Successfully reported this slideshow.
We use your LinkedIn profile and activity data to personalize ads and to show you more relevant ads. You can change your ad preferences anytime.
Re-Engineering the Operating Room Using Variability
Methodology to Improve Health Care Value
C Daniel Smith, MD, FACS, Tho...
Although these efforts remain important, and some
would argue that improving patient flow through an indi-
vidual operating...
best match fixed resources with the needs of the patient
and surgeon.
Unscheduled and scheduled surgical patients compete
according to the categories and definitions used for the
redesign and future management.
After data collection was complete...
Results are summarized in Table 7. One year after imple-
mentation of the redesign, both surgical volume (þ4%)
cases. When taken together, these results led to improved
financial performance. Though not directly measured,
one could ar...
been structured. At its core, the cultural change asks
providers to transition from managing their practices,
and especial...
Table 7. Results: Changes in Operational Performance of Operating Room
Variable Pre-redesign Postredesign Change, %
schedule to facilitate that surgeon delivering surgical care
in that hospital. This business model has worked for all
here do not replace these important elements of a well-
managed suite of operating rooms.
In summary, we have ...
Upcoming SlideShare
Loading in …5

2013 re engineering the operating room using variability methodology to improve health care value


Published on

Re-engineering the operating room using variability methodology to improve health care value.

Published in: Health & Medicine
  • Be the first to comment

2013 re engineering the operating room using variability methodology to improve health care value

  1. 1. Re-Engineering the Operating Room Using Variability Methodology to Improve Health Care Value C Daniel Smith, MD, FACS, Thomas Spackman, MD, Karen Brommer, RN, Michael W Stewart, MD, Michael Vizzini, MBA, James Frye, MBA, William C Rupp, MD BACKGROUND: Variability in flow of patients through operating rooms has a dramatic impact on a hospital’s performance and finances. Natural variation (uncontrollable) and artificial variation (control- lable) differ and require different resources and management. The aim of this study was to use variability methodology for a hospital’s surgical services to improve operational performance. STUDY DESIGN: Over a 3-month period, all operations at a referral center were classified as either scheduled (artificial variation) or unscheduled (natural variation). Data regarding patient flow were collected for all cases. From these data, mathematical models determined explicit resources to be allocated for scheduled and unscheduled cases, with isolation of the 2 flow streams. Services were allocated block time based on 80% prime time use, and scheduled cases were capped at 5:00 PM. Guidelines for operating room access were implemented to smooth the daily schedule and minimize artificial variation on the day of surgery. After implementation of this redesign, 12 months of data were compared with the previous 12-month period. Metrics analyzed included prime time use, overtime minutes, access for urgent or emergent cases, the number of room changes to the elective schedule on the day of surgery, and variation of daily schedules. RESULTS: Surgical volume and surgical minutes increased by 4% and 5%, respectively. Prime time use increased by 5%. Overtime staffing decreased by 27%. Day-to-day variability decreased by 20%. The number of elective schedule same day changes decreased by 70%. Staff turnover rate decreased by 41%. Net operating income and margin improved by 38% and 28%, respectively. CONCLUSIONS: Variability management results in improvement in operating room operational and financial performance. This optimization may have a significant impact on a hospital’s ability to adapt to health care reform. (J Am Coll Surg 2013;216:559e570. Ó 2013 by the American College of Surgeons) Our current health care system is heavily leveraged to deliver complex care through a hospital system. This model of care is inefficient, expensive, and unsustainable in its current form. Preventative and predictive medicine promise to improve an individual’s overall health while moving much of this care out of hospitals and into outpatient settings or even patients’ homes. Although exciting, it will take decades before this promise can be fully realized, and until then we will remain dependent on our hospitals as substantial care delivery platforms. The heavy dependence on hospitals is especially true for the delivery of surgical care. Surgical care generates substantial revenue for hospitals, but it is also one of the largest drivers of cost within the hospital, and health care reform’s mandate to cut costs while simultaneously providing care to millions of currently uninsured Ameri- cans will significantly affect our hospitals’ operating rooms and surgical services. Over the years, numerous studies have looked at improving the efficiency of an operating room’s perfor- mance.1-5 Most have tried to identify and eliminate waste to improve throughput without increasing resources; put simply, if cases start on time and room turnover time is decreased, more cases will be completed in a day. Disclosure Information: Dr Smith served as a consultant for the Institute for Healthcare Optimization, a not-for-profit entity, and received hono- raria for helping teach others the methodology used as part of the study detailed in this article. All other authors have nothing to declare. Presented at the Southern Surgical Association 124th Annual Meeting, Palm Beach, FL, December 2012. Received December 12, 2012; Accepted December 12, 2012. From the Departments of Surgery (Smith), Anesthesiology (Spackman), Ophthalmology (Stewart), and Medicine, Division of Oncology (Rupp); Nursing Administration (Brommer); and Administration and Finances (Vizzini, Frye); Mayo Clinic in Florida, Jacksonville, FL. Correspondence address: C Daniel Smith, MD, FACS, Department of Surgery, Mayo Clinic Florida, 4200 San Pablo Rd, Jacksonville, FL 32224. email: 559 ª 2013 by the American College of Surgeons ISSN 1072-7515/13/$36.00 Published by Elsevier Inc.
