531614
research-article2014
AJMXXX10.1177/1062860614531614American Journal of Medical QualityReddy et al
Article
Impact of Throughput Optimization on Intensive Care Unit Occupancy
American Journal of Medical Quality 2015, Vol. 30(4) 317–322 © The Author(s) 2014 Reprints and permissions: sagepub.com/journalsPermissions.nav DOI: 10.1177/1062860614531614 ajmq.sagepub.com
Anita J. Reddy, MD1, Rita Pappas, MD, FAAP, FHM1, Sanjeev Suri, MD, MBA1, Christopher Whinney, MD1, Lisa Yerian, MD1, and Jorge A. Guzman, MD1
Abstract Intensive care unit (ICU) resources are scarce, yet demand is increasing at a rapid rate. Optimizing throughput efficiency while balancing patient safety and quality of care is of utmost importance during times of high ICU utilization. Continuous improvement methodology was used to develop a multidisciplinary workflow to improve throughput in the ICU while maintaining a high quality of care and minimizing adverse outcomes. The research team was able to decrease ICU occupancy and therefore ICU length of stay by implementing this process without increasing mortality or readmissions to the ICU. By improving throughput efficiency, more patients were able to be provided with care in the ICU. Keywords ICU, throughput, Little’s law, census Intensive care unit (ICU) resources are in high demand and the demand is growing at a rapid rate, particularly because the increase in critical care patient days has outpaced the increase in ICU beds.1 ICU census and resultant workload play a key part in patient safety and quality of care, especially during times of increased ICU utilization. A high ICU census has been associated with increased risk of central line–associated bloodstream infections and ventilator-associated pneumonias, and higher rates of postoperative complications after complex surgeries,2,3 thus the increasing efforts to avoid delays in patient transfers out of the ICU. A number of interventions can increase ICU throughput, particularly processes that affect ICU length of stay or decrease ICU-related complications.4-7 Additionally, step-down units or special telemetry wards are being utilized to manage ICU census and reduce excessive workload. ICU occupancy plays a role in triaging decisions because patient transfer into the ICU occurs at a higher rate when there are more empty beds in the unit, allowing the admission of less acutely ill patients; an increase in patient transfers out of the ICU occurs when the census is high, sometimes running the risk of moving patients out prematurely.8 The research team experienced increasing delays in getting patients out of the ICU once their clinical condition no longer required ICU care (increased callout time), resulting in increasing waiting times and fewer ICU
admissions. This article reports the outcomes of a process improvement intended to reduce the throughput time for patients ready to be discharged from the ICU and its impact on hospital resources.
Methods Process Improvement The research team sought to apply a process improvement methodology to improve the efficiency of the ICU throughput process in a collaborative fashion without adversely affecting patient outcomes. At the time this work began, the team’s medical ICU had 53 beds spread across 4 geographic areas in a 1400-bed tertiary care hospital (the number of beds assigned to the medical ICU has since been increased by 11 for a total of 64 beds). The process changes took place in the medical ICU and excluded all other ICUs, such as those in the surgical, neurologic, and cardiovascular areas. All patients in the medical ICU are under the primary care of the critical care service (24 hours a day, 7 days a 1
Cleveland Clinic, Cleveland, OH
Corresponding Author: Anita J. Reddy, MD, 9500 Euclid Avenue, Desk A90, Cleveland, OH 44195. Email: [email protected]
Downloaded from ajm.sagepub.com at Stockholm University Library on August 11, 2015
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American Journal of Medical Quality 30(4)
week), and all other teams serve as consultants (closed model). Protocols are currently used to reduce sedation use/daily sedation vacation, reduce ventilator-associated pneumonia, perform daily spontaneous breathing trials, among other processes, and no new clinical protocols were implemented during the duration of the process described herein. A multidisciplinary workgroup composed of ICU physicians, ICU nurses, medical operations, transport administration, environmental services, nursing throughput, unit secretaries, hospitalist physicians, and chief internal medicine residents was tasked with creating a more efficient throughout process. As part of the overall assessment, a current state process map of the entire patient discharge to floor process was created in order to (a) define key steps in the current discharge to floor process, (b) make these steps visible to all members of the cross-functional team, and (c) enable clear communication of process changes within the context of the current process. In addition to the key process steps, process details were added to ensure that key activities required for each process step were made clear to all team members. Opportunities to enhance throughput efficiency were identified and indicated on the map, and an initial “future state” map was developed. This workgroup met approximately 6 times, both before and after the process changes were implemented, to fine-tune the algorithm. The ICU throughput process changes were effective May 21, 2012. Through a series of workgroup huddles held daily for several weeks the team iterated on the process through cycles of “plan-do-check-act,” updating the key process steps and the process details daily as appropriate. Each adjustment or clarification made to the process was clearly document on the map, which became the authoritative document detailing the process. The final process map (including both key process steps and process details) is shown in Figure 1. Prior to implementation, the procedure to transfer a stable patient to the floor involved identifying an accepting physician on the floor and, after discussion of patient status with that physician, requesting a floor bed and notifying the accepting team of availability of the bed; then, the accepting team would arrive in the ICU to evaluate the patient and write orders for transfer to the floor. The changes implemented in the ICU discharge process are listed in Table 1. In addition, bed transfer requests were encouraged to be made by 9 am, and the unit secretary would schedule patient transport at the time the floor bed was assigned to reduce wait times. The research team kept track of rapid response team activation as a surrogate for patients being transferred without a prompt assessment on arrival to the floors by the accepting team or because of clinical instability.
