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doi: 10.1007/bf02257264
pmid: 8522909
Reduction of health care costs is of paramount importance in our time. This paper is a part of the research which proposes an expert hospital decision support system for resource scheduling. The proposed system combines mathematical programming, knowledge base, and database technologies, and what is more, its friendly interface is suitable for any novice user. Operating rooms in hospitals represent big investments and must be utilized efficiently. In this paper, first a mathematical model similar to job shop scheduling models is developed. The model loads surgical cases to operating rooms by maximizing room utilization and minimizing overtime in a multiple operating room setting. Then a prototype expert system which replaces the expertise of the operations research analyst for the model, drives the modelbase, database, and manages the user dialog is developed. Finally, an overview of the sequencing procedures for operations within an operating room is also presented.
Operating Room Information Systems, Operating Rooms, Health Care Rationing, Expert Systems, Models, Theoretical, Artificial Intelligence, Decision Support Systems, Management, Humans, Computer Simulation, Surgery Department, Hospital, Information Systems
Operating Room Information Systems, Operating Rooms, Health Care Rationing, Expert Systems, Models, Theoretical, Artificial Intelligence, Decision Support Systems, Management, Humans, Computer Simulation, Surgery Department, Hospital, Information Systems
citations This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically). | 41 | |
popularity This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network. | Top 10% | |
influence This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically). | Top 1% | |
impulse This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network. | Average |