- Email: [email protected]
Improving Operational Efficiency in the Emergency Department—The Children's Hospital of Michigan Experience
Abstract: Efficiency, the ability to accomplish a task with a minimum expenditure of time and effort, is essential in any emergency department (ED). Traditional quality of care measures evaluate systems using individual patient data and aggregate these data to identify opportunities for improvement. Operational efficiency (OE) tools examine population-based data and focus on the processes that impact systems performance. This macroperspective facilitates the delivery of the greatest care to the greatest number of patients. In view of the challenges faced in measuring quality of care to a population, it is imperative to accept that OE is a critical driver in determining quality of care. This article will describe the OE model used at the Children's Hospital of Michigan to change an existing system in a tertiary pediatric ED. Our work resulted in reducing ED admission time by 83%, the left without being seen rate by 91%, and the ED length of stay by 48%; improved the door-to-doctor time; and eliminated waiting room deaths.
Improving Operational Efficiency in the Emergency Department— The Children's Hospital of Michigan Experience
Keywords: emergency department; operational efficiency; community focused care; performance improvement; throughput *Pediatrics and Emergency Medicine, Wayne State University; †Pediatric Emergency Medicine, Children's Hospital of Michigan, Detroit, MI; ‡Emergency Medicine and Pediatrics, Johns Hopkins Emergency Medical Services, Howard County General Hospital, Columbia, MD; §Pediatric Emergency Department, Children's Hospital of Michigan, Detroit, Michigan. Reprints requests and correspondence: Stephen R. Knazik, DO, MBA, Children's Hospital of Michigan, Emergency
Stephen R. Knazik, DO, MBA*†‡, Kathleen De Baker, RN, MSN, CPNP§
O
ne might define quality emergency care as delivering the greatest good to the greatest number of patients when resources are constrained. Therefore, quality metrics used in emergency medicine focus on both threshold metrics (eg, patient safety monitoring of near-miss events for individual patients) and population-based metrics (eg, the acceptable rate of compliance with evidence-based guidelines). The parameter, efficiency, and the accomplishment of (or ability to accomplish) a task with a minimum expenditure of time and effort can thus be applied to systems performance in emergency medicine by using the business term, operational efficiency (OE). As we use this definition, it
OPERATIONAL EFFICIENCY IN THE EMERGENCY DEPARTMENT / KNAZIK AND DE BAKER • VOL. 12, NO. 2 133
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Department, 3901 Beaubien Boulevard, Detroit, Michigan 48201. [email protected]
1522-8401/$ - see front matter © 2011 Elsevier Inc. All rights reserved.
is intentionally broader than that used by the Institute of Medicine (IOM) where, in describing the 6 domains of quality, efficiency is limited to merely avoiding waste.1 It is imperative to accept that OE is a critical driver in delivering timely high-quality care to patients with acute presentations of injury and illness. Timely segmentation of emergency care permits diagnostic and therapeutic moments of opportunity that simply do not exist when care is delayed. Extreme examples might be the providing defibrillation, or performing a testicular flow study for a patient with testicular torsion. Thus, in our emergency department (ED), we tie OE with traditional views of quality in a unique way that refutes the unfortunate reluctance of many both within and outside our field to view OE as a tool valid only to address patient satisfaction, rather than as an overarching principle intrinsic to our practice. We confirmed, before the IOM articulated the 6 domains of quality, that OE, comprising the 2 IOM domains of efficient and timely, is interdependent with the other 4 domains of safe, effective, equitable, and patient centered. The special challenge in the ED is that volume, arrival times, acuity, and specific patients' or families' needs are highly variable. The opportunity that this presents is that we may use macrotools to evaluate department-specific data to model flow. Patient arrivals and ED utilization may be represented using simulations based on a Poisson distribution (Figure 1). This formula has been restated by Professor Charles Noon at The University of Tennessee College of Business Administration, making it usable for simulation modeling. Hence, knowing the rate of patient arrivals (λ) over a defined period of time, how long each increment of service takes, the number of service
Figure 1. Poisson distribution formula.
providers (k), and the acceptable threshold of performance allows careful analysis of data to define the component parts of an overall process. A simulation tool using these principles can be used to predict examination room use, staffing, laboratory or radiology turnaround time, patient transport times, environmental service needs, discharge process tasks, and computerized order entry, to name but a few. For our change process, this was key to understanding our systems and our data, as well as to predict, measure, and justify each attempt at change. The following will describe one OE model and the methods used to change an existing system using both traditions, quality and operational tools, to effect performance improvement and monitor quality in a pediatric ED in an urban tertiary pediatric hospital.
