Dexter, Franklin MD, PhD; Wachtel, Ruth E. PhD, MBA
The Perioperative Surgical Home (“Surgical Home”) is an approach, adopted by the American Society of Anesthesiologists, designed in part to increase quality, patient safety, shared decision-making, and to decrease costs per patient by reducing waste.1
Many health care interventions have increased cost, but the increase is justified by the patient benefit. This is referred to as having good cost utility.2 Because of the increased cost, widespread use of such technology can be challenging to achieve and often depends on national regulations and insurance contracts that support use of the technology. This Special Article is about achieving net cost reduction, independent of the benefits gained (Table 1).
The Surgical Home involves anesthesiologists coordinating preoperative, intraoperative, and postoperative care.3 This Special Article is limited to net cost reduction achieved as anesthesiologists in the Surgical Home provide new expertise and input, not classically considered within the scope of essential knowledge of anesthesiologists (e.g., systems-based practice).4 This Special Article is not about the economics of anesthesiologists improving the quality of their routine care (e.g., using series of evidence-based techniques and anesthesia information management system [AIMS] prompts to prevent complications of central line placement, to choose antiemetics,5–8 or to adjust ventilator tidal volumes intraoperatively).9 Anesthesiologists currently are paid to provide such routine care. Progressive improvements in quality are their professional responsibility.
This Special Article has 5 sections:
1. Reduce Unnecessary Interventions That Do Not Have Potential to Benefit Patients
2. Staff Scheduling, Case Scheduling, and Staff Assignment
3. Preoperative: Reducing Nonadministrative Causes of Cancellations
4. Intraoperative: Reducing Operating Room Time (i.e., Increased “Throughput”)
5. Postoperative: Reducing Length of Stay (“Enhanced Recovery”)
It is noteworthy that the sequence of our first 4 sections is essentially analogous to O’Sullivan et al.10’s 2005 “systematic and comprehensive review” of “evidence-based methods that contribute to a positive return on investment from anesthesia information management systems (AIMS).” The key implication is that cost reduction from the Surgical Home depends largely on anesthesiologists learning about and applying medical informatics and analytics. Reducing length of stay is another important goal of the Surgical Home,3 but this has different economics and relies on separate information systems.
1. REDUCE UNNECESSARY INTERVENTIONS THAT DO NOT HAVE POTENTIAL TO BENEFIT PATIENTS
One substantial opportunity for cost reduction with the Surgical Home is decreasing unnecessary interventions in the preoperative, intraoperative, and/or postoperative periods (i.e., waste).1 Preoperative and intraoperative interventions are described in this section. Cost reduction from changes in the use of different implant and disposable products and negotiation of contracts are considered in the Discussion.
Performing routine screening tests in patients who are otherwise healthy is of little or no known value in detecting diseases and in changing anesthetic management or outcome.11 At some hospitals, preoperative tests are ordered excessively, causing unnecessary costs.12,13 When anesthesiologists in preoperative assessment clinics (e.g., Medical Director of the Preoperative Assessment Clinic) determine which laboratory tests are ordered, the rate of ordering is less than if internists or surgeons are involved.12 When anesthesiologists manually select the tests ordered, the number and cost of the preoperative tests is reduced.14
The “Choosing Wisely” campaign is encouraging because its focus is logical and its choices of interventions to be reduced are evidence-based.a The criteria are those that are “(1) common clinical practices for which there is (2) little or no evidence of benefit to patients,” and for which “(3) avoidance would lead to improved quality of care and/or (4) reduced costs.”ab For example, “Do not obtain baseline laboratory studies in patients without significant systemic disease …”a Another example is not “to obtain baseline diagnostic cardiac testing” (e.g., echocardiography) “in asymptomatic stable patients with known cardiac disease …”15–18
The Medical Director of the Preoperative Assessment Clinic can manage implementation of the reduction in unnecessary interventions.12 Data for monitoring performance can be obtained from the AIMS. Statistical analysis can be performed by someone with a background in analytics including process improvement control charts. For example, the percentage of patients without significant systemic disease but having at least 1 preoperative test can be monitored.12 Methods of monitoring unnecessaryab 19 type and screen and blood ordering also have been developed.20,21 Future comparative effectiveness research will identify more opportunities, and these should be embraced.
Additional cost reduction can be achieved using the AIMS data. An anesthesiologist serving as the “Director of Anesthesia Informatics” can lead its use. The American Board of Preventive Medicine recently offered its first Board Certification in Clinical Informatics, and several anesthesiologists became certified.c
AIMS and their data can be used to decrease drug and supply costs and wastage. Anesthetic practice guidelines were developed and updated with information compiled using an anesthesiology department’s AIMS.22–24 Using the AIMS, anesthesia providers were given ongoing monthly feedback, summarizing their use of expensive drugs relative to their peers.23,24 Anesthetic drug costs were reduced.23,24 Reducing fresh gas flow by providing feedback to providers by e-mail25 or real-time alerts26,27 was effective because wastage was not a problem of a few rare cases having very large flows but principally small excess usage for many cases by many providers.28 The expert in analytics can assist by adjusting feedback based on each clinician’s drug and supply costs, available since the AIMS provides the necessary data to permit valid comparisons between providers.29
An advantage of cost reduction by reducing unnecessary interventions is that essentially no one argues against reducing waste. However, the opportunities for cost reduction among all patients often are small, because many of the laboratory tests and anesthetic drugs are inexpensive (<$10). Substantial cost reduction may be achieved only for a small minority of patients. For example, consider the Choosing Wisely criterion of not “to administer packed red blood cells in a young healthy patient without ongoing blood loss” and low hemoglobin “unless symptomatic or hemodynamically unstable.”ab 19 This is important medically, resulting in substantial net cost reduction for these patients. Blood transfusion is performed in 12% of hospitalized patients and is the most common procedure during hospitalization.d However, far fewer than 5% of patients undergoing anesthesia receive a blood transfusion.20,30,31
2. STAFF SCHEDULING, CASE SCHEDULING, AND STAFF ASSIGNMENT
An opportunity for cost reduction through the Surgical Home’s enhanced coordination of care is literally the coordination of care: staff scheduling months in advance, case scheduling into the staffed time, and then staff assignment to the most appropriate cases.32–34 Such coordination has substantive opportunity for net cost reduction because: (1) for every surgical case, anesthesiologists, nurses, and so on are scheduled, the case is scheduled, and personnel are assigned, and (2) labor is the largest total variable cost of surgical care.35–37 Unlike interventions in the preceding section (e.g., not all patients undergo preoperative echocardiogramab 15–17), this section applies to all patients undergoing anesthesia.
