Enhancing detection of labor violations in the agricultural sector: A multilevel generalized linear regression model of H-2A violation counts.

Agricultural workers are essential to the supply chain for our daily food, and yet, many face harmful work conditions, including garnished wages, and other labor violations. Workers on H-2A visas are particularly vulnerable due to the precarity of their immigration status being tied to their employer. Although worksite inspections are one mechanism to detect such violations, many labor violations affecting agricultural workers go undetected due to limited inspection resources.

Expanding Our Understanding of Traffickers and Their Operations: A Review of the Literature and Path Forward.

Human trafficking is a serious crime and violation of human rights that results in numerous harms. Although the phenomenon is not new, scholarship on the issue has grown substantially since the first legal framework was passed in 2000. However, the existing literature has been criticized for its skewed focus on victims, among other things.

Operations research and analytics to combat human trafficking: A systematic review of academic literature.

Human trafficking is a widespread and compound social, economic, and human rights issue occurring in every region of the world. While there have been an increasing number of anti-human trafficking studies from the Operations Research and Analytics domains in recent years, no systematic review of this literature currently exists. We fill this gap by providing a systematic literature review that identifies and classifies the body of Operations Research and Analytics research related to the anti-human trafficking domain, thereby illustrating the collective impact of the field to date.

A discrete event simulation to evaluate impact of radiology process changes on emergency department computed tomography access.

Background: Hospitals face the challenge of managing demand for limited computed tomography (CT) resources from multiple patient types while ensuring timely access.

Methods: A discrete event simulation model was created to evaluate CT access time for emergency department (ED) patients at a large academic medical center with six unique CT machines that serve unscheduled emergency, semi-scheduled inpatient, and scheduled outpatient demand. Three operational interventions were tested: adding additional patient transporters, using an alternative creatinine lab, and adding a registered nurse dedicated to monitoring CT patients in the ED.

Life and expectations post-kidney transplant: a qualitative analysis of patient responses.

Background: The effect of a kidney transplant on a recipient extends beyond the restoration of kidney function. However, there is limited qualitative analysis of recipient perspectives on life following transplantation, particularly in the United States. To understand the full patient experience, it is necessary to understand recipient views on life adjustments after kidney transplantation, medical management, and quality of life.

Development of data integration and visualization tools for the Department of Radiology to display operational and strategic metrics.

Visualizing process metrics can help identify targets for improvement initiatives. Dashboards and scorecards are tools to visualize important metrics in an easily interpretable manner. We describe the development of two visualization systems: a dashboard to provide real-time situational awareness to frontline coordinators, and a scorecard to display aggregate monthly performance metrics for strategic process improvement efforts.

Comparison of patient and provider goals, expectations, and experiences following kidney transplantation.

Objective: This study examined whether kidney transplant recipients' post-transplant goals and expectations align with those as perceived by their healthcare providers.

Methods: Post-transplant goals and expectations across four domains were assessed via a descriptive survey of healthcare providers (N=72) and kidney transplant recipients (N=476) at the University of Michigan from March 23 - October 1, 2015. Demographic and transplant-related data were collected via a retrospective review of medical records, and survey responses were compared using Chi-square tests, Wilcoxon two-sample tests, and logistic regression.

Incorporating nurse absenteeism into staffing with demand uncertainty.

Increased nurse-to-patient ratios are associated negatively with increased costs and positively with improved patient care and reduced nurse burnout rates. Thus, it is critical from a cost, patient safety, and nurse satisfaction perspective that nurses be utilized efficiently and effectively. To address this, we propose a stochastic programming formulation for nurse staffing that accounts for variability in the patient census and nurse absenteeism, day-to-day correlations among the patient census levels, and costs associated with three different classes of nursing personnel: unit, pool, and temporary nurses.