Analysis An internally developed electronic sign-out tool was used to facilitate tracking of patient handovers. Outcomes of the project were evaluated by comparing the time from requesting a bed until the bed was assigned (Request-toAssign), the time the bed was assigned until the patient was transferred to the hospital ward (Assign-to-Transfer), and the overall callout time from bed request until the patient was transferred to the hospital ward (Request-toTransfer). These data as well as data regarding average daily admissions (ADM) were obtained from the homegrown Cleveland Clinic admission-discharge-transferregistration system, which was able to track callout times at each step of the process. Little’s law was applied to assess the impact of reduced transfer times. Little’s law links ICU census and ICU throughput by stating that the long-term average number of customers (patients) in a stable system (the ICU) L is equal to the long-term average effective arrival rate (number of admissions), λ, multiplied by the average time a customer spends in the system (ICU length of stay [LOS]), W or L = λW. Therefore, average daily ICU census is equal to average daily ICU admissions divided by average ICU throughput (1/average LOS). L = λW or ADC = ADM * LOS where ADC is the average daily census. Using this equation, the only 2 ways to decrease ICU census is to admit fewer patients or increase ICU throughput.9 This process improvement project targeted increasing ICU throughput by decreasing time spent in the process on noncritical activities (reduction of Assign-to-Transfer and Request-to-Transfer times) and by increasing the capacity of the process, which was contiguous to the ICU process (ie, all beds considered equal on the floors) to reduce the ICU process bottleneck at the point of exit from the ICU. Nominal data are expressed as n (%) and were analyzed with Pearson χ2. Because of the normality assumption not being fulfilled, continuous data are expressed as median (interquartile range) and were analyzed with the Mann–Whitney U test. All tests of significance were 2-tailed, and P < .05 was considered statistically significant. All statistical tests were performed with SPSS version 15.0 for Windows (SPSS Inc, Chicago, IL).
Results In all, 1185 patients were discharged from the medical ICU between April 1, 2012, and August 31, 2012. These months were selected to avoid the potential seasonal impact of winter months on the process. A total of 83 of these patients were excluded from the analysis because of death or discharge to another nursing facility; 1102 patients were discharged from the ICU to the regular
Downloaded from ajm.sagepub.com at Stockholm University Library on August 11, 2015
Downloaded from ajm.sagepub.com at Stockholm University Library on August 11, 2015
319
Abbreviations: ICU, intensive care unit; MICU, medical intensive care unit; ADTR, admission-discharge-transfer-registration system; QB, quarterback; MET, medical emergency team.
Figure 1. Process map to enhance ICU throughput process—future state.
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American Journal of Medical Quality 30(4)
Table 1. Process Interventions to Increase Efficiency of Intensive Care Unit (ICU) Throughput. 1. The ICU team had the ultimate triaging authority, deciding the accepting floor service rather than searching for an accepting physician. 2. Patients were assigned any open hospital bed if a bed on the respective service’s ward was not immediately available (but the patient remained under the care of the appropriate service). 3. Identifying patients suitable for floor transfer by 9 am. 4. The ICU team was responsible for writing the transfer orders once a ward bed has been assigned (instead of the hospital ward team).
