Background: Prioritization of waiting lists for elective surgery represents a major issue in public systems in view of the fact that patients often suffer from consequences of long waiting times. In addition, administrative and standardized data on waiting lists are generally lacking in Italy, where no detailed national reports are available. This is true although since 2002 the National Government has defined implicit Urgency-Related Groups (URGs) associated with Maximum Time Before Treatment (MTBT), similar to the Australian classification. The aim of this paper is to propose a model to manage waiting lists and prioritize admissions to elective surgery.
In this paper we develop a three-phase, hierarchical approach for the weekly scheduling of operating rooms. This approach has been implemented in one of the surgical departments of a public hospital located in Genova (Genoa), Italy. Our aim is to suggest an integrated way of facing surgical activity planning in order to improve overall operating theatre efficiency in terms of overtime and throughput as well as waiting list reduction, while improving department organization. In the first phase we solve a bin packing-like problem in order to select the number of sessions to be weekly scheduled for each ward; the proposed and original selection criterion is based upon an updated priority score taking into proper account both the waiting list of each ward and the reduction of residual ward demand. Then we use a blocked booking method for determining optimal time tables, denoted Master Surgical Schedule (MSS), by defining the assignment between wards and surgery rooms. Lastly, once the MSS has been determined we use the simulation software environment Witness 2004 in order to analyze different sequencings of surgical activities that arise when priority is given on the basis of a) the longest waiting time (LWT), b) the longest processing time (LPT) and c) the shortest processing time (SPT). The resulting simulation models also allow us to outline possible organizational improvements in surgical activity. The results of an extensive computational experimentation pertaining to the studied surgical department are here given and analyzed.
The European Medicines Agency has approved a multicomponent serogroup B meningococcal vaccine (Bexsero®) for use in individuals of 2 months of age and older. A cost-effectiveness analysis (CEA) from the societal and Italian National Health Service perspectives was performed in order to evaluate the impact of vaccinating Italian infants less than 1 y of age with Bexsero®, as opposed to non-vaccination. The analysis was carried out by means of Excel Version 2011 and the TreeAge Pro® software Version 2012. Two basal scenarios that differed in terms of disease incidence (official and estimated data to correct for underreporting) were considered. In the basal scenarios, we considered a primary vaccination cycle with 4 doses (at 2, 4, 6 and 12 months of age) and 1 booster dose at the age of 11 y, the societal perspective and no cost for death. Sensitivity analyses were carried out in which crucial variables were changed over probable ranges. In Italy, on the basis of official data on disease incidence, vaccination with Bexsero® could prevent 82.97 cases and 5.61 deaths in each birth cohort, while these figures proved to be three times higher on considering the estimated incidence. The results of the CEA showed that the Incremental Cost Effectiveness Ratio (ICER) per QALY was €109,762 in the basal scenario if official data on disease incidence are considered and €26,599 if estimated data are considered. The tornado diagram indicated that the most influential factor on ICER was the incidence of disease. The probability of sequelae, the cost of the vaccine and vaccine effectiveness also had an impact. Our results suggest that vaccinating infants in Italy with Bexsero® has the ability to significantly reduce meningococcal disease and, if the probable underestimation of disease incidence is considered, routine vaccination is advisable.
BackgroundThe Operating Room (OR) is a key resource of all major hospitals, but it also accounts for up 40 % of resource costs. Improving cost effectiveness, while maintaining a quality of care, is a universal objective. These goals imply an optimization of planning and a scheduling of the activities involved. This is highly challenging due to the inherent variable and unpredictable nature of surgery.MethodsA Business Process Modeling Notation (BPMN 2.0) was used for the representation of the “OR Process” (being defined as the sequence of all of the elementary steps between “patient ready for surgery” to “patient operated upon”) as a general pathway (“path”). The path was then both further standardized as much as possible and, at the same time, keeping all of the key-elements that would allow one to address or define the other steps of planning, and the inherent and wide variability in terms of patient specificity. The path was used to schedule OR activity, room-by-room, and day-by-day, feeding the process from a “waiting list database” and using a mathematical optimization model with the objective of ending up in an optimized planning.ResultsThe OR process was defined with special attention paid to flows, timing and resource involvement. Standardization involved a dynamics operation and defined an expected operating time for each operation. The optimization model has been implemented and tested on real clinical data. The comparison of the results reported with the real data, shows that by using the optimization model, allows for the scheduling of about 30 % more patients than in actual practice, as well as to better exploit the OR efficiency, increasing the average operating room utilization rate up to 20 %.ConclusionsThe optimization of OR activity planning is essential in order to manage the hospital’s waiting list. Optimal planning is facilitated by defining the operation as a standard pathway where all variables are taken into account. By allowing a precise scheduling, it feeds the process of planning and, further up-stream, the management of a waiting list in an interactive and bi-directional dynamic process.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.