- Email: [email protected]
Improving Efficiency of Interventional Service by Lean Six Sigma
CASE STUDIES IN CLINICAL PRACTICE MANAGEMENT
Improving Efficiency of Interventional Service by Lean Six Sigma Li Zhang, PhD, Karin Runzheimer, RN, Elisabeth Bonifer, MD, Annika Keulers, MD, Eike Piechowiak, MD, Andreas Mahnken, MD INTRODUCTION TO THE PROBLEM WE ADDRESSED Interventional radiology (IR) suites are scarce resources that are often highly congested and identified as bottlenecks in patient flow. These bottlenecks cause delays in treatment that diminish care quality, increase the length of hospital stay and total inpatient costs, and are detrimental to both patients and health care providers [1]. Effective management of such delays can produce dramatic improvements in medical outcomes, patient satisfaction, and access to service, while reducing the costs of health care simultaneously. At most hospitals, however, accurate data on capacity, demand, and the extent of delay are not always available, and the relationships among these individual parameters are only partly understood. Often average capacity is incorrectly used to predict performance, quality, or timeliness. Long wait times for central venous access services and percutaneous gastrostomy perceived by referring physicians at our hospital made it urgent to improve our processes and to understand how capacity affects access to and delays in the aforementioned services. WHAT WE DID TO ADDRESS THE PROBLEM In the IR suite at our academic hospital (with about 1,200 beds),
488 cases of central venous access and percutaneous gastrostomy were performed in 2012, on working days from 8 AM to 1 PM. The actual start time for procedures is about 8:30 AM. After 1 PM, the room is used for fluoroscopic procedures, which were not the subject of this study. Both inpatients (61%) and outpatients (39%) were treated, solely by residents, who during this study possessed different degrees of professional experience. Generally a resident is replaced after 3 months of training by another inexperienced resident and supervised by a staff interventional radiologist. A Lean Six Sigma team, including radiologists and technologists, was established to address the problem [2]. Measurements in the IR suite were carried out at the beginning of the project and about 10 months later, in each case for 5 consecutive days. Measurements documented process steps, times, failures, and the physical motions of technologists. Cycle time and lead time were calculated from the measurement results. Cycle time is the length of patient stay in the IR suite plus the time for IR suite preparation. Lead time is the sum of cycle and waiting times between subprocesses. Eleven and 17 procedures measured individually for phase 1 and phase 2 were analyzed.
ª 2015 American College of Radiology 1546-1440/15/$36.00 n http://dx.doi.org/10.1016/j.jacr.2015.05.016
Cause-and-effect analysis and failure mode and effect analysis (FMEA) [3,4] were performed. Rolled percentage complete and accurate [5] was calculated. Value-stream mappings were carried out [6]. These analyses served to identify and eliminate process waste. Demand for procedures was evaluated by the number of procedures requested per day. Throughput was measured by the number of procedures performed per time unit on the basis of data from the radiology information system. The data were separately analyzed according to two project phases as described previously. The takt time is the daily room uptime divided by daily procedures requested. It is the rate at which venous access services and percutaneous gastrostomy should be provided to satisfy the need of referring physicians. The outcome quality of central venous access was measured by the number of procedures requiring explantations and revisions relative to the number of implantations. Performance parameters were calculated on the basis of demand and capacity by adopting an M/M/1 queuing model [7]. Parameters included utilization of the IR, the expected number of procedures in the waiting queue, the expected length of delay of delayed procedures, and service level. Data are presented as mean SD or as medians. Normal distribution
1
of data was verified by the AndersonDarling test and Poisson distribution by the goodness-of-fit test, with P values > .05 considered to indicate statistical significance. One-way analysis of variance was used to identify differences between population means. Inequality of two population medians was verified by using the Mann-Whitney test, with P values < .05 considered to indicate statistical significance. All statistical analyses were performed using Minitab version 15 (Minitab Inc, State College, Pennsylvania).
