Ways to reduce patient turnaround

Transcription

1 The Emerald Research Register for this journal is available at The current issue and full text archive of this journal is available at JHOM 9, 88 Ways to reduce patient turnaround time and improve service quality in emergency departments David Sinreich and Yariv Marmor Davidison Faculty of Industrial Engineering and Management, Technion Israel Institute of Technology, Haifa, Israel Journal of Health Organization and Management Vol. 9 No., 005 pp q Emerald Group Publishing Limited DOI 0.08/ Abstract Purpose Recent years have witnessed a fundamental change in the function of emergency departments (EDs). The emphasis of the ED shifts from triage to saving the lives of shock-trauma rooms equipped with state-of-the-art equipment. At the same time walk-in clinics are being set up to treat ambulatory type patients. Simultaneously ED overcrowding has become a common sight in many large urban hospitals. This paper recognises that in order to provide quality treatment to all these patient types, ED process operations have to be flexible and efficient. The paper aims to examine one major benchmark for measuring service quality patient turnaround time, claiming that in order to provide the quality treatment to which EDs aspire, this time needs to be reduced. Design/methodology/approach This study starts by separating the process each patient type goes through when treated at the ED into unique components. Next, using a simple model, the impact each of these components has on the total patient turnaround time is determined. This in turn, identifies the components that need to be addressed if patient turnaround time is to be streamlined. Findings The model was tested using data that were gathered through a comprehensive time study in six major hospitals. The analysis reveals that waiting time comprises 5-6 per cent of total patient turnaround time in the ED. Its major components are: time away for an x-ray examination; waiting time for the first physician s examination; and waiting time for blood work. Originality/value The study covers several hospitals and analyses over 0,000 process components; as such the common findings may serve as guidelines to other hospitals when addressing this issue. Keywords Hospitals, Emergency services, Emergency treatment, Patient care, Turnarounds, Customer services quality Paper type Research paper. Introduction and background In recent years we have been witnessing a fundamental change in the way physicians and other healthcare policy makers view the role of the emergency department (ED). Literature describes this change as a shift in the ED s emphasis from triage to saving the lives of patients needing urgent and immediate care. To support this shift, new operation policies are being adopted whereby patients are grouped according to their condition severity. In addition, the physical structure of the ED has been modified and many hospitals now have walk-in clinics designed to treat ambulatory patients. At the same time shock-trauma rooms have been established and equipped with state-of-the-art equipment to treat critically injured patients. This research was supported by the Israel National Institute for Health Policy and Health Services Research NIHP under grants number 000/7/a and 000/7/b. The authors would also like to thank the Research Center for Work Safety and Human Engineering at the Technion and all the student teams who assisted them in gathering data.

2 In order to provide quality treatment to the large variety of patient types, ED process operations have to be flexible and efficient. Putting aside clinical considerations, ED service quality can be measured in terms of patient turnaround time before being discharged or admitted to one of the hospital departments (Rapoport et al., 00).The Clinical Advisory Board concluded in their 00 report that patient satisfaction is highly dependent on patient turnaround time in the ED. Another factor that has been repeatedly identified as a source of patient discontent is the long time they must wait before and after the different procedures they undergo (Hall and Press, 996, Fernandes et al., 994). These factors are especially important since EDs have had to cope with an increase in the number of patients seeking medical care for many years, with no solution to this state of affairs in sight. At the same time, due to dwindling resources, EDs are unable to augment medical staff attending these patients. These circumstances are resulting in a rise in ED overcrowding (Gallagher and Lynn, 990; Derlet et al., 00; Schneider et al., 00) and consequently, it is not unusual to find patients sitting or lying in hallways rather than in the ED. This phenomenon can be easily described through Little s Law l ¼ W =L (Little, 96) where l denotes the average arrival rate of entities to a system, L denotes the average turnaround time of these entities in the system and W denotes the average number of entities in the system at any given point in time. Little s Law clearly shows that any rise in the patient arrival rate, while the patient turnaround time remains constant, will result in an increase in the number of patients in the ED until a state of overcrowding is reached. Overcrowding usually aggravates patient discontent, particularly due to a decrease in the privacy these patients experience in the ED. Patient discontent may also raise frustration among the medical staff. When frustration leads to an increase in the staff s treatment time, i.e. when patient turnaround time becomes a function of patients arrival rate LðlÞ, the outcome may be even worse. At this point, ED overcrowding can become critical. It is obvious that under such circumstances the service quality offered to patients, patients health, and even the clinical outcome may be affected (Regan 000). Therefore, turnaround time needs to be addressed, analysed and, wherever possible, reduced in order to improve patient satisfaction and recovery. The understanding that patient turnaround time is directly related to service quality prompted a large number of studies to focus on how to reduce this time (Gandhi et al., 00; Lane et al., 000; Gonzalez et al., 997; Braly, 995). These and other studies used ED simulation models to evaluate the impact operational changes such as staffing levels and schedules (Evans et al., 996; Rossetti et al., 999) or structural changes such as setting up a fast-track alongside the ED (Garcia et al. 995) have on ED performance measures. Other studies (Daniel et al., 996; Heckerling, 984) used time analysis to determine the main causes for patient-related delays in specific EDs. A more general study analysed patient time delays in six major hospitals in Dublin (Regan 000). The study identified inappropriate staffing levels of nurses and physicians, confusing medical staff role definitions, long distances to adjacent facilities and inappropriate ED layout structures as the primary causes for patient delays. However, most of these studies did not attempt to identify a more general pattern of the issues that need to be addressed in order to reduce patient turnaround time in the ED. A more general approach is described in Sinreich and Marmor (005). This study shows that it is possible to aggregate the processes each patient goes through when treated in the ED according to patient type (internal, surgical, orthopedic, etc.), Ways to reduce patient turnaround 89

