A survey of health care models that encompass multiple departments

In this survey we review quantitative health care models to illustrate the extent to which they encompass multiple hospital departments. The paper provides general overviews of the relationships that exists between major hospital departments and describes how these relationships are accounted for by researchers. We find the atomistic view of hospitals often taken by researchers is partially due to the ambiguity of patient care trajectories. To this end clinical pathways literature is reviewed to illustrate its potential for clarifying patient flows and for providing a holistic hospital perspective

Using an Agent-based Simulation for Predicting the Effects of Patients Derivation Policies in Emergency Departments

AbstractThe increasing demand of urgent care, overcrowding of hospital emergency departments (ED) and limited economic resources are phenomena shared by health systems around the world. It is estimated that up to 50% of patients that are attended in ED have non complex conditions that could be resolved in ambulatory care services. The derivation of less complex cases from the ED to other health care devices seems an essential measure to allocate properly the demand of care service between the different care units. This paper presents the results of an experiment carried out with the objective of analyzing the effects on the ED (patients’ Length of Stay, the number of patients attended and the level of activity of ED Staff) of different derivation policies. The experiment has been done with data of the Hospital of Sabadell (a big hospital, one of the most important in Catalonia, Spain), making use of an Agent-Based model and simulation formed entirely of the rules governing the behaviour of the individual agents which populate the ED, and due to the great amount of data that should be computed, using High Performance Computing

Simulation Modelling in Healthcare: Challenges and Trends

AbstractIn this paper, we describe simulation models in healthcare that have been developed in the past two decades. Simulation systems, ranging from simulation of patient flow in emergency rooms to simulation of populations with a specific chronic diseases, are reviewed. Simulation types included discrete event simulation (DES) and agent based simulation (ABS). A trend of variability and scalability were identified, and discussed in terms of platform used to develop the model, data sources, and computational power needed to run the simulation. In the synthesis of simulation models, programming languages and products emerged as clusters. Design models and systems engineering development processes are examined with a focus on requirements discovery, models and scenarios of simulation. Graphic user interfaces in the simulation tools in healthcare are reviewed in terms of visual design and human factors. Furthermore, interaction modes and trends of information visualization techniques used for the simulations are reported. Agent-based simulation models in particular were reviewed, and findings suggest agent characteristics varied across literature researched in aspects such as socio-demographic design considerations

A Simulation-Based Evaluation Of Efficiency Strategies For A Primary Care Clinic With Unscheduled Visits

In the health care industry, there are strategies to remove inefficiencies from the health delivery process called efficiency strategies. This dissertation proposed a simulation model to evaluate the impact of the efficiency strategies on a primary care clinic with unscheduled walk-in patient visits. The simulation model captures the complex characteristics of the Orlando Veteran\u27s Affairs Medical Center (VAMC) primary care clinic. This clinic system includes different types of patients, patient paths, and multiple resources that serve them. Added to the problem complexity is the presence of patient no-shows characteristics and unscheduled patient arrivals, a problem which has been until recently, largely neglected. The main objectives of this research were to develop a model that captures the complexities of the Orlando VAMC, evaluate alternative scenarios to work in unscheduled patient visits, and examine the impact of patient flow, appointment scheduling, and capacity management decisions on the performance of the primary care clinic system. The main results show that only a joint policy of appointment scheduling rules and patient flow decisions has a significant impact on the wait time of scheduled patients. It is recommended that in the future the clinic addresses the problem of serving additional walk-in patients from an integrated scheduling and patient flow viewpoint

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

Analysis of scheduling in a diagnostic imaging department: A simulation study

In this thesis we present an Agent-Based Modelling Tool (ABMT) for use in the investigation of the impact that operational level changes have on diagnostic imaging scheduling and patient wait times. This tool represents a novel application of agent-based modelling in the outpatient scheduling/simulation fields. The ABMT is a decision support tool with a user friendly graphical user interface that is capable of modelling a wide array of outpatient scheduling scenarios. The tool was verified and validated using data and expertise from Hotel Dieu Grace Hospital, Windsor, Ontario, Canada. The ABMT represents a technological advancement in the modelling of multi-server, multi-priority class customer queueing systems with deterministic service times and uneven distribution of server up-time

Robotic Assistance in Coordination of Patient Care

We conducted a study to investigate trust in and dependence upon robotic decision support among nurses and doctors on a labor and delivery floor. There is evidence that suggestions provided by embodied agents engender inappropriate degrees of trust and reliance among humans. This concern is a critical barrier that must be addressed before fielding intelligent hospital service robots that take initiative to coordinate patient care. Our experiment was conducted with nurses and physicians, and evaluated the subjects’ levels of trust in and dependence on high- and low-quality recommendations issued by robotic versus computer-based decision support. The support, generated through action-driven learning from expert demonstration, was shown to produce high-quality recommendations that were ac- cepted by nurses and physicians at a compliance rate of 90%. Rates of Type I and Type II errors were comparable between robotic and computer-based decision support. Furthermore, em- bodiment appeared to benefit performance, as indicated by a higher degree of appropriate dependence after the quality of recommendations changed over the course of the experiment. These results support the notion that a robotic assistant may be able to safely and effectively assist in patient care. Finally, we conducted a pilot demonstration in which a robot assisted resource nurses on a labor and delivery floor at a tertiary care center.National Science Foundation (U.S.) (Grant 2388357

Integrated Planning in Hospitals: A Review

Efficient planning of scarce resources in hospitals is a challenging task for which a large variety of Operations Research and Management Science approaches have been developed since the 1950s. While efficient planning of single resources such as operating rooms, beds, or specific types of staff can already lead to enormous efficiency gains, integrated planning of several resources has been shown to hold even greater potential, and a large number of integrated planning approaches have been presented in the literature over the past decades. This paper provides the first literature review that focuses specifically on the Operations Research and Management Science literature related to integrated planning of different resources in hospitals. We collect the relevant literature and analyze it regarding different aspects such as uncertainty modeling and the use of real-life data. Several cross comparisons reveal interesting insights concerning, e.g., relations between the modeling and solution methods used and the practical implementation of the approaches developed. Moreover, we provide a high-level taxonomy for classifying different resource-focused integration approaches and point out gaps in the literature as well as promising directions for future research