MEASURE We examined our system in great detail to try to determine what we could measure and thus change. It was clear that we first needed to streamline our entry registration process because this was not value added and was extremely laborious. We timed staff in the performance of registration. As a team, we determined what essential information was necessary to initiate care and limited entry registration (mini-reg) to these data only. We also examined each step of our existing process for each patient segment to determine if opportunities existed differentially for each group. Our initial findings revealed that our problems were a mix of process and physical constraints. To our delight, the greatest constraint to throughput appeared to be room capacity. This changed our use of simulation in that the key service time metric was room turnover, not physician encounter time. Our solution was to optimize our admission process as a means to optimize room utilization. Using the patient/family experience as our filter, we decided that there were few time-sensitive processes that required a delay in bed placement that would be experienced by a family. For example, we studied 6 months of admission data to determine that we delayed more than 6000 inpatient admissions due to an experience limited to only 2 patients whose insurance required preauthorization. Similar investigation led to the conclusion that silo admission thinking from specialty services was delaying bed placement decisions, even to the detriment of patients served by those specialty services. This desegmentation of admissions resulted in more flexibility and improved overall systems performance by providing an opportunity
OPERATIONAL EFFICIENCY IN THE EMERGENCY DEPARTMENT / KNAZIK AND DE BAKER • VOL. 12, NO. 2 135
Figure 2. Kotter's principles of change. Data from reference.3
for more parallel and fewer serial processes. This was accurately predicted by our simulation model. Only after room use was optimized could we then address the subordinate factors of physician medical decision-making time, staffing, and information flow metrics. Our other quality concerns served as checks on our performance for unintended consequences of change such as patient and family satisfaction, return visits within 72 hours, and ED staff satisfaction. These balancing measures ensured that all quality measures were aligned with each change.
IMPROVE/INTERVENE Traditional operational metrics can be viewed as a series of constraint analyses.2 When each choke point that hinders efficiency in a serial process can be identified, they can then be addressed, tested, refined, and change implemented. Finding the key constraints requires specific knowledge gleaned from enlightened experts, be they frontline staff, administrators, or consultants. Ideally, each factor identified
can be addressed in order of impact on the process and inversely with respect to negative consequences of change. Although incremental changes can be made by easy transitions, which may not have major impact on the overall process, the impact of these changes is typically limited. Therefore, planning for major operational change requires the determination of the major impediment to efficiency and addressing that aspect first. Subsequent change efforts can then target those next in importance. This represents classic constraint theory. Sometimes, local culture and other leadership decisions may affect the order of processes tested and refined. Some of the leadership reasons for this may be found in John Kotter's3,4 work on strategies for organizational change (Figure 2). For example, to get buy-in from registration staff, their input and their impact on care delivery must be valued. Likewise, this staff must be enabled with the means to improve specific features of flow and create short-term wins. Process redesign must include the community in a meaningful way to reinforce the vision of change, despite the
136
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perception of inefficiency inherent in large group input. Urgency can be created by emphasizing cost and revenue enhancements anticipated with portions of the larger project. The guiding team must be relentless in planning for the next improvement even before existing projects are completed to ensure organizational momentum. Making process change the organizational way of thinking and OE a part of the culture requires putting the right people in place and enabling them to try change.
TOOLS USED TO EVALUATE QUALITY AND EFFECT CHANGE We felt that success could only be achieved with the complete support of senior hospital administration, and enjoining hospital-wide staff and senior administration in each phase of this project was essential. This was achieved through collegiality and by clearly and repeatedly stating institutional benefit. For some in senior administration, this was best accomplished by focusing on the potential financial benefits, whereas for others, the driver was community responsibility and patient satisfaction, and for some, the traditional quality improvement approach worked best. Capitalizing on a deep understanding of the Poisson's distribution and its effect on throughput, we used simulation to predict flow through constrained systems. Rigorously based on Poisson's law of random events, this simulation tool accurately captured many critical components of our processes identified by key stakeholders that simply would have been overwhelmingly complex to understand without its use. We became experts in communicating this to various stakeholders. The very nature of restating problems in a mathematically predictable plus experimentally verifiable way facilitated this communication and lent credibility and trust to our initial efforts, which we were able to translate into process improvement across our organization. We also used traditional medical quality and business metric methods to accurately measure
Plan-Do-Check-Act Cycle Plan Do
Act Check
Figure 3. The PDCA cycle.
