Operative parameters of La Fuenfría Hospital such as: hospitalized patients; daily admissions and discharges were studies for the hospital as a whole, and for each hospital’s service unit (henceforth called ‘services’). Conventional statistical analyzes and fractal dimension analyzes were performed on daily In-Patient series. The sequence of daily admissions and patients staying on each service were found to be a kind of random series known as random walks (Rw), sequences where what happens next, depends on what happens now plus a random variable. Rw analyzed with parametric or nonparametric statistics may simulate cycles and drifts which resemble seasonal variations or fake trends which reduce the Hospital’s efficiency. Globally, inpatients Rw s in LFH, were found to be determined by the time elapsed between daily discharges and admissions. The factors determining LFH R were found to be the difference between daily admissions and discharges. The discharges are replaced by admissions with some random delay and the random difference determines LFH Rw s. These findings show that if the daily difference between admissions and discharges is minimized, the number of inpatients would fluctuate less and the number of unoccupied beds would be reduced, thus optimizing the Hospital service.
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 variations. 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 daily admitted admissions and patients staying on each service were found to be a kind of random series of a kind called random walks (Rw ). Rw, sequences where what happens next, depends on what happens now plus a random variable. Rw analysed with parametric or nonparametric statistics may simulate cycles and drifts which resemble seasonal variations or fake trends which reduce the Hospital's efficiency. Globally, inpatients Rw s in LFH, 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 determinates LFH Rw s. 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 inpatients would fluctuate less and the number of unoccupied beds would be reduced optimising the Hospital service.
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