3 JHOM 9, 90 disregarding the specific ED in which the patient is treated. Based on this finding, Sinreich and Marmor (005) develop a generic process for each patient type and combine these processes into one unified process, as shown in Figure. This study will use the above generic process to determine which of its components, as recorded in real-life hospital EDs, has the largest impact on the time patients spend in the ED and as such should be addressed first in any attempt to reduce ED patient turnaround time. Figure. The unified process chart according to which ED patients are treated

4 The rest of the paper is organised as follows: Section presents the individual components that make up the entire generic process and describes the model that is used to calculate the impact of each process component on patient turnaround time. Section describes the data gathering process and Section 4 presents the data analysis. Finally, Section 5 presents conclusions and closing remarks.. Determining the impact each component has on the entire process.. Identifying the different process components The first step in this study is to identify the unique components that make up the generic process illustrated in Figure. The process comprises 77 unique components[] that are listed in Table I. This list includes all components each patient type undergoes when admitted to the ED. (Most patients, obviously, do not experience all of them.) The second step are observations and a time study through which the duration and values of each of the above components is gathered. Once these data are gathered we can analyse them using the following model and determine the impact each component has on patient turnaround time in the ED. Ways to reduce patient turnaround 9.. Impact calculation model Based on the duration of each component, revealed in the time study, averages are calculated via (): P tiph ðþ j t iph ¼ j n iph ðþ where t iph ðjþdenotes the duration of the jth observed component of type i for patient type p at hospital h, andn iph denotes the number of times this component was observed. Some components such as the physicians examination are performed several times while other components such as discharge or admission are performed only once. Hence, it is important to determine the actual contribution each component makes to patient turnaround time as a whole. However, we cannot clearly state how many times each component is performed in each case since different patients are processed differently. We do, nevertheless, know how many times each component is observed as indicated by n iph and we also know how many times we observed a component that is performed only once during the process. Choosing the one which was observed the largest amount of times denoted by q ph and dividing the former by the latter, we can estimate how many times on average each component is performed (a result which is less than indicates the proportion of the patients which undergo this procedure). Using this result, the weighted duration of component i of patient p at hospital h can be calculated via (): ~t iph ¼ t iph n iph q ph : ðþ Finally, based on these calculations, the weight or impact each component has on the duration of overall patient processing can be calculated via ():

