La Fuenfr\'ia 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+\Delta t}$), depends on what happens now ($ y_{t}$) plus a random variable ($ \epsilon $), $ y_{t+\Delta t}= y_t + \epsilon $. 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.

中文翻译：

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导致随机游走的隐性混沌因素是医院手术效率的制约因素

La Fuenfr\'ia Hospital (LFH) 手术参数，例如：住院患者；每天的入院和出院是针对整个医院和每个医院的服务单位（这里简称为“服务”）的研究。数据用于建立操作参数值系列及其变化。对该系列进行了常规统计分析和分形维数分析。统计分析表明数据不遵循高斯（即“正态”）分布，因此选择非参数统计分析来描述数据。发现每天入院和住院患者的顺序是一种称为随机游走 (Rw) 的随机序列。Rw 是接下来会发生什么的序列 ($ y_{t+\Delta t}$)，取决于现在发生了什么 ($ y_{t}$) 加上一个随机变量 ($ \epsilon $)，$ y_{t+\Delta t}= y_t + \epsilon $。使用参数或非参数统计分析的 Rw 可以模拟类似于季节性变化或虚假趋势的循环和漂移。在全球范围内，发现 LFFH 中入院患者的 Rws 取决于每日出院和入院之间的时间。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，出院被一些随机延迟的入院所取代，随机差异决定因素 LFH Rws。住院患者之间的每日差异与入院和出院之间的每日差异遵循相同的统计分布。