Hospitalization admission control of emergency patients using markovian decision processes and discrete event simulation

International audienceThis paper addresses the hospitalization admission control policies of patients from an emergency department that should be admitted shortly or transferred. When an emergency patient arrives, depending on his/her health condition, a physician may decide to hospitalize him/her in a specific department. Patient admission depends on the availability of beds, the length of stay (LOS) and the reward of hospitalization which are both patient-class specific. The problem consists in determining patient admission policies in order to maximize the overall gain. We first propose a Markov Decision Process (MDP) Model for determination of the optimal patient admission policy under some restrictive and necessary assumptions such as exponentially distributed LOS. A simulation model is then built to assess MDP admission policies under realistic conditions. We show that MDP policies significantly improve the overall gain for different types of facilities

EUROPEAN CONFERENCE ON QUEUEING THEORY 2016

International audienceThis booklet contains the proceedings of the second European Conference in Queueing Theory (ECQT) that was held from the 18th to the 20th of July 2016 at the engineering school ENSEEIHT, Toulouse, France. ECQT is a biannual event where scientists and technicians in queueing theory and related areas get together to promote research, encourage interaction and exchange ideas. The spirit of the conference is to be a queueing event organized from within Europe, but open to participants from all over the world. The technical program of the 2016 edition consisted of 112 presentations organized in 29 sessions covering all trends in queueing theory, including the development of the theory, methodology advances, computational aspects and applications. Another exciting feature of ECQT2016 was the institution of the Takács Award for outstanding PhD thesis on "Queueing Theory and its Applications"

A DISCRETE EVENT SIMULATION (DES) BASED APPROACH TO MAXIMIZE THE PATIENT THROUGHPUT IN OUTPATIENT CLINIC

The healthcare system is a complex system which exhibits conditions of uncertainty, ambiguity emergence that incurs incoming patient congestion. Discrete event simulation (FlexSim) is considered as a viable decision support tool in analyzing a system for improvement. Using a data-driven discrete event simulation approach, this paper portrays a comprehensive analysis to maximize the number of patients in an on-campus clinic, located at Mississippi State University. The outcome of the analysis of current system exhibits that deploying a few nurse practitioners results in bottlenecks which decreases the systems’ throughput substantially due to the overall longer patients’ waiting time.  Access to the laboratory is characterized through multi-server queuing network, arrival process is followed discrete distributions, and batch sizes and arrival times are stochastic in nature. In an effort to plummet inpatient congestion at the outpatient clinic, by using empirically calibrated simulation model, we will figure out the best balance between the number of the lab technician and incoming patient during working hour. An analysis of optimal solutions is demonstrated, which is followed by recommendation and avenues for future research

Implementation and validation of a new method to model voluntary departures from emergency departments

In the literature, several organizational solutions have been proposed for determining the probability of voluntary patient discharge from the emergency department. Here, the issue of self-discharge is analyzed by Markov theory-based modeling, an innovative approach diffusely applied in the healthcare field in recent years. The aim of this work is to propose a new method for calculating the rate of voluntary discharge by defining a generic model to describe the process of first aid using a “behavioral” Markov chain model, a new approach that takes into account the satisfaction of the patient. The proposed model is then implemented in MATLAB and validated with a real case study from the hospital “A. Cardarelli” of Naples. It is found that most of the risk of self-discharge occurs during the wait time before the patient is seen and during the wait time for the final report; usually, once the analysis is requested, the patient, although not very satisfied, is willing to wait longer for the results. The model allows the description of the first aid process from the perspective of the patient. The presented model is generic and can be adapted to each hospital facility by changing only the transition probabilities between states

Managing Hospital Care: Data-driven decisions and comparisons

This dissertation focuses on utilizing data-driven approaches to objectively measure variation in the quality of care across different hospitals, understand how physicians make dynamic admission and routing decisions for patients, and propose potential changes in practice to improve the quality of care and patient flow management. This analysis was performed in the context of Intensive Care Units (ICUs) and the Emergency Department (ED). In the first part, we assess variation in the overall quality of care provided by both urban and rural hospitals under the same integrated healthcare delivery system when augmenting administrative data with detailed patient severity scores from the electronic medical records (EMRs). Using a new template matching methodology for more objective comparison, we found that the use of granular EMR data significantly reduces the variation across hospitals in common patient severity-of-illness levels. Further, we found that hospital rankings on 30-day mortality and estimates of length-of-stay (LOS) are statistically different from rankings based on administrative data. In the second part, we study ICU admission decision-making dynamically throughout a patient’s stay in the general ward/the Transitional Care Unit (TCU). We first used an instrumental variable approach and modern multivariate matching methods to rigorously estimate the potential benefits and costs of transferring patients to the ICU based on a real-time risk score for deterioration. We then used the quantified impact to calibrate a comprehensive simulation model to evaluate system performances under various new ICU transfer policies. We show that proactively transferring the most severe patients to the ICU could reduce mortality rates and LOS without increasing ICU congestion and causing other adverse effects. In the third part, we focus on understanding how physicians make ICU admission decisions for patients in the ED. We first used two sets of reduced-form regressions to understand 1) what and how patient risk factors and system controls impact the admission decision from the ED; and 2) what are the potential benefits of admitting patients from the ED to the ICU. We then proposed a dynamic discrete choice structural model to estimate to what extent physicians account for the inter-temporal externalities when deciding to admit a specific patient to the ICU, to the ward or let him/her wait in the ED. Note that the structural model estimation is still an ongoing process and more investigation is required to fine tune the details. Therefore, we will not discuss the structural model estimation results in this chapter, but only present the modeling framework and key estimation strategy