Multi-objective analysis of hospital delivery systems
Introduction
This research represents a continuation of the work presented in Rossetti, Felder, and Kumar (2000). In this work, we expand our analysis of the automation of hospital distribution services by exploring the effect of mobile robots on the elevator response, reliability of both the elevators and robots, and a multiple criteria formulation of the automation introduction decision problem. The University of Virginia Health Sciences Center (UVA-HSC) is a 591-bed facility, with an average daily census of 454. In the fiscal year between July 1, 1996 and June 30, 1997 the system cared for 29,189 inpatients, including newborns. Outpatient and emergency visits totaled 514,307 for the year. The UVA-HSC is an eight-floor complex plus an additional underground floor. The hospital has roughly the shape of a W and the floors are connected to each other by two banks of elevators located respectively in the East and in the West side of the building. Each elevator bank consists of six elevators in two rows of three. Three elevators or one-half of each bank is dedicated to staff use. The other half is dedicated to patient and visitor use. Currently, human couriers perform delivery tasks. Three couriers per shift are involved in specimen delivery, while two are used in pharmacy delivery. Three shifts of eight hours each ensure 24-hour full time service.
Pyxis, a subsidiary of Cardinal Heath, has developed a mobile robot of commercial success in the emerging field of service robots. The HelpMate® robot is a fully autonomous robot capable of carrying out delivery missions between hospital departments and nursing stations. HelpMate® robots use a specific world model for both mission planning and local navigation. The world is represented as a network of links (hallways) and an elemental move for the robot is navigating in a single hallway, avoiding people and other obstacles. In situations where more than one robot is present, a system supervisor properly spaces the robots along the hallways, since they compete for space and for the elevators. HelpMate® robots have two main elements for the human interface: a screen and a keypad on the control console and voice output. For more information concerning the capabilities of the robot, the interested reader is referred to Evans, 1994, Evans et al., 1992 and to the Pyxis web page at http://www.pyxis.com/products/helpmate.asp.
This paper is divided into seven sections. Section 2 presents the definition of the decision problem through the Analytic Hierarchy Process (AHP). Section 3 is dedicated to the description of the computer simulation models developed to assess the quantitative performance measures of the systems. Section 4 discusses how the performance measures were determined. Section 5 is dedicated to the evaluation of the alternatives. In order to determine the sensitivity of the final results to input factors, an ANOVA analysis was performed. In Section 6, the stability of the results with respect to changes in the relative importance of the performance measures are checked through a sensitivity analysis. The paper concludes with recommendations and potential areas for further study.
Section snippets
Formulation of the decision problem
This research examines the internal delivery system of middle to large sized hospital facilities with respect to economic, social and technical factors. The purpose of this examination is to synthesize and apply a methodology that can be used to assist hospital decision-makers faced with whether or not to utilize advanced automation strategies to improve the performance of hospital delivery systems. In particular, the methodology will be applied to the problem of determining whether or not to
Simulation model
Two simulation models were developed: one for the robotic, the other for the human-based delivery system. The language utilized was MODSIM III, by CACI Products Company, La Jolla, CA, USA. MODSIM III is a general-purpose, modular, block-structured, high-level programming language, which provides direct support for object-oriented programming and discrete-event simulation; therefore, we describe the features of the main objects interacting in the system.
Performance measure determination
The performance measures introduced in Fig. 1 can be divided into three groups as far as their assessment process is concerned: technical performance measures, economic performance measures, and qualitative performance measures.
Evaluation of the alternatives and ANOVA analysis
In order to compare the two alternatives, we computed the average difference between the AHP objective functions based on the thirty-two scenarios given by the factorial experimental design and the 64 replications assigned to each design point. Let Di be the difference between the values of the AHP objective function for the robotic and human based system observed in the ith day:
The AHP objective function for the ith day is defined by , ; GPj is the global priority
Sensitivity analysis
The stability of the system response after modifications in the decision-maker preference structure affecting the AHP Global Priorities was checked through a sensitivity analysis. The analysis investigated whether or not preference structure modifications could benefit the human-based solution. The sensitivity analysis was focused mostly on variations of the priority of the elements in the higher levels of the hierarchy structure: since the decision-maker was an operations manager, judgments on
Conclusions and future extensions
Based on our analysis, hospitals should seriously consider the application of mobile robots within their delivery systems. Mobile robot solutions are cost effective and can meet or exceed the performance of human based delivery systems. For this particular application at the University of Virginia Health Sciences Center, we showed that the robotic delivery system is preferable with respect to the human-based system with an overall confidence level of 99.986% based on the preference structure of
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