5 JHOM 9, 9 Table I. The components that comprise the generic patient processes Code a Element description Average waiting time for the administrative reception process Average duration of the administrative reception process Average waiting time for the triage nurse 4 Average duration of the triage performed by a nurse 5 Average waiting time for a bed 6 Average delay time after discharge 7 Average duration of administrative discharge 8 Average patient delay time on an ambulance stretcher 9 Average duration of patient transfer from the stretcher to bed 0 Average duration of bed preparation by a nurse Average waiting time for taking vital signs by a nurse Average duration of taking vital signs by a nurse 4 Average duration of the ECG check by a nurse 5 Average waiting time to be treated by a nurse 6 Average duration of the treatment by a nurse 8 Average duration of general arrangements of the patient s space and bed by a nurse 0 Average duration of the explanations given by a nurse to the patient and family Average duration of patient s follow-up check-ups by a nurse Average waiting time for instructing patients before discharge by a nurse 4 Average duration of instructing patients before discharge by a nurse 0 Average waiting time for the first physician s examination Average duration of the first physician s examination Average waiting time for second or third physician s examination 4 Average duration of second or third physician s examination 6 Average duration of patient s follow-up by a physician 8 Average waiting time for the discharge/hospitalisation decision by a senior physician 9 Average duration of the discharge/hospitalisation procedure by a senior physician 40 Average duration of the explanations given by a physician to the patient and family 4 Average duration of physician s treatment 4 Average duration of consultation with a senior physician 4 Average waiting time for the physician to fill in the forms for the imaging center 44 Average waiting time to be treated by a physician 45 Average waiting time for a physician s referral 46 Average duration of the physician s referral other than the x-ray 50 Average waiting time for consulting a specialist 5 Average duration of the consultation with a specialist 5 Average duration of an additional consultation with a specialist 5 Average waiting time for a consulting physician from another part of the ED 54 Average duration of the consultation with another ED physician 55 Average duration of an additional consultation with another ED physician 56 Average waiting time for a social worker 57 Average duration of the meeting with the social worker 58 Average duration of the physical x-ray exam including forms (excluding all other delays) 59 Average waiting time for an x-ray examination 60 Average time away for an x-ray examination 6 Average duration of an x-ray exam done in the ED 6 Average time away for an ultrasound examination 6 Average duration of an ultrasound exam done in the ED 64 Average time away for a CT exam (continued)

6 Code a Element description 65 Average waiting time of x-ray results 66 Average waiting time for ultrasound results 67 Average waiting time of CT results 68 Average waiting time for urgent blood results 69 Average waiting time for regular blood results 70 Average waiting time for blood results (priority unknown) 7 Average waiting time for urine results 7 Average waiting time to be sent for a checkup to one of the hospital department 7 Average time away for checkup in one of the hospital wards 74 Average waiting time before an x-ray examination 75 Average waiting time before a CT examination 76 Average waiting time before an ultrasound examination 80 Average time away for a cast 8 Average time away for stitches 8 Average time away for disinfecting septic wounds 8 Average time away for personal reasons 84 Average waiting time for logistics personnel 85 Average duration of the patient s delay for observations 87 Average waiting time for hospitalisation approval by hospital ward 88 Average time away for an unknown imaging examination 89 Average waiting time for an x-ray technician to come to the ED 90 Average duration of moving a patient from the bed onto a stretcher to be discharged 9 Average waiting time for unknown reasons 9 Average time away to give a urine sample 9 Average waiting time for a vehicle to pick-up a patients who needs assistance 94 Average patient delay for effects of a medical procedure to wear off 95 Average waiting time for other ED personal 96 Average time away to another part of the ED Ways to reduce patient turnaround 9 Note: a The code numbers do not run consecutively since this list was built over time and open spots were left for additional components as they were identified, so even though the list ends at 96, there are as yet only 77 components Table I. w iph ¼ ~ t iph P : ~ t iph ðþ i. Implementing the model and calculating component impact There are 5 general hospitals operating in Israel. These hospitals can be classified based on size as measured by the number of hospital beds: large (over 700 beds), medium ( beds) and small (less than 400 beds). Six of these hospitals agreed to participate in this study. According to our measure, two of the six are large (hospitals and 5), two are medium (hospitals and 4) and two are small (hospitals and 6). The first step in the study included interviews with the senior physicians and head nurses of each of the participating EDs to learn what specific procedures are routinely performed by ED staff. Based on these interviews, the patient classification in each ED was determined, as listed in Table II. This classification shows that there are nine basic