  2. 2. Although these efforts remain important, and some would argue that improving patient flow through an indi- vidual operating room remains the holy grail of operating room efficiency, managing the flow of surgical patients into hospitals and operating rooms is a relatively unex- plored area that could yield significant gains in operating room performance. Tools used outside of health care help industries such as manufacturing and telecommunica- tions predict demand on resources and optimally manage flow into a system to allow consistent product delivery.6-8 These efforts focus largely on understanding and managing variability in demands on a system. Using these concepts and tools is a promising way to redesign our hospital management strategies and deliver high value care consistent with health care reform mandates.7,9-11 To meet an increased demand for surgical services at the Mayo Clinic Florida practice, construction of addi- tional operating rooms was being seriously considered. However, baseline data suggested that operating rooms were being underused during regular working hours (prime time), despite the incurrence of considerable over- time. We hypothesized that by using operations manage- ment principles and variability theory, we could expand the capacity of our hospital’s operating rooms and increase surgical throughput without adding infrastruc- ture or expense. The aim of this project was to test this hypothesis by designing and implementing a new oper- ating room management strategy. Herein we present a case study of this work with 1-year results. METHODS This project was undertaken in collaboration with the Institute for Healthcare Optimization (Boston, MA,, an independent not-for-profit research, education, and service organization that uses operations management principles and variability meth- odology to help design strategies to manage patient flow through hospitals. With the direction and full support of the hospital’s CEO, goals with measurable endpoints were established (Table 1). The focus of the project was to manage the flow of surgical patients into the hospital and operating rooms to optimize the use of existing resources. This initiative was designated as the “Managing Variability Program (MVP),” with the “Program” designation to indicate its enduring presence as opposed to a “project,” which is of finite duration. The executive group that managed the day-to-day operations of the operating rooms formed the program team (Table 2). This is a subcommittee of the Surgical Committee, which is composed of the chairs of all the surgical departments and divisions and provides gover- nance and approval for all activities related to the hospital’s surgical services. Beginning in November 2009 and extending through the implementation phase and first year of management, this executive team met twice weekly to design and implement the operating room redesign. The program development was broken into 3 separate but inter-related components: design, implementation, and management. Each was pursued concurrently to implement the redesign on November 1, 2010 and assess its impact after 1 full year. Design Overall concepts of model Design features are detailed in Table 3. The redesign of the management of the operating rooms relied on under- standing and defining variability in surgical patient flow. Variability theory defines 2 types of variation: natural variation (over which we have no control) and artificial variation (which can be controlled). An example of natural variation (an emergency or unscheduled case) would be a patient presenting with an acute abdomen requiring urgent surgery. Appropriate resources must be available at all times to care for these unscheduled cases. In contrast, artificial variation results from uneven sched- uling of elective operations. This creates artificially light and busy surgical schedules, which may vary considerably from day to day. The scheduling of an elective case can be managed according to pre-set clinical criteria, allowing flexibility in creating an operating room schedule to Table 1. Study Goals (Endpoints) Primary goals (endpoints) Increased surgical volume (no. of cases and minutes of surgery) Decreased overtime (nonprime time minutes of surgery) Maintain appropriate access for emergency surgery (classification compliance) Secondary endpoints Predictable elective operating room schedule (no. of same day changes to elective case schedule) Assure surgeons work with their primary team (block utilization) Staff satisfaction (staff turnover rate) Financial impact (net operating income) Table 2. Operating Room Redesign Team Chair, Surgical Committee e Chair, Department of Surgery Vice Chair, Surgical Committee e Chair, Department of Anesthesiology Member Surgical Committee e Chair, Department of Ophthalmology Associate Administrator, Surgery and Procedure Operations Director, Surgical Services Director, Systems and Procedures Financial Analysts Institute for Healthcare Optimization Team Members 560 Smith et al Operating Room Optimization J Am Coll Surg
