David Claudio

Experiments in multiple criteria selection problems with multiple decision makers

Multiple criteria selection problems deal with ranking alternatives under conflicting criteria. In the resolution of such problems, scaling methods are used to standardise the data and weighting methods are used to assign a preference to each criterion based on the decision makers assessment. Several methods have been proposed for weighting and scaling but the most favourable combinations of methods are uncertain. This study presents the results of experiments in which data from human decision makers is used to determine the best combination of weighting-scaling methods for single and multiple decision makers using the weighted sum decision making model.

Utility function-based patient prioritisation in the emergency department

With the increased awareness of productivity problems in healthcare operations, many researchers have proposed the implementation of tools and methods developed in other fields to benefit healthcare delivery. Accordingly, we present in this paper an exploratory work using a hypothetical example of a decision-making methodology, the multi-attribute utility analysis, to healthcare. The hypothetical sample problem presented involves patient prioritisation in an Emergency Department (ED), where several patients require immediate attention and they all have the same acuity level. Utility theory is selected for this application to appropriately account for the uncertainty in the decision problem. [Submitted 01 March 2008; Revised 05 September 2008; Revised 02 February 2009; Accepted 07 February 2009]

Alarm fatigue and its influence on staff performance

An alarm is a warning of an approaching situation which requires a response. The Emergency Care Research Institute considered alarm hazard as the number one health technology hazard for the years 2012 through 2014. In response, The Joint Commission set a standard for all hospitals in the United States to assess alarm fatigue in their monitoring process and to develop a systematic, coordinated approach to clinical alarm system management. In order to comply with this requirement, a working definition of alarm fatigue is necessary. This observational study undertook the objective of defining alarm fatigue, measuring it and exploring its role in performance deterioration. A conceptual model was developed considering the significance of working conditions and staff individuality on alarm fatigue and, consequently, alarm fatigue on staff performance. The results show that in general, performance deterioration is actually influenced by a combination of alarm fatigue, working conditions and staff individuality. In fact, in the case of nurses and response time, alarm fatigue plays no role, only working conditions and staff individuality. These findings suggest that the role of alarm fatigue as a health hazard in the clinical environment should be reevaluated.

A dynamic multi-attribute utility theory-based decision support system for patient prioritization in the emergency department

The triage process may result in long waiting periods during which vital indicators of patients with apparently less urgent problems are not monitored after the initial triage. The integration of technology and decision theory has the potential to assist nurses in recognizing priorities by collecting data on the changing clinical information of patients and methodically organizing it. This study investigates the potential for integrating technology and multi-attribute utility theory (MAUT) to develop a dynamic decision support system (DSS) for patient prioritization in Emergency Department (ED) settings. An enhancement to the conventional MAUT model was made to incorporate changes in vital signs over time. A pilot study was conducted with data from 12 nurses and 47 patients. The dynamic MAUT model was assessed with a physician who made prioritization decisions independent of the model. A statistical analysis shows no significant difference between the recommendation proposed by the model and the decisions made by the physician. The results from the analysis give evidence for the potential benefits of combining technology with decision theory to aid nurses in prioritizing ED patients. These results can be used to further develop a DSS for dynamic patient prioritization in ED settings.

A simulation study for a hybrid inventory control strategy with advance demand information

Choosing the appropriate production and inventory control strategy is a key factor for the success of modern enterprises. Some industries adopt a Make-To-Order (MTO) policy to improve their punctuality and flexibility, while others adopt a Make-To-Stock (MTS) policy to minimise and control the inventory. This study proposes a hybrid strategy combining MTS and MTO with prioritisation. In the proposed strategy, the pull policy is considered for regular demands while a push policy with priority is applied to customers who notify their demand needs in advance. Through a set of simulation experiments this strategy is proved to be of great effectiveness.

Variability based surgical scheduling: a simulation approach

Variability in the duration of surgical procedures is one cause of delayed start times for scheduled procedures in operating theaters. While historical procedure durations are frequently used in assigning surgery times to schedule surgery blocks, taking into account the level of variability associated with specific procedures is not commonly utilized in creating surgery schedules in a multiple room operating suite. This article proposes a new methodology for surgical scheduling which sequences procedures based on duration groups and their level of variability. Discrete event simulation was used to model and validate the ratio of delayed starts versus on-time starts due to incorrectly estimated procedure length using a hospitals current scheduling algorithm and historical data. A statistical analysis was used to compare the proposed methodology against the current scenario to determine if delayed starts can be reduced by sequencing procedures based on duration variability.

A simulation study of practical methods for technical debt management in agile software development

Technical debt is a well understood yet understudied phenomena. A current issue is the verification and validation of proposed methods for technical debt management in the context of agile development. In practice, such evaluations are either too costly or too time consuming to be conducted using traditional empirical methods. In this paper, we describe a set of simulations based on models of the agile development process, Scrum, and the integration of technical debt management. The purpose of this study is to identify which strategy is superior and to provide empirical evidence to support existing claims. The models presented are based upon conceptual and industry models concerning defects and technical debt. The results of the simulations provide compelling evidence for current technical debt management strategies proposed in the literature that can be immediately applied by practitioners.

A detailed simulation model of an infusion treatment center

Oncology clinics face several complexities in their processes. When patients arrive at the infusion chairs, nurses and pharmacy technicians must be available to get the patients ready for the infusion and mix their drug treatments. This requires having the right information at the right moment. This research develops a detailed discrete event simulation model which considers the interactions between resources, information, and patient flow. The model was used to evaluate different scheduling policies and determine which of them could be incorporated in the clinic with the objective of increasing daily throughput without affecting patient wait time or total time in the system.

Using Abstraction Hierarchy as a Structured Approach in Verifying Data Quality in Driving Research

Data lost or misconstrued due to malfunctioning research equipment affects accuracy, results and analyses that driving researchers report. Carefully studying the data collection system with an Abstraction Hierarchy gathers necessary information about the equipment location and capabilities which can highlight issues in the collected data where completeness and quality are lacking. Use of this structured method to describe the instrumented research vehicle information system is a new application; however, Abstraction Hierarchy has been used in complex systems such as power, chemical and nuclear industries in order to improve the reliability of the human-machine interfaces. In this application, the Abstraction Hierarchy identified potential operating temperature issues affecting the quality of the data, provided avenues to clearly define how behavioral variables were measured, equated input and output sampling rates to show potential problems in the system and used similar variables to cross-check the consistency of the data. This is the first step in developing a structured approach to verifying data quality.

Time series forecasting in an outpatient cancer clinic using common-day clustering

The use of forecasting methods in healthcare settings can lead to operational improvements and improved patient care. However, many outpatient care facilities do not engage in demand forecasting and those that do often use rudimentary methods without exploring the best technique to forecast their patient demand. This research study examines the application of time series forecasting techniques to daily patient volume levels at an outpatient cancer treatment clinic. The work focuses on the optimal methods for accurate day-ahead forecasting in this healthcare setting with particular attention given to the differing forecast performance characteristics between traditional calendar sequencing and common-day clustering of the time series data. Through the construction of various forecasting models across multiple patient treatment duration categories, it is found that modifying a time series to a common-day clustered sequence can provide a statistically significant improvement in the accuracy of a forecast.