7 JHOM 9, 94 patient types: Triage patients before classification, Fast track, Internal, Surgical, Orthopedic, Trauma, Internal Walk-in, Surgical Walk-in and Orthopedic Walk-in. Some patient classifications are more significant than others as they appear in all or most of the EDs. As explained earlier and analysed in Sinreich and Marmor (005), although these patients are handled slightly differently in each of the EDs, similar components (summarised in Table I) make up each of the processes these patients undergo. The second step in the study was a planned array of observations and an organised time study that were conducted by supervised student teams at the different EDs. Close to,700 man-hours were invested in the study, during which a total of 0,464 process components were observed and recorded. The distribution of the components among the six EDs was as follows:,95 in hospital,,596 in hospital,,60 in hospital, 4, in hospital 4, 4,95 in hospital 5 and,879 in hospital 6. Using equations ()-(), the weights (impact) of the 77 different process components were calculated. This enabled us to identify the components that are more likely to reduce patient turnaround time when addressed (More Bang for Your Buck). For example, let us use the model to calculate the impact process component (PC) 69 (Average waiting time for regular blood results) with respective to a surgical type patient has on the duration of this patient s overall process in hospital.pc 69 was observed eight times in the case of surgical patients. The average duration t 69;S; and standard deviation of this component were 56.4 and 8.8 minutes, respectively. Next, using PC (average duration of taking vital signs by a nurse), which is only performed once during the process, and was observed the largest amount of times 7, the adjusted time ~t 69;S; of the PC 69 can be calculated as follows: ~t 69;S; ¼ 56:4 8 ¼ :9: 7 This means that PC 69 is not performed for all surgical patients. Therefore, its adjusted time is less compared to the average observed time. Finally, based on the combined duration of all process components P t i;s; 5:44, the impact process PC 69 has on the entire process duration can be calculated as follows: w 69;S; ¼ :9 5:44 ¼ 0:09: Based on these values, 9 components, having the largest impact[] on patient turnaround time in the ED, were chosen. The values of these 9 components for each patient type in hospital are listed in Table III. The impact values for all patient types in the other five hospitals that participated in the study are listed in Tables IV-VIII. Hospital Type of patients defined Table II. Patient types at the different EDs Triage þ Fast-track, Internal, Surgical, Orthopedic Internal, Surgical, Orthopedic Triage þ Fast-track, Internal, Surgical, Orthopedic 4 Fast-track, Orthopedic Walk-in, Surgical Walk-in, Internal, Orthopedic, Surgical 5 Fast-track, Orthopedic Walk-in, Surgical Walk-in, Internal, Surgical, Orthopedic, Trauma 6 Triage, Internal, Internal Surgical Minor, Orthopedic

8 Code Element weight (in percent) w iph, value t iph, sample size n iph Triage/fast-track Internal Orthopedic Surgical 5 0,4, 0,5, 0,4.9,07,4.5,9 0,4,,6,5,.,66,.6,65 0,.,,.5,7 5,7.,8,8.,9,.,7,7., 6,.5,4,,44,.7,9,.7,6 0 6,9.7, 7,0.4,89 4,6., 6,0.6,9,,44 6,6.,9 6,.8,55 4,4,68 0,.6,5 4,4.8, 8,6.5,,7.8, 4 0,.6,5,.5,48,.8,8,.8,9 8,.8,5 4,6.7, 7,9.4,8,.8, 4 0,6,,4,8 5,8,,5.6, 44,6.6, 6,.8,6 0,7.5, 50 4,9.6,6,8.4,,9, 6,9.5,0 5,5.,8,.5, 0,,,5., 58 0,, 0,.5,,.4, 0,., 59 0,5.8,4 0,5,,4.5,9 60,7.,6 9,.,7 4,6.9, 7,40,7 6,6, 64 4,49.5,5 65,7.8,4 69 7,,,5.6,9 5,44.8,4 9,56.4,8 7,.5,5,8.,0 7 8,4.,,60,6 5,4.5,7 84,6.5,5,8.4,74,.,4 7,, 85,7, 0,8, 0,4, 0,, 9 8,6.,6 8,9.5,4,6.7,,8.9,0 9,4.7,6 0,5,5 0,9, 94,79.,,., 0,8, 96,6, Total impact (%) Ways to reduce patient turnaround 95 Table III. The elements having the highest impact on patient processing in hospital It is clear from the results listed in Table III that these 9 elements account for at least 80 per cent of turnaround time of the different patient types in hospital. This is also true in the case of the other five hospitals. 4. Data analysis Patient turnaround time in the ED does not always reveal the entire story when talking about quality service. Other factors that affect ED service quality are the delays (Hall and Press, 996 and Fernandes et al. 994) that patients experience while being treated. These include waiting for resources such as nurses, physicians, experts and logistics personnel, waiting for a bed, waiting for test results, etc. If patients feel that the majority of time they are in the ED they are under treatment, they will probably perceive their hospital stay as a positive experience. However, if most of their length-of-stay is spent waiting, doing nothing, it will probably reflect badly on their service quality perceptions. Table IX lists the observed average waiting time in each of the six hospitals and its share (percentage) of the entire patient turnaround time.