Substantial Heterogeneity Among Hospitals Limits the Maximum Potential Cost Reduction
Substantial heterogeneity in net cost reduction should be expected among hospitals. The percentages of operating rooms (ORs) with >8 hours of cases vary markedly not only among hospitals38–40 but also among services and days of the week at individual hospitals.41 For example, suppose every OR at one hospital has 5.5 to 7.5 hours of cases and turnovers each workday. Staff schedules are for at least 8 hours daily. At this hospital, there generally would be no opportunity for cost reduction by revising staff schedules, case scheduling, and/or staff assignment. In contrast, suppose every OR at a different hospital has at least 8.5 hours of cases daily and >10 hours on most days. At this second hospital, there is substantial opportunity to use statistical methods to reduce costs through changes in staff scheduling.
How There Can be Substantial Cost Reduction?
Vocabulary terms used in scientific studies of OR management have been reviewed.42 Without knowing these terms, managers may be unable to find articles relevant to their hospitals in the scientific literature.43 For example, the “allocated hours” are the hours into which cases are scheduled.42–45 How to calculate the OR allocations appropriately (to maximize the efficiency of use of OR time), given the hours of cases forecasted months in advance, has been reviewed.42 How to calculate OR allocations for services with single ORs has been reviewed.45 Statistical methods have been compared.46 Decision-making on the day of surgery has been reviewed.47 The derivations in the Appendices of the articles show that the methods are rational and sufficient for case scheduling decisions beginning the morning of the workday before surgery through the day of surgery.44,47,48
Substantial excess costs are caused partly by a common cognitive bias.49 The hours of each OR into which cases are scheduled are chosen to be briefer than the optimal choice of allocated time.42,45 For example, some schedulers simplify the problem of scheduling cases into allocated hours by treating all ORs at the hospital identically, allocating the same number of hours to each OR. They base their decisions on what would be appropriate on average, neglecting to consider service and day of the week combination.49 Although some managers express concern about extra labor costs because of personnel (anesthesiologists, nurses, etc.) working late, they then perform staff scheduling and staff assignment that result in the personnel working late.32,33 These are some of the sources of excess and reducible labor costs.32,42
Anesthesiologists’ Role in OR Management
An OR Medical Director having knowledge of scientific OR management is essential to achieve the cost reduction.50 This anesthesiologist plays a pivotal role in achieving cost reduction by being responsible for staff scheduling and for case scheduling starting a few days before surgery.50,51 This responsibility is important in part because OR nursing directors often are not rewarded for making decisions that increase anesthesiologists’ productivity.51 The relative costs based on wages are anesthesiologists >nurse anesthetists >OR nurses >surgical technologists, and thus, the bottlenecks economically to providing care generally should also be in that sequence.
The OR Medical Director should use the OR allocations for staff scheduling, months before the day of surgery, to achieve suitable numbers of anesthesiologists, nurse anesthetists, OR nurses, and so on with appropriate skills each morning.52,53 However, the Director’s principal use of the OR allocations is on the workday before surgery and on the day of surgery. These management decisions rarely need to be made at night (e.g., 7:00 PM to 6:59 AM), because during such periods a mean of only 0.2% of OR-date combinations have a change made to their schedules.54 The use of the OR allocations the workday before surgery through the day of surgery is necessary, because most ORs have a change in one or more cases within 1 workday of surgery.54 Many changes in cases are made, because at hospitals approximately 20% of OR scheduled hours are derived from patients who are inpatient preoperatively.55 These cases need to be done promptly for patient safety, patient centered care, etc.
The OR Medical Director also can work as a facilitator of group level decision-making,50 guiding surgeons in adjusting their days of surgery, if physical hospital bed capacity (wards or intensive care unit) is the perioperative bottleneck. By physical, we mean not nurses but numbers of beds.56–59 At these facilities, adjusting days of the week that individual surgeons work can decrease health care costs by leveling capacity among weekdays and thereby reducing disruption of admission of medical and emergency medicine patients.60,61 Similarly, at hospitals with extensive extra hospital capacity to accommodate peaks caused by variation in surgical workload (e.g., overall hospital occupancy 65%), smoothing by changing the days of the week that certain surgeons operate can decrease nursing ward labor costs.61 Such changes can be done one hospital ward at a time, with the necessary engagement of all surgeons admitting to that ward. Costs are reduced when nursing staff scheduling is changed.
Role of Anesthesia Information Management Systems for Decisions on the Day of Surgery
Staff scheduling and assignment of anesthesiologists, nurse anesthetists, and so on depend not only on the OR information system but also on the AIMS. Decisions that can be made with either information system are interchangeable,62 but an AIMS includes all anesthetizing locations. Therefore, traditionally use of AIMS data has been superior, although this may be less of an issue in the future as enterprise wide systems are used more commonly. Regardless, the AIMS data are essential on the day of surgery for case and staff assignment to ORs. Such decision-making is based not simply on having displays showing data from the AIMS such as real-time usage63–65 but displays with recommendations or displays and brief checklists.66–69 Consequently, in our experience, achieving a cost reduction32–34,37,38,42,44–46 depends not only on having a knowledgeable50 OR Medical Director and someone with an analytics background but also on the Director of Anesthesia Informatics. Many of the AIMS data used for this purpose are the basic signals (e.g., when there is pulse oximetry and heart rate monitoring in use in an OR, the OR is occupied by a patient).64,65,67–69
3. PREOPERATIVE: REDUCING NONADMINISTRATIVE CAUSES OF CANCELLATIONS
Absence of Substantive Net Cost Reduction in Reducing Typical Cancellation Rates to Low Rates
The Surgical Home likely will decrease the incidence of nonadministrative causes of cancellations. However, at hospitals with typical or less than average cancellation rates (e.g., 1% to 8%),70,71 this will not significantly reduce total costs (i.e., among all patients).54,71 Rather, cancellations within 1 workday of surgery (slightly) reduce variability in services’ workloads among days (P < 0.0001).54,71 Cancellations soon before the day of surgery are associated with (slightly) increased net hours of cases scheduled.54 This apparent paradox occurs because cancellation of cases influences the timing of decision-making in the scheduling office.71 The workload for the date of surgery is known incompletely when cases are scheduled many weeks in advance. When a cancelled case is replaced by an inpatient close to the day of surgery, the schedulers have a better idea of the anticipated workload.46,54,55,72,73 Consequently, schedulers can (and in practice do) fully fill, but not exceed, the allocated hours for the specific OR into which the case is placed.54 Furthermore, when the cancelled case is rescheduled (as are 90%) for a later day, usually it is rescheduled to a day that the surgeon has slightly less than the surgeon’s median workload (P = 0.022).74 In other words, as long as the OR allocations have been calculated using the appropriate statistical methods, decreasing (typical) cancellation rates will not reduce OR costs at hospitals. Reducing cancellations is important from a patient perspective, but its value is intangible.75,76
AIMS data and analytics were needed for anesthesiologists to understand the economic cost of cancellation.54,71,74 The traditional U.S. health care system of fee-for-service (i.e., more compensation for doing more cases) has been in use for decades and creates an incentive not to cancel cases. Reducing administrative causes of cancellations (e.g., insufficient remaining OR time) does substantively reduce costs, but doing so involves interventions of the OR Medical Director (i.e., of the preceding Section 2).e 35,37,77 Private U.S. hospitals achieve very low cancellation rates (e.g., <2%).71 A survey study nearly 2 decades ago even evaluated anesthesiologists’ perceptions of production pressure not to cancel cases.78 Thus, incremental reductions in nonadministrative causes of cancellations would be expected not to achieve substantial net cost reduction when considered among all surgical patients.
