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Queuing Theory Accurately Models the Need for Critical Care Resources
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| Zeitschriftentitel: | Anesthesiology |
|---|---|
| Personen und Körperschaften: | , , , |
| In: | Anesthesiology, 100, 2004, 5, S. 1271-1276 |
| Format: | E-Article |
| Sprache: | Englisch |
| veröffentlicht: |
Ovid Technologies (Wolters Kluwer Health)
|
| Schlagwörter: |
| author_facet |
McManus, Michael L. Long, Michael C. Cooper, Abbot Litvak, Eugene McManus, Michael L. Long, Michael C. Cooper, Abbot Litvak, Eugene |
|---|---|
| author |
McManus, Michael L. Long, Michael C. Cooper, Abbot Litvak, Eugene |
| spellingShingle |
McManus, Michael L. Long, Michael C. Cooper, Abbot Litvak, Eugene Anesthesiology Queuing Theory Accurately Models the Need for Critical Care Resources Anesthesiology and Pain Medicine |
| author_sort |
mcmanus, michael l. |
| spelling |
McManus, Michael L. Long, Michael C. Cooper, Abbot Litvak, Eugene 0003-3022 Ovid Technologies (Wolters Kluwer Health) Anesthesiology and Pain Medicine http://dx.doi.org/10.1097/00000542-200405000-00032 <jats:sec> <jats:title>Background</jats:title> <jats:p>Allocation of scarce resources presents an increasing challenge to hospital administrators and health policy makers. Intensive care units can present bottlenecks within busy hospitals, but their expansion is costly and difficult to gauge. Although mathematical tools have been suggested for determining the proper number of intensive care beds necessary to serve a given demand, the performance of such models has not been prospectively evaluated over significant periods.</jats:p> </jats:sec> <jats:sec> <jats:title>Methods</jats:title> <jats:p>The authors prospectively collected 2 years' admission, discharge, and turn-away data in a busy, urban intensive care unit. Using queuing theory, they then constructed a mathematical model of patient flow, compared predictions from the model to observed performance of the unit, and explored the sensitivity of the model to changes in unit size.</jats:p> </jats:sec> <jats:sec> <jats:title>Results</jats:title> <jats:p>The queuing model proved to be very accurate, with predicted admission turn-away rates correlating highly with those actually observed (correlation coefficient = 0.89). The model was useful in predicting both monthly responsiveness to changing demand (mean monthly difference between observed and predicted values, 0.4+/-2.3%; range, 0-13%) and the overall 2-yr turn-away rate for the unit (21%vs. 22%). Both in practice and in simulation, turn-away rates increased exponentially when utilization exceeded 80-85%. Sensitivity analysis using the model revealed rapid and severe degradation of system performance with even the small changes in bed availability that might result from sudden staffing shortages or admission of patients with very long stays.</jats:p> </jats:sec> <jats:sec> <jats:title>Conclusions</jats:title> <jats:p>The stochastic nature of patient flow may falsely lead health planners to underestimate resource needs in busy intensive care units. Although the nature of arrivals for intensive care deserves further study, when demand is random, queuing theory provides an accurate means of determining the appropriate supply of beds.</jats:p> </jats:sec> Queuing Theory Accurately Models the Need for Critical Care Resources Anesthesiology |
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Anesthesiology |
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| title |
Queuing Theory Accurately Models the Need for Critical Care Resources |
| title_unstemmed |
Queuing Theory Accurately Models the Need for Critical Care Resources |
| title_full |
Queuing Theory Accurately Models the Need for Critical Care Resources |
| title_fullStr |
Queuing Theory Accurately Models the Need for Critical Care Resources |
| title_full_unstemmed |
Queuing Theory Accurately Models the Need for Critical Care Resources |
| title_short |
Queuing Theory Accurately Models the Need for Critical Care Resources |
| title_sort |
queuing theory accurately models the need for critical care resources |
| topic |
Anesthesiology and Pain Medicine |
| url |
http://dx.doi.org/10.1097/00000542-200405000-00032 |
| publishDate |
2004 |
| physical |
1271-1276 |
| description |
<jats:sec>
<jats:title>Background</jats:title>
<jats:p>Allocation of scarce resources presents an increasing challenge to hospital administrators and health policy makers. Intensive care units can present bottlenecks within busy hospitals, but their expansion is costly and difficult to gauge. Although mathematical tools have been suggested for determining the proper number of intensive care beds necessary to serve a given demand, the performance of such models has not been prospectively evaluated over significant periods.</jats:p>
</jats:sec>
<jats:sec>
<jats:title>Methods</jats:title>
<jats:p>The authors prospectively collected 2 years' admission, discharge, and turn-away data in a busy, urban intensive care unit. Using queuing theory, they then constructed a mathematical model of patient flow, compared predictions from the model to observed performance of the unit, and explored the sensitivity of the model to changes in unit size.</jats:p>
</jats:sec>
<jats:sec>
<jats:title>Results</jats:title>
<jats:p>The queuing model proved to be very accurate, with predicted admission turn-away rates correlating highly with those actually observed (correlation coefficient = 0.89). The model was useful in predicting both monthly responsiveness to changing demand (mean monthly difference between observed and predicted values, 0.4+/-2.3%; range, 0-13%) and the overall 2-yr turn-away rate for the unit (21%vs. 22%). Both in practice and in simulation, turn-away rates increased exponentially when utilization exceeded 80-85%. Sensitivity analysis using the model revealed rapid and severe degradation of system performance with even the small changes in bed availability that might result from sudden staffing shortages or admission of patients with very long stays.</jats:p>
</jats:sec>
<jats:sec>
<jats:title>Conclusions</jats:title>