9 JHOM 9, Code Element weight (in percent) w iph, value t iph, sample size n iph Internal Orthopedic Surgical 96 Table IV. The elements having the highest impact on patient processing in hospital 5 0,4,,.6,04,6.,6,.9,7,.4,48,.,6,.,0 5,4.5,6,7.5,4,8.6, 6,.7,65,.8,4,.,6 0,5.,9,,58,5.9,74,5.8, 4,,7,.9,89,.4,4 4,.,9 4,9.,4 4,.,6,.8,4,.,8 8 6,4,56 5,8.,5 5,9.,54 4 0,.6,9 0,.9,8,6.4,9 44,.7,6,8.,9,6.5,4 50 4,0.,7,4,4 8,8.9, 5,7,8,7.,6,6.4,0 58 0,., 0,.,7 59 0,6, 4,0., 60 8,.,54,5.,56 9,7.5,4 6,9.,7 8,74.4, 64,56.8,4,56,,9.,4 65,, 0,6, 69 0,58.8,6 6,95.7,6 7,57.,,, 7,4,6 9,50,8 84,.,40,8.8,6,8.,4 85 8,5.5,,79,,0.,4 9,8.5,8 0,, 4,64,6 9 0,5.7,0 0,.8, 0,.,8 94,64.4,9 0,9,,78.8, 96 Total impact (%) Table IX reveals that waiting time makes up 5-6 per cent of patients total time in the ED. In other words, in all six hospitals observed, patients spent more time in the ED doing nothing compared to the amount of time they were being treated. A similar analysis was done for the different patient types. Table X lists the average waiting time each patient type experienced and its share (percentage) of the entire patient turnaround time. Table X shows that for all patient types, except moderate-to-severe orthopedic patients, more time is spent doing nothing compared to the time spent being treated. This is particularly apparent in the case of moderate-to-severe internal patients and ambulatory surgical patients who spent 65 per cent and 7 per cent, respectively, of their time in the ED waiting, doing nothing. Table X also reveals that compared to more severe patients, ambulatory type patients spent a larger portion of their time in the ED waiting (7.9 per cent compared to 5. per cent surgical and 55. per cent compared to 4. per cent - orthopedic). This fact contradicts the fundamental reason for setting up walk-in clinics in the ED. It is clear, based on the above analysis, that some intervention is needed to reduce the ED patient turnaround time in general, and patient waiting time in particular.

10 Code Element weight (in percent) w iph, value t iph, sample size n iph Triage fast-track Internal Orthopedic Surgical 5 0,.8, 8,6.4,8,4.4,6,4,,4.5,4 5,.9,4 6,4.,5,.7,50 4,.4,55 5,5.,,4.7,9,6.5,4,4.8, 6,.6,9,.,,.4,50,.5,9 0 0,.4,60 4,7.,6,0.6,94 8,5.,5 7,6.8,70 4,5.6,77 5,.9,5 5,.,76,9.6,7,.4,,8.,,7.4,8 4,5.8,,.8,4,.,7,.7,7 8,8.,7,8.5,8,0, 5,.,6 4 0,.8,7,8.7,4 0,.9,7 0,, 44,.8,4 0,.4,4 0,7., 0,, 50 5,0.5,5,40.8,7 0,,,4, 5,.5,4 0,5.9,7 0,,,0,4 58 0,4,8 0,.7,5,.8,8,.4,4 59,.4,7 0,0, 6,0.6,48,0.6,5 60 4,0.8,0 5,.6,6 6,4.9,6,8,7 6,0.7,4 64 4,66,6,4,,54, 65,.5,4 0,9,,8.5, 69 8,49.5, 7,54.4,,60.,5,6, 7,,4,4.5, 7 9,,0 6,54.9,,5.7,5,4, 84,8.5,8,7.,40,5.8,,0.,7 85,5, 0,86.4,,77,,7.7, 9,.5,,.6,8,9.,9,.6,9 9,.5, 0,.9,8,.9,8,.7, 94,.4,5 4,47.7,0,4.9,8,5, 96 0,6, Total impact (5) Ways to reduce patient turnaround 97 Table V. The elements having the highest impact on patient processing in hospital Therefore, the components, which have the largest impact on the length-of-stay of all patient types in each of the six EDs surveyed, need to be identified. Addressing and improving these components will be the most effective way to reduce patient turnaround time and improve ED service quality. 4.. Determining the most crucial process components in each hospital Table XI shows that the three process components that have the largest impact on the total process duration, account for around 0 per cent of the time patients spent in the ED in all six hospitals. Therefore, these components should be the prime candidates to address when time reduction is sought. The average time patients are out of the ED for an x-ray examination (PC 60), regardless of whether the hospital operates several dedicated x-ray sites or just one general site, has the largest impact on ED patient time for all patient types in all six hospitals. The second process component, which has a considerable effect on patient turnaround time in most hospitals, is the average waiting time for the results of regular

