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Index system used to measure the attention in the Emergency Department 5. The index does not need large computational background, shares the data of each hospital facility for the entire network, and identifies in which cumulative sum CUSUM algorithm 9 there is an unusual demand of the aforementioned supplies. With the information, the model warns over possible respiratory epidemic outbreaks when the number of warnings by area unit exceeds the parameters described later. This system is not predictive it only gives a fast count that could improve predictive models 10, 11 as there is a post hoc analysis of the evolution of those variables which provides elements to understand the propagation of the epidemic. We think that this model helps to solve the problem of personnel and material shortages in the network created by an epidemic outbreak, as it enables the network to reallocate in real time physicians and supplies, particularly but not limitedto the Emergency Department and the Intensive Care Unit, supporting the alert that the system gives and optimizing time and resources in the affected areas.Computational and Mathematical Methods in Medicine It also provides recommendations for the final distribution and logistics of these resources, which can be considered an additional asset over other models already known.Table 2 Main saturation stages. No. 3 2 1 0 Level 200 100 200 100 10032. Materials and MethodsWe developed an algorithm to monitor daily reported needs of materials and specialized personnel in a network of healthcare facilities. The model was evaluated using simulated multiple outbreaks superimposed on historical baseline data and the 2009 H1N1 influenza outbreak in Mxico 1216, e with figures taken from the National Institute of Medical Sciences and Nutrition Salvador Zubiran INNSZ census. Later there is a description of the model, scenario evaluation, algorithms, and performance indicators. As said before, the model was inspired by the Modified Overcrowd Index, recently developed by our group 5. 2.1. OvercrowdSevereRespiratoryDiseaseIndex OSRDI. The OSRDI model is a CUSUM 9 algorithm. The CUSUMbased calculation means the calculation of a cumulative sum. Samples from a process have been assigned weights and summed as follows 0 , 1 max 0, . 1Description Extremely saturated Highly saturated Saturated NormalLevel acceptance range for OSRDI saturation index Section 2.1 5.3 100 available bedshemodynamic monitors mechanical ventilators. This saturation rate gives the relation between usable beds and the equipment in the hospital. This quotient is a referential of possible relative growth in the healthcare facility considering the total beds usable and the equipment. If there are no hemodynamic monitors or mechanical ventilators at the time of taking the inventory, the quotient will be calculated as 999. Additionally, there is one quotient given at the outbreak itself taken from the number of patients requiring a specialty bed see Table 1 and the number of available beds in the healthcare facility. 4 300 patientsavailable beds. This saturation rate measures the attention quality in terms of the number of patients requiring a specialty bed and the equipment for the acute respiratory failure, related to the number of available beds. In case the denominator is zero, meaning there are no available beds in the facility at the time of taking the inventory, the quotient will be calculated as 999. The model also displays two different subindexes A and B. A The first subindex shows the resources distribution that a respiratory disease outbreak rapidly consumes, taking the maximum quotient from 1, 2, and 3. B The second subindex is related to the potential respiratory disease outbreak itself. Quotient 4 is pondered 30 if any quotient 1, 2, or 3 is greater than 90. The model uses these two quotients taking the biggest. For instance, if the quotient 1 has 100 as value, quotient 2 has 165, quotient 3 has 201, and quotient 4 has 350, then the OSRDIA would be 201 3 extremely saturated and the OSRDIB would be 350 1.30 455 see Table 2. The model has four levels of alert used also by the Modified Overcrowd Index 5 Table 2 levels A and B are associated with a warning message. The daily use of the system will place in context the level of alert in the hospital facility the method will take the highest value from subindexes A and B at the time of the alarm, in other words, OSRDI max OSRDIA, OSRDIB. 2.2. Geographical Regions. The model also takes into account the geographical area of each affiliated hospital the same way as it does in the Modified Overcrowd Index 5. TheWith this formula, the OSRDI model weights the variables described in Table 1, detecting any unusual consumption of resources in the entire hospital network, generating warning alerts in the facility where it is located and in nearby region facilities as the algorithm builds up specific areas by postal code. This space distribution varies depending on the fluctuation of the network nodes. The method considers four quotients in two different levels of information to give a warning from the variables mentioned previously. The first three quotients are given before the outbreak. 1 100 available bedsdoctors respiratory technicians nurses. This saturation rate gives the relation between available beds and doctors, respiratory technicians and nurses. The quotient is a referential of the attention given to patients hospitalized by the specialized healthcare personnel. If there are no doctors, respiratory technicians, or nurses at the time of taking the inventory, the quotient will be calculated as 999 being so, the range of the quotient fluctuates between 0 and 999. 2 100 available areashemodynamic monitors mechanical ventilators. This saturation rate gives the relation between available areas in the hospital facility and the equipment. This quotient is a referential of possible relative growth in the facility, considering the maximum available areas where an equipped bed can be placed with the available equipment. In case of denominator zero because of no hemodynamic monitors or mechanic ventilators available at the time of taking the inventory, the quotient will be calculated as 999.4Computational and Mathematical Methods in MedicineTable 3 Warning rate for an OSRDI network.Hospitals Area codes OSRDI Warning rate1 10500 150 2502 10501 200 2503 10500 250 2504 10503 110 2505 10510 3006 10988 199 1997 10987 189 1998 10985 170 1999 10986 165 19910 10300 5011 10301 6012 10303 9913 10600 400
