An Objective Method for Bed Capacity Planning in a Hospital Department

Objectives: To propose an objective approach in order to determine the number of beds required for a hospital department by considering how recruitment fluctuates over time. To compare this approach with classical bed capacity planning techniques.

Methods: Asimulated data-based evaluation of the impact that the variability in hospital department activity produces upon the performance of methods used for determining the number of beds required. The evaluation criteria included productive efficiency measured bythe bed occupancy rate, accessibility measured by the transfer rate of patients due to lack of available beds and a proxy of clinical effectiveness, by the proportion of days during which there is no possibility forunscheduled admission.

Results: When the variability of the number of daily patients increases, the Target Occupancy Rate favors productive efficiency at the expense of accessibility and proxy clinical effectiveness. On the contrary, when the variability of the department activity is marginal, the Target Activity Rate penalizes the proxy of clinical effectiveness, and the Target Occupancy Rate under-optimizes productive efficiency.

The method we propose led to a superior performance in terms of accessibility and proxy of clinical effectiveness at the expense of productive efficiency. Such a situation is suitable for intensive care units. In the case of other departments, a weighting procedure should be used to improve productive efficiency.

Conclusions: This approach could be considered as the first step of a family of methods for quantitative healthcare planning.

Six methodological steps to build medical data warehouses for research

PURPOSE

We propose a simple methodology for heterogeneous data collection and central repository-style database design in healthcare. Our method can be used with or without other software development frameworks, and we argue that its application can save a relevant amount of implementation effort. Also, we believe that the method can be used in other fields of research, especially those that have a strong interdisciplinary nature.

BACKGROUND

The idea emerged during a healthcare research project, which consisted among others in grouping information from heterogeneous and distributed information sources. We developed this methodology by the lessons learned when we had to build a data repository, containing information about elderly patients flows in the UK's long-term care system (LTC).

DESIGN

We explain thoroughly those aspects that influenced the methodology building. The methodology is defined by six steps, which can be aligned with various iterative development frameworks. We describe here the alignment of our methodology with the RUP (rational unified process) framework. The methodology emphasizes current trends, as early identification of critical requirements, data modelling, close and timely interaction with users and stakeholders, ontology building, quality management, and exception handling.

RESULTS

Of a special interest is the ontological engineering aspect, which had the effects with the highest impact after the project. That is, it helped stakeholders to perform better collaborative negotiations that brought better solutions for the overall system investigated. An insight into the problems faced by others helps to lead the negotiators to win-win situations. We consider that this should be the social result of any project that collects data for better decision making that leads finally to enhanced global outcomes.

A simulation modelling approach to evaluating length of stay, occupancy, emptiness and bed blocking in a hospital geriatric department

The flow of patients through geriatric hospitals has been previously described in terms of acute (short-stay), rehabilitation (medium-stay), and long-stay states where the bed occupancy at a census point is modelled by a mixed exponential model using BOMPS (Bed Occupancy Modelling and Planning System). In this a patient is initially admitted to acute care. The majority of the patients are discharged within a few days into their own homes or through death. The rest are converted into medium-stay patients where they could stay for a few months and thereafter either leave the system or move on to a long-stay compartment where they could stay until they die. The model forecasts the average length of stay as well as the average number of patients in each state. The average length of stay in the acute compartment is artificially high if some would-be long-term patients are kept waiting in the short-stay compartment until beds become available in long-stay (residential and nursing homes). In this paper we consider the problem as a queueing system to assess the effect of blockage on the flow of patients in geriatric departments. What-if analysis is used to allow a greater understanding of bed requirements and effective utilisation of resources.