change and identify causes, including rapid cycle testing, the Plan-Do-Check-Act (PDCA) cycle of improvement, Pareto charts, and extensive statistical analysis of each discrete change process with verification from expert stakeholders that we were measuring that which was essential and causal, and not just associated events. Rapid cycle testing (one patient, one process, one time, and then analyze for modifications) worked best for simple steps, such as where to deploy furniture in triage space. The PDCA cycle (Figure 3) was used to trial change for a variety of processes including room cleaning, bed tracking, and patient transport. These process improvements were transparent, published, and discussed at many levels of the organization for verification of validity and to discern other opportunities for improvement. Pareto charts use a diagrammatic way of identifying timing of steps in serial processes to determine the time contribution of each step to the overall process. These charts then became a communication tool allowing many eyes to examine complex ED care functions for opportunities. An example was tracking representative patients for certain segments, such as acuity, specialty services needed, or specific special needs, through our admissions process with times for each step indicated, so that constraint opportunities were highlighted. These same tools were used to monitor performance and identify new opportunities to improve quality. Importantly, these OE quality measures were not the only monitored parameters in our ED at the time. Other metrics included patient/ family satisfaction data, care alignment with multiple clinical guidelines, 72-hour return visits, morbidity and mortality, data interpretation errors, transfers out, appropriate use of consultants, and procedural sedation.
RESULTS Implementing these process changes occurred over a 5-year span from 2000 to 2005. The setting was an urban pediatric ED with more than 60 000 visits in 2001, within a 235-bed facility, with an average daily census of 175. Our ED OE markers in 2001 included an average ED length of stay (LOS) of 295 minutes, average decision to admit to arrival on the inpatient unit of 606 minutes, and annual left without being seen (LWBS) rate of 7.95%. An average of 60% of patients were seen within 29 minutes of arrival at the beginning of 2004. Importantly, in 2001, we experienced the death of 2 children, which we identified as possibly preventable if there had been no delay in delivering ED
OPERATIONAL EFFICIENCY IN THE EMERGENCY DEPARTMENT / KNAZIK AND DE BAKER • VOL. 12, NO. 2 137
Figure 4. Children's Hospital of Michigan ED annual volume, 1999-2009.
services after their arrival. These deaths were partly attributable to ED crowding precluding examination room placement and timely assessment, exactly our focus of improving OE. We analyzed the profile of our LWBS patients by individually contacting each family by telephone and determined that 18% of our LWBS patients required admission to an acute care hospital within 48 hours of leaving our institution, compared with our admission rate of 11.3% for patients who stayed to be seen. Such data dispelled our organization's widely held belief that those that left were less acutely ill and that families left because they were convenience shopping. Inquiry and rigorous science found that the exact opposite was true; families departed to seek more expeditious care because they believed their child was too sick to wait. Before embarking on our change process, we performed a gap analysis. We did this by seeking national benchmark data. We examined best practices at the time to determine what was operationally possible. Where appropriate, we used our national contacts to probe into systems approaches that were working at other centers as well as the operational steps to implement change. It was immediately evident that ED crowding, a hospital-wide problem, was a significant and integral opportunity for improving OE. Concurrent with these changes, our directed opportunities to improve OE in the ED provided examples, fostered rapid cycle projects, and allowed field testing of initiatives that could be
translated to the hospital as a whole, such as minireg, bedside registration, the use of an electronic tracking board, and the use of access managers to facilitate placement decisions in the ED.
GOALS OF CHANGE PROJECT We chose the following 5 metrics as important OE and care quality markers: • Reduce the interval of decision to admit to patient placement in an inpatient bed • Reduce the LWBS rate • Reduce the ED LOS
TABLE 1. Key OE performance metrics prechange and postchange. Prechange Admit to bed, average LWBS rate ED LOS Door-to-doctor time in b29 min Waiting room deaths
Immediate Postchange
606 min 7.95% (2000) 295 min (2002) 59.54% (2004)
102 min 0.68% (2005) 151 min (2005) 93.03% (2005)
2 in 1 y
0 in 10 y
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Figure 5. Emergency department LOS, 2002-2008.