nursing floor and these patients were included in the analysis. Patients were divided into 2 groups for comparison: 319 patients discharged prior to the process change and 783 patients discharged after the process change (excluding patients who had died or were discharged to longterm care nursing facilities from the ICU). There were no differences in age, sex, or source of ICU admission in the pre- and postintervention groups. Initial APACHE (Acute Physiology and Chronic Health Evaluation) diagnoses on admission were similar among the 2 groups and APACHE III scores also were also comparable (Table 2). The percentage of patients ventilated pre versus post intervention was not significantly different, and ventilator days and 24-hour readmission rates remained similar as well (Table 3). Request-to-Assign time, Assignto-Transfer time, and Request-to-Transfer time were all significantly reduced after process implementation. Adjusted ICU occupancy decreased over time: 93.5% in April, 94.7% in May, 86.3% in June, 91.6% in July, and 84.4% in August. Medical ICU LOS also was decreased significantly post intervention (Table 3). Applying Little’s law, the reduction of ICU callout time by 1.27 hours translates into 157 additional admissions per year. ADC = ADM * LOS = 53
ADM = * 2.59
20.46 ADM / d
With a reduction of 1.27 hours of throughput time (0.0528 (d) = [1.5 hours/24 hours/day]), therefore LOS decreases to 2.5372 days. = 53
ADM = * 2.5372
20.89 ADM / d
∆ADM / d = 0.43 0.43 × 365 = 157 more admissions / year Therefore, on a cost basis, reducing overall ICU LOS by an average of 0.47 days would result in a cost
Table 2. Patient Demographics, Source of Intensive Care Unit (ICU) Admission, and APACHE Diagnosis on Admission to the ICUa. Preintervention Postintervention P (n = 319) (n = 783) Value Age (years) Sex (male) Source of admission Emergency department Regular nursing floor ICU in same hospital Another hospital (all levels of care) Otherb Mechanical ventilation APACHE III score Diagnosis ARDS Acute renal failure Cardiac arrest Congestive heart failure Diabetic ketoacidosis GI bleed Obstructive lung disease Sepsis, all sources Otherb
60 (50-71) 163 (51.1)
61 (51-71) 426 (54.4)
120 (37.6) 120 (37.6) 8 (2.5) 65 (20.4)
314 (40.1) 272 (34.7) 12 (1.5) 165 (21.1)
6 (1.9) 108 (33.9) 65 (50-81)
20 (2.6) 270 (34.5) 60 (46-79)
9 (2.8) 5 (1.6) 6 (1.9) 6 (1.9) 7 (2.2) 39 (12.2) 18 (5.6) 55 (17.2) 174 (54.5)
17 (2.2) 16 (2.0) 19 (2.4) 11 (1.4) 10 (1.3) 86 (11.0) 40 (5.1) 120 (15.3) 464 (59.3)
.760 .351 .640 .889 .082 .797
Abbreviations: APACHE, Acute Physiology and Chronic Health Evaluation; ARDS, acute respiratory distress syndrome; GI, gastrointestinal. a Data presented as n (%) or median (interquartile range). b Other sources of admission include physical rehabilitation, skilled care facility, long-term acute care facility, home, postanesthesia care unit, and operating room.
reduction of an average of $1947 per ICU admission based on average room and board charges per night for patients admitted to the medical ICU, where average room and board charges per night for an ICU stay is $4143. Hospital LOS during the same time periods also was reduced; rapid response team activation rates also remained unchanged during the study period despite shorter ICU LOS (Table 3).