OUTCOMES An average takt time as defined before of 55 min was determined to satisfy 90% of daily demand, equivalent to a daily capacity of about 5.5 procedures. Current-state value-stream mapping revealed waiting times and improvement potential in process steps and determined average lead time and cycle time (Fig. 1). Causeand-effect analysis and FMEA identified 69 failure modes contributing to process failures. A total of 117 potential failure causes contributed to these modes. The percentage complete and accurate from order entry to
signed report was calculated to be 9% before improvement. The 69 failure modes were prioritized according to the principles of FMEA, and 46 were chosen, treated, or eliminated in the first iteration. In particular, the process of obtaining preoperative informed consent as legally required and the postoperative follow-up procedure for outpatients were established or optimized. Preoperative preparations such as obtaining results from blood testing and intravenous access were secured or performed outside the IR suite. A patient information sheet was provided to inform patients about the entire periprocedural process, to ensure that relevant patient preparations were done at home, to improve patients’ ontime arrival, and to reduce no-shows among outpatients. Fine-grained responsibilities of radiologists and technologists were redefined for the entire process. Standard operating procedures, including patient preparation, material needs, device positioning, and process flow, were refined. An additional staff member was added to support the IR technologist and to minimize changeover and setup times. The length and the number of scheduling time slots
were adjusted so that they could accommodate measured cycle time, including the training time needed for inexperienced residents and emergency requests. The Kanban method was introduced for securing the availability of materials, reducing the space needed for excess material stock, and eliminating wait times for collecting materials during a procedure. In addition, the procedure itself was refined, and some steps, such as routine cardiac monitoring, were eliminated if safely feasible. The sequence of disinfection and sterile draping was reorganized, and the positions of devices and medications were optimized to reduce the motions of IR technologists.
Comparison of Demand, Throughput, and Delay Between the Two Project Phases Daily demand and throughput of procedures have been determined from radiology information system data. Analysis of variance demonstrated no statistical difference in monthly demand between the two project phases (P ¼ .42). Demand followed a Poisson distribution (P ¼ .10), with a Poisson mean of
Fig 1. Current value-stream mapping. The symbols denoted as Kaizen identify process steps to be improved. Triangles indicate waiting-time waste to be removed.
2
Journal of the American College of Radiology Volume - n Number - n - 2015
3.06 procedures/day. The cumulative distribution function of demand showed that a capacity of 5.5 procedures per day would cover 90% of daily demand. Daily throughput, however, is constrained by IR suite uptime and lead time. Because uptime is limited to 300 min/day, the maximum possible daily throughput was three procedures in project phase 1. Taking the late start of about 30 min into account, the realistic maximum average throughput in project phase 1 was expected to be less than 2.7. The actual daily median throughput values were 3.2 and 3.6 procedures/day during project phases 1 and 2, respectively (P ¼ .08).
Improved Value Stream Mapping, Cycle Time, and Potential Capacity Key to the effort toward improvement was enabling timely and highquality procedures to flow smoothly throughout our process. Minimizing process variation and eliminating waste were thus the major tasks in the process redesign. Mean cycle time decreased from 75 7 to 53 9 min (P < .00) in project phase 2, a significant reduction of 28%. Dividing cycle time period into different segments, as shown in Table 1, it is obvious that the major reduction of cycle time (11 min) from phase 1 to phase 2 could be attributed to steps occurring before skin incision. The cycle time of the steps performed solely by the radiologist was reduced from 29 to 23 min. There were no relevant differences in the procedural steps taken before the radiologist entered the IR suite or after skin suture (Table 1). The reduction in cycle time led to a decrease in lead time. With room uptime being 300 min, the potential capacity after the Lean Six Sigma improvement was 4.6 procedures/day, an increase of 52%
Table 1. Comparison of process cycle time in the two project phases Variable Time from patient entry to radiologist entry Time from radiologist entry to skin incision Time from skin incision to sticking plaster Time from skin suture to patient exit Room preparation time
Phase 1 (min) 10 (9)
Phase 2 (min) 7 (5)
Change
P .308*
27 (6)
16 (5)
40%
.000*
29 (8)
23 (8)
21%
.035*
6 2
5.5 1
.936† —
*Analysis of variance. † Mann-Whitney test.
compared with the capacity of 3.0 procedures in project phase 1. Taking the late start in the morning into consideration, the realistic maximum daily throughput after improvement was 4.1 procedures/day. The outcome quality of central venous access showed no change between the two project phases (P ¼ .14).