  3. 3. best match fixed resources with the needs of the patient and surgeon. Unscheduled and scheduled surgical patients compete for the same resources, but each represents a uniquely different demand on hospital resources and patient flow. Despite the distinctly different needs for each of these cases, their scheduling into operating rooms is often determined by patient and surgeon preference, and on the day of surgery, managed on the fly by a “board runner.” This leads to mixing of the scheduled and unscheduled flow streams and resources, resulting in significant unpre- dictability and unnecessary variability. To compensate for this, operating rooms become chronically under- or over- staffed and under- or overused, creating an expensive and poorly used resource that leads to significant staff and surgeon dissatisfaction. At a very high level, once this definition of patient flow variability is established and understood, a careful accounting and analysis of a hospital’s specific case types (scheduled vs unscheduled) and volume can be collected and used for mathematical modeling of resource alloca- tion (eg, rooms, staff, equipment). This allows separation of the unscheduled cases and their resources from those needed for elective cases. This isolation of unscheduled from scheduled surgical cases is an essential component of variability method- ology. At its core, variability methodology involves iden- tification, quantification, and elimination of artificial variability so that the flow of elective patients can be managed to optimize the operating rooms’ performance and produce a smooth day-to-day schedule that is predictable and reliable. If unscheduled cases are allowed to blend into the elective schedule, the predictability and reliability are lost, and if scheduled cases blend into the resource allocated for unscheduled cases, access for those unscheduled cases becomes blocked, inducing unaccept- able delays for emergent care. To fully model and redesign a surgical practice, a comprehensive characterization of the surgical volume is needed to do the mathematical modeling required for decisions about resource allocation. In its most basic form, this includes classifying all cases as either scheduled or unscheduled, establishing urgency classifications for unscheduled cases (Table 4), measuring the start and end times of each case, and defining prime time (when the regular work day starts and stops). With these data, mathematical modeling creates numerous probability sce- nariosdeach uniquely dependent on how resources are allocateddwith risks calculated for each allocation strategy. Risk is defined as not being able to accommo- date all emergency surgery within urgency classifications or bumping of elective cases. Examples of several risk scenarios are shown in Table 5. Selection of an allocation model must only be done once the various probability and risk scenarios are considered. Once an allocation model has been selected, case scheduling tools, daily management strategies, and metrics with reporting tools can be developed to facilitate implementation and management of the program. Mayo Hospital Specific Design We defined an urgent/emergent case as one that must be performed within 24 hours for clinical reasons. We sub- divided the urgent/emergent case classification into 5 distinct urgency classifications (Table 4). When posting an urgent/emergent case, the surgeon was asked to declare the urgency classification. Cases that could wait more than 24 hours, but needed to be completed within 5 days, were classified as work-in cases. Work-in cases were further classified according to either clinical need (eg, gallstone pancreatitis needing cholecystectomy before discharge) or administrative reasons (eg, surgeon or patient required surgery within 5 days but not for clinical reasons). All other cases were classified as elective. We defined prime time as 7:30 AM to 5:00 PM Monday through Friday. Data were then collected for 3 months (Table 6). No changes to how the operating rooms were assigned or managed were made during this 3-month data collection phase. These data allowed characterization of the practice Table 3. DesignFeaturesforOperatingRoomRe-Engineering 1. Understand and define variability in patient flow. 2. Isolate natural variation from artificial variation. 3. Define prime time and establish a “hard stop” to an operative day. 4. Establish urgency classifications. 5. Collect prospective data based on preliminary definitions and assumptions. 6. Use mathematical modeling and test probability scenarios to decide on room allocations for elective and emergent cases. 7. Allocate service blocks based on actual service-specific needs. 8. Assign block time to effect smoothing of volumes throughout week. 9. Re-evaluate staffing levels and tie to block time allocations. 10. Set expectations around block utilization thresholds to gain or lose block. Table 4. Urgency Classifications for Urgent and Emergent Cases A e must start within 45 min B e must start within 2 h C e must start within 4 h D e must start within 8 h E e must start within 24 h Vol. 216, No. 4, April 2013 Smith et al Operating Room Optimization 561
