Healthcare Management Primer

This primer was written by students enrolled in HMP 721.01, Management of Health Care Organizations, in the Health Management & Policy Program, College of Health and Human Services, University of New Hampshire. This course was taught by Professor Mark Bonica in Fall 2017

Improving Patient Safety, Patient Flow and Physician Well-Being in Emergency Departments

Over 151 million people visit US Emergency Departments (EDs) annually. The diverse nature and overwhelming volume of patient visits make the ED one of the most complicated settings in healthcare to study. ED overcrowding is a recognized worldwide public health problem, and its negative impacts include patient safety concerns, increased patient length of stay, medical errors, patients left without being seen, ambulance diversions, and increased health system expenditure. Additionally, ED crowding has been identified as a leading contributor to patient morbidity and mortality. Furthermore, this chaotic working environment affects the well-being of all ED staff through increased frustration, workload, stress, and higher rates of burnout which has a direct impact on patient safety. This research takes a step-by-step approach to address these issues by first forecasting the daily and hourly patient arrivals, including their Emergency Severity Index (ESI) levels, to an ED utilizing time series forecasting models and machine learning models. Next, we developed an agent-based discrete event simulation model where both patients and physicians are modeled as unique agents for capturing activities representative of ED. Using this model, we develop various physician shift schedules, including restriction policies and overlapping policies, to improve patient safety and patient flow in the ED. Using the number of handoffs as the patient safety metric, which represents the number of patients transferred from one physician to another, patient time in the ED, and throughput for patient flow, we compare the new policies to the current practices. Additionally, using this model, we also compare the current patient assignment algorithm used by the partner ED to a novel approach where physicians determine patient assignment considering their workload, time remaining in their shift, etc. Further, to identify the optimal physician staffing required for the ED for any given hour of the day, we develop a Mixed Integer Linear Programming (MILP) model where the objective is to minimize the combined cost of physician staffing in the ED, patient waiting time, and handoffs. To develop operations schedules, we surveyed over 70 ED physicians and incorporated their feedback into the MILP model. After developing multiple weekly schedules, these were tested in the validated simulation model to evaluate their efficacy in improving patient safety and patient flow while accounting for the ED staffing budget. Finally, in the last phase, to comprehend the stress and burnout among attending and resident physicians working in the ED for the shift, we collected over 100 hours of physiological responses from 12 ED physicians along with subjective metrics on stress and burnout during ED shifts. We compared the physiological signals and subjective metrics to comprehend the difference between attending and resident physicians. Further, we developed machine learning models to detect the early onset of stress to assist physicians in decision-making

An Assessment of Health Care Safety Net Services in Seven Metropolitan Atlanta Counties

The Georgia Health Policy Center (GHPC), the Centers for Disease Control and Prevention (CDC) and the National Network of Public Health Institutes began collaboration in the summer of 2007 on a project designed to establish a framework for public health to help inform the health reform debate. The partnership set out to broaden the health reform conversation to include health promotion, health improvement, and disease prevention. The effort included background research, focus groups, key interviews with internal and external stakeholders from local, state, and national groups, and additional convenings of local, state, and national partners. This report highlights opportunities for public health to bridge the different levels of health reform and create strategies and policies that could be implemented on each level

Can Pay Regulation Kill? Panel Data Evidence on the Effect of Labor Markets on Hospital Performance

Labor market regulation can have harmful unintended consequences. In many markets, especially for public sector workers, pay is regulated to be the same for individuals across heterogeneous geographical labor markets. We would predict that this will mean labor supply problems and potential falls in the quality of service provision in areas with stronger labor markets. In this paper we exploit panel data from the population of English acute hospitals where pay for medical staff is almost flat across the country. We predict that areas with higher outside wages should suffer from problems of recruiting, retaining and motivating high quality workers and this should harm hospital performance. We construct hospital-level panel data on both quality - as measured by death rates (within hospital deaths within thirty days of emergency admission for acute myocardial infarction, AMI) - and productivity. We present evidence that stronger local labor markets significantly worsen hospital outcomes in terms of quality and productivity. A 10% increase in the outside wage is associated with a 4% to 8% increase in AMI death rates. We find that an important part of this effect operates through hospitals in high outside wage areas having to rely more on temporary "agency staff" as they are unable to increase (regulated) wages in order to attract permanent employees. By contrast, we find no systematic role for an effect of outside wages of performance when we run placebo experiments in 42 other service sectors (including nursing homes) where pay is unregulated.labor market regulation, hospital quality, hospital productivity, skills

Emergency Department Management: Data Analytics for Improving Productivity and Patient Experience

The onset of big data, typically defined by its volume, velocity, and variety, is transforming the healthcare industry. This research utilizes data corresponding to over 23 million emergency department (ED) visits between January 2010 and December 2017 which were treated by physicians and advanced practice providers from a large national emergency physician group. This group has provided ED services to health systems for several years, and each essay aims to address operational challenges faced by this group’s management team. The first essay focuses on physician performance. We question how to evaluate performance across multiple sites and work to understand the relationships between patient flow, patient complexity, and patient experience. Specifically, an evaluation system to assess physician performance across multiple facilities is proposed, the relationship between productivity and patient experience scores is explored, and the drivers of patient flow and complexity are simultaneously identified. The second essay explores the relationship between physician performance and malpractice claims as we investigate whether physicians’ practice patterns change after they are named in a malpractice lawsuit. Overall, the results of this analysis indicate that the likelihood of being named in a malpractice claim is largely a function of how long a physician has practiced. Furthermore, physician practice patterns remain consistent after a physician is sued, but patient experience scores increase among sued physicians after the lawsuit is filed. Such insights are beneficial for management as they address the issue of medical malpractice claims. The final essay takes a closer look at the relationship between advanced practice providers (APPs) and physicians. Can EDs better utilize APPs to reduce waiting times and improve patient flow? A systematic data-driven approach which incorporates descriptive, predictive, and prescriptive analyses is employed to provide recommendations for ED provider staffing practices

Emergency Department Utilization and Capacity

Synthesizes research on who utilizes emergency departments, how often for non-urgent or preventable conditions and why, how cost-sharing affects utilization, and how utilization patterns affect hospital finances, overcrowding, and cost implications

Taxonomic classification of planning decisions in health care: a review of the state of the art in OR/MS

We provide a structured overview of the typical decisions to be made in resource capacity planning and control in health care, and a review of relevant OR/MS articles for each planning decision. The contribution of this paper is twofold. First, to position the planning decisions, a taxonomy is presented. This taxonomy provides health care managers and OR/MS researchers with a method to identify, break down and classify planning and control decisions. Second, following the taxonomy, for six health care services, we provide an exhaustive specification of planning and control decisions in resource capacity planning and control. For each planning and control decision, we structurally review the key OR/MS articles and the OR/MS methods and techniques that are applied in the literature to support decision making

Community Health Centers: The Challenge of Growing to Meet the Need for Primary Care in Medically Underserved Communities

Provides an overview of community health centers, their patients, and recent federal and state funding cuts, as well as funding prospects for the centers' expansion to meet greater demand among patients newly eligible for Medicaid or private coverage