Relationship Among Demand, Capacity, and Service Level On the basis of the M/M/1 queuing model, the following performance parameters could be calculated. In
phase 1, the IR suite exhibited a utilization rate of 95%, with 19 procedures constantly on the waiting list. The average expected delay to the procedure was about 6.4 working days. Only 19% of procedures were carried out within 2 days, as expected by referring physicians. In phase 2, room utilization decreased by 9% to 86%. During the same time period, the number of procedures waiting in the queue decreased from 19 to 5.4, a decrease of 72%, and the expected delay decreased from 6.4 to 2.1 days, a
Fig 2. Relationship of number of requests waiting (Lq), expected length of delay, interventional radiology suite utilization (percentage), and service level as described by an M/M/1 queuing model with a demand rate of 3.06 procedures. At 3.1 procedures/day, the system overflows, the number of procedures waiting in the queue can equal or exceed 70, and the length of delay reaches 25 working days, resulting in an extremely congested system. A moderate increase in capacity of about 13% (ie, from 3.1 to 3.5 procedures/day) decreases queue length from more than 70 to 6 procedures (a 91% reduction) and the expected length of delay from 25 to about 2 days (a 92% reduction). The service level increases exponentially with increasing capacity (service rate).
Journal of the American College of Radiology Zhang et al n Case Studies in Clinical Practice Management
3
decrease of 67%. At the same time, the service level improved from 19% to 47%. A remarkable impact of the capacity in terms of service rate on waiting was observed, especially in the range of 3.1 to 3.5 procedures per day (Fig. 2). A 13% increase in capacity, achieved by reducing lead time (eg, from 100 to 86 min), led to an exponential reduction in waiting time.
CONCLUSIONS We found in this study that the application of Lean Six Sigma improved efficiency in our IR suite. The approach used is applicable to any IR environment because the aim
of the undertaking was to eliminate process delays and failures, which are present in many IR suites. The extra capacity gained in this way has had a dramatic effect on access and on wait times. Providing proper capacity through process improvement or resource allocation is critical to maintaining a short waiting list.
ACKNOWLEDGMENTS We thank Anne-Kathrin Reuter, Anna-Christina Stamm, Marga Rominger, and Mariana Gurschi for their partial participation in this project.
2. 3.
4.
5.
6.
REFERENCES 1. Belsori D. Managing a patient flow improvement project. In: Hall RW, ed. Patient
7.
flow: reducing delay in healthcare delivery. New York, New York: Springer; 2006. Turney J. Six Sigma and Lean Six Sigma. Radiol Technol 2007;79:191-2. Abujudeh HH, Kaewlai R. Radiology failure mode and effect analysis: what is it? Radiology 2009;252:544-50. Zhang L, Hefke A, Figiel J, Romminger M, Klose K. Enhancing same day access to magnetic resonance imaging. J Am Coll Radiol 2011;8:649-56. Martin K, Osterling M. Metrics-based process mapping: identifying and eliminating waste in office and service processes. New York, New York: Productivity Press; 2012. Cima RR, Brown MJ, Hebl JR, et al; Surgical Process Improvement Team, Mayo Clinic, Rochester. Use of Lean and Six Sigma methodology to improve operating room efficiency in a high-volume tertiary-care academic medical center. J Am Coll Surg 2011;213:83-92. Bhat UN. An introduction to queueing theory. Boston: Birkhäuser; 2008.
Li Zhang, PhD, Karin Runzheimer, Elisabeth Bonifer, MD, Annika Keulers, MD, Eike Piechowiak, MD, and Andreas Mahnken, MD, are from the Department of Diagnostic and Interventional Radiology, University Hospital Giessen and Marburg, Philipps University of Marburg, Marburg, Germany. The authors have no conflicts of interest to disclose. Li Zhang, PhD: University Hospital Giessen and Marburg, Department of Diagnostic Radiology, Baldinger Strasse, 35033 Marburg, Germany; e-mail: [email protected].