Surgical Home Is Not Necessary to Substantially Lower Cancellation Rates
The above analysis is fully in contrast to that for hospitals with substantial (e.g., >8%) cancellation rates.70,71 Having an active Medical Director of the Preoperative Assessment Clinic and the provision of multidisciplinary care may facilitate a reduction in cancellation rates for nonadministrative causes from substantial to typical.79–82 However, the benchmarking studies predating Surgical Home show70,71 that to achieve low percentages the Surgical Home model is neither a necessary criterion nor novel.
4. INTRAOPERATIVE: REDUCING OPERATING ROOM TIME (I.E., INCREASED “THROUGHPUT”)
Another area where costs can be reduced but substantial net cost reduction among all surgical patients should not be expected is from reducing OR time, including delays. The reason why we expect overall limited reduced cost is that most perioperative costs are fixed (e.g., building and AIMS).83,84 Yet since new equipment and hospitals must be built, fixed costs are included in reimbursement by Diagnosis Related Groups (DRG). Consequently, averaged among all surgical patients, the difference between reimbursement and variable costs vastly exceeds $1000/h of OR time.85–87 Thus, there has been for decades strong financial incentives to increase throughput (e.g., for surgeons to work as quickly as possible so that they or their corporation can bill more Relative Value Units). Nevertheless, we review the topic.
Anesthesiologists Have Managerial Role in Reducing Total (Overall) OR Times
Reductions in OR times do not appear to be influenced directly by anesthesiologists while they supervise nurse anesthetists and anesthesiology residents but rather by anesthesiologists functioning as OR Medical Directors. (Supervision is used here generically, not as a U.S. billing term). For example, coordination of anesthesiologists’ physical presence in ORs does not appear to reduce OR time substantively.39,88–90 OR times can be segmented into surgical times (positioning to application of dressing) plus nonsurgical times (i.e., anesthesia-controlled times). OR times are affected principally by surgical times, not anesthesia-controlled times.88–90 Mean anesthesia times are brief relative to standard deviations (SDs) of surgical times.88 Among joint replacement cases at several community hospitals, there was no association between increases in the physical presence of the anesthesiologist from 0% to 21% of the case and reductions in OR times.39 At an academic “day surgery” center, anesthesia-controlled times were no briefer (<1 minute, P = 0.19) with anesthesiologist supervision of nurse anesthetists (i.e., 2 providers present) than with an anesthesiologist practicing alone.91
The anesthesiologist as OR Medical Director can achieve decreases in OR times by hiring additional nonphysician providers. Examples include not only having more (1) housekeeping teams to reduce turnover times,92,93 but also more (2) postanesthesia care unit (PACU) nurses,41,94–98 (3) transporters to reduce delays in PACU exit when patients are ready for discharge,99 and/or (4) nurses to setup, assist anesthesiologists, and monitor patients after peripheral nerve blocks.100 More expensive anesthetic drugs can be used to reduce OR times101,102 and staff waiting103 by reducing104,105 annoying106 prolonged times to extubation (i.e., 15 minutes or longer from end of surgery). Finally, the OR Medical Director can provide leadership to ensure the availability of 1 extra staffed OR when a service with 3 or 4 surgeons each has scheduled at least 8 hours of cases.107–110
Achieving Reduction in OR Time Requires Investment of Increased Resources (Variable Costs)
Examination of the resources used92–110 reveals one of the limitations of decreasing OR time, including delays. Changes described in Section 1 occupy the time of a Medical Director of the Preoperative Assessment Clinic, a Director of Anesthesia Informatics, and a few hours a month of an analyst (statistician). Changes described in Section 2 take the time of an OR Medical Director, a Director of Anesthesia Informatics, and a few hours a month of an analyst. The interventions in the preceding paragraph to decrease OR time include the time of the latter three individuals. However, the interventions also include incremental variable costs (e.g., more PACU nurses).92–110
Potential to Achieve Net Cost Reduction Is Limited to ORs with More Than 8 Hours of Cases
Another limitation to decreasing costs through a reduction in OR time is that it is rarely effective except when there are at least 8 hours of OR time consistently for the service on the day of the week.42,45,66,101,111,112 Many hospitals do not satisfy this condition.38,39 There are even fewer hospitals that have many individual surgeons consistently filling an OR with at least 8 hours of cases each workday that the surgeon is doing at least one case.113–115 Consequently, the potential for substantive net cost reduction is limited by the fact that although reducing OR time does make very good economic sense, this is so only when targeted to those individual surgeons with many hours of cases. It is close to meaningless to read a statement111 that reducing OR time by 10 minutes reduces costs by $33.50 (or some even larger value), because OR time is not simply a variable cost (see Section 2).42,101,104
Suppose that reducing OR time can be achieved. When calculating the reduction in costs achieved through a decrease in the OR time of an OR with >8 hours of cases,101,105,111 do not blindly use a value for the cost per minute of OR time derived from the hospital financial cost accounting system. Often the values are vast overestimates because they attribute costs that are not decreased (fixed costs) into the value. Instead, identify through observation the personnel who would have been in the OR had the time not been reduced (e.g., for prolonged extubations: anesthesiologist, surgical physician assistant, OR nurse, and surgical technologist).103,116 The cost per minute of the OR time reduced should be similar to the sums of those individuals’ typical salaries plus benefits for the time period (e.g., $3.35/min or $200/h).111
5. POSTOPERATIVE: REDUCING LENGTH OF STAY (“ENHANCED RECOVERY”)
The intraoperative role of anesthesiologists to enhance recovery has changed over the past decade, and we expect it will continue to improve, thereby reducing patient morbidity. For example, antibiotic administration was not traditionally considered an anesthesiologist role, being limited “simply” to following surgeons’ orders. With informatics, automatic AIMS messages can be sent if the antibiotic has not been administered, resulting in an increased rate of appropriate antibiotic administration.117–119 Near 100% compliance can be achieved when there are not only real-time alerts but also e-mail feedback and summary reports by provider.117–121 Incidences of surgical site infections can be decreased,121 likely with concomitant reductions in length of stays. Patients likely are far better off because of such programs. However, this does not imply that net costs will be less unless these interventions are applied consistently by multiple providers to many patients.