• Achieve an acceptable rate of door to doctor (arrival to physician examination) within 29 minutes • No deaths attributable to prolonged wait Like most EDs in America, we have experienced an explosion of growth (Figure 4). Despite this growth, which was predicted by leadership, we were able to achieve significant change in key performance indicators. As shown in Table 1, we achieved reductions in the time interval from ED admission decision to inpatient bed placement by 83%, reduced the LWBS rate by 91% and the ED LOS by 48%. We also improved the rate of b 29 minute doorto-doctor time and eliminated waiting room deaths. All metrics have shown sustained improvement over time. An example of optimized performance for ED LOS is depicted in Figure 5. Figure 6 demonstrates sustained performance improvement achieved for admission times. Because the parameter LWBS is highly volume dependent, it will
Figure 6. Average times for inpatient admissions from the ED.
exhibit a wide variation from summer to winter, as can be seen in Figure 7. This operations performance information has been shared monthly with all ED staff and hospital system administration. Being able to have patients seen by a physician within 29 minutes in all of our hospital system's EDs became a 7-hospital challenge in 2004. This metric continues to be a significant service expectation and driver of marketing and thus was added as a performance measure in 2004. This reworked process has been tracked daily and is widely shared with staff in all 7 centers from the ED to the boardroom. Much like LWBS, this metric is also volume driven, with seasonal fluctuations amidst improvement(Figure 8).
SUMMARY: LESSONS LEARNED 1. Rigorous stakeholder-specific data that are transparent, verifiable, and important must be identified, validated, and valued as a marker for quality. Articulating data to staff and stakeholders needs to be customized using language specific to task and talent. It is incumbent to frame the question and results to those empowered to change or support change. 2. Operational efficiency and quality tools are interrelated in achieving both traditional quality and business goals in emergency medicine. These reflect the interdependence of precepts of OE and traditional quality efforts. Cross-discipline measures and metrics allow integration among stakeholders, opening avenues to organizational change. 3. For those to whom quality matters, each business goal must be communicated with its corresponding quality implication attached, just as quality goals must be tied to business possibilities in most hospital
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20.00% 18.00% 16.00%
LWBS monthly tracking
14.00% 12.00% 10.00% 8.00% 6.00% 4.00% 2.00%
Sep
May
Sep
Jan-09
May
Sep
8-Jan
May
Sep
7-Jan
May
Sep
6-Jan
May
Sep
5-Jan
Jan
May
Sep
May
Sep
3-Jan
May
Sep
Jan-02
May
Sep
Jan-01
May
Jan-00
0.00%
Figure 7. Emergency department patient LWBS rates, 2000-2010.
discussions. None of these operational changes were implemented in a quality vacuum. We continued and expanded traditional quality measures during this time including alignment with existing pathways,
improving accuracy of medical data interpretation, morbidity and mortality review, utilization and timeliness of consultations, transfers of care, procedural sedation, and regulatory compliance.
120.00%
100.00%
80.00%
60.00%
40.00%
29 minute Door to Doc 20.00%
Au g N ov Fe b M ay Au g N ov Fe b M ay Au g N ov Fe b M ay Au g N ov Fe b M ay Au g N ov Fe b M ay Au g N ov
0.00%
Figure 8. Percent of patients meeting the 29-minute door-to doctor interval metric.
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4. Emphasis was placed on communication with administrators who are key and yet far removed from operations in the ED. A key point with our business and satisfaction stakeholders is the concept of the optimal LOS rather than merely shortening LOS. Presented in the discussion of the acute care model are strategies for segmenting care designed to achieve the twin goals of fast things being fast and slow things being slow. Some emergency decisions do and should take time, such as those requiring serial evaluations of response to treatment. An OE principle is to reduce wasted time, thus speeding care for “snapshot” problems, those requiring recognition and a resultant quick disposition, as well as those “movie problems,” where elapsed time is integral to best medical and business practice including the appropriate use of inpatient and observation resources. These concepts provide an opportunity to use the 3 W's of Wit, Wisdom, and War stories to engage diverse stakeholders in ways that are meaningful to them. 5. Using modeling and simulations that are accurate and mathematically neat is crucial to understanding complex systems where constraints are involved. These tools allow an individual or small group to predict
outcomes of interventions before asking staff to change operations. The simulation inputs need to be simple and easily understood for communicating validity.
ACKNOWLEDGMENTS The authors wish to thank the senior administration at Children's Hospital of Michigan who were directly involved as well as those from the Detroit Medical Center, without whose support the component projects to improve our care would not have been possible. Special thanks to our interdisciplinary team of champions from across the hospital's operating units who gave tirelessly for a sustained number of years to implement and cement change that continues to benefit the children and families of our community.