4
Journal of the American College of Radiology Volume - n Number - n - 2015
Improving Efficiency of Interventional Service by Lean Six Sigma Li Zhang, PhD, Karin Runzheimer, RN, Elisabeth Bonifer, MD, Annika Keulers, MD, Eike Piechowiak, MD, Andreas Mahnken, MD INTRODUCTION TO THE PROBLEM WE ADDRESSED Interventional radiology (IR) suites are scarce resources that are often highly congested and identified as bottlenecks in patient flow. These bottlenecks cause delays in treatment that diminish care quality, increase the length of hospital stay and total inpatient costs, and are detrimental to both patients and health care providers [1]. Effective management of such delays can produce dramatic improvements in medical outcomes, patient satisfaction, and access to service, while reducing the costs of health care simultaneously. At most hospitals, however, accurate data on capacity, demand, and the extent of delay are not always available, and the relationships among these individual parameters are only partly understood. Often average capacity is incorrectly used to predict performance, quality, or timeliness. Long wait times for central venous access services and percutaneous gastrostomy perceived by referring physicians at our hospital made it urgent to improve our processes and to understand how capacity affects access to and delays in the aforementioned services. WHAT WE DID TO ADDRESS THE PROBLEM In the IR suite at our academic hospital (with about 1,200 beds),
488 cases of central venous access and percutaneous gastrostomy were performed in 2012, on working days from 8 AM to 1 PM. The actual start time for procedures is about 8:30 AM. After 1 PM, the room is used for fluoroscopic procedures, which were not the subject of this study. Both inpatients (61%) and outpatients (39%) were treated, solely by residents, who during this study possessed different degrees of professional experience. Generally a resident is replaced after 3 months of training by another inexperienced resident and supervised by a staff interventional radiologist. A Lean Six Sigma team, including radiologists and technologists, was established to address the problem [2]. Measurements in the IR suite were carried out at the beginning of the project and about 10 months later, in each case for 5 consecutive days. Measurements documented process steps, times, failures, and the physical motions of technologists. Cycle time and lead time were calculated from the measurement results. Cycle time is the length of patient stay in the IR suite plus the time for IR suite preparation. Lead time is the sum of cycle and waiting times between subprocesses. Eleven and 17 procedures measured individually for phase 1 and phase 2 were analyzed.
ª 2015 American College of Radiology 1546-1440/15/$36.00 n http://dx.doi.org/10.1016/j.jacr.2015.05.016
Cause-and-effect analysis and failure mode and effect analysis (FMEA) [3,4] were performed. Rolled percentage complete and accurate [5] was calculated. Value-stream mappings were carried out [6]. These analyses served to identify and eliminate process waste. Demand for procedures was evaluated by the number of procedures requested per day. Throughput was measured by the number of procedures performed per time unit on the basis of data from the radiology information system. The data were separately analyzed according to two project phases as described previously. The takt time is the daily room uptime divided by daily procedures requested. It is the rate at which venous access services and percutaneous gastrostomy should be provided to satisfy the need of referring physicians. The outcome quality of central venous access was measured by the number of procedures requiring explantations and revisions relative to the number of implantations. Performance parameters were calculated on the basis of demand and capacity by adopting an M/M/1 queuing model [7]. Parameters included utilization of the IR, the expected number of procedures in the waiting queue, the expected length of delay of delayed procedures, and service level. Data are presented as mean SD or as medians. Normal distribution
1
of data was verified by the AndersonDarling test and Poisson distribution by the goodness-of-fit test, with P values > .05 considered to indicate statistical significance. One-way analysis of variance was used to identify differences between population means. Inequality of two population medians was verified by using the Mann-Whitney test, with P values < .05 considered to indicate statistical significance. All statistical analyses were performed using Minitab version 15 (Minitab Inc, State College, Pennsylvania).