  4. 4. according to the categories and definitions used for the redesign and future management. After data collection was complete, modeling was per- formed and probability scenarios, as outlined above and depicted in Table 5, were considered. Rooms (including staff, equipment, instruments, and supplies) were allo- cated for urgent/emergent, work-in, or elective cases (Fig. 1). These data were also used to allocate elective rooms to the various surgical services as elective block time. Sufficient operating room block time was assigned to meet 125% of each service’s current demand. Put differently, a service that continued its current volume of work would use 80% of its elective block room alloca- tion. After determining each service’s operating room requirements, elective block was assigned to assure that cases were evenly distributed throughout the week to avoid disparate peaks and valleys in daily surgical volume (ie, the weekly volume was “smoothed” to allow a more predictable end to each elective day). This resulted in an overall redesign of the operating room resource alloca- tions based on understanding and managing variability, to increase prime time capacity and use, and smooth the weekly volume of elective surgery performed. Implementation Concurrent with the redesign efforts, tactics for effective implementation of the redesign were developed. Although beyond the scope of this manuscript, implementation followed principles of quality improvement and change management. All existing policies related to the day-to- day function of the operating rooms were reviewed and revised to be consistent with anticipated redesign. Consid- eration was given to staged implementation by selected services vs simultaneous implementation by all services. To maximize the flexibility and impact of the redesign, it was decided to implement the program for the entire surgical practice at the same time. All policy changes and resource allocations were vetted and approved by the Surgical Committee (composed of the chairs of all surgical departments and divisions) and the Executive Operations Team of Mayo Clinic in Florida. The new program was implemented on November 1, 2010. Management The design team served as the management team (Table 2). Dashboards for day-to-day, weekly, monthly, and rolling quarterly data were used (Fig. 2). Decisions trees were developed to help manage conflicts and facili- tate real-time decision-making regarding access to the operating rooms. Anesthesia and Certified Registered Nurse Anesthetist board runners were educated regarding the principles of the program. Consistent with quality improvement and change management principles, changes in the program (based on feedback and data analysis) were considered after 3 months. Table 5. Examples of Probability Scenarios Used for Risk Modeling and Choice of Redesign Plans Cases included Variables Scenario 3 - weekday prime time* All urgent/emergent, rooms needed, n 1 Average room use, % 51.4 Case urgency classification Average waiting time, min A - within 45 min 92 B - within 2 h 107 C - within 4 h 132 D - within 8 h 171 E - within 24 h 268 Frequency of classification A bumps 1 every 2.7 wk (30% of all A cases) Scenario 1 - weekday prime timey All urgent/emergent, rooms needed, n 3 Average room use, % 17.1 Case urgency classification Average waiting time, min A - within 45 min 1 B - within 2 h 1 C - within 4 h 1 D - within 8 h 1 E - within 24 h 1 Frequency of classification A bumps 1 every 167 wk Scenario 15 - weekday prime timez All urgent/emergent þ kidney transplant, rooms needed, n 2 Average room utilization, % 33.5 Case urgency classification Average waiting time, min A - within 45 min 16 B - within 2 h 17 C - within 4 h 19 D - within 8 h 22 E - within 24 h 28 Frequency of classification A bumps 1 every 8 wk *Isolating 1 room for all urgent/emergent cases. y Isolating 3 rooms for all urgent/emergent cases. z Isolating 2 rooms for all urgent/emergent cases. Table 6. Preliminary Data Collection (3 Months) Type of case e elective or urgent/emergent Urgency classification if urgent/emergent: A, B, C, D, E Urgent/emergent case request time Wheels in time: time patient entered operating room Wheels out time: time patient left operating room 562 Smith et al Operating Room Optimization J Am Coll Surg