11 JHOM 9, Code Element weight (in percent) w iph, value t iph, sample size n iph Internal walk-in Internal Orthopedic Orthopedic walk-in Surgical Surgical walk-in 98 Table VI. The elements having the highest impact on patient processing in hospital 4 5 0,, 0,,4 0,4, 5,6.7,8,.5,67 0,7.9,45 5,5,9 8,8.7,7 5,.5,,.,59 6,.7,6 6,.9,67 8,4.,7 5,.,4 0,4.8,4 5,0.,0,.6,55,4.5,7,.5, 0,.5,4 5,5, 6,.7,4,.,0,.8,9,.6,5,4.9,6,5.,4 0 7,9,4 8,9.6,0 7,8.5,,4.,8 4,.7,9 5,5, 8,6.9,6 8,7.,58 4,.7,4 5,8.,6 8,7.4,0,9.4,8,.7,7,9.4,5 0,.5, 4,4.,4,.4,59,4.,8 0,, 0,.8,4,4.8,5 8,.9, 5,6.5,67 8,4.,7 0,44.5,,7, 4,6.,7,4.4,48 0,5.5,,6, 44 0,.8, 0,7.5, 0,,,6, 50 5,65.,4,5.6,,8,,0,,6, 5,0.,5,0.,7,4.5, 4,40, 0,8, ,.5,,7.6,8 0,5.5, 0,4, 60 4,6.5, 0,7,6,44.,7 9,8, 9,49., 6 0,,,47, 64,5.5, 4,68.4,,5, 9,84, 65 5,5.5,0,8.9,8 0,7, 9,84,,6, 69 4,64., 4,7.5,8 7,.4,9 7,5.9,,4, 84 7,5.,5 9,7,6,.4,9 0,, 4,0.4,0,5., 85,8.4,4 4,5.9,4,, 0,,,6, 9,8.6,5,47.,9,74, 9,.5, 0,5.6,9,5.7,9,6.7,7 5,5, 94,7,,8.8, 96 Total impact (%) blood tests (PC 69). Similar time delays were experienced by patients in hospitals,, and 6. The third process component, which has a considerable effect on patient turnaround time in most hospitals, is the average waiting time for the first physician s examination (PC 0). Similar time delays were experienced by patients in hospitals, 4, 5 and 6. We next classified the six hospitals that participated in the study according to size and physical location and checked whether these classifications have an impact on our determination of the most significant (in terms of turnaround time) process components. Based on the average number of admissions per day, hospitals (0) and 6 () were labelled as small versus hospitals (9), (05), 4 (0) and 5 (9), which were labelled as big. Based on their locations, hospitals and 4 were labelled as regional hospitals while the rest were labelled as inner city hospitals. As it turns out, there was no statistical difference between the process components rankings in the different hospital groups in both classifications.

12 Element weight (in percent) wiph, value tiph, sample size niph Internal Walk-in Internal Orthopedic Orthopedic Walk-in Surgical Surgical Walk-in Trauma Code 5 0,.6,7 5,8.6,5,6.,64 6,8.7,4 0,,,6,5,5.,6 8,.6,9 4,5.,4,.,,.,7,.6,46 5,9.,7,8.9,4,6.6, 6,.6,6,.,0,.9,0 0,.,4,.,97 0 4,6.9,74 4,.,49 5,6.8,7 6,6.,6 9,4.,6 4,7.8, 4,0.,7 5,6,78,5.8,64 4,.7,9 6,.7,6,.7,6 8,6.5,,.7,86 5,0.9,0,0.6,,6.4,8 6,.5,8 8,9.9,9 0,4.8,4 4,.6,7 4,4.,,5.6,49,5.4,5 4,4.,5 4,6.,6,6,4,.4,49 8,7, 5,9.4,7 0,4.5,4 7,9,,.8, 4 0,4.5,0 0,.7,,7, 0,4.7,4 44 0,, 0,4, 0,9.7, 50 4,80,4 7,.9,4 8,7.5, 7,5.,47 5,.,9,8.,8,4,,5., ,, 0,, 0,,4 0,.4,6 59 0,6, 5,0.,5,8.9,7,.8,0 60 6,4.4, 6,4.9,6 0,48.4, 8,0.5,48,5, 9,.,5,7.6,80 6 4,9,,5.,6 64,40.8,9,4,,9.,4 65,46.5,4 4,68.9, 69,7.4,5 7,48.,5 4,7.5,,8.,5 7 4,69.7,0 4,,,, 4,4.5,8 7 9,7.5,,8.6,8 0,55.8, 84,0.6,5,8.9,6 85,9, 8,8.8,6 0,6, 9,.,6 6,.9,4,66,,8.,5 9,.6,5 0,4, 0,4, 0,0.,7 94,4,,78.4,7,0, 5,48, 0,8.5, 96 9,69., Total impact (%) Ways to reduce patient turnaround 99 Table VII. The elements having the highest impact on patient processing in hospital 5