REFERENCES 1. Institute of Medicine Committee on Quality of Healthcare in America. Crossing the quality chasm: a new health system for the 21st century. Washington, DC: National Academies Press; 2001. 2. Goldratt EM, Cox J. The goal: a process of ongoing improvement. Great Barrington, MA: North River Press; 2004. 3. Kotter JP. Leading change. Boston, MA: Harvard Business School Press; 1996. 4. Kotter International. Eight steps for leading change. Available at: http://www.kotterinternational.com/kotterprinciples/ ChangeSteps.aspx. Accessed May, 2011.
Improving Operational Efficiency in the Emergency Department— The Children's Hospital of Michigan Experience
Keywords: emergency department; operational efficiency; community focused care; performance improvement; throughput *Pediatrics and Emergency Medicine, Wayne State University; †Pediatric Emergency Medicine, Children's Hospital of Michigan, Detroit, MI; ‡Emergency Medicine and Pediatrics, Johns Hopkins Emergency Medical Services, Howard County General Hospital, Columbia, MD; §Pediatric Emergency Department, Children's Hospital of Michigan, Detroit, Michigan. Reprints requests and correspondence: Stephen R. Knazik, DO, MBA, Children's Hospital of Michigan, Emergency
Stephen R. Knazik, DO, MBA*†‡, Kathleen De Baker, RN, MSN, CPNP§
O
ne might define quality emergency care as delivering the greatest good to the greatest number of patients when resources are constrained. Therefore, quality metrics used in emergency medicine focus on both threshold metrics (eg, patient safety monitoring of near-miss events for individual patients) and population-based metrics (eg, the acceptable rate of compliance with evidence-based guidelines). The parameter, efficiency, and the accomplishment of (or ability to accomplish) a task with a minimum expenditure of time and effort can thus be applied to systems performance in emergency medicine by using the business term, operational efficiency (OE). As we use this definition, it
OPERATIONAL EFFICIENCY IN THE EMERGENCY DEPARTMENT / KNAZIK AND DE BAKER • VOL. 12, NO. 2 133
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VOL. 12, NO. 2 • OPERATIONAL EFFICIENCY IN THE EMERGENCY DEPARTMENT / KNAZIK AND DE BAKER
Department, 3901 Beaubien Boulevard, Detroit, Michigan 48201. [email protected]
1522-8401/$ - see front matter © 2011 Elsevier Inc. All rights reserved.
is intentionally broader than that used by the Institute of Medicine (IOM) where, in describing the 6 domains of quality, efficiency is limited to merely avoiding waste.1 It is imperative to accept that OE is a critical driver in delivering timely high-quality care to patients with acute presentations of injury and illness. Timely segmentation of emergency care permits diagnostic and therapeutic moments of opportunity that simply do not exist when care is delayed. Extreme examples might be the providing defibrillation, or performing a testicular flow study for a patient with testicular torsion. Thus, in our emergency department (ED), we tie OE with traditional views of quality in a unique way that refutes the unfortunate reluctance of many both within and outside our field to view OE as a tool valid only to address patient satisfaction, rather than as an overarching principle intrinsic to our practice. We confirmed, before the IOM articulated the 6 domains of quality, that OE, comprising the 2 IOM domains of efficient and timely, is interdependent with the other 4 domains of safe, effective, equitable, and patient centered. The special challenge in the ED is that volume, arrival times, acuity, and specific patients' or families' needs are highly variable. The opportunity that this presents is that we may use macrotools to evaluate department-specific data to model flow. Patient arrivals and ED utilization may be represented using simulations based on a Poisson distribution (Figure 1). This formula has been restated by Professor Charles Noon at The University of Tennessee College of Business Administration, making it usable for simulation modeling. Hence, knowing the rate of patient arrivals (λ) over a defined period of time, how long each increment of service takes, the number of service
Figure 1. Poisson distribution formula.
providers (k), and the acceptable threshold of performance allows careful analysis of data to define the component parts of an overall process. A simulation tool using these principles can be used to predict examination room use, staffing, laboratory or radiology turnaround time, patient transport times, environmental service needs, discharge process tasks, and computerized order entry, to name but a few. For our change process, this was key to understanding our systems and our data, as well as to predict, measure, and justify each attempt at change. The following will describe one OE model and the methods used to change an existing system using both traditions, quality and operational tools, to effect performance improvement and monitor quality in a pediatric ED in an urban tertiary pediatric hospital.