OUTCOMES An average takt time as defined before of 55 min was determined to satisfy 90% of daily demand, equivalent to a daily capacity of about 5.5 procedures. Current-state value-stream mapping revealed waiting times and improvement potential in process steps and determined average lead time and cycle time (Fig. 1). Causeand-effect analysis and FMEA identified 69 failure modes contributing to process failures. A total of 117 potential failure causes contributed to these modes. The percentage complete and accurate from order entry to
signed report was calculated to be 9% before improvement. The 69 failure modes were prioritized according to the principles of FMEA, and 46 were chosen, treated, or eliminated in the first iteration. In particular, the process of obtaining preoperative informed consent as legally required and the postoperative follow-up procedure for outpatients were established or optimized. Preoperative preparations such as obtaining results from blood testing and intravenous access were secured or performed outside the IR suite. A patient information sheet was provided to inform patients about the entire periprocedural process, to ensure that relevant patient preparations were done at home, to improve patients’ ontime arrival, and to reduce no-shows among outpatients. Fine-grained responsibilities of radiologists and technologists were redefined for the entire process. Standard operating procedures, including patient preparation, material needs, device positioning, and process flow, were refined. An additional staff member was added to support the IR technologist and to minimize changeover and setup times. The length and the number of scheduling time slots
were adjusted so that they could accommodate measured cycle time, including the training time needed for inexperienced residents and emergency requests. The Kanban method was introduced for securing the availability of materials, reducing the space needed for excess material stock, and eliminating wait times for collecting materials during a procedure. In addition, the procedure itself was refined, and some steps, such as routine cardiac monitoring, were eliminated if safely feasible. The sequence of disinfection and sterile draping was reorganized, and the positions of devices and medications were optimized to reduce the motions of IR technologists.
Comparison of Demand, Throughput, and Delay Between the Two Project Phases Daily demand and throughput of procedures have been determined from radiology information system data. Analysis of variance demonstrated no statistical difference in monthly demand between the two project phases (P ¼ .42). Demand followed a Poisson distribution (P ¼ .10), with a Poisson mean of
Fig 1. Current value-stream mapping. The symbols denoted as Kaizen identify process steps to be improved. Triangles indicate waiting-time waste to be removed.
2
Journal of the American College of Radiology Volume - n Number - n - 2015
3.06 procedures/day. The cumulative distribution function of demand showed that a capacity of 5.5 procedures per day would cover 90% of daily demand. Daily throughput, however, is constrained by IR suite uptime and lead time. Because uptime is limited to 300 min/day, the maximum possible daily throughput was three procedures in project phase 1. Taking the late start of about 30 min into account, the realistic maximum average throughput in project phase 1 was expected to be less than 2.7. The actual daily median throughput values were 3.2 and 3.6 procedures/day during project phases 1 and 2, respectively (P ¼ .08).
Improved Value Stream Mapping, Cycle Time, and Potential Capacity Key to the effort toward improvement was enabling timely and highquality procedures to flow smoothly throughout our process. Minimizing process variation and eliminating waste were thus the major tasks in the process redesign. Mean cycle time decreased from 75 7 to 53 9 min (P < .00) in project phase 2, a significant reduction of 28%. Dividing cycle time period into different segments, as shown in Table 1, it is obvious that the major reduction of cycle time (11 min) from phase 1 to phase 2 could be attributed to steps occurring before skin incision. The cycle time of the steps performed solely by the radiologist was reduced from 29 to 23 min. There were no relevant differences in the procedural steps taken before the radiologist entered the IR suite or after skin suture (Table 1). The reduction in cycle time led to a decrease in lead time. With room uptime being 300 min, the potential capacity after the Lean Six Sigma improvement was 4.6 procedures/day, an increase of 52%
Table 1. Comparison of process cycle time in the two project phases Variable Time from patient entry to radiologist entry Time from radiologist entry to skin incision Time from skin incision to sticking plaster Time from skin suture to patient exit Room preparation time
Phase 1 (min) 10 (9)
Phase 2 (min) 7 (5)
Change
P .308*
27 (6)
16 (5)
40%
.000*
29 (8)
23 (8)
21%
.035*
6 2
5.5 1
.936† —
*Analysis of variance. † Mann-Whitney test.
compared with the capacity of 3.0 procedures in project phase 1. Taking the late start in the morning into consideration, the realistic maximum daily throughput after improvement was 4.1 procedures/day. The outcome quality of central venous access showed no change between the two project phases (P ¼ .14).