Traditional Hospital Financing in the United States and Length of Stay
A system of reimbursing hospitals and surgeons based on DRG and primary surgical procedure creates incentives for reducing lengths of stay. The reason is that longer lengths of stay fill hospitals and thereby impair throughput. If no physical beds are available, the hospital cannot do more cases and make even more money (see 2nd to last paragraph of Section 2). Thus, surgeons in the United States and other countries with fee-for-service type compensation have had incentives to reduce lengths of stay when this can be achieved without substantive increases in costs. For specific procedures, decreases in lengths of stay that are accompanied by decreases in costs will be achievable as medical advances are made.122
Recently, Eappen et al.123 studied the “relationship between occurrence of surgical complications and hospital finances.” Among U.S. Medicare patients (i.e., DRG payments), one or more complications were associated with $1700 extra contribution margin per patient (i.e., the hospital benefited financially from the complications through recoding into higher paying DRGs, P < 0.001).123 Such results do not contradict our perspective, because 93.3% of patients have no complications. Among all payers, 94.7% have no complications. The financial benefit of reducing length of stay is achieved principally not by reducing complications in a few patients but by reducing the average length of stay of the vast majority of patients.124 This is evident from an end point typically studied, the median length of stay. The median will not be decreased by reducing the incidence of complications and longer lengths of stay occurring in only 5% to 10% of patients.
Patients Per Day Benefiting from Enhanced Recovery programs
Enhanced Recovery programs have achieved, and we expect will continue to achieve reductions in length of stay.125–129 A single anesthesiologist can “work in tandem with the same group of … certified registered nurse practitioners and a registered nurse … case coordinator to consistently provide … focused and integrated postoperative patient care—from the PACU to the intensive care unit and/or the regular inpatient unit.”3 More frequent rounding on patients can be done, with discharge in early evenings, thereby reducing length of stay.130 However, the economics of enhanced recovery is very sensitive to the numbers of patients per day.131
Suppose that an intervention will be applied to many patients on a surgical ward, reducing the median length of stay by several days. Then, the intervention will reduce costs because the number of surgical ward nurses employed will progressively be reduced. In contrast, suppose that the intervention will be applied to <1 patient every other day per hospital,126,127,129 the reduction in length of stay will be a median of 1 day, and the specific days will be variable due to patient and surgeon factors.60,61 Then, it is unlikely that nurse staff scheduling can be changed to achieve the cost reduction.131 As summarized at the end of Section 2, hospitals with large variability among days in the admission of Enhanced Recovery patients have fewer opportunities for cost reduction.131–133
Estimating Variable Costs Per Day
When quantifying the cost reduction from each reduction in length of stay, be cautious in calculating precisely what has been reduced.84,134,135 Quoting the title of Taheri et al.135 2000 cost accounting study in the Journal of the American College of Surgeons, “Length of stay has minimal impact on the cost of hospital admission.” Hospital cost accounting systems may attribute substantial costs to a hospital ward but those costs are not reduced by decreasing length of stay. The labor costs in caring for a surgical patient are substantially more on the first few days following surgery.84,134,135 When length of stay is reduced for the average patient, as applies to net cost reduction, sometimes only minimal nursing labor and supply costs are reduced. As a check on estimates of cost reductions for reducing length of stay by 1 day, divide the national annual compensation for the surgical ward nurses by 250 workdays per year and by the typical number of patients cared for by each such nurse (e.g., 5).136
Finally, as part of the Surgical Home, anesthesiologists may also be able to reduce length of stay through educational interventions made as part of the Preoperative Assessment. For example, patient expectations postoperatively may be established so that arrangements for home care may be made. The economics of such interventions made preoperatively are the same as the economics for the reductions in length of stay described in this section.
The Perioperative Surgical Home is an approach designed to increase quality, patient safety, and shared decision-making and to decrease costs. This Special Article is limited to the net cost reducing interventions (Table 1). The article is of limited scope in that topics with increased utility to patients, absent small incremental decreases in costs, were not reviewed.2 In addition, strategies that increase profit and patients served were not reviewed (e.g., reducing length of stay with the objective of doing more surgery). Such profit opportunities for anesthesiology departments have been reviewed.137
In Sections 1 to 5, we showed that two principal economic opportunities for cost reduction from the Surgical Home are the same as those derived from the use of AIMS data.10 One opportunity is ceasing ineffective interventions (Section 1). Doing this means: anesthesiologists involved (1) in preoperative assessment and (2) in informatics. The other opportunity is from coordinating patients and personnel (e.g., anesthesiologists and nurses), including evidence-based staff scheduling, case scheduling, and staff assignment (Section 2). Doing this means: (1) using mathematics when scheduling staff; (2) knowledgeable anesthesiologist(s) (i.e., OR Medical Director) working with the surgical scheduling office(s) starting late afternoons two workdays before surgery through the day of surgery; and (3) anesthesiologists involved in informatics. These two principal economic opportunities for cost reduction from the Surgical Home are the same as for AIMS.10
Once the OR Medical Director is doing these activities, the anesthesiologist can also be engaged in other substantial perioperative cost reduction activities, such as leading surgical group discussions on implant and disposable costs, purchasing, and preference cards.36,50,51,134,138–140 Using both a payment (price) cap model when negotiating with manufacturers141 and providing relative cost information to surgeons when >1 type of implant may be suitable142 are multiple specialty interventions that can benefit from physician leadership. Computational and other engineering methods can be used to optimize numbers of preference cards.139,140
In Sections 3 to 5, we showed that although there are other potential strategies for net cost reduction from the Surgical Home, currently well-managed organizations will probably not achieve substantial (if any) net cost reductions from them: improved preoperative assessment, faster intraoperative care, and/or enhanced recovery (reduced length of stay). They have economic value when used selectively (e.g., for specific ORs with long workdays of single surgeons). However, the traditional system of surgeon and hospital paid per case (e.g., DRG) created incentives for those strategies and has been in use for decades. Our economic assessment for hospitals would have been different if there had not been many years of such a method of reimbursement.
Franklin Dexter is the Statistical Editor and Section Editor for Economics, Education, and Policy for Anesthesia & Analgesia. This manuscript was handled by Dr. Steven L. Shafer, Editor-in-Chief, and Dr. Dexter was not involved in any way with the editorial process or decision.
