Modeling patient flow in the emergency department using machine learning and simulation

Recently, the combination of machine learning (ML) and simulation is gaining a lot of attention. This paper presents a novel application of ML within the simulation to improve patient flow within an emergency department (ED). An ML model used within a real ED simulation model to quantify the effect of detouring a patient out of the ED on the length of stay (LOS) and door-to-doctor time (DTDT) as a response to the prediction of patient admission to the hospital from the ED. The ML model trained using a set of six features including the patient age, arrival day, arrival hour of the day, and the triage level. The prediction model used a decision tree (DT) model, which is trained using historical data achieves a 75% accuracy. The set of rules extracted from the DT are coded within the simulation model. Given a certain probability of free inpatient beds, the predicted admitted patient is then pulled out from the ED to inpatient units to alleviate the crowding within the ED. The used policy combined with adding specific ED resources achieve 9.39% and 8.18% reduction in LOS and DTDT, respectively

Energy Forensics Analysis

The energy consumed by a building can reveal information about the occupants and their activities inside the building. This could be utilized by industries and law enforcement agencies for commercial or legal purposes. Utility data from Smart Meter (SM) readings can reveal detailed information that could be mapped to foretell resident occupancy and type of appliance usage over desired time intervals. However, obtaining SM data in the United States is laborious and subjected to legal and procedural constraints. This research develops a user-driven simulation tool with realistic data options and assumptions of potential human behavior to determine energy usage patterns over time without any utility data. In this work, factors such as occupant number, the possibility of place being occupied, thermostat settings, building envelope, appliances used in households, appliance capacities, and the possibility of using each appliance, weather, and heating-cooling systems specifications are considered. For five specific benchmarked scenarios, the range of the random numbers is specified based on assumed potential human behavior for occupancy and energy-consuming appliances usage possibility, with respect to the time of the day, weekday, and weekends. The simulation is developed using the Visual Basic Application (VBA)® in Microsoft Excel®, based on the discrete-event Monte Carlo Simulation (MCS). This simulation generates energy usage patterns and electricity and natural gas costs over 30-minutes intervals for one year. The simulated energy usage and the cost are reflected in the sensitivity analysis by comparing factors such as occupancy, appliance type, and time of the week. This work is intended to facilitate the analysis of building occupants\u27 activities by various stakeholders, subject to all legal provisions that apply. It is not intended for the general public to pursue these activities because legal ramifications might be involved

Emergency department design evaluation and optimization using discrete event simulation

The proposed research would help any architect/owner decide the number of rooms/ cubicles for each sub-department of the ED, as well as have an estimated price for the ED, in order to optimally serve patients entering the ED with a known arrival rate. A thorough literature review was undertaken to collect data concerning the application of decision support tools for minimizing patient waiting times and maximizing the utilization rate in health care systems. Interviews were made with hospital managers in order to verify process flow, waiting times, activity durations, and resources. In addition, several floor plans of EDs have been studied in order to assure the logical flow of the process. Based on the data collected and the several verifications, a discrete event simulation model was developed using ARENA software. This simulation model was then verified by building a similar model on different software, which was AnyLogic. The results proved the accuracy of the model. Twenty additional simulation runs were performed to be used for the regression analysis. The equations resulted from the regression analysis were used for the optimization model. A genetic algorithm was used for the purpose of obtaining optimized resource allocation for different arrival rates within a constrained budget, area, and patient waiting time in the system

Analysis of topics in health microeconomics through flexible regression models

The object of discussion is To calculate a new flexible hospital production function by means of a Generalized Additive Model including interactions and to compare it with the classic models Cobb-Douglas and Translog in the prediction of the behavior of productive factors and to study how the number of beds in the hospital affects the hospital activity, the length of stays and, consequently, the waiting list. The GAM model is more appropriate than the Cobb-Douglas or the translog to evaluate hospital production functions, for public hospitals located in Galicia and for the study period. The study also demonstrates the usefulness of simulation techniques to examine a hospital system. There are no significant differences in terms of waiting lists and occupancy rates when the number of beds in larger hospitals increases. Supply-side policies can also be disappointing in their effects on waiting times for small rural hospitals. Greater capacity in terms of more beds is associated with shorter waiting times for medium size hospitals

Development and verification of a simplified building energy model

The purpose of this research is to develop and verify a simplified and concise building simulation model suitable for high-level applications such as preliminary design or for embedding into adaptive control systems. An actual complex modern building and its energy system has been monitored. The monitored energy performance of this building has been compared with the empirical performance predicted by two simulation modeling programs and, alternatively, by a simplified single-zone model. This project is composed of several related tasks. The first component is the monitoring of the energy consumption rates, pertinent environmental data, and load indicators of the new Klaus Advanced Computing Building on the Georgia Institute of Technology's Atlanta campus. The Klaus building was chosen because it represents a typical non-residential building. Subsequently, these findings have been compared with results from DOE-2 and eQUEST, well established energy simulation modeling programs. These comparisons allow for an empirical verification of the modeling program for Atlanta conditions. Finally, a simplified single-zone building model has been developed, and its predictions compared with the empirical data and with the results of the more complex programs. The results verify both the more complex programs and the single-zone model, and also demonstrate the use of a single-zone model for future work and predictions.M.S.Committee Chair: Dr. Sheldon Jeter; Committee Member: Dr. Ruchi Choudhary; Committee Member: Dr. Srinivas Garimell

