In this paper, we present the Integrated Physician and Surgery Scheduling Problem (IPSSP) as a new approach for solving operating room scheduling problems where staff rosters for the physicians are integrated in the optimization. A mixed integer linear programming formulation is created based on the most frequently observed objective and restrictions of the surgery scheduling and the physician rostering problem in the literature. We analyze schedules by relaxing both surgery and physician related constraints. We then measure the implications of setting these physician preferences on the surgery schedule. Our experiments show two main interesting insights for physician roster schedulers as well as operating theatre scheduling managers.
We here describe a normalization method to combine quantitative proteomics data. By merging the output of two popular quantification software packages, we obtained a 20% increase (on average) in the number of quantified human proteins without suffering from a loss of quality. Our integrative workflow is freely available through our user-friendly, open-source Rover software (http://compomics-rover.googlecode.com/).
Hospital resource modelling literature is primarily focussed on productivity and efficiency measures. In this paper, our focus is on the alignment of the most valuable revenue factor, the operating room (OR) with the most valuable cost factor, the staff. When aligning these economic and social decisions, respectively, into one sustainable model, simulation results justify the integration of these factors. This research shows that integrating staff decisions and OR decisions results in better solutions for both entities. A discrete event simulation approach is used as a performance test to evaluate an integrated and an iterative model. Experimental analysis show how our integrated approach can benefit the alignment of the planning of the human resources as well as the planning of the capacity of the OR based on both economic related metrics (lead time, overtime, number of patients rejected) and social related metrics (personnel preferences, aversions, roster quality).
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