MEASURE We examined our system in great detail to try to determine what we could measure and thus change. It was clear that we first needed to streamline our entry registration process because this was not value added and was extremely laborious. We timed staff in the performance of registration. As a team, we determined what essential information was necessary to initiate care and limited entry registration (mini-reg) to these data only. We also examined each step of our existing process for each patient segment to determine if opportunities existed differentially for each group. Our initial findings revealed that our problems were a mix of process and physical constraints. To our delight, the greatest constraint to throughput appeared to be room capacity. This changed our use of simulation in that the key service time metric was room turnover, not physician encounter time. Our solution was to optimize our admission process as a means to optimize room utilization. Using the patient/family experience as our filter, we decided that there were few time-sensitive processes that required a delay in bed placement that would be experienced by a family. For example, we studied 6 months of admission data to determine that we delayed more than 6000 inpatient admissions due to an experience limited to only 2 patients whose insurance required preauthorization. Similar investigation led to the conclusion that silo admission thinking from specialty services was delaying bed placement decisions, even to the detriment of patients served by those specialty services. This desegmentation of admissions resulted in more flexibility and improved overall systems performance by providing an opportunity
OPERATIONAL EFFICIENCY IN THE EMERGENCY DEPARTMENT / KNAZIK AND DE BAKER • VOL. 12, NO. 2 135
Figure 2. Kotter's principles of change. Data from reference.3
for more parallel and fewer serial processes. This was accurately predicted by our simulation model. Only after room use was optimized could we then address the subordinate factors of physician medical decision-making time, staffing, and information flow metrics. Our other quality concerns served as checks on our performance for unintended consequences of change such as patient and family satisfaction, return visits within 72 hours, and ED staff satisfaction. These balancing measures ensured that all quality measures were aligned with each change.
IMPROVE/INTERVENE Traditional operational metrics can be viewed as a series of constraint analyses.2 When each choke point that hinders efficiency in a serial process can be identified, they can then be addressed, tested, refined, and change implemented. Finding the key constraints requires specific knowledge gleaned from enlightened experts, be they frontline staff, administrators, or consultants. Ideally, each factor identified
can be addressed in order of impact on the process and inversely with respect to negative consequences of change. Although incremental changes can be made by easy transitions, which may not have major impact on the overall process, the impact of these changes is typically limited. Therefore, planning for major operational change requires the determination of the major impediment to efficiency and addressing that aspect first. Subsequent change efforts can then target those next in importance. This represents classic constraint theory. Sometimes, local culture and other leadership decisions may affect the order of processes tested and refined. Some of the leadership reasons for this may be found in John Kotter's3,4 work on strategies for organizational change (Figure 2). For example, to get buy-in from registration staff, their input and their impact on care delivery must be valued. Likewise, this staff must be enabled with the means to improve specific features of flow and create short-term wins. Process redesign must include the community in a meaningful way to reinforce the vision of change, despite the
136
VOL. 12, NO. 2 • OPERATIONAL EFFICIENCY IN THE EMERGENCY DEPARTMENT / KNAZIK AND DE BAKER
perception of inefficiency inherent in large group input. Urgency can be created by emphasizing cost and revenue enhancements anticipated with portions of the larger project. The guiding team must be relentless in planning for the next improvement even before existing projects are completed to ensure organizational momentum. Making process change the organizational way of thinking and OE a part of the culture requires putting the right people in place and enabling them to try change.
TOOLS USED TO EVALUATE QUALITY AND EFFECT CHANGE We felt that success could only be achieved with the complete support of senior hospital administration, and enjoining hospital-wide staff and senior administration in each phase of this project was essential. This was achieved through collegiality and by clearly and repeatedly stating institutional benefit. For some in senior administration, this was best accomplished by focusing on the potential financial benefits, whereas for others, the driver was community responsibility and patient satisfaction, and for some, the traditional quality improvement approach worked best. Capitalizing on a deep understanding of the Poisson's distribution and its effect on throughput, we used simulation to predict flow through constrained systems. Rigorously based on Poisson's law of random events, this simulation tool accurately captured many critical components of our processes identified by key stakeholders that simply would have been overwhelmingly complex to understand without its use. We became experts in communicating this to various stakeholders. The very nature of restating problems in a mathematically predictable plus experimentally verifiable way facilitated this communication and lent credibility and trust to our initial efforts, which we were able to translate into process improvement across our organization. We also used traditional medical quality and business metric methods to accurately measure
Plan-Do-Check-Act Cycle Plan Do
Act Check
Figure 3. The PDCA cycle.