Index system used to measure the attention in the Emergency Department 5. The index does not need large computational background, shares the data of each hospital facility for the entire network, and identifies in which cumulative sum CUSUM algorithm 9 there is an unusual demand of the aforementioned supplies. With the information, the model warns over possible respiratory epidemic outbreaks when the number of warnings by area unit exceeds the parameters described later. This system is not predictive it only gives a fast count that could improve predictive models 10, 11 as there is a post hoc analysis of the evolution of those variables which provides elements to understand the propagation of the epidemic. We think that this model helps to solve the problem of personnel and material shortages in the network created by an epidemic outbreak, as it enables the network to reallocate in real time physicians and supplies, particularly but not limitedto the Emergency Department and the Intensive Care Unit, supporting the alert that the system gives and optimizing time and resources in the affected areas.Computational and Mathematical Methods in Medicine It also provides recommendations for the final distribution and logistics of these resources, which can be considered an additional asset over other models already known.Table 2 Main saturation stages. No. 3 2 1 0 Level 200 100 200 100 10032. Materials and MethodsWe developed an algorithm to monitor daily reported needs of materials and specialized personnel in a network of healthcare facilities. The model was evaluated using simulated multiple outbreaks superimposed on historical baseline data and the 2009 H1N1 influenza outbreak in Mxico 1216, e with figures taken from the National Institute of Medical Sciences and Nutrition Salvador Zubiran INNSZ census. Later there is a description of the model, scenario evaluation, algorithms, and performance indicators. As said before, the model was inspired by the Modified Overcrowd Index, recently developed by our group 5. 2.1. OvercrowdSevereRespiratoryDiseaseIndex OSRDI. The OSRDI model is a CUSUM 9 algorithm. The CUSUMbased calculation means the calculation of a cumulative sum. Samples from a process have been assigned weights and summed as follows 0 , 1 max 0, . 1Description Extremely saturated Highly saturated Saturated NormalLevel acceptance range for OSRDI saturation index Section 2.1 5.3 100 available bedshemodynamic monitors mechanical ventilators. This saturation rate gives the relation between usable beds and the equipment in the hospital. This quotient is a referential of possible relative growth in the healthcare facility considering the total beds usable and the equipment. If there are no hemodynamic monitors or mechanical ventilators at the time of taking the inventory, the quotient will be calculated as 999. Additionally, there is one quotient given at the outbreak itself taken from the number of patients requiring a specialty bed see Table 1 and the number of available beds in the healthcare facility. 4 300 patientsavailable beds. This saturation rate measures the attention quality in terms of the number of patients requiring a specialty bed and the equipment for the acute respiratory failure, related to the number of available beds. In case the denominator is zero, meaning there are no available beds in the facility at the time of taking the inventory, the quotient will be calculated as 999. The model also displays two different subindexes A and B. A The first subindex shows the resources distribution that a respiratory disease outbreak rapidly consumes, taking the maximum quotient from 1, 2, and 3. B The second subindex is related to the potential respiratory disease outbreak itself. Quotient 4 is pondered 30 if any quotient 1, 2, or 3 is greater than 90. The model uses these two quotients taking the biggest. For instance, if the quotient 1 has 100 as value, quotient 2 has 165, quotient 3 has 201, and quotient 4 has 350, then the OSRDIA would be 201 3 extremely saturated and the OSRDIB would be 350 1.30 455 see Table 2. The model has four levels of alert used also by the Modified Overcrowd Index 5 Table 2 levels A and B are associated with a warning message. The daily use of the system will place in context the level of alert in the hospital facility the method will take the highest value from subindexes A and B at the time of the alarm, in other words, OSRDI max OSRDIA, OSRDIB. 2.2. Geographical Regions. The model also takes into account the geographical area of each affiliated hospital the same way as it does in the Modified Overcrowd Index 5. TheWith this formula, the OSRDI model weights the variables described in Table 1, detecting any unusual consumption of resources in the entire hospital network, generating warning alerts in the facility where it is located and in nearby region facilities as the algorithm builds up specific areas by postal code. This space distribution varies depending on the fluctuation of the network nodes. The method considers four quotients in two different levels of information to give a warning from the variables mentioned previously. The first three quotients are given before the outbreak. 1 100 available bedsdoctors respiratory technicians nurses. This saturation rate gives the relation between available beds and doctors, respiratory technicians and nurses. The quotient is a referential of the attention given to patients hospitalized by the specialized healthcare personnel. If there are no doctors, respiratory technicians, or nurses at the time of taking the inventory, the quotient will be calculated as 999 being so, the range of the quotient fluctuates between 0 and 999. 2 100 available areashemodynamic monitors mechanical ventilators. This saturation rate gives the relation between available areas in the hospital facility and the equipment. This quotient is a referential of possible relative growth in the facility, considering the maximum available areas where an equipped bed can be placed with the available equipment. In case of denominator zero because of no hemodynamic monitors or mechanic ventilators available at the time of taking the inventory, the quotient will be calculated as 999.4Computational and Mathematical Methods in MedicineTable 3 Warning rate for an OSRDI network.Hospitals Area codes OSRDI Warning rate1 10500 150 2502 10501 200 2503 10500 250 2504 10503 110 2505 10510 3006 10988 199 1997 10987 189 1998 10985 170 1999 10986 165 19910 10300 5011 10301 6012 10303 9913 10600 400