  5. 5. RESULTS Results are summarized in Table 7. One year after imple- mentation of the redesign, both surgical volume (þ4%) and surgical minutes (þ5%) had increased. Prime time use increased by 5%, while overtime staffing decreased by 27%. Day-to-day variability in case volumes and minutes of surgery decreased by 20% and 22%, respec- tively (Fig. 3), indicating a smoothing of the surgical schedule. The number of same day changes to the elective surgical schedule decreased by 70% (Fig. 4). A 41% decrease in staff turnover suggested improved job satisfac- tion (Fig. 5). These results were accompanied by improvements in net operating income and net operating margin (38% and 28%, respectively). DISCUSSION Optimizing the function of a hospital’s operating rooms is critical to delivering safe, cost-effective surgical care. For many, the focus of optimizing the performance of oper- ating rooms has centered on increasing efficiency.5,12,13 Most attempts have tried to shorten the duration of oper- ating room processes (eg, room turnover time) to create capacity for additional surgical cases. LEAN and Six Sigma are commonly used managerial techniques to eliminate waste and improve efficiency.1,2 Although these improve- ments are important, emerging concepts from nonhealth care sectors centered around variability methodology promise to expand capacity beyond what can be gained by efficiently running the operating room. Variability methodology aims to manage the flow of patients into a hospital’s operating rooms and surgical services, as opposed to flow through the operating rooms themselves. To effect improvements, the required methodology, processes, and metrics are vastly different from those that improve efficiency within a single operating room. For example, efficiency efforts geared to improving in-room operating room performance include strategies such as parallel processing, use of induction rooms, on-time starts, and shortened room turn-over times.3–5 In contrast, vari- ability methodology aims to isolate scheduled cases (artifi- cial variation) from unscheduled cases (natural variation), distribute scheduled cases throughout the week to smooth the weekly volumes, and allocate appropriate resources for unscheduled cases to avoid access restrictions.6,7,12,14,15 The predictability and subsequent operational gains achieved with this methodology create capacity otherwise consumed by unmanaged artificial variation. This allows greater over- all throughput (more surgical cases) without the addition of incremental resources. Ideally, the 2 efforts, operating room efficiency and variability management, are both pursued and optimized. This project and case study explored the use of variability methodology to achieve the stated goals. The work encom- passed not only application of this methodology, but also the design, re-engineering, implementation, and subse- quent impact of these concepts. To date, this is the most comprehensive application of these concepts to a hospital’s surgical services, and through this effort and experience, significantly positive results were achieved. The results regarding performance outcomes are self- evident. Throughput was increased without incremental expense, overtime was reduced, staff satisfaction was improved, and the same day changes to scheduled cases were significantly decreased, all while maintaining appro- priate operating room access for urgent and emergent Figure 1. Breakdown of room allocations for final redesign. CTS, cardiothoracic surgery; GS, general surgery; H/L, heart/lung; NS, neurosurgery; Ortho, orthopedic surgery; Tx, transplant. Vol. 216, No. 4, April 2013 Smith et al Operating Room Optimization 563
  6. 6. cases. When taken together, these results led to improved financial performance. Though not directly measured, one could argue that this redesign should also lead to safer surgery. Increasing prime time, service-specific block utili- zation means surgeons are consistently working with their usual teams, thereby enhancing team work. Furthermore, by limiting the number of same-day changes to the elective schedule, fewer cases are rerouted to rooms and teams not previously expecting these cases, limiting the errors than can accompany multiple “handoffs.” Other significant consequences of this work not readily evident from these data, and beyond the scope of this manuscript, deserve mention. Many of these concern the cultural change required to implement such a program. Although the management concepts devel- oped and used may appear obvious and simple to someone knowledgeable and versed in these principles, and the data are certainly compelling, the actual day-to- day application of variability methodology is counterintu- itive to how surgical practices and hospital systems have Figure 2. Example of dashboard used for reporting metrics to leaders of surgical practice. MVP, managing vari- ability program. 564 Smith et al Operating Room Optimization J Am Coll Surg
  7. 7. been structured. At its core, the cultural change asks providers to transition from managing their practices, and especially their surgical schedules, from what is best for the surgeon and patient, to what is best for the hospital. That’s not to say that the patient ceases to be a focus of this redesign concept because the entire model is built around defining the patient’s clinical needs and assuring appropriate resources are available to meet those needs. One could argue that this concept is very patient centric in assuring the availability of the right team at the right time to meet the patient’s surgical needs. However, hospitals generally cater to the surgeon’s Figure 2. Continued Vol. 216, No. 4, April 2013 Smith et al Operating Room Optimization 565