change and identify causes, including rapid cycle testing, the Plan-Do-Check-Act (PDCA) cycle of improvement, Pareto charts, and extensive statistical analysis of each discrete change process with verification from expert stakeholders that we were measuring that which was essential and causal, and not just associated events. Rapid cycle testing (one patient, one process, one time, and then analyze for modifications) worked best for simple steps, such as where to deploy furniture in triage space. The PDCA cycle (Figure 3) was used to trial change for a variety of processes including room cleaning, bed tracking, and patient transport. These process improvements were transparent, published, and discussed at many levels of the organization for verification of validity and to discern other opportunities for improvement. Pareto charts use a diagrammatic way of identifying timing of steps in serial processes to determine the time contribution of each step to the overall process. These charts then became a communication tool allowing many eyes to examine complex ED care functions for opportunities. An example was tracking representative patients for certain segments, such as acuity, specialty services needed, or specific special needs, through our admissions process with times for each step indicated, so that constraint opportunities were highlighted. These same tools were used to monitor performance and identify new opportunities to improve quality. Importantly, these OE quality measures were not the only monitored parameters in our ED at the time. Other metrics included patient/ family satisfaction data, care alignment with multiple clinical guidelines, 72-hour return visits, morbidity and mortality, data interpretation errors, transfers out, appropriate use of consultants, and procedural sedation.
RESULTS Implementing these process changes occurred over a 5-year span from 2000 to 2005. The setting was an urban pediatric ED with more than 60 000 visits in 2001, within a 235-bed facility, with an average daily census of 175. Our ED OE markers in 2001 included an average ED length of stay (LOS) of 295 minutes, average decision to admit to arrival on the inpatient unit of 606 minutes, and annual left without being seen (LWBS) rate of 7.95%. An average of 60% of patients were seen within 29 minutes of arrival at the beginning of 2004. Importantly, in 2001, we experienced the death of 2 children, which we identified as possibly preventable if there had been no delay in delivering ED
OPERATIONAL EFFICIENCY IN THE EMERGENCY DEPARTMENT / KNAZIK AND DE BAKER • VOL. 12, NO. 2 137
Figure 4. Children's Hospital of Michigan ED annual volume, 1999-2009.
services after their arrival. These deaths were partly attributable to ED crowding precluding examination room placement and timely assessment, exactly our focus of improving OE. We analyzed the profile of our LWBS patients by individually contacting each family by telephone and determined that 18% of our LWBS patients required admission to an acute care hospital within 48 hours of leaving our institution, compared with our admission rate of 11.3% for patients who stayed to be seen. Such data dispelled our organization's widely held belief that those that left were less acutely ill and that families left because they were convenience shopping. Inquiry and rigorous science found that the exact opposite was true; families departed to seek more expeditious care because they believed their child was too sick to wait. Before embarking on our change process, we performed a gap analysis. We did this by seeking national benchmark data. We examined best practices at the time to determine what was operationally possible. Where appropriate, we used our national contacts to probe into systems approaches that were working at other centers as well as the operational steps to implement change. It was immediately evident that ED crowding, a hospital-wide problem, was a significant and integral opportunity for improving OE. Concurrent with these changes, our directed opportunities to improve OE in the ED provided examples, fostered rapid cycle projects, and allowed field testing of initiatives that could be
translated to the hospital as a whole, such as minireg, bedside registration, the use of an electronic tracking board, and the use of access managers to facilitate placement decisions in the ED.
GOALS OF CHANGE PROJECT We chose the following 5 metrics as important OE and care quality markers: • Reduce the interval of decision to admit to patient placement in an inpatient bed • Reduce the LWBS rate • Reduce the ED LOS
TABLE 1. Key OE performance metrics prechange and postchange. Prechange Admit to bed, average LWBS rate ED LOS Door-to-doctor time in b29 min Waiting room deaths
Immediate Postchange
606 min 7.95% (2000) 295 min (2002) 59.54% (2004)
102 min 0.68% (2005) 151 min (2005) 93.03% (2005)
2 in 1 y
0 in 10 y
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VOL. 12, NO. 2 • OPERATIONAL EFFICIENCY IN THE EMERGENCY DEPARTMENT / KNAZIK AND DE BAKER
Figure 5. Emergency department LOS, 2002-2008.
