Yariv N. Marmor

Readmissions after implantation of axial flow left ventricular assist device.

OBJECTIVES The purpose of this study was to determine the occurrence and causes of readmissions after implantation of axial flow left ventricular assist device (LVAD). BACKGROUND Based on the REMATCH (Randomized Evaluation of Mechanical Assistance for the Treatment of Congestive Heart Failure) study experience, readmissions after LVAD implantation are thought to be frequent. METHODS We retrospectively analyzed admissions to our facility in a cohort of 115 patients implanted between January 2008 and July 2011 with the HeartMate II axial flow LVAD, of whom 42 were bridged to transplant. To account for repeated events, Andersen-Gill models were used to determine possible predictors. RESULTS The patients were followed for 1.4 ± 0.9 years. There were 224 readmissions in 83 patients. The overall readmission rate was 1.64 ± 1.97 per patient-year of follow-up. The readmission rate for the first 6 months was 2.0 ± 2.3 and decreased to 1.2 ± 2.1 during subsequent follow-up. Leading causes were bleeding (66 readmissions in 34 patients), mostly gastrointestinal bleed (51 in 27 patients), cardiac (51 in 36 patients, most for HF or arrhythmia), infections (32 in 25 patients) of which 6 were pump related, and thrombosis (20 in 15 patients) including 13 readmissions due to hemolysis. Preoperative variables associated with (fewer) readmissions in a multivariate model include residence within our hospital-extended referral zone of Minnesota and the neighboring states (hazard ratio: 0.66; 95% confidence interval: 0.48 to 0.91; p = 0.011), hemoglobin (hazard ratio: 0.91, 95% confidence interval: 0.84 to 0.99; p = 0.027) and N-terminal pro-B-type natriuretic peptide (hazard ratio: 0.98; 95% confidence interval: 0.96 to 1.0 per 1,000-unit increase, p = 0.022). C-statistic for the model: 0.63. CONCLUSIONS Readmission rates after axial flow LVAD implantation decrease during the first 6 months and then stabilize. The leading causes are bleeding, cardiac (heart failure and arrhythmia), infections, and thrombosis.

On patient flow in hospitals: A data-based queueing-science perspective

Hospitals are complex systems with essential societal benefits and huge mounting costs. These costs are exacerbated by inefficiencies in hospital processes, which are often manifested by congestion and long delays in patient care. Thus, a queueing-network view of patient flow in hospitals is natural for studying and improving its performance. The goal of our research is to explore patient flow data through the lens of a queueing scientist. The means is exploratory data analysis (EDA) in a large Israeli hospital, which reveals important features that are not readily explainable by existing models. Questions raised by our EDA include: Can a simple (parsimonious) queueing model usefully capture the complex operational reality of the Emergency Department (ED)? What time scales and operational regimes are relevant for modeling patient length of stay in the Internal Wards (IWs)? How do protocols of patient transfer between the ED and the IWs influence patient delay, workload division and fairness? EDA also unde...

Simulation-based models of emergency departments:: Operational, tactical, and strategic staffing

The Emergency Department (ED) of a modern hospital is a highly complex system that gives rise to numerous managerial challenges. It spans the full spectrum of operational, clinical, and financial perspectives, over varying horizons: operational—a few hours or days ahead; tactical—weeks or a few months ahead; and strategic, which involves planning on monthly and yearly scales. Simulation offers a natural framework within which to address these challenges, as realistic ED models are typically intractable analytically. We apply a general and flexible ED simulator to address several significant problems that arose in a large Israeli hospital. The article focuses mainly, but not exclusively, on workforce staffing problems over these time horizons. First, we demonstrate that our simulation model can support real-time control, which enables short-term prediction and operational planning (physician and nurse staffing) for several hours or days ahead. To this end, we present a novel simulation-based technique that implements the concept of offered-load and discover that it performs better than a common alternative. Then we evaluate ED staff scheduling that adjusts for midterm changes (tactical horizon, several weeks or months ahead). Finally, we analyze the design and staffing problems that arose from physical relocation of the ED (strategic yearly horizon). Application of the simulation-based approach led to the implementation of our design and staffing recommendations.

Ways to reduce patient turnaround time and improve service quality in emergency departments

PURPOSE Recent years have witnessed a fundamental change in the function of emergency departments (EDs). The emphasis of the ED shifts from triage to saving the lives of shock-trauma rooms equipped with state-of-the-art equipment. At the same time walk-in clinics are being set up to treat ambulatory type patients. Simultaneously ED overcrowding has become a common sight in many large urban hospitals. This paper recognises that in order to provide quality treatment to all these patient types, ED process operations have to be flexible and efficient. The paper aims to examine one major benchmark for measuring service quality--patient turnaround time, claiming that in order to provide the quality treatment to which EDs aspire, this time needs to be reduced. DESIGN/METHODOLOGY/APPROACH This study starts by separating the process each patient type goes through when treated at the ED into unique components. Next, using a simple model, the impact each of these components has on the total patient turnaround time is determined. This in turn, identifies the components that need to be addressed if patient turnaround time is to be streamlined. FINDINGS The model was tested using data that were gathered through a comprehensive time study in six major hospitals. The analysis reveals that waiting time comprises 51-63 per cent of total patient turnaround time in the ED. Its major components are: time away for an x-ray examination; waiting time for the first physicians examination; and waiting time for blood work. ORIGINALITY/VALUE The study covers several hospitals and analyses over 20,000 process components; as such the common findings may serve as guidelines to other hospitals when addressing this issue.