Discussion Processes emphasizing speed and efficiency can be applied to ICU throughput by reducing wait times for admission, optimizing the discharge of patients once they no longer have ICU needs, reducing unnecessarily long LOS, and avoiding ICU-acquired infections and readmissions. Little’s law can be applied to the ICU model and provide objective and quantifiable evidence of the impact the interventions implemented have on ICU capacity.9,10 High-quality patient care and hospital efficiency also are strongly associated with optimal hospital occupancy.11 Historically, the Queuing Theory developed by Erlang a century ago suggested that systems are the most efficient
Downloaded from ajm.sagepub.com at Stockholm University Library on August 11, 2015
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Reddy et al Table 3. Pre- and Postintervention Impact on Throughput Times, Length of Stay, Ventilator Days, and Readmission Ratesa. Preintervention (n = 319) Request-to-Assign 103 (33-297) timeb, minutes Assign-to-Transfer 238 (162-321) time, minutes Request-to-Transfer 380 (281-563) time, minutes ICU LOS, days 2.59 (1.63-5.02) Hospital LOS, days 11.57 (5.83-21.79) Ventilator days 2 (1-5) Readmission rate 3.30% within 24 hours
Postintervention (n = 783)
P Value
89 (25-236)
.020
176 (118-250)
research-article2014
AJMXXX10.1177/1062860614531614American Journal of Medical QualityReddy et al
Article
Impact of Throughput Optimization on Intensive Care Unit Occupancy
American Journal of Medical Quality 2015, Vol. 30(4) 317–322 © The Author(s) 2014 Reprints and permissions: sagepub.com/journalsPermissions.nav DOI: 10.1177/1062860614531614 ajmq.sagepub.com
Anita J. Reddy, MD1, Rita Pappas, MD, FAAP, FHM1, Sanjeev Suri, MD, MBA1, Christopher Whinney, MD1, Lisa Yerian, MD1, and Jorge A. Guzman, MD1
Abstract Intensive care unit (ICU) resources are scarce, yet demand is increasing at a rapid rate. Optimizing throughput efficiency while balancing patient safety and quality of care is of utmost importance during times of high ICU utilization. Continuous improvement methodology was used to develop a multidisciplinary workflow to improve throughput in the ICU while maintaining a high quality of care and minimizing adverse outcomes. The research team was able to decrease ICU occupancy and therefore ICU length of stay by implementing this process without increasing mortality or readmissions to the ICU. By improving throughput efficiency, more patients were able to be provided with care in the ICU. Keywords ICU, throughput, Little’s law, census Intensive care unit (ICU) resources are in high demand and the demand is growing at a rapid rate, particularly because the increase in critical care patient days has outpaced the increase in ICU beds.1 ICU census and resultant workload play a key part in patient safety and quality of care, especially during times of increased ICU utilization. A high ICU census has been associated with increased risk of central line–associated bloodstream infections and ventilator-associated pneumonias, and higher rates of postoperative complications after complex surgeries,2,3 thus the increasing efforts to avoid delays in patient transfers out of the ICU. A number of interventions can increase ICU throughput, particularly processes that affect ICU length of stay or decrease ICU-related complications.4-7 Additionally, step-down units or special telemetry wards are being utilized to manage ICU census and reduce excessive workload. ICU occupancy plays a role in triaging decisions because patient transfer into the ICU occurs at a higher rate when there are more empty beds in the unit, allowing the admission of less acutely ill patients; an increase in patient transfers out of the ICU occurs when the census is high, sometimes running the risk of moving patients out prematurely.8 The research team experienced increasing delays in getting patients out of the ICU once their clinical condition no longer required ICU care (increased callout time), resulting in increasing waiting times and fewer ICU
admissions. This article reports the outcomes of a process improvement intended to reduce the throughput time for patients ready to be discharged from the ICU and its impact on hospital resources.
Methods Process Improvement The research team sought to apply a process improvement methodology to improve the efficiency of the ICU throughput process in a collaborative fashion without adversely affecting patient outcomes. At the time this work began, the team’s medical ICU had 53 beds spread across 4 geographic areas in a 1400-bed tertiary care hospital (the number of beds assigned to the medical ICU has since been increased by 11 for a total of 64 beds). The process changes took place in the medical ICU and excluded all other ICUs, such as those in the surgical, neurologic, and cardiovascular areas. All patients in the medical ICU are under the primary care of the critical care service (24 hours a day, 7 days a 1
Cleveland Clinic, Cleveland, OH
Corresponding Author: Anita J. Reddy, MD, 9500 Euclid Avenue, Desk A90, Cleveland, OH 44195. Email: [email protected]
Downloaded from ajm.sagepub.com at Stockholm University Library on August 11, 2015
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American Journal of Medical Quality 30(4)
week), and all other teams serve as consultants (closed model). Protocols are currently used to reduce sedation use/daily sedation vacation, reduce ventilator-associated pneumonia, perform daily spontaneous breathing trials, among other processes, and no new clinical protocols were implemented during the duration of the process described herein. A multidisciplinary workgroup composed of ICU physicians, ICU nurses, medical operations, transport administration, environmental services, nursing throughput, unit secretaries, hospitalist physicians, and chief internal medicine residents was tasked with creating a more efficient throughout process. As part of the overall assessment, a current state process map of the entire patient discharge to floor process was created in order to (a) define key steps in the current discharge to floor process, (b) make these steps visible to all members of the cross-functional team, and (c) enable clear communication of process changes within the context of the current process. In addition to the key process steps, process details were added to ensure that key activities required for each process step were made clear to all team members. Opportunities to enhance throughput efficiency were identified and indicated on the map, and an initial “future state” map was developed. This workgroup met approximately 6 times, both before and after the process changes were implemented, to fine-tune the algorithm. The ICU throughput process changes were effective May 21, 2012. Through a series of workgroup huddles held daily for several weeks the team iterated on the process through cycles of “plan-do-check-act,” updating the key process steps and the process details daily as appropriate. Each adjustment or clarification made to the process was clearly document on the map, which became the authoritative document detailing the process. The final process map (including both key process steps and process details) is shown in Figure 1. Prior to implementation, the procedure to transfer a stable patient to the floor involved identifying an accepting physician on the floor and, after discussion of patient status with that physician, requesting a floor bed and notifying the accepting team of availability of the bed; then, the accepting team would arrive in the ICU to evaluate the patient and write orders for transfer to the floor. The changes implemented in the ICU discharge process are listed in Table 1. In addition, bed transfer requests were encouraged to be made by 9 am, and the unit secretary would schedule patient transport at the time the floor bed was assigned to reduce wait times. The research team kept track of rapid response team activation as a surrogate for patients being transferred without a prompt assessment on arrival to the floors by the accepting team or because of clinical instability.
