Efficiency analysis and management strategies of hospital beds based on diagnosis-related groups / 中华医院管理杂志

Objective:To improve the evaluation method of hospital beds efficiency based on diagnosis-related groups (DRG), and to provide a basis for hospitals to allocate beds reasonably and improve bed efficiency.Methods:Taking a tertiary hospital in Beijing as the research object, the types of beds were evaluated by the beds utilization matrix with the time consumption index as the X-axis and the bed utilization rate as the Y-axis. The types of beds in the department were divided into efficiency type, pressure type, turnover type, and idle type. The efficiency of medical services and the level of diagnosis and treatment were evaluated by the weight of DRG per bed. The calculation method of theoretical number of beds was improved by incorporating hospital case mix index as a risk adjustment factor into the formula to evaluate the status of beds allocation. Combining the bed type, DRG weight per bed, and bed allocation status, the improvement emphasis and management strategy of bed utilization could be comprehensively analyzed.Results:Among the 24 departments in the hospital, there were 5, 9, 1 and 9 departments being efficiency type, pressure type, turnover type and idle type, respectively. The weight per bed of 11 departments was higher than the average level of the hospital. There were 16, 5, and 3 departments with appropriate, fewer, and excessive beds, respectively.Conclusions:The comprehensive analysis of beds utilization type, allocation status and weight of each bed based on DRG is an effective method to evaluate the efficiency of hospital beds, and can provide decision-making basis for hospital bed resource allocation, hospital operation focus adjustment, and subject development planning.

The Development of Automated Bed-allocation Expert System in Clinical Research Ward

BACKGROUND: Demands for complicated and long-term administration clinical trials have been increased since investigators actively involved in early stage clinical trials including first-in-human (FIH) trials. Research wards in our clinical trial center were mainly used for phase 1 trials. In order to perform several clinical trials simultaneously during a short period with a minimum number of rooms, beds, and equipment, staffs have to spend a lot of time for efficient operation of limited numbers of facilities. In this study, automated bed-allocation system was developed for efficient scheduling of the research ward based on clinical trial condition and status like experts. METHODS: The system was developed based on clinical trial design, schedule, and the information on research bed and availability stored and updated in database (DB). Automatic assignment system was designed to find an optimal schedule according to the given information using expert rules and algorithms. The optimal solution can be visualized on Gantt chart using C# and Chart FX API. RESULTS: The system was developed to demonstrate the schedule on color chart. It turned out to be well-designed to find an optimal schedule for bed allocation. The system also allows automatic updating of the schedule and information in the DB. CONCLUSION: Automated bed-allocation system developed in this study could save time and improve the efficiency for using space and equipment in clinical trial center. The system can be also applied to similar works or tasks in other fields.

Study of the estimating method for setting the criteria for hospital bed allocation in Shandong Province / 中华医院管理杂志

Objective To provide the administrative departments in the health sector with a method for estimating hospital beds when making regional health plans. Methods The method of general surveys was adopted to make relevant investigation on all the cities and districts in Shandong Province. Data collected were estimated by borrowed and self-developed formulas. Results By 2005, the total number of hospital beds in the province will be reduced by 5.19%, the number of beds in the city seats will be reduced by 4.97%, the number of beds in the county seats will be reduced by 1.03%, and the number of beds in the township seats will be reduced by 9.68%. Conclusion The findings of the study have filled in a gap at present when there is a lack of estimating methods. They are viable and yet still need to be polularized and perfected in practice.