Toward simulation-based real-time decision-support systems for emergency departments

Emergency Departments (EDs) require advanced support systems for monitoring and controlling their processes: clinical, operational, and financial. A prerequisite for such a system is comprehensive operational information (e.g. queueing times, busy resources,…), reliably portraying and predicting ED status as it evolves in time. To this end, simulation comes to the rescue, through a two-step procedure that is hereby proposed for supporting real-time ED control. In the first step, an ED manager infers the EDs current state, based on historical data and simulation: data is fed into the simulator (e.g. via location-tracking systems, such as RFID tags), and the simulator then completes unobservable state-components. In the second step, and based on the inferred present state, simulation supports control by predicting future ED scenarios. To this end, we estimate time-varying resource requirements via a novel simulation-based technique that utilizes the notion of offered-load.

Designing patient flow in emergency departments

Emergency Department (ED) managers can choose from several operational models, for example, Triage or Fast-Track. The following questions thus naturally arise: why does a hospital choose to work with its particular operational model rather than another? Or what is the best model to operate under? More specifically, how to fit an operational model to an EDs uncontrollable (environmental) parameters? To address such questions, we develop a methodology for ED Design (EDD): we apply it to data collected over a period of two to four years from eight hospitals, of various sizes and deploying various ED operational models. (To cover all size-model combinations, we enrich our data via accurate ED simulation.) The EDD methodology first feeds the data into a Data Envelopment Analysis (DEA) program, which determines the relative efficiency of each month of the different operational models of each hospital. Then, after taking into account the individual hospitals effect, we identify the operational model that is dominant under each set of uncontrollable parameters. We discovered that different operational models dominate others over different combinations of uncontrollable parameters. For example, a hospital catering to an aging population is best served by a fast-track operational model.

Mental models as a practical tool in the engineer's toolbox

Industrial engineering methods are very successful in coping with well-structured systems and processes. However, when it comes to analysing, planning and controlling systems which contain unstructured processes, managers and engineers are faced with a much more difficult task. This is especially true in systems where teams and individuals have a significant role in the daily operation, monitoring and decision-making. In these situations, the processes may be performed differently by different individuals depending on their perceptions, concepts, ideas and perceived system status, all of which are denoted as the operators’ Mental Model (MM) of the system. This study develops a similarity measure to quantify the differences between MMs. This is done by eliciting the operators’ subjective perceptions of the system and their role within it (Mental Model), and comparing them to a standard description reference model which represents managements policy of how the system should be operated. Analysing the differences between these models may facilitate intervention approaches in closing these gaps and may help in creating better-synchronized and synergistic teamwork. Use of this similarity measure is demonstrated in a hospital ED environment.

Mental Health and Posttraumatic Growth in Civilians Exposed to Ongoing Terror

This study investigated negative and positive emotional outcomes among civilians exposed to ongoing terror. The measures included direct, indirect, and subjective exposure to terror; human resources; posttraumatic stress (PTS) symptoms; distress; and posttraumatic growth (PTG). The results indicate that whereas direct exposure is not related to the outcomes, exposure of family members to terror is positively related to PTS and to PTG. Path analysis revealed an indirect relationship between subjective exposure and PTG mediated by PTS, suggesting the role of emotional suffering in inducing growth. Clinical implications of incorporating PTG strategies into the treatment of terror victims are discussed.

Generating and evaluating simulation scenarios to improve emergency department operations

Overcrowding and long patient length of stay, staff shortage, arrival volume increases, and budget constraints are problems hampering ED operations (Sinreich and Marmor, 2005; Maull et al., 2009; NHS, 2010). This paper suggests a framework for schematic generation and evaluation of simulation scenarios to improve ED processes in real-life environments. We illustrate the application of our methodology in a specific ED. We contribute to the area of ED computer simulation by suggesting a methodology that offers the following advantages: (1) Simulation scenarios can be schematically formulated rather than based on trial-and-error experiments. (2) Scenario development can be integrated in the different stages of simulation model development to support designers and management in understanding ED problems, improvement goals, data that should be collected and operational changes that should be applied.

Capacity management and patient scheduling in an outpatient clinic using discrete event simulation

Capacity management and scheduling decisions are important for managing an outpatient clinic in which multiple classes of patients are treated. After an appointment is scheduled, it can be rescheduled, cancelled, or a patient may not show-up on their appointment day. This study simulates the behavior of patients with regard to the time to appointment, examining different demand rates and service times for each patient class (new external patients, internal patients, established patients and subsequent visit patients); we also consider different delay-dependent reschedule, cancellation, and no-show rates. A discrete event simulation model is developed to analyze the effects of allowing different appointment windows, i.e., the maximum time between the appointment request date and the actual appointment date, for different patient classes. Capacity utilization, patient access, and financial rewards are used as the performance indicators.