OBJECTIVE To estimate the required number of public beds for adults in intensive care units in the state of Rio de Janeiro to meet the existing demand and compare results with recommendations by the Brazilian Ministry of Health.METHODS The study uses a hybrid model combining time series and queuing theory to predict the demand and estimate the number of required beds. Four patient flow scenarios were considered according to bed requests, percentage of abandonments and average length of stay in intensive care unit beds. The results were plotted against Ministry of Health parameters. Data were obtained from the State Regulation Center from 2010 to 2011.RESULTS There were 33,101 medical requests for 268 regulated intensive care unit beds in Rio de Janeiro. With an average length of stay in regulated ICUs of 11.3 days, there would be a need for 595 active beds to ensure system stability and 628 beds to ensure a maximum waiting time of six hours. Deducting current abandonment rates due to clinical improvement (25.8%), these figures fall to 441 and 417. With an average length of stay of 6.5 days, the number of required beds would be 342 and 366, respectively; deducting abandonment rates, 254 and 275. The Brazilian Ministry of Health establishes a parameter of 118 to 353 beds. Although the number of regulated beds is within the recommended range, an increase in beds of 122.0% is required to guarantee system stability and of 134.0% for a maximum waiting time of six hours.CONCLUSIONS Adequate bed estimation must consider reasons for limited timely access and patient flow management in a scenario that associates prioritization of requests with the lowest average length of stay.

ABSTRACT. Critical Care is a medical specialty which addresses the life-saving and lifesustaining management of patients at risk of imminent death. The number of Intensive Care Unit (ICU) beds has an impact on patient's prognosis. This paper aims to determine the optimal number of ICU beds to reduce patient's waiting time. Time series was applied to predict demand making use of information on the daily patient's requests for ICU beds to obtain a demand forecast by means of exponential smoothing and Box-Jenkins models, which provided the input of a Queuing model. The outputs were the optimal number of ICU beds, in different scenarios, based on demand rate and patient's length of stay (LOS). A maximum waiting time in the queue of 6 hours was proposed and compared to government recommendation (118-353 beds). The need for ICU beds varied from 345 to 592 for a 6-hour waiting time (for a LOS of 6.5 to 11.2 days, respectively). The results show that managing demand and discharge timing could control the queue. Moreover, they also suggest that the current recommendation is inadequate for the demand.

Learning Objectives: ICU beds (ICUb) overcrowding results in delay for healthcare, long waits and higher mortality. There is an entry flow obstruction when the number of patients requiring ICU is greater than beds available. Determining the optimal number of ICUb is essential due to both the expensive cost and the increasing demand for them. We strive to find the optimal number of ICUb for RJ. In order to tackle the problem, we make use of time series of a Queuing model. Methods:The authors collected a series of daily requests for ICU admissions and outcomes, from 2010 to 2011. According to the information extracted from the Rio's Regulation database, they employed time series theory to forecast future demand rates. The novelty is that the model predicts the input rate of a queuing model which takes into account the stochastic variations during the actual operation of the system.The output rate was obtained from the DATASUS 2013. For carrying out all predictions they used the statistical software R Development Core Team, 2012. To estimate number of beds they compared total requests X longer and shorter lengths of stay (LOS). For each scenario the optimal number of ICUb was predicted for a prescribed maximum waiting time in such a way that only a prescribed percentage 0

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