Relationship Among Demand, Capacity, and Service Level On the basis of the M/M/1 queuing model, the following performance parameters could be calculated. In
phase 1, the IR suite exhibited a utilization rate of 95%, with 19 procedures constantly on the waiting list. The average expected delay to the procedure was about 6.4 working days. Only 19% of procedures were carried out within 2 days, as expected by referring physicians. In phase 2, room utilization decreased by 9% to 86%. During the same time period, the number of procedures waiting in the queue decreased from 19 to 5.4, a decrease of 72%, and the expected delay decreased from 6.4 to 2.1 days, a
Fig 2. Relationship of number of requests waiting (Lq), expected length of delay, interventional radiology suite utilization (percentage), and service level as described by an M/M/1 queuing model with a demand rate of 3.06 procedures. At 3.1 procedures/day, the system overflows, the number of procedures waiting in the queue can equal or exceed 70, and the length of delay reaches 25 working days, resulting in an extremely congested system. A moderate increase in capacity of about 13% (ie, from 3.1 to 3.5 procedures/day) decreases queue length from more than 70 to 6 procedures (a 91% reduction) and the expected length of delay from 25 to about 2 days (a 92% reduction). The service level increases exponentially with increasing capacity (service rate).
Journal of the American College of Radiology Zhang et al n Case Studies in Clinical Practice Management
3
decrease of 67%. At the same time, the service level improved from 19% to 47%. A remarkable impact of the capacity in terms of service rate on waiting was observed, especially in the range of 3.1 to 3.5 procedures per day (Fig. 2). A 13% increase in capacity, achieved by reducing lead time (eg, from 100 to 86 min), led to an exponential reduction in waiting time.
CONCLUSIONS We found in this study that the application of Lean Six Sigma improved efficiency in our IR suite. The approach used is applicable to any IR environment because the aim
of the undertaking was to eliminate process delays and failures, which are present in many IR suites. The extra capacity gained in this way has had a dramatic effect on access and on wait times. Providing proper capacity through process improvement or resource allocation is critical to maintaining a short waiting list.
ACKNOWLEDGMENTS We thank Anne-Kathrin Reuter, Anna-Christina Stamm, Marga Rominger, and Mariana Gurschi for their partial participation in this project.
2. 3.
4.
5.
6.
REFERENCES 1. Belsori D. Managing a patient flow improvement project. In: Hall RW, ed. Patient
7.
flow: reducing delay in healthcare delivery. New York, New York: Springer; 2006. Turney J. Six Sigma and Lean Six Sigma. Radiol Technol 2007;79:191-2. Abujudeh HH, Kaewlai R. Radiology failure mode and effect analysis: what is it? Radiology 2009;252:544-50. Zhang L, Hefke A, Figiel J, Romminger M, Klose K. Enhancing same day access to magnetic resonance imaging. J Am Coll Radiol 2011;8:649-56. Martin K, Osterling M. Metrics-based process mapping: identifying and eliminating waste in office and service processes. New York, New York: Productivity Press; 2012. Cima RR, Brown MJ, Hebl JR, et al; Surgical Process Improvement Team, Mayo Clinic, Rochester. Use of Lean and Six Sigma methodology to improve operating room efficiency in a high-volume tertiary-care academic medical center. J Am Coll Surg 2011;213:83-92. Bhat UN. An introduction to queueing theory. Boston: Birkhäuser; 2008.
Li Zhang, PhD, Karin Runzheimer, Elisabeth Bonifer, MD, Annika Keulers, MD, Eike Piechowiak, MD, and Andreas Mahnken, MD, are from the Department of Diagnostic and Interventional Radiology, University Hospital Giessen and Marburg, Philipps University of Marburg, Marburg, Germany. The authors have no conflicts of interest to disclose. Li Zhang, PhD: University Hospital Giessen and Marburg, Department of Diagnostic Radiology, Baldinger Strasse, 35033 Marburg, Germany; e-mail: [email protected].
4
Journal of the American College of Radiology Volume - n Number - n - 2015