13 JHOM 9, Code Element weight (in percent) w iph, value t iph, sample size n iph Internal/surgical Internal Orthopedic Triage 00 Table VIII. The elements having the highest impact on patient processing in hospital 6 5 0,.,6,, 9,5.,6,5.,5 4,4.8,9 0,5, 5,.8,7,.5,56,5.,4 0,, 4,,47 5,5,4,.8,5,,5 6,.,8,.,0 0,, 4,.,4 0 5,6.4,90 6,5.6,9 0,.8, 5,5.,0 5,5.7, 7,4.,7,9.8, 0,, 0,, 4 0,.,8,.7,9 8,.9, 7,4.6,7,9.,,.5,9 4,6.,,.8,0 6,.,8 44,9,5,6.4, 4,0.,6 50,7.7,8 0,4, 5,.8,4 0,7, 58 6,.6,6 59 7,7.9,4 60,46.,0,6.5,7 8,9.7,0 6,7.5,,, 64,4,,4, 65,4,6 0,5, 69,5.,8 8,6.,7 7,.,,6., 6,.4,5 7 5,56.9,9,8, 84 6,.5,58,9.4,7 85,99,,6, 9 5,9.8,0 4,9.,5,6.7,,.8,7 9 0,7.4,5 0,5.5, 6,.6,8 94,47.8,4 96 Total impact (%) Hospital Table IX. Waiting time percentages in each of the hospitals Average time in system regardless of patient type (minutes) Average overall waiting time (minutes) Waiting time impact (%) Determining the most crucial process components for each patient classification The study highlights, as shown in Tables IX and X, the importance of reducing patient delay time, especially in the case of ambulatory patients who spend most of their time in the ED waiting and doing nothing. The summary of the data analysis reported in Tables XI-XIII underlines the process components that need to be addressed, studied and evaluated first when trying to reduce patient turnaround time. This study does not offer any ready-made solutions on

14 Patient Orthopedic Walk-in Surgical Walk-in Internal Walk-in and Fast-track Triage Internal Surgical Orthopedic Component Average time in system regardless of patient type (minutes) Average overall delay time (minutes) Delay impact (%) Ways to reduce patient turnaround 0 Table X. Waiting time percentages for each of the patient types

15 JHOM 9, Component Hospital Table XI. The three most critical elements in each of the six hospitals and their average duration (minutes) Average waiting time for the first physician s examination (PC 0) (8.9) (9.9) (4.8) (7.5) Average time away for an x-ray examination (PC 60) (0.9) (.9) (.5) (46.) (9.7) (7.5) Average waiting time for regular blood results (PC 69) (54.) (66.6) (54.6) (55.5) Average time away for checkup in one of the hospital departments (PC 7) (57.8) Average waiting time for logistics aid personnel (PC 84) (9.9) (6) Average duration of the patient s delay for observation (PC 85) (9.8) Total impact of the top three components (%) Component Patient Internal Surgical Orthopedic Table XII. The three most influential component each patient type and their average duration (minutes) Average waiting time for the first physician s examination (PC 0) (9.5) (8.) (.7) Average duration of the first physician s examination (PC ) (.) Average time away for an x-ray examination (PC 60) (5.9) (0.) (8.7) Average waiting time for regular blood results (PC 69) (5.5) (0.) Total impact of the top three components (%) how to improve each of these components since each ED is unique to some extent. Therefore, each hospital is required to evaluate its current state of operations and conditions of these components. Only then can a plan be devised for how to, cost effectively, improve management and control over these process components and cut any unnecessary, non-productive operation time. Tables XII and XIII show that the different patient types can be divided into two groups. The first group contains all patients whose condition ranges from moderate to severe and who require a bed and full attention. The second group comprises all ambulatory patients whose condition requires less attention, and in most cases do not mandate a bed. Although PC 0 (average waiting time for the first physician s examination) and PC 60 (average time away for an x-ray examination) have a considerable impact on both these patient groups, other process elements have a much larger impact on one group as opposed to the other group. Process components such as average duration of the first physician s examination (PC ) and average waiting time for regular blood results (PC 69) have a higher impact on the acute and severe patients, while process components such as average waiting time for taking vital signs by a nurse (PC ), average duration of taking vital signs by

16 Patient Internal Walk-in and Fast-track Triage Surgical Walk-in Orthopedic Walk-in Component (.8) (8.6) Average waiting time for taking vital signs by a nurse (PC ) () (6.6) (.7) (4.) Average duration of taking vital signs by a nurse (PC ) Average waiting time for the first physician s examination (PC 0) (.5) (4.8) Average waiting time for second or third physician s examination (PC ) (8.7) Average time away for an x-ray examination (PC 60) Total impact of the top three components (%) (.) (4.5) Ways to reduce patient turnaround 0 Table XIII. The three most influential component each patient type and their average duration (minutes)