Name: Franklin Dexter, MD, PhD.
Contribution: This author helped design and conduct the study and write the manuscript
Attestation: Franklin Dexter has approved the final manuscript.
Conflicts of Interest: The University of Iowa, Department of Anesthesia, Division of Management Consulting, performs some of the analyses described in this paper for hospitals and companies. The Division also assists companies in adding the analyses into their products. FD has tenure and receives no funds personally, including honoraria, other than his salary and allowable expense reimbursements from the University of Iowa. Income from the Division’s consulting work is used to fund future Division research.
Name: Ruth E. Wachtel, PhD, MBA.
Contribution: This author helped write the manuscript.
Attestation: Ruth Wachtel has approved the final manuscript.
Conflicts of Interest: The author has no conflicts of interest to declare.
a Onuoha OC, Arkoosh V, Fleisher LA. ‘Choosing wisely’ in Anesthesiology: Top-5 list – addressing the gap between evidence and practice. ASA Newsletter 2014;78(1):44–45 Cited Here...
b Available at: http://www.choosingwisely.org/doctor-patient-lists/american-society-of-anesthesiologists/ Accessed January 8, 2014 Cited Here...
c Available at: http://www.theabpm.org/public/newci.cfm. Accessed January 8, 2014 Cited Here...
d Pfuntner A, Wier LM, Stocks C. Most frequent procedures performed in U.S. hospitals, 2011. HCUP Statistical Brief #165. October 2013. Agency for Healthcare Research and Quality, Rockville, MD. Available at: http://www.hcup-us.ahrq.gov/reports/statbriefs/sb165.pdf. Accessed January 20, 2014 Cited Here...
e For administrative causes, the number needed to treat to prevent the cancellation is 1, because the specific patient who would be cancelled for “lack” of OR time is known. However, for changes made in the Preoperative Assessment Clinic, the number needed to treat is at least 6.75 Cited Here...
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16. Fleisher LAAmerican College of Cardiology/American Heart Association. . Cardiac risk stratification for noncardiac surgery: update from the American College of Cardiology/American Heart Association 2007 guidelines. Cleve Clin J Med. 2009;76(Suppl 4):S9–15
17. Wijeysundera DN, Beattie WS, Karkouti K, Neuman MD, Austin PC, Laupacis A. Association of echocardiography before major elective non-cardiac surgery with postoperative survival and length of hospital stay: population based cohort study. BMJ. 2011;342:d3695
18. Sheffield KM, McAdams PS, Benarroch-Gampel J, Goodwin JS, Boyd CA, Zhang D, Riall TS. Overuse of preoperative cardiac stress testing in medicare patients undergoing elective noncardiac surgery. Ann Surg. 2013;257:73–80
19. Carson JL, Carless PA, Hebert PC. Transfusion thresholds and other strategies for guiding allogeneic red blood cell transfusion. Cochrane Database Syst Rev. 2012;4:CD002042
20. Dexter F, Ledolter J, Davis E, Witkowski TA, Herman JH, Epstein RH. Systematic criteria for type and screen based on procedure’s probability of erythrocyte transfusion. Anesthesiology. 2012;116:768–78
21. Frank SM, Rothschild JA, Masear CG, Rivers RJ, Merritt WT, Savage WJ, Ness PM. Optimizing preoperative blood ordering with data acquired from an anesthesia information management system. Anesthesiology. 2013;118:1286–97
22. Coleman RL, Sanderson IC, Lubarsky DA. Anesthesia information management systems as a cost containment tool. CRNA. 1997;8:77–83
23. Lubarsky DA, Glass PS, Ginsberg B, Dear GL, Dentz ME, Gan TJ, Sanderson IC, Mythen MG, Dufore S, Pressley CC, Gilbert WC, White WD, Alexander ML, Coleman RL, Rogers M, Reves JG. The successful implementation of pharmaceutical practice guidelines. Analysis of associated outcomes and cost savings. SWiPE Group. Systematic Withdrawal of Perioperative Expenses. Anesthesiology. 1997;86:1145–60
24. Lubarsky DA, Sanderson IC, Gilbert WC, King KP, Ginsberg B, Dear GL, Coleman RL, Pafford TD, Reves JG. Using an anesthesia information management system as a cost containment tool. Description and validation. Anesthesiology. 1997;86:1161–9
25. Body SC, Fanikos J, DePeiro D, Philip JH, Segal BS. Individualized feedback of volatile agent use reduces fresh gas flow rate, but fails to favorably affect agent choice. Anesthesiology. 1999;90:1171–5
26. Nair BG, Peterson GN, Neradilek MB, Newman SF, Huang EY, Schwid HA. Reducing wastage of inhalation anesthetics using real-time decision support to notify of excessive fresh gas flow. Anesthesiology. 2013;118:874–84
27. Luria I, Lampotang S, Schwab W, Cooper LA, Lizdas D, Gravenstein N. Automated, real-time fresh gas flow recommendations alter isoflurane consumption during the maintenance phase of anesthesia in a simulator-based study. Anesth Analg. 2013;117:1139–47
28. Dexter F, Maguire D, Epstein RH. Observational study of anaesthetists’ fresh gas flow rates during anaesthesia with desflurane, isoflurane and sevoflurane. Anaesth Intensive Care. 2011;39:460–4
29. Dexter F, Lubarsky DA, Gilbert BC, Thompson C. A method to compare costs of drugs and supplies among anesthesia providers: a simple statistical method to reduce variations in cost due to variations in casemix. Anesthesiology. 1998;88:1350–6
30. Ferraris VA, Davenport DL, Saha SP, Austin PC, Zwischenberger JB. Surgical outcomes and transfusion of minimal amounts of blood in the operating room. Arch Surg. 2012;147:49–55
31. Turan A, Yang D, Bonilla A, Shiba A, Sessler DI, Saager L, Kurz A. Morbidity and mortality after massive transfusion in patients undergoing non-cardiac surgery. Can J Anaesth. 2013;60:761–70
32. Dexter F, Epstein RH, Marsh HM. A statistical analysis of weekday operating room anesthesia group staffing costs at nine independently managed surgical suites. Anesth Analg. 2001;92:1493–8
33. Lehtonen JM, Kujala J, Kouri J, Hippeläinen M. Cardiac surgery productivity and throughput improvements. Int J Health Care Qual Assur. 2007;20:40–52
34. Abouleish AE, Dexter F, Epstein RH, Lubarsky DA, Whitten CW, Prough DS. Labor costs incurred by anesthesiology groups because of operating rooms not being allocated and cases not being scheduled to maximize operating room efficiency. Anesth Analg. 2003;96:1109–13
35. Tessler MJ, Kleiman SJ, Huberman MM. A “zero tolerance for overtime” increases surgical per case costs. Can J Anaesth. 1997;44:1036–41