Analysis An internally developed electronic sign-out tool was used to facilitate tracking of patient handovers. Outcomes of the project were evaluated by comparing the time from requesting a bed until the bed was assigned (Request-toAssign), the time the bed was assigned until the patient was transferred to the hospital ward (Assign-to-Transfer), and the overall callout time from bed request until the patient was transferred to the hospital ward (Request-toTransfer). These data as well as data regarding average daily admissions (ADM) were obtained from the homegrown Cleveland Clinic admission-discharge-transferregistration system, which was able to track callout times at each step of the process. Little’s law was applied to assess the impact of reduced transfer times. Little’s law links ICU census and ICU throughput by stating that the long-term average number of customers (patients) in a stable system (the ICU) L is equal to the long-term average effective arrival rate (number of admissions), λ, multiplied by the average time a customer spends in the system (ICU length of stay [LOS]), W or L = λW. Therefore, average daily ICU census is equal to average daily ICU admissions divided by average ICU throughput (1/average LOS). L = λW or ADC = ADM * LOS where ADC is the average daily census. Using this equation, the only 2 ways to decrease ICU census is to admit fewer patients or increase ICU throughput.9 This process improvement project targeted increasing ICU throughput by decreasing time spent in the process on noncritical activities (reduction of Assign-to-Transfer and Request-to-Transfer times) and by increasing the capacity of the process, which was contiguous to the ICU process (ie, all beds considered equal on the floors) to reduce the ICU process bottleneck at the point of exit from the ICU. Nominal data are expressed as n (%) and were analyzed with Pearson χ2. Because of the normality assumption not being fulfilled, continuous data are expressed as median (interquartile range) and were analyzed with the Mann–Whitney U test. All tests of significance were 2-tailed, and P < .05 was considered statistically significant. All statistical tests were performed with SPSS version 15.0 for Windows (SPSS Inc, Chicago, IL).
Results In all, 1185 patients were discharged from the medical ICU between April 1, 2012, and August 31, 2012. These months were selected to avoid the potential seasonal impact of winter months on the process. A total of 83 of these patients were excluded from the analysis because of death or discharge to another nursing facility; 1102 patients were discharged from the ICU to the regular
Downloaded from ajm.sagepub.com at Stockholm University Library on August 11, 2015
Downloaded from ajm.sagepub.com at Stockholm University Library on August 11, 2015
319
Abbreviations: ICU, intensive care unit; MICU, medical intensive care unit; ADTR, admission-discharge-transfer-registration system; QB, quarterback; MET, medical emergency team.
Figure 1. Process map to enhance ICU throughput process—future state.
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American Journal of Medical Quality 30(4)
Table 1. Process Interventions to Increase Efficiency of Intensive Care Unit (ICU) Throughput. 1. The ICU team had the ultimate triaging authority, deciding the accepting floor service rather than searching for an accepting physician. 2. Patients were assigned any open hospital bed if a bed on the respective service’s ward was not immediately available (but the patient remained under the care of the appropriate service). 3. Identifying patients suitable for floor transfer by 9 am. 4. The ICU team was responsible for writing the transfer orders once a ward bed has been assigned (instead of the hospital ward team).