17 JHOM 9, 04 a nurse (PC ) and the average waiting time for a second or third physician s examination (PC ) have a higher impact on ambulatory patient types. 5. Conclusions and final remarks The study highlights, as shown in Tables IX and X, the importance of reducing patient delay time especially in the case of ambulatory type patients which spend most of their time in the ED waiting and doing nothing. The summary of the data analysis shown in Tables XI-XIII highlights the process components which need to be addressed, studied and evaluated first when trying to reduce patient turnaround time. This study does not offer any ready-made solutions on how to improve each of these components since each ED is unique to some extent. Therefore, each hospital is required to evaluate first the current operation state and condition of these suggested components. Only then can a plan be devised on how to, cost effectively, improve management and control over these process components and cut any unnecessary, non-productive operation time. Notes. A component is defined as a unique operation a patient undergoes/experiences or one that a member of the hospital staff, medical or logistical, performs.. An element is included in the 9 most influential elements if the value of w iph for at least one patient classification in at least one of the six EDs is greater than or equal to 5 per cent. References Braly, D. (995), Seek alternatives via simulation software, Health Management Technology, Vol. 6 No., pp. -5. Daniel, A.L., Fung, H. and Hedley, A.J. (996), Time studies in A&E departments a useful tool for management, Journal of Management in Medicine, Vol. 0 No., pp. 5-. Derlet, R., Richards, J. and Kravitz, R. (00), Frequent overcrowding in US emergency departments, Academic Emergency Medicine, Vol. 8 No., pp Evans, G.W., Unger, E. and Gor, T.B. (996), A simulation model for evaluating personnel schedules in a hospital emergency department, Proceedings of the 96 Winter Simulation Conference, pp Fernandes, C.M.B., Daya, M.R., Barry, S. and Palmer, N. (994), Emergency department patient who leave without seeing a physician: the Toronto Hospital experience, Annals of Emergency Medicine, Vol. 4 No. 6, pp Gallagher, E.J. and Lynn, S.G. (990), The etiology of medical gridlock: causes of emergency overcrowding in New York City, Journal of Emergency Medicine, Vol. 8, pp Gandhi, T., Nagarkar, K., DeGennaro, M. and Srihari, K. (00), Reducing patient turnaround time in an emergency room, Proceedings of the International Conference on Production Research (in a CD). Garcia, M., Centeno, M.A., Rivera, C. and DeCarlo, N. (995), Reducing time in an emergency room via fast-track, Proceedings of the 95 Winter Simulation Conference, pp Gonzalez, C.J., Gonzalez, M. and Rios, N.M. (997), Improving the quality of service in an emergency room using simulation-animation and total quality management, Computer and Industrial Engineering., Vol. No. -, pp

18 Hall, M.F. and Press, I. (996), Keys to patient satisfaction in the emergency department: results of a multiple facility study, Hospitals and Health Service Administration, Vol. 4, pp Heckerling, P.S. (984), Time study of an emergency room, identification of sources of patient delay, Illinois Medical Journal, Vol. 66, pp Lane, D.C., Monefeldt, C. and Rosenhead, J.V. (000), Looking in the wrong palace for healthcare improvements: a system dynamics study on an A&E Department, Journal of the Operations Research Society, Vol. 5, pp Little, J.D.C. (96), A proof for the queuing formula L ¼ l W, Operations Research, Vol. 9 No., pp Rapoport, J., Teres, D., Zhao, Y. and Lemeshow, S. (00), Length of stay data as a guide to hospital economic performance for ICU patients, Medical Care, Vol. 4 No., pp Regan, G. (000), Making a difference to A&E Analysis of the operational inefficiencies in A&E departments in major acute hospitals in Dublin, Accident & Emergency Nursing, Vol. 8, pp Rossetti, M., Trzeinski, G. and Syerud, S. (999), Emergency department simulation and determination of optimal attending physician staffing schedules, Proceedings of the 99 Winter Simulation Conference, pp Schneider, S.M., Gallery, M.E., Schafermeyer, R. and Zwemer, F.L. (00), Emergency department overcrowding: a point in time, Annals of Emergency Medicine, Vol. 4 No., pp Sinreich, D. and Marmor, Y. (005), The operations of hospital emergency departments: the basis for developing a simulation tool, IIE Transactions,, Vol. 7 No., in press. Ways to reduce patient turnaround 05 Further reading Clinical Advisory Board (00), Best Practices for Eliminating Bottlenecks and Delays, Technical Report, Clinical Advisory Board, p. 4.