Within-year variation in hospital utilization and its implications for hospital costs

Baker, Laurence C.; Phibbs, Ciaran S.; Guarino, Cassandra M.; Supina, Dylan; Reynolds, James L.

doi:10.1016/j.jhealeco.2003.09.005

Cited by 37 publications

(44 citation statements)

References 21 publications

(28 reference statements)

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“…Hospital administrators are motivated to maintain this reserve capacity for many reasons. First, the public pressure for health care availability forces hospitals to avoid waiting lists and to avert turning away patients [1,2]. Second, the seasonality is an important feature of hospital admissions [3][4][5][6][7][8][9][10].…”

Section: Introductionmentioning

confidence: 99%

Hospital Costs and Unexpected Demand: The Case of Greece

Boutsioli¹

2011

TOECONSJ

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Demand for hospital inpatient care varies in a consistent way. However, a part of this variability is unpredictable. Hospitals react to this situation by holding standby capacity in order to be prepared to meet unanticipated surges in demand. This paper examines the production responses to unexpected hospital demand on a sample of Greek public hospitals over the period [2001][2002][2003][2004][2005]. The hospital output is measured by the number of inpatient admissions, distinguishing them into elective and emergency. The unexpected demand is measured as the difference between actual emergency admissions and forecasted emergency admissions using an Autoregressive Moving Average model. The results support the hypothesis that production reactions to unexpected demand have a significant impact on hospital costs.

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Section: Introductionmentioning

confidence: 99%

Hospital Costs and Unexpected Demand: The Case of Greece

Boutsioli¹

2011

TOECONSJ

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“…However, as Joskow himself claims the normal distribution is a good approximation when the mean is relatively large, as is the case in hospitals. More recently, Baker et al (2004) have concluded that the daily census probability has a characteristic normal shape for all hospitals they observed. Thus, hospital face demand z which is a random variable that can be approximated by the normal distribution…”

Section: Demand Variability and Uncertaintymentioning

confidence: 94%

“…To compute estimates of variation in hospital utilization, the daily 'census' for about 500 Californian hospitals for 12 years were used. Baker et al (2004) found that only a negligible number of hospitals in their sample are almost full. Considerable day-to-day variation in hospital utilization and significant differences in utilization patterns among hospitals were found.…”

Section: Review Of Empirical Studiesmentioning

confidence: 97%

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Demand Variability, Demand Uncertainty and Hospital Costs: A Selective Survey of the Empirical Literature

Boutsioli¹

2010

GJHS

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Demand for hospital services varies in an uncertain way and affects both the optimal size and the variable cost of hospitals. From this selective review the following main points for further research are resulted. The theory of hospital costs needs to take into consideration the impact of demand variability. The insurance role of hospital excess capacity should be treated endogenously. Better econometric techniques can improve the efficiency of empirical results. Disaggregated data may be more appropriate, and finally, more countries in order to be able to generalize the conclusions concerning the impact of demand variability on hospital costs are needed.

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“…Litvak et al (2005) reduced the variability in the demand for nursing staff by smoothing the elective admissions. Lynk (1995) described that merged hospitals would likely face lower variability in demand relative to the mean, while Baker et al (2004) explored the relationship between demand variability and hospital costs. Elkhuizen et al (2007) showed that a variability reduction in the duration of CT scans would lead to lower waiting times and a higher utilization.…”

Section: Variabilitymentioning

confidence: 99%

Reduce fluctuations in capacity to improve the accessibility of radiotherapy treatment cost-effectively

et al. 2011

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This paper is motivated by a case study to reduce the throughput times for radiotherapy treatment. The goal is to find a cost-effective way to meet future throughput targets. A combination of queuing theory and computer simulation was used. First, computer simulation to detect the bottleneck(s) in a multi-step radiotherapy process. Despite, the investment in an additional linear accelerator, the main bottleneck turned out to be the outpatient department (OPD). Next, based on queuing theory, waiting times were improved by reducing the fluctuations in the OPD capacity. Computer simulation was used again to quantify the effect on the total throughput time of a radiotherapy patient. The results showed a reduction in both access times as well as waiting times prior to the consecutive steps: the preparation phase and actual treatment. The paper concludes with practical suggestions on how to reduce the fluctuations in capacity, and seems of interest for other radiotherapy departments or other multi-step situations in a hospital.

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Within-year variation in hospital utilization and its implications for hospital costs

Cited by 37 publications

References 21 publications

Hospital Costs and Unexpected Demand: The Case of Greece

Hospital Costs and Unexpected Demand: The Case of Greece

Demand Variability, Demand Uncertainty and Hospital Costs: A Selective Survey of the Empirical Literature

Reduce fluctuations in capacity to improve the accessibility of radiotherapy treatment cost-effectively

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