论文标题
隐藏的混乱因素引起随机步行，以降低医院的手术效率
Hidden chaos factors inducing random walks which reduce hospital operative efficiency
论文作者
论文摘要
LaFuenfría医院（LFH）手术参数，例如：住院的患者；每日入院和出院是整个医院的研究，根据每个医院的服务部门（在这里称为“服务”）。数据用于构建手术参数值序列及其变化。在该系列上进行了常规的统计分析和分形维分析。统计分析表明，数据未遵循高斯（即“正常”）分布，因此选择了非参数统计分析来描述数据。发现每日入院和每次服务的患者的序列被发现是一种随机系列，称为随机步行（RW）。 RW是接下来发生的事情（$ y_ {t+Δt} $），取决于现在发生的事情（$ y__ {t} $）加一个随机变量（$ε$），$ y_ {t+Δt} = y_t+ε$。使用参数或非参数统计分析的RW可能会模拟类似于季节性变化或假趋势的周期和漂移。在全球范围内，发现LFFH的RWS患者是根据每日出院和入院之间经过的时间确定的。发现确定LFH RW的因素是每日入院和出院之间的差异。分析表明，排放量被以一定的延迟入院而取代，并且随机差异决定因素LFH RWS。住院患者之间的每日差异遵循与入院和出院之间的每日差异相同的统计分布。这些表明，如果将入院和出院之间的每日差异最小化，即，在出院时，患者会毫不延迟入院，那么入院内裤的数量将减少，并且无人职业床的数量将减少优化医院服务。
La Fuenfría Hospital (LFH) operative parameters such as: hospitalised patients; daily admissions and discharges were studies for the hospital as a whole, and per each Hospital's service unit (just called "service" here). Data were used to build operative parameter value series and their variation. Conventional statistical analyses and fractal dimension analyses were performed on the series. Statistical analyses indicated that the data did not follow a Gauss (i.e. "normal") distribution, thus nonparametric statistical analyses were chosen to describe data. The sequence of admitted daily admissions and patients staying on each service were found to be a kind of random series of a kind called random walks (Rw). Rw are sequences where what happens next ($ y_{t+Δt}$), depends on what happens now ($ y_{t}$) plus a random variable ($ ε$), $ y_{t+Δt}= y_t + ε$. Rw analysed with parametric or non parametric statistics may simulate cycles and drifts which resemble seasonal variations or fake trends. Globally, admitted patients Rws in LFFH, were found to be determined by the time elapsed between daily discharges and admissions. The factor determining LFH Rw were found to be the difference between daily admissions and discharges. The analysis suggests discharges are replaced by admissions with some random delay and that the random difference determinants LFH Rws. The daily difference between hospitalised patients follows the same statistical distribution as the daily difference between admissions and discharges. These suggest that if the daily difference between admissions and discharges is minimised, i.e., a patient is admitted without delay when another is discharged, the number of admitted panties would fluctuate less and the number of unoccupied beds would be reduced optimising the Hospital service.