36. Dexter F, Blake JT, Penning DH, Sloan B, Chung P, Lubarsky DA. Use of linear programming to estimate impact of changes in a hospital’s operating room time allocation on perioperative variable costs. Anesthesiology. 2002;96:718–24
37. Stepaniak PS, Mannaerts GH, de Quelerij M, de Vries G. The effect of the Operating Room Coordinator’s risk appreciation on operating room efficiency. Anesth Analg. 2009;108:1249–56
38. Abouleish AE, Prough DS, Whitten CW, Zornow MH, Lockhart A, Conlay LA, Abate JJ. Comparing clinical productivity of anesthesiology groups. Anesthesiology. 2002;97:608–15
39. Dexter F, Weih LS, Gustafson RK, Stegura LF, Oldenkamp MJ, Wachtel RE. Observational study of operating room times for knee and hip replacement surgery at nine U.S. community hospitals. Health Care Manag Sci. 2006;9:325–39
40. Berry M, Berry-Stölzle T, Schleppers A. Operating room management and operating room productivity: the case of Germany. Health Care Manag Sci. 2008;11:228–39
41. Marcon E, Dexter F. An observational study of surgeons’ sequencing of cases and its impact on postanesthesia care unit and holding area staffing requirements at hospitals. Anesth Analg. 2007;105:119–26
42. McIntosh C, Dexter F, Epstein RH. The impact of service-specific staffing, case scheduling, turnovers, and first-case starts on anesthesia group and operating room productivity: a tutorial using data from an Australian hospital. Anesth Analg. 2006;103:1499–516
43. Wachtel RE, Dexter F. Difficulties and challenges associated with literature searches in operating room management, complete with recommendations. Anesth Analg. 2013;117:1460–79
44. Strum DP, Vargas LG, May JH, Bashein G. Surgical suite utilization and capacity planning: a minimal cost analysis model. J Med Syst. 1997;21:309–22
45. Pandit JJ, Dexter F. Lack of sensitivity of staffing for 8-hour sessions to standard deviation in daily actual hours of operating room time used for surgeons with long queues. Anesth Analg. 2009;108:1910–5
46. He B, Dexter F, Macario A, Zenios S. The timing of staffing decisions in hospital operating rooms: incorporating workload heterogeneity into the newsvendor problem. Manuf Serv Op. 2012;14:99–114
47. Dexter F, Epstein RH, Traub RD, Xiao Y. Making management decisions on the day of surgery based on operating room efficiency and patient waiting times. Anesthesiology. 2004;101:1444–53
48. Dexter F, Traub RD. How to schedule elective surgical cases into specific operating rooms to maximize the efficiency of use of operating room time. Anesth Analg. 2002;94:933–42
49. Wachtel RE, Dexter F. Review article: review of behavioral operations experimental studies of newsvendor problems for operating room management. Anesth Analg. 2010;110:1698–710
50. Prahl A, Dexter F, Braun MT, Van Swol L. Review of experimental studies in social psychology of small groups when an optimal choice exists and application to operating room management decision-making. Anesth Analg. 2013;117:1221–9
51. Masursky D, Dexter F, Nussmeier NA. Operating room nursing directors’ influence on anesthesia group operating room productivity. Anesth Analg. 2008;107:1989–96
52. Dexter F, Wachtel RE, Epstein RH, Ledolter J, Todd MM. Analysis of operating room allocations to optimize scheduling of specialty rotations for anesthesia trainees. Anesth Analg. 2010;111:520–4
53. Chelly JE, Horne JL, Hudson ME, Williams JP. Factors impacting on-time transfer to the operating room in patients undergoing peripheral nerve blocks in the preoperative area. J Clin Anesth. 2010;22:115–21
54. Dexter F, Shi P, Epstein RH. Descriptive study of case scheduling and cancellations within 1 week of the day of surgery. Anesth Analg. 2012;115:1188–95
55. Dexter F, Epstein RH, Elgart RL, Ledolter J. Forecasting and perception of average and latest hours worked by on-call anesthesiologists. Anesth Analg. 2009;109:1246–52
56. Vanberkel PT, Boucherie RJ, Hans EW, Hurink JL, van Lent WA, van Harten WH. Accounting for inpatient wards when developing master surgical schedules. Anesth Analg. 2011;112:1472–9
57. Chow VS, Puterman ML, Salehirad N, Huang W, Atkins D. Reducing surgical ward congestion through improved surgical scheduling and uncapacitated simulation. Prod Oper Manag. 2011;20:418–30
58. Meyfroidt G, Güiza F, Cottem D, De Becker W, Van Loon K, Aerts JM, Berckmans D, Ramon J, Bruynooghe M, Van den Berghe G. Computerized prediction of intensive care unit discharge after cardiac surgery: development and validation of a Gaussian processes model. BMC Med Inform Decis Mak. 2011;11:64
59. van Essen JT, Bosch JM, Hans EW, van Houdenhoven M, Hurink JL. Reducing the number of required beds by rearranging the OR-schedule. OR Spectrum. 2014
60. McManus ML, Long MC, Cooper A, Mandell J, Berwick DM, Pagano M, Litvak E. Variability in surgical caseload and access to intensive care services. Anesthesiology. 2003;98:1491–6
61. Litvak E, Fineberg HV. Smoothing the way to high quality, safety, and economy. N Engl J Med. 2013;369:1581–3
62. Dexter F, Epstein RH. Optimizing second shift OR staffing. AORN J. 2003;77:825–30
63. Junger A, Benson M, Quinzio L, Michel A, Sciuk G, Brammen D, Marquardt K, Hempelmann G. An Anesthesia Information Management System (AIMS) as a tool for controlling resource management of operating rooms. Methods Inf Med. 2002;41:81–5
64. Xiao Y, Hu P, Hu H, Ho D, Dexter F, Mackenzie CF, Seagull FJ, Dutton RP. An algorithm for processing vital sign monitoring data to remotely identify operating room occupancy in real-time. Anesth Analg. 2005;101:823–9
65. Epstein RH, Dexter F, Piotrowski E. Automated correction of room location errors in anesthesia information management systems. Anesth Analg. 2008;107:965–71
66. Dexter F, Willemsen-Dunlap A, Lee JD. Operating room managerial decision-making on the day of surgery with and without computer recommendations and status displays. Anesth Analg. 2007;105:419–29
67. Dexter F, Epstein RH, Lee JD, Ledolter J. Automatic updating of times remaining in surgical cases using bayesian analysis of historical case duration data and “instant messaging” updates from anesthesia providers. Anesth Analg. 2009;108:929–40