  8. 8. Table 7. Results: Changes in Operational Performance of Operating Room Variable Pre-redesign Postredesign Change, % Surgical cases, n 11,874 12,367 4 Surgical min 1,757,008 1,844,479 5 Prime time OR use, % 61 64 5 Number of overtime full time employees, average, n 7.4 5.4 À27 Staff turnover rate, % 20.3 11.5 À43 Daily case volume variation (upper-lower control limit) 55.24 44.06 À20 Daily case minutes variation (upper-lower control limit) 6,531 5,124 À22 Daily elective room changes, average/mo 80 25 À69 Daily elective room changes, % 8 2 À70 Cost/case (added 15 OR staff full time employees), $ 1,062 1,070 0 Cost/min of surgery (added 15 OR staff full time employees), $ 7.18 7.26 1 Staff turnover cost (millions), $ 2.47 1.40 À43 Overtime cost savings, $ 111,488 Total OR net revenue (fee increase adjusted), $ 93,929,569 98,686,693 5 Net operating income, $ 15,877,986 21,957,708 38 Net operating margin, % 17 22 28 OR, operating room. Figure 3. Control charts showing change in variability after implementation of oper- ating room redesign. LCL, lower confidence limit; UCL, upper confidence limit. 566 Smith et al Operating Room Optimization J Am Coll Surg
  9. 9. schedule to facilitate that surgeon delivering surgical care in that hospital. This business model has worked for all parties because it generally meets the patient’s needs, enhances the surgeon’s ability to deliver care even when faced with several competing demands, and keeps high- revenue surgical care at the given hospital. The hospital’s revenue has been sufficiently favorable to allow a “what- ever, whenever” culture for the surgeon and still maintain good operating margins. Today, those favorable margins enjoyed by hospitals are evaporating, forcing hospitals to cut costs while main- taining high-quality outcomes. At the same time, these quality-focused outcomes are becoming increasingly scru- tinized and will soon factor into reimbursement formulas. Hospitals across the country are aggressively pursuing cost-cutting strategies, and the high-value, high-cost envi- ronment of the operating room is a prime target for cost reduction. Applying variability methodology swings the pendulum for access to the hospital’s operating rooms from “whatever and whenever” the surgeon wants, to what is best for the hospital. Put more directly, in this model, the surgeon is asked to compromise to meet the hospital’s financial needs. The resultant tension between a surgeon and hospital administration can become intense and was certainly present during the redesign and imple- mentation detailed in this case study. Before embarking on such a program and applying variability methodology, it is critical that a detailed assessment of the hospital’s culture, its providers, and their willingness to accept change be performed. Process improvement and change management strategies and tools should be assessed and liberally applied because gains like those demonstrated here may take considerable time to realize. Software and information technology tools to help schedule surgical cases within the redesign goals, and reporting tools within a quantitative dashboard are essential to facil- itate adoption of this program. Transparency regarding leadership decisions and frequent feedback to all providers about performance improvements should be emphasized. Change management and analytics support should be identified either internally or pursued exter- nally before starting such a program. Finally, the more commonly pursued efficiency efforts remain an essential component to realizing the gains possible with variability methodology. Perfect manage- ment of the flow of patients into the surgical practice without an efficient and well-run operating room will produce suboptimal results. The perfect schedule that is theoretically predictable and reliable will disintegrate if patients cannot enter and exit operating rooms in an expeditious and efficient manner. The methods described Figure 4. Number of changes to elective surgical schedule on the day of surgery before and after implementation of operating room redesign. MVP, managing variability program. Figure 5. Staff turnover rate and cost over first 12 months after implementation of operating room redesign. Vol. 216, No. 4, April 2013 Smith et al Operating Room Optimization 567