nursing floor and these patients were included in the analysis. Patients were divided into 2 groups for comparison: 319 patients discharged prior to the process change and 783 patients discharged after the process change (excluding patients who had died or were discharged to longterm care nursing facilities from the ICU). There were no differences in age, sex, or source of ICU admission in the pre- and postintervention groups. Initial APACHE (Acute Physiology and Chronic Health Evaluation) diagnoses on admission were similar among the 2 groups and APACHE III scores also were also comparable (Table 2). The percentage of patients ventilated pre versus post intervention was not significantly different, and ventilator days and 24-hour readmission rates remained similar as well (Table 3). Request-to-Assign time, Assignto-Transfer time, and Request-to-Transfer time were all significantly reduced after process implementation. Adjusted ICU occupancy decreased over time: 93.5% in April, 94.7% in May, 86.3% in June, 91.6% in July, and 84.4% in August. Medical ICU LOS also was decreased significantly post intervention (Table 3). Applying Little’s law, the reduction of ICU callout time by 1.27 hours translates into 157 additional admissions per year. ADC = ADM * LOS = 53
ADM = * 2.59
20.46 ADM / d
With a reduction of 1.27 hours of throughput time (0.0528 (d) = [1.5 hours/24 hours/day]), therefore LOS decreases to 2.5372 days. = 53
ADM = * 2.5372
20.89 ADM / d
∆ADM / d = 0.43 0.43 × 365 = 157 more admissions / year Therefore, on a cost basis, reducing overall ICU LOS by an average of 0.47 days would result in a cost
Table 2. Patient Demographics, Source of Intensive Care Unit (ICU) Admission, and APACHE Diagnosis on Admission to the ICUa. Preintervention Postintervention P (n = 319) (n = 783) Value Age (years) Sex (male) Source of admission Emergency department Regular nursing floor ICU in same hospital Another hospital (all levels of care) Otherb Mechanical ventilation APACHE III score Diagnosis ARDS Acute renal failure Cardiac arrest Congestive heart failure Diabetic ketoacidosis GI bleed Obstructive lung disease Sepsis, all sources Otherb
60 (50-71) 163 (51.1)
61 (51-71) 426 (54.4)
120 (37.6) 120 (37.6) 8 (2.5) 65 (20.4)
314 (40.1) 272 (34.7) 12 (1.5) 165 (21.1)
6 (1.9) 108 (33.9) 65 (50-81)
20 (2.6) 270 (34.5) 60 (46-79)
9 (2.8) 5 (1.6) 6 (1.9) 6 (1.9) 7 (2.2) 39 (12.2) 18 (5.6) 55 (17.2) 174 (54.5)
17 (2.2) 16 (2.0) 19 (2.4) 11 (1.4) 10 (1.3) 86 (11.0) 40 (5.1) 120 (15.3) 464 (59.3)
.760 .351 .640 .889 .082 .797
Abbreviations: APACHE, Acute Physiology and Chronic Health Evaluation; ARDS, acute respiratory distress syndrome; GI, gastrointestinal. a Data presented as n (%) or median (interquartile range). b Other sources of admission include physical rehabilitation, skilled care facility, long-term acute care facility, home, postanesthesia care unit, and operating room.
reduction of an average of $1947 per ICU admission based on average room and board charges per night for patients admitted to the medical ICU, where average room and board charges per night for an ICU stay is $4143. Hospital LOS during the same time periods also was reduced; rapid response team activation rates also remained unchanged during the study period despite shorter ICU LOS (Table 3).
Discussion Processes emphasizing speed and efficiency can be applied to ICU throughput by reducing wait times for admission, optimizing the discharge of patients once they no longer have ICU needs, reducing unnecessarily long LOS, and avoiding ICU-acquired infections and readmissions. Little’s law can be applied to the ICU model and provide objective and quantifiable evidence of the impact the interventions implemented have on ICU capacity.9,10 High-quality patient care and hospital efficiency also are strongly associated with optimal hospital occupancy.11 Historically, the Queuing Theory developed by Erlang a century ago suggested that systems are the most efficient
Downloaded from ajm.sagepub.com at Stockholm University Library on August 11, 2015
321
Reddy et al Table 3. Pre- and Postintervention Impact on Throughput Times, Length of Stay, Ventilator Days, and Readmission Ratesa. Preintervention (n = 319) Request-to-Assign 103 (33-297) timeb, minutes Assign-to-Transfer 238 (162-321) time, minutes Request-to-Transfer 380 (281-563) time, minutes ICU LOS, days 2.59 (1.63-5.02) Hospital LOS, days 11.57 (5.83-21.79) Ventilator days 2 (1-5) Readmission rate 3.30% within 24 hours
Postintervention (n = 783)
P Value
89 (25-236)
.020
176 (118-250)