68. Epstein RH, Dexter F. Mediated interruptions of anaesthesia providers using predictions of workload from anaesthesia information management system data. Anaesth Intensive Care. 2012;40:803–12
69. Epstein RH, Dexter F, Ehrenfeld JM, Sandberg WS. Implications of event entry latency on anesthesia information management decision support systems. Anesth Analg. 2009;108:941–7
70. Schuster M, Neumann C, Neumann K, Braun J, Geldner G, Martin J, Spies C, Bauer MCASCAES Study Group. . The effect of hospital size and surgical service on case cancellation in elective surgery: results from a prospective multicenter study. Anesth Analg. 2011;113:578–85
71. Dexter F, Maxbauer T, Stout C, Archbold L, Epstein RH. Relative influence on total cancelled operating room time from patients who are inpatients or outpatients preoperatively. Anesth Analg. 2014;118:1072–80
72. Dexter F, Traub RD, Macario A. How to release allocated operating room time to increase efficiency: predicting which surgical service will have the most underutilized operating room time. Anesth Analg. 2003;96:507–12
73. Dexter F, Macario A. When to release allocated operating room time to increase operating room efficiency. Anesth Analg. 2004;98:758–62
74. Epstein RH, Dexter F. Rescheduling of previously cancelled surgical cases does not increase variability in operating room workload when cases are scheduled based on maximizing efficiency of use of operating room time. Anesth Analg. 2013;117:995–1002
75. Tait AR, Voepel-Lewis T, Munro HM, Gutstein HB, Reynolds PI. Cancellation of pediatric outpatient surgery: economic and emotional implications for patients and their families. J Clin Anesth. 1997;9:213–9
76. Ivarsson B, Kimblad PO, Sjöberg T, Larsson S. Patient reactions to cancelled or postponed heart operations. J Nurs Manag. 2002;10:75–81
77. Tung A, Dexter F, Jakubczyk S, Glick DB. The limited value of sequencing cases based on their probability of cancellation. Anesth Analg. 2010;111:749–56
78. Gaba DM, Howard SK, Jump B. Production pressure in the work environment. California anesthesiologists’ attitudes and experiences. Anesthesiology. 1994;81:488–500
79. Pollard JB, Zboray AL, Mazze RI. Economic benefits attributed to opening a preoperative evaluation clinic for outpatients. Anesth Analg. 1996;83:407–10
80. Fischer SP. Development and effectiveness of an anesthesia preoperative evaluation clinic in a teaching hospital. Anesthesiology. 1996;85:196–206
81. van Klei WA, Moons KG, Rutten CL, Schuurhuis A, Knape JT, Kalkman CJ, Grobbee DE. The effect of outpatient preoperative evaluation of hospital inpatients on cancellation of surgery and length of hospital stay. Anesth Analg. 2002;94:644–9
82. Ferschl MB, Tung A, Sweitzer B, Huo D, Glick DB. Preoperative clinic visits reduce operating room cancellations and delays. Anesthesiology. 2005;103:855–9
83. Roberts RR, Frutos PW, Ciavarella GG, Gussow LM, Mensah EK, Kampe LM, Straus HE, Joseph G, Rydman RJ. Distribution of variable vs fixed costs of hospital care. JAMA. 1999;281:644–9
84. Taheri PA, Butz DA. Health care as a fixed-cost industry: implications for delivery. Surg Innov. 2005;12:365–71
85. Macario A, Dexter F, Traub RD. Hospital profitability per hour of operating room time can vary among surgeons. Anesth Analg. 2001;93:669–75
86. Dexter F, Blake JT, Penning DH, Lubarsky DA. Calculating a potential increase in hospital margin for elective surgery by changing operating room time allocations or increasing nursing staffing to permit completion of more cases: a case study. Anesth Analg. 2002;94:138–42
87. Dexter F, Ledolter J, Wachtel RE. Tactical decision making for selective expansion of operating room resources incorporating financial criteria and uncertainty in subspecialties’ future workloads. Anesth Analg. 2005;100:1425–32
88. Dexter F, Coffin S, Tinker JH. Decreases in anesthesia-controlled time cannot permit one additional surgical operation to be reliably scheduled during the workday. Anesth Analg. 1995;81:1263–8
89. Abouleish AE, Dexter F, Whitten CW, Zavaleta JR, Prough DS. Quantifying net staffing costs due to longer-than-average surgical case durations. Anesthesiology. 2004;100:403–12
90. Silber JH, Rosenbaum PR, Zhang X, Even-Shoshan O. Estimating anesthesia and surgical procedure times from medicare anesthesia claims. Anesthesiology. 2007;106:346–55
91. Urman RD, Sarin P, Mitani A, Philip B, Eappen S. Presence of anesthesia resident trainees in day surgery unit has mixed effects on operating room efficiency measures. Ochsner J. 2012;12:25–9
92. Dexter F, Marcon E, Aker J, Epstein RH. Numbers of simultaneous turnovers calculated from anesthesia or operating room information management system data. Anesth Analg. 2009;109:900–5
93. Wang J, Dexter F, Yang K. A behavioral study of daily mean turnover times and first case of the day start tardiness. Anesth Analg. 2013;116:1333–41
94. Dexter F, Epstein RH, Penning DH. Statistical analysis of postanesthesia care unit staffing at a surgical suite with frequent delays in admission from the operating room–a case study. Anesth Analg. 2001;92:947–9
95. Dexter F, Epstein RH, Marcon E, de Matta R. Strategies to reduce delays in admission into a postanesthesia care unit from operating rooms. J Perianesth Nurs. 2005;20:92–102
96. Schoenmeyr T, Dunn PF, Gamarnik D, Levi R, Berger DL, Daily BJ, Levine WC, Sandberg WS. A model for understanding the impacts of demand and capacity on waiting time to enter a congested recovery room. Anesthesiology. 2009;110:1293–304
97. Ehrenfeld JM, Dexter F, Rothman BS, Minton BS, Johnson D, Sandberg WS, Epstein RH. Lack of utility of a decision support system to mitigate delays in admission from the operating room to the postanesthesia care unit. Anesth Analg. 2013;117:1444–52
98. Smith MP, Sandberg WS, Foss J, Massoli K, Kanda M, Barsoum W, Schubert A. High-throughput operating room system for joint arthroplasties durably outperforms routine processes. Anesthesiology. 2008;109:25–35
99. Marcon E, Kharraja S, Smolski N, Luquet B, Viale JP. Determining the number of beds in the postanesthesia care unit: a computer simulation flow approach. Anesth Analg. 2003;96:1415–23
100. Russell RA, Burke K, Gattis K. Implementing a regional anesthesia block nurse team in the perianesthesia care unit increases patient safety and perioperative efficiency. J Perianesth Nurs. 2013;28:3–10