<jats:p>The stochastic nature of patient flow may falsely lead health planners to underestimate resource needs in busy intensive care units. Although the nature of arrivals for intensive care deserves further study, when demand is random, queuing theory provides an accurate means of determining the appropriate supply of beds.</jats:p>
</jats:sec> |
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| author | McManus, Michael L., Long, Michael C., Cooper, Abbot, Litvak, Eugene |
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| description | <jats:sec> <jats:title>Background</jats:title> <jats:p>Allocation of scarce resources presents an increasing challenge to hospital administrators and health policy makers. Intensive care units can present bottlenecks within busy hospitals, but their expansion is costly and difficult to gauge. Although mathematical tools have been suggested for determining the proper number of intensive care beds necessary to serve a given demand, the performance of such models has not been prospectively evaluated over significant periods.</jats:p> </jats:sec> <jats:sec> <jats:title>Methods</jats:title> <jats:p>The authors prospectively collected 2 years' admission, discharge, and turn-away data in a busy, urban intensive care unit. Using queuing theory, they then constructed a mathematical model of patient flow, compared predictions from the model to observed performance of the unit, and explored the sensitivity of the model to changes in unit size.</jats:p> </jats:sec> <jats:sec> <jats:title>Results</jats:title> <jats:p>The queuing model proved to be very accurate, with predicted admission turn-away rates correlating highly with those actually observed (correlation coefficient = 0.89). The model was useful in predicting both monthly responsiveness to changing demand (mean monthly difference between observed and predicted values, 0.4+/-2.3%; range, 0-13%) and the overall 2-yr turn-away rate for the unit (21%vs. 22%). Both in practice and in simulation, turn-away rates increased exponentially when utilization exceeded 80-85%. Sensitivity analysis using the model revealed rapid and severe degradation of system performance with even the small changes in bed availability that might result from sudden staffing shortages or admission of patients with very long stays.</jats:p> </jats:sec> <jats:sec> <jats:title>Conclusions</jats:title> <jats:p>The stochastic nature of patient flow may falsely lead health planners to underestimate resource needs in busy intensive care units. Although the nature of arrivals for intensive care deserves further study, when demand is random, queuing theory provides an accurate means of determining the appropriate supply of beds.</jats:p> </jats:sec> |
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| spelling | McManus, Michael L. Long, Michael C. Cooper, Abbot Litvak, Eugene 0003-3022 Ovid Technologies (Wolters Kluwer Health) Anesthesiology and Pain Medicine http://dx.doi.org/10.1097/00000542-200405000-00032 <jats:sec> <jats:title>Background</jats:title> <jats:p>Allocation of scarce resources presents an increasing challenge to hospital administrators and health policy makers. Intensive care units can present bottlenecks within busy hospitals, but their expansion is costly and difficult to gauge. Although mathematical tools have been suggested for determining the proper number of intensive care beds necessary to serve a given demand, the performance of such models has not been prospectively evaluated over significant periods.</jats:p> </jats:sec> <jats:sec> <jats:title>Methods</jats:title> <jats:p>The authors prospectively collected 2 years' admission, discharge, and turn-away data in a busy, urban intensive care unit. Using queuing theory, they then constructed a mathematical model of patient flow, compared predictions from the model to observed performance of the unit, and explored the sensitivity of the model to changes in unit size.</jats:p> </jats:sec> <jats:sec> <jats:title>Results</jats:title> <jats:p>The queuing model proved to be very accurate, with predicted admission turn-away rates correlating highly with those actually observed (correlation coefficient = 0.89). The model was useful in predicting both monthly responsiveness to changing demand (mean monthly difference between observed and predicted values, 0.4+/-2.3%; range, 0-13%) and the overall 2-yr turn-away rate for the unit (21%vs. 22%). Both in practice and in simulation, turn-away rates increased exponentially when utilization exceeded 80-85%. Sensitivity analysis using the model revealed rapid and severe degradation of system performance with even the small changes in bed availability that might result from sudden staffing shortages or admission of patients with very long stays.</jats:p> </jats:sec> <jats:sec> <jats:title>Conclusions</jats:title> <jats:p>The stochastic nature of patient flow may falsely lead health planners to underestimate resource needs in busy intensive care units. Although the nature of arrivals for intensive care deserves further study, when demand is random, queuing theory provides an accurate means of determining the appropriate supply of beds.</jats:p> </jats:sec> Queuing Theory Accurately Models the Need for Critical Care Resources Anesthesiology |
| spellingShingle | McManus, Michael L., Long, Michael C., Cooper, Abbot, Litvak, Eugene, Anesthesiology, Queuing Theory Accurately Models the Need for Critical Care Resources, Anesthesiology and Pain Medicine |
| title | Queuing Theory Accurately Models the Need for Critical Care Resources |
| title_full | Queuing Theory Accurately Models the Need for Critical Care Resources |
| title_fullStr | Queuing Theory Accurately Models the Need for Critical Care Resources |
| title_full_unstemmed | Queuing Theory Accurately Models the Need for Critical Care Resources |
| title_short | Queuing Theory Accurately Models the Need for Critical Care Resources |
| title_sort | queuing theory accurately models the need for critical care resources |
| title_unstemmed | Queuing Theory Accurately Models the Need for Critical Care Resources |
| topic | Anesthesiology and Pain Medicine |
| url | http://dx.doi.org/10.1097/00000542-200405000-00032 |