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Results for “Hospital Bed Capacity”

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At least 19 recordsLinked to original sources
PubMed2005

A simple method to optimize hospital beds capacity.

OBJECTIVE: The number of acute hospital beds is determined by health authorities using methods based on ratios and/or target bed occupancy rates. These methods fail to consider the variability in hospitalization demands over time. On the other hand, the implementation of sophisticated models requires the decision concerning the number of beds to be made by an expert. Our aim is to develop a new method that is as simple to use as the ratio method while minimizing the roundabout approaches of these methods. METHOD: A score was constructed with three parameters: number of transfers due to lack of space, number of days with no possibility for S unscheduled admissions and number of days with at least a threshold of U unoccupied beds. The optimal number of beds is the number for which both the mean and the standard deviation of the score reach their minimum. We applied this method to two internal medicine departments and one urological surgery department and we compared the solutions proposed by this method with those put forward by the ratio method. RESULTS: The solutions proposed by this method were intermediate to those calculated by the local and national length of Stays ratio methods. Simulating an unusual increase in admission requests had no consequence on the bed number selected, indicating that the method was robust. CONCLUSION: Our tool represents a real alternative to the ratio methods. A software has been developed and is now available for use.

Algorithms↗
PubMed2003

Tapping into hidden hospital bed capacity.

Using existing resources more efficiently can help hospitals relieve their bed-capacity problems cost-effectively. Hospitals need to: Measure performance in key areas that affect patient flow and capacity Identify the drivers of capacity constraints and key opportunities for performance improvement Understand how patients flow through the hospital to identify the causes of capacity problems.

Bed Occupancy↗
PubMed2006

A network flow approach to optimizing hospital bed capacity decisions.

The delivery of cost-effective and quality hospital-based health care remains an important and ongoing challenge for the American health care industry. Despite numerous advances in medical procedures and technologies, a growing array of outpatient health care options, limits on inpatient reimbursements, and almost two decades of hospital contraction and consolidation, annual inpatient admissions in the United States are currently at levels not seen since the early 1980s. This combination of increased demand and diminished resources makes planning for hospital bed capacity a difficult problem for health care decision makers. We examine this problem by developing a network flow model that incorporates facility performance and budget constraints to determine optimal hospital bed capacity over a finite planning horizon. Under modest assumptions, we demonstrate that for realistic sized capacity planning problems, our network formulation is not computationally intensive, and allows us to obtain optimal bed capacity plans quickly.

Decision Making, Organizational↗
PubMed2003

[Efficient use of the hospital bed capacity].

The development of rather precise methods for determining the efficiency of health service activity as well as the measures directed to maintain and to strengthen the population health is extremely difficult but very important task. However till now the problems of using the medical resources more effectively, the choice of well-founded criteria and indices, the ways with the help of which they are obtained and the methods of their assessment are studied not enough. The systemic analysis of efficiency of using the RF AF medical service hospital bed fund conducted in the State Institute for physicians' advanced training showed that its evaluation should be based on the analysis of three main parameters: the provision and need of the treated contingents for the hospital beds. It will allow to determine the correspondence of available bed capacity to the total number of contingents treated; the number of patients treated in hospitals taking into account the possibilities of the institution; the quality of the rendered medical care.

Bed Occupancy↗
PubMedDate not supplied

How many hospital beds?

For many years, average bed occupancy level has been the primary measure that has guided hospital bed capacity decisions at both policy and managerial levels. Even now, the common wisdom that there is an excess of beds nationally has been based on a federal target of 85% occupancy that was developed about 25 years ago. This paper examines data from New York state and uses queueing analysis to estimate bed unavailability in intensive care units (ICUs) and obstetrics units. Using various patient delay standards, units that appear to have insufficient capacity are identified. The results indicate that as many as 40% of all obstetrics units and 90% of ICUs have insufficient capacity to provide an appropriate bed when needed. This contrasts sharply with what would be deduced using standard average occupancy targets. Furthermore, given the model's assumptions, these estimates are likely to be conservative. These findings illustrate that if service quality is deemed important, hospitals need to plan capacity based on standards that reflect the ability to place patients in appropriate beds in a timely fashion rather than on target occupancy levels. Doing so will require the collection and analysis of operational data-such as demands for and use of beds, and patient delays--which generally are not available.

Bed Occupancy↗
PubMedDate not supplied

Hospital bed surge capacity in the event of a mass-casualty incident.

INTRODUCTION: Traditional strategies to determine hospital bed surge capacity have relied on cross-sectional hospital census data, which underestimate the true surge capacity in the event of a mass-casualty incident. OBJECTIVE: To determine hospital bed surge capacity for the County more accurately using physician and nurse manager assessments for the disposition of all in-patients at multiple facilities. METHODS: Overnight- and day-shift nurse managers from each in-patient unit at four different hospitals were approached to make assessments for each patient as to their predicted disposition at 2, 24, and 72 hours post-event in the case of a mass-casualty incident, including transfer to a hypothetical, onsite nursing facility. Physicians at the two academic institutions also were approached for comparison. Age, gender, and admission diagnosis also were recorded for each patient. RESULTS: A total of 1,741 assessments of 788 patients by 82 nurse managers and 25 physicians from the four institutions were included. Nurse managers assessed approximately one-third of all patients as dischargeable at 24 hours and approximately one-half at 72 hours; one-quarter of the patients were assessed as being transferable to a hypothetical, on-site nursing facility at both time points. Physicians were more likely than were nurse managers to send patients to such a facility or discharge them, but less likely to transfer patients out of the intensive care unit (ICU). Inter-facility variability was explained by differences in the distribution of patient diagnoses. CONCLUSIONS: A large proportion of in-patients can be discharged within 24 and 72 hours in the event of a mass-casualty incident (MCI). Additional beds can be made available if an on-site nursing facility is made available. Both physicians and nurse managers should be included on the team that makes patient dispositions in the event of a MCI.

Bed Occupancy↗