101. Epstein RH, Dexter F, Brull SJ. Cohort study of cases with prolonged tracheal extubation times to examine the relationship with duration of workday. Can J Anaesth. 2013;60:1070–6
102. Dexter F, Epstein RH. Increased mean time from end of surgery to operating room exit in a historical cohort of cases with prolonged time to extubation. Anesth Analg. 2013;117:1453–9
103. Masursky D, Dexter F, Kwakye MO, Smallman B. Measure to quantify the influence of time from end of surgery to tracheal extubation on operating room workflow. Anesth Analg. 2012;115:402–6
104. Agoliati A, Dexter F, Lok J, Masursky D, Sarwar MF, Stuart SB, Bayman EO, Epstein RH. Meta-analysis of average and variability of time to extubation comparing isoflurane with desflurane or isoflurane with sevoflurane. Anesth Analg. 2010;110:1433–9
105. Dexter F, Bayman EO, Epstein RH. Statistical modeling of average and variability of time to extubation for meta-analysis comparing desflurane to sevoflurane. Anesth Analg. 2010;110:570–80
106. Apfelbaum JL, Grasela TH, Hug CC Jr, McLeskey CH, Nahrwold ML, Roizen MF, Stanley TH, Thisted RA, Walawander CA, White PF. The initial clinical experience of 1819 physicians in maintaining anesthesia with propofol: characteristics associated with prolonged time to awakening. Anesth Analg. 1993;77:S10–4
107. Torkki PM, Marjamaa RA, Torkki MI, Kallio PE, Kirvelä OA. Use of anesthesia induction rooms can increase the number of urgent orthopedic cases completed within 7 hours. Anesthesiology. 2005;103:401–5
108. Hanss R, Buttgereit B, Tonner PH, Bein B, Schleppers A, Steinfath M, Scholz J, Bauer M. Overlapping induction of anesthesia: an analysis of benefits and costs. Anesthesiology. 2005;103:391–400
109. Williams BA, Starling SL, Bircher NG, Wilks DH, Watkins WD. Optimization of anesthesia staffing using simulation modeling. Am J Anesthesiol. 1998;25:113–20
110. Marjamaa RA, Torkki PM, Hirvensalo EJ, Kirvelä OA. What is the best workflow for an operating room? A simulation study of five scenarios. Health Care Manag Sci. 2009;12:142–6
111. Dexter F, Epstein RH. Typical savings from each minute reduction in tardy first case of the day starts. Anesth Analg. 2009;108:1262–7
112. Macario A, Dexter F. Effect of compensation and patient scheduling on OR labor costs. AORN J. 2000;71:860, 863–9
113. Wachtel RE, Dexter F. Influence of the operating room schedule on tardiness from scheduled start times. Anesth Analg. 2009;108:1889–901
114. Sulecki L, Dexter F, Zura A, Saager L, Epstein RH. Lack of value of scheduling processes to move cases from a heavily used main campus to other facilities within a health care system. Anesth Analg. 2012;115:395–401
115. Dexter F, Masursky D, Ledolter J, Wachtel RE, Smallman B. Monitoring changes in individual surgeon’s workloads using anesthesia data. Can J Anaesth. 2012;59:571–7
116. Wachtel RE, Dexter F, Epstein RH, Ledolter J. Meta-analysis of desflurane and propofol average times and variability in times to extubation and following commands. Can J Anaesth. 2011;58:714–24
117. Wax DB, Beilin Y, Levin M, Chadha N, Krol M, Reich DL. The effect of an interactive visual reminder in an anesthesia information management system on timeliness of prophylactic antibiotic administration. Anesth Analg. 2007;104:1462–6
118. Nair BG, Newman SF, Peterson GN, Wu WY, Schwid HA. Feedback mechanisms including real-time electronic alerts to achieve near 100% timely prophylactic antibiotic administration in surgical cases. Anesth Analg. 2010;111:1293–300
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121. Schwann NM, Bretz KA, Eid S, Burger T, Fry D, Ackler F, Evans P, Romancheck D, Beck M, Ardire AJ, Lukens H, McLoughlin TM. Point-of-care electronic prompts: an effective means of increasing compliance, demonstrating quality, and improving outcome. Anesth Analg. 2011;113:869–76
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124. Dexter F, Tinker JH. The cost efficacy of hypothetically eliminating adverse anesthetic outcomes from high-risk, but neither low- nor moderate-risk, surgical operations. Anesth Analg. 1995;81:939–44
125. Kariv Y, Delaney CP, Senagore AJ, Manilich EA, Hammel JP, Church JM, Ravas J, Fazio VW. Clinical outcomes and cost analysis of a “fast track” postoperative care pathway for ileal pouch-anal anastomosis: a case control study. Dis Colon Rectum. 2007;50:137–46
126. Maessen J, Dejong CH, Hausel J, Nygren J, Lassen K, Andersen J, Kessels AG, Revhaug A, Kehlet H, Ljungqvist O, Fearon KC, von Meyenfeldt MF. A protocol is not enough to implement an enhanced recovery programme for colorectal resection. Br J Surg. 2007;94:224–31
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131. Dexter F, Macario A, Dexter EU. Computer simulation of changes in nursing productivity from early tracheal extubation of coronary artery bypass graft patients. J Clin Anesth. 1998;10:593–8
132. Dexter F, Macario A, Manberg PJ, Lubarsky DA. Computer simulation to determine how rapid anesthetic recovery protocols to decrease the time for emergence or increase the phase I postanesthesia care unit bypass rate affect staffing of an ambulatory surgery center. Anesth Analg. 1999;88:1053–63
133. Hsu SH. Cost information and pricing: empirical evidence. Contemporary Accounting Research. 2011;28:554–79
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136. Patterson J. The effects of nurse to patient ratios. Nurs Times. 2011;107:22–5
137. Scurlock C, Dexter F, Reich DL, Galati M. Needs assessment for business strategies of anesthesiology groups’ practices. Anesth Analg. 2011;113:170–4
138. Wachtel RE, Dexter F, Lubarsky DA. Financial implications of a hospital’s specialization in rare physiologically complex surgical procedures. Anesthesiology. 2005;103:161–7
139. Reymondon F, Pellet B, Marcon E. Optimization of hospital sterilization costs proposing new grouping choices of medical devices into packages. Int J Production Economics. 2008;112:326–35
140. Dexter F, Dexter EU, Ledolter J. Influence of procedure classification on process variability and parameter uncertainty of surgical case durations. Anesth Analg. 2010;110:1155–63
141. Montgomery K, Schneller ES. Hospitals’ strategies for orchestrating selection of physician preference items. Milbank Q. 2007;85:307–35
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