEVAC Force Structure Requirements Using an Updated Army Scenario, Total Army Analysis Admission Data, Monte Carlo Simulation, and Theater Structure.

In this study, we re-evaluate air ambulance requirements (rules of allocation) and planning considerations based on an Army-approved, Theater Army Analysis scenario. A previous study using workload only estimated a requirement of 0.4 to 0.

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

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.

Aeromedical evacuation planning using geospatial decision-support.

In this study, we proffer an algorithmic, geospatial-based decision-support methodology that assists military decision-makers in determining which aeromedical evacuation (MEDEVAC) assets to launch after receiving an injury location, given knowledge only of terrain, aircraft location, and aircraft capabilities. The objective is for military medical planners to use this decision-support tool (1) to improve real-time situational awareness by visualization of MEDEVAC coverage, showing which areas can be reached within established timelines; (2) to support medical planning by visualizing the impact of changes in the medical footprint to the MEDEVAC coverage; and (3) to support decision-making by providing a time-sorted list of MEDEVAC asset packages to select from, given the location of the patients. This same geospatial-based decision tool can be used for proper emplacement of evacuation assets such that the theater is covered within a truly representative 1-hour response time.

Analyzing the future of army aeromedical evacuation units and equipment: a mixed methods, requirements-based approach.

We utilize a mixed methods approach to provide three new, separate analyses as part of the development of the next aeromedical evacuation (MEDEVAC) platform of the Future of Vertical Lift (FVL) program. The research questions follow: RQ1) What are the optimal capabilities of a FVL MEDEVAC platform given an Afghanistan-like scenario and parameters associated with the treatment/ground evacuation capabilities in that theater?; RQ2) What are the MEDEVAC trade-off considerations associated with different aircraft engines operating under variable conditions?; RQ3) How does the additional weight of weaponizing the current MEDEVAC fleet affect range, coverage radius, and response time? We address RQ1 using discrete-event simulation based partially on qualitative assessments from the field, while RQ2 and RQ3 are based on deterministic analysis. Our results confirm previous findings that travel speeds in excess of 250 knots and ranges in excess of 300 nautical miles are advisable for the FVL platform design, thereby reducing the medical footprint in stability operations.

The Future of Vertical Lift: initial insights for aircraft capability and medical planning.

The U.S. Army continues to evaluate capabilities associated with the Future of Vertical Lift (FVL) program-a futures program (with a time horizon of 15 years and beyond) intended to replace the current helicopter fleet.

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

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.