Bed capacity and surgical waiting lists: a simulation analysis

Waiting time for elective surgery is a key problem in the current medical world. This paper aims to reproduce, by a Monte Carlo simulation model, the relationship between hospital capacity, inpatient activity, and surgery waiting list size in teaching hospitals. Inpatient activity is simulated by fitting a Normal distribution to real inpatient activity data, and the effect of the number of beds on inpatient activity is modelled with a linear regression model. Analysis is performed with data of the University Multi-Hospital Complex of Santiago de Compostela (Santiago de Compostela, Spain), by considering two scenarios regarding the elastiticity of demand with bed increase. If demand does not grow with an increase on bed capacity, small changes lead to drastic reductions in the waiting lists. However, if demand grows as bed capacity does, adding additional capacity merely makes waiting lists worse

A systematized approach for reduction of medical appointment waiting list

Paper aims: This work aims to develop a systematized approach for the reduction of medical appointment waiting lists, proposing an optimization decision-making model followed by continuous people engagement towards a systematic approach for waiting list problem-solving. Originality: There are several studies related to waiting lists in healthcare contexts, however, the present study presents an innovative approach for waiting list problem-solving by proposing prescriptive decision-making models followed by continuous improvement and people engagement. Research method: A research approach with the following phases was developed: system analysis, problem quantification, and development of an optimization model. After these phases, the model was applied, and the results were analysed, as contributions to a systematized model. Main findings: The model was applied to the screening waiting list for orthopaedics appointments followed by the fundamental involvement of medical doctors, which made it possible to implement the optimal solution generated by the model, resulting in a reduction of 90% by 56 days in waiting time for the screening process. Implications for theory and practice: This model contributes for theory and for practice as a way to deal with different scenarios for waiting list reduction in the upcoming days during and after the pandemic.This work was supported by projects UIDB/00319/2020 and POCI-01-0145-FEDER-030299, from Fundação para a Ciência e Tecnologia (FCT), Portugal

Manpower allocation problem with heterogeneous skills.

Kuo, Yong Hong."August 2010."Thesis (M.Phil.)--Chinese University of Hong Kong, 2010.Includes bibliographical references (p. 130-133).Abstracts in English and Chinese.Abstract --- p.iAcknowledgement --- p.iiiChapter 1 --- Introduction --- p.1Chapter 2 --- Ground Staff Allocation at Airports --- p.4Chapter 2.1 --- Background --- p.4Chapter 2.2 --- Literature Review --- p.8Chapter 2.3 --- Model --- p.12Chapter 2.3.1 --- Notation --- p.13Chapter 2.3.2 --- Basic Model --- p.17Chapter 2.3.3 --- Model Structure --- p.20Chapter 2.3.4 --- Definition of Tasks --- p.22Chapter 2.3.5 --- Job x Language Model --- p.25Chapter 2.3.6 --- Job + Language Model --- p.27Chapter 2.4 --- Methodology --- p.35Chapter 2.4.1 --- Branch-and-Cut Algorithm --- p.36Chapter 2.4.2 --- Constraint-Driven Approach --- p.40Chapter 2.5 --- Optimization Tool --- p.51Chapter 2.6 --- Computational Results --- p.53Chapter 2.6.1 --- Branch-and-Cut Algorithm --- p.53Chapter 2.6.2 --- Constraint-Driven Approach --- p.59Chapter 2.7 --- Conclusions and Future Work --- p.64Chapter 3 --- Staff Scheduling in Emergency Departments --- p.67Chapter 3.1 --- Background --- p.67Chapter 3.1.1 --- Patient Flows --- p.69Chapter 3.1.2 --- Doctor Duties --- p.71Chapter 3.2 --- Simulation Model --- p.72Chapter 3.2.1 --- Assumptions --- p.73Chapter 3.2.2 --- Event-Scheduling --- p.74Chapter 3.2.3 --- Arrival Events --- p.80Chapter 3.2.4 --- Service Activities --- p.82Chapter 3.2.5 --- Paperwork-Processing --- p.84Chapter 3.2.6 --- Impact of Doctors' Schedules --- p.85Chapter 3.3 --- Parameter Estimation --- p.87Chapter 3.3.1 --- Data Scarcity --- p.87Chapter 3.3.2 --- Estimation of Service Time Distributions --- p.87Chapter 3.3.3 --- Search Procedure for Parameter Estimation --- p.90Chapter 3.3.4 --- Parameter Estimation by Descent Method --- p.91Chapter 3.3.5 --- Parameter Estimation by Simulated An- nealing --- p.93Chapter 3.4 --- Data Analysis and Simulated Results --- p.97Chapter 3.5 --- Conclusions and Future Work --- p.114Chapter A --- Mathematical Proofs --- p.116Chapter B --- Getting Started with the Manpower Optimization Tool --- p.122Chapter B.l --- Required Files or Programs --- p.122Chapter B.2 --- Input Parameters --- p.123Chapter B.3 --- Operational Tasks --- p.125Chapter B.4 --- Language Requirements --- p.126Chapter B.5 --- Flight Schedules --- p.126Chapter B.6 --- Availabilities of Workers --- p.128Chapter B.7 --- Staff Assignments --- p.128Bibliography --- p.13

Preparing financial models; AICPA practice aid series 06-2

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ACHIEVING UNIVERSAL LIAISONS AND HEALTHCARE CONTACT CENTER CENTRALIZATION THROUGH THE USE OF DECISION SUPPORT TOOLS

Healthcare contact centers often experience a large volume of calls and traditional standardized guidelines can be difficult to follow during an active call. While more common workflows can be memorized, they change often because Healthcare is a dynamic field. Constant updates to workflows, an abundance of different processes and provider preferences, and a fast paced environment can lead Customer Service Liaisons (CSLs) to handle patient inquiries incorrectly. Active decision support tools enable a CSL to follow an updated workflow without needing to navigate through complex guidelines and emails. This research shows that contact center centralization through the use of decision support tools can reduce Average Speed to Answer by 70 seconds even with an increase to Average Handle Time by 30 seconds. This research also identifies key features the tool may need to facilitate widespread adoption by clinicians and CSL alike