  10. 10. here do not replace these important elements of a well- managed suite of operating rooms. CONCLUSIONS In summary, we have shown that redesigning operating room management around variability theory and meth- odology allows increased throughput while increasing prime time use, decreasing overtime, and improving staff satisfaction. At the same time, day-to-day variability in case volume and within-day changes to the elective schedule are decreased, resulting in a more predictable and reliable flow of cases through the operating rooms. Overall, these improvements result in better financial performance and support the hypothesis that more surgical cases can be performed without incrementally increasing the cost of delivering that care. This strategy holds great promise for helping hospitals and surgeons adapt to the challenges created by impending health care reform. Author Contributions Studyconceptionanddesign:Smith,Spackman, Brommer, Stewart, Rupp Acquisition of data: Brommer, Vizzini, Frye Analysisand interpretationofdata:Smith,Stewart,Vizzini, Frye Drafting of manuscript: Smith Critical revision: Smith, Stewart, Vizzini REFERENCES 1. Cima RR, Brown MJ, Hebl JR, et al. 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;213:83e92; discussion 93e94. 2. Collar RM, Shuman AG, Feiner S, et al. Lean management in academic surgery. J Am Coll Surg 2012;214:928e936. 3. Pandit JJ, Abbott T, Pandit M, et al. Is ‘starting on time’ useful (or useless) as a surrogate measure for ‘surgical theatre efficiency’? Anaesthesia 2012;67:823e832. 4. Peters JA, Dean HM. Enhancing OR capacity and utilization. Healthc Financ Manage 2011;65:66e71. 5. Wolf FA, Way LW, Stewart L. The efficacy of medical team training: improved team performance and decreased operating room delays: a detailed analysis of 4863 cases. Ann Surg 2010; 252:477e483; discussion 483e485. 6. Litvak E, Buerhaus PI, Davidoff F, et al. Managing unneces- sary variability in patient demand to reduce nursing stress and improve patient safety. Joint Commission Journal on Quality & Patient Safety 2005;31:330e338. 7. McManus ML, Long MC, Cooper A, Litvak E. Queuing theory accurately models the need for critical care resources. Anesthesiology 2004;100:1271e1276. 8. Ryckman FC, Yelton PA, Anneken AM, et al. Redesigning intensive care unit flow using variability management to improve access and safety. Joint Commission Journal on Quality & Patient Safety 2009;35:535e543. 9. Best Care at Lower Cost: The Path to Continuously Learning Health Care in America. Washington, DC: The National Academies Press, 2012. 10. Jeang A, Chiang A-J. Economic and quality scheduling for effective utilization of operating rooms. J Med Syst 2012;36: 1205e1222. 11. Milstein A, Shortell S. Innovations in care delivery to slow growth of US health spending. JAMA 2012;308:1439e1440. 12. Dhupar R, Evankovich J, Klune JR, et al. Delayed operating room availability significantly impacts the total hospital costs of an urgent surgical procedure. Surgery 2011;150:299e305. 13. van Det MJ, Meijerink WJ, Hoff C, Pierie JP. Interoperative efficiency in minimally invasive surgery suites. Surg Endosc 2009;23:2332e2337. 14. Litvak E, Bisognano M. More patients, less payment: increasing hospital efficiency in the aftermath of health reform. Health Affairs 2011;30:76e80. 15. McManus ML, Long MC, Cooper A, et al. Variability in surgical caseload and access to intensive care services. Anesthe- siology 2003;98:1491e1496. Discussion DR JULIE A FREISCHLAG (Baltimore, MD): I actually had spoken to Dr Smith quite a bit when we, too, became involved in this process and used the Institute for Healthcare Optimization to improve our operating room variability at Johns Hopkins. We also knew we were going to be moving into a new hospital and we wanted to do things better before we moved. The 3 key things we found in our process were preparation and buy-in of all the staff, nursing, anesthesia, and surgery, and that months of collecting data and meetings were very important. We have to have champions in each area. And you have to stay focused on what’s best for the patient, because, frankly, the way we run the OR is what’s best for the surgeon. When are you available? When do you have clinic? When do you have research? When are you out of town? We do a lot of negotiation about whether or not the patient needs surgery right away or not, and we have to be really transparent about the real urgency of the case. Is the surgeon really available? You know, most of us put in a slip and go do something else for 8 hours because it’s never going to get on. And what were the reasons that the case didn’t go as planned? A third of the time, it is the surgeon; a third of the time, the patient; a third of the time, anesthesia and others. And this is not for the faint of heart. Dr Smith and I have both taken major body blows for doing this kind of process in an oper- ating room. And when you redesign the culture and take away block time, you can imagine how painful that will be. We, too, now do 6 to 8 more cases a day. We did that even before we got into the new operating room with the new capacity. And we have seen similar decreases in costs and more efficiency and less pain in getting that elective case not interrupted, that urgent case on, and even work-ins, of which we have a lot. Our elective rooms started off with more than 80% use, and now it’s close to 95%. We have more block time to offer to others, and we do take it away. The minute you’re under 80%, your block time goes, because we have to have 95% use of block time. We have 5 emergent rooms that run about 60%. 568 Smith et al Discussion J Am Coll Surg