• Achieve an acceptable rate of door to doctor (arrival to physician examination) within 29 minutes • No deaths attributable to prolonged wait Like most EDs in America, we have experienced an explosion of growth (Figure 4). Despite this growth, which was predicted by leadership, we were able to achieve significant change in key performance indicators. As shown in Table 1, we achieved reductions in the time interval from ED admission decision to inpatient bed placement by 83%, reduced the LWBS rate by 91% and the ED LOS by 48%. We also improved the rate of b 29 minute doorto-doctor time and eliminated waiting room deaths. All metrics have shown sustained improvement over time. An example of optimized performance for ED LOS is depicted in Figure 5. Figure 6 demonstrates sustained performance improvement achieved for admission times. Because the parameter LWBS is highly volume dependent, it will
Figure 6. Average times for inpatient admissions from the ED.
exhibit a wide variation from summer to winter, as can be seen in Figure 7. This operations performance information has been shared monthly with all ED staff and hospital system administration. Being able to have patients seen by a physician within 29 minutes in all of our hospital system's EDs became a 7-hospital challenge in 2004. This metric continues to be a significant service expectation and driver of marketing and thus was added as a performance measure in 2004. This reworked process has been tracked daily and is widely shared with staff in all 7 centers from the ED to the boardroom. Much like LWBS, this metric is also volume driven, with seasonal fluctuations amidst improvement(Figure 8).
SUMMARY: LESSONS LEARNED 1. Rigorous stakeholder-specific data that are transparent, verifiable, and important must be identified, validated, and valued as a marker for quality. Articulating data to staff and stakeholders needs to be customized using language specific to task and talent. It is incumbent to frame the question and results to those empowered to change or support change. 2. Operational efficiency and quality tools are interrelated in achieving both traditional quality and business goals in emergency medicine. These reflect the interdependence of precepts of OE and traditional quality efforts. Cross-discipline measures and metrics allow integration among stakeholders, opening avenues to organizational change. 3. For those to whom quality matters, each business goal must be communicated with its corresponding quality implication attached, just as quality goals must be tied to business possibilities in most hospital
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20.00% 18.00% 16.00%
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Figure 7. Emergency department patient LWBS rates, 2000-2010.
discussions. None of these operational changes were implemented in a quality vacuum. We continued and expanded traditional quality measures during this time including alignment with existing pathways,
improving accuracy of medical data interpretation, morbidity and mortality review, utilization and timeliness of consultations, transfers of care, procedural sedation, and regulatory compliance.
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Au g N ov Fe b M ay Au g N ov Fe b M ay Au g N ov Fe b M ay Au g N ov Fe b M ay Au g N ov Fe b M ay Au g N ov
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Figure 8. Percent of patients meeting the 29-minute door-to doctor interval metric.
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4. Emphasis was placed on communication with administrators who are key and yet far removed from operations in the ED. A key point with our business and satisfaction stakeholders is the concept of the optimal LOS rather than merely shortening LOS. Presented in the discussion of the acute care model are strategies for segmenting care designed to achieve the twin goals of fast things being fast and slow things being slow. Some emergency decisions do and should take time, such as those requiring serial evaluations of response to treatment. An OE principle is to reduce wasted time, thus speeding care for “snapshot” problems, those requiring recognition and a resultant quick disposition, as well as those “movie problems,” where elapsed time is integral to best medical and business practice including the appropriate use of inpatient and observation resources. These concepts provide an opportunity to use the 3 W's of Wit, Wisdom, and War stories to engage diverse stakeholders in ways that are meaningful to them. 5. Using modeling and simulations that are accurate and mathematically neat is crucial to understanding complex systems where constraints are involved. These tools allow an individual or small group to predict
outcomes of interventions before asking staff to change operations. The simulation inputs need to be simple and easily understood for communicating validity.
ACKNOWLEDGMENTS The authors wish to thank the senior administration at Children's Hospital of Michigan who were directly involved as well as those from the Detroit Medical Center, without whose support the component projects to improve our care would not have been possible. Special thanks to our interdisciplinary team of champions from across the hospital's operating units who gave tirelessly for a sustained number of years to implement and cement change that continues to benefit the children and families of our community.
REFERENCES 1. Institute of Medicine Committee on Quality of Healthcare in America. Crossing the quality chasm: a new health system for the 21st century. Washington, DC: National Academies Press; 2001. 2. Goldratt EM, Cox J. The goal: a process of ongoing improvement. Great Barrington, MA: North River Press; 2004. 3. Kotter JP. Leading change. Boston, MA: Harvard Business School Press; 1996. 4. Kotter International. Eight steps for leading change. Available at: http://www.kotterinternational.com/kotterprinciples/ ChangeSteps.aspx. Accessed May, 2011.