کد مقاله | کد نشریه | سال انتشار | مقاله انگلیسی | نسخه تمام متن |
---|---|---|---|---|
10523882 | 957123 | 2014 | 8 صفحه PDF | دانلود رایگان |
عنوان انگلیسی مقاله ISI
Intensive care unit discharge policies prior to treatment completion
ترجمه فارسی عنوان
قبل از اتمام درمان، خطوط تخلیه بخش مراقبتهای ویژه
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کلمات کلیدی
واحد مراقبت های ویژه، پذیرش بیمارستان، برنامه نویسی دینامیک، شبیه سازی بهینه سازی،
موضوعات مرتبط
علوم پزشکی و سلامت
پزشکی و دندانپزشکی
سیاست های بهداشت و سلامت عمومی
چکیده انگلیسی
In this study we explore a model to optimize the Intensive Care Unit (ICU) discharging decisions prior to service completion as a result of capacity-constrained situation under uncertainty. Discharging prior to service completion, which is called demand-driven discharge or premature discharging, increases the chance that a patient to be readmitted to the ICU in the near future. Since readmission imposes an additional load on ICUs, the cost of demand-driven discharge is pertained to the surge of readmission chance and the length of stay (LOS) in the ICU after readmission. Hence, the problem is how to select a current patient in the ICU for demand-driven discharge to accommodate a new critically ill patient. In essence, the problem is formulated as a stochastic dynamic programming model. However, even in the deterministic form i.e. knowing the arrival and treatment times in advance, solving the dynamic programming model is almost unaffordable for a sizable problem. This is illustrated by formulating the problem by an integer programming model. The uncertainties and difficulties in the problem are convincing reasons to use the optimization-simulation approach. Thus, using simulations, we evaluate various scenarios by considering Weibull distribution for the LOS. While it is known that selecting a patient with the lowest readmission risk is optimum under certain conditions and supposing a memory-less distribution for LOS; we remark that when LOS is non-memory-less, considering readmission risk and remaining LOS rather than just readmission risk leads to better results.
ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Operations Research for Health Care - Volume 3, Issue 3, September 2014, Pages 168-175
Journal: Operations Research for Health Care - Volume 3, Issue 3, September 2014, Pages 168-175
نویسندگان
S. Zahra Hosseinifard, Babak Abbasi, James P. Minas,