This paper presents the development, validation, and implementation of a data-driven optimization model designed to dynamically plan the assignment of anesthesiologists across multiple hospital locations within a large multi-specialty healthcare system. We formulate the problem as a multi-stage robust mixed-integer program incorporating on-call flexibility to address demand uncertainty. In the first stage, anesthesiologists are assigned to specific locations or an on-call pool several weeks before the day of surgery. In the second stage, on-call staff are deployed to particular locations based on demand forecasts received days before the surgeries. Finally, in the third stage, overtime and idle time are realized. To ensure practicality and real-world applicability, the model considers individual anesthesiologist location constraints and incorporates fairness considerations for on-call assignments. We exploit the problem's structure to reformulate the multi-stage robust optimization problem into a large-scale mixed-integer linear program. To solve this optimization problem efficiently, we propose a nested column and constraint generation method. Uncertainty in demand forecasts and workload is estimated based on historical data, with a calibration procedure that balances optimality and conservatism. The optimized dynamic staffing plan has been successfully implemented in the University of Pittsburgh Medical Center healthcare system, leading to estimated annual cost savings of 12\% compared to current practice, or about \$800,000 annually. We also provide managerial insights into the importance of improved forecasts, location flexibility, and the impact of fairness constraints. The proposed methodology is generalizable to other areas of healthcare staffing, such as nurse staffing, with similar workforce planning challenges.

Measuring and comparing clinical productivity of individual anesthesiologists is confounded by anesthesiologist-independent factors, including facility-specific factors (case duration, anesthetizing site utilization, type of surgical procedure, and non–operating room locations), staffing ratio, number of calls, and percentage of clinical time providing anesthesia. Further, because anesthesia care is billed with different units than relative value units, comparing work with other types of clinical care is difficult. Finally, anesthesia staffing needs are not based on productivity measurements but primarily the number and hours of operation of anesthetizing sites. The intent of this review is to help anesthesiologists, anesthesiology leaders, and facility leaders understand the limitations of anesthesia unit productivity as a comparative metric of work, how this metric often devalues actual work, and the impact of organizational differences, staffing models and coverage requirements, and effectiveness of surgical case load management on both individual and group productivity.

Background and objectives: This study seeks to establish Provider Team Efficiency (PTE) as a useful operating room efficiency metric and to associate a dollar value as a cost to the institution for a decrement in PTE for each site within our academic center's network. In the secondary analysis, multiple weighted linear regressions were performed to determine additional institutional factors, which might affect the corresponding PTE. Methods: PTE is defined here as total surgical hours per total CRNA staffed hours per day, from 07:00 to 17:00, for the calendar year of 2018, within each surgical site represented as a percentage. Six institutional locations with operating rooms in a large academic health network were involved in data collection, where multivariate weighted linear regressions were performed on PTE, Operating Cost per Hour, Number of operating rooms working per day, Number of surgical cases in a year, Hours per case, and Hours per OR per day. Results: The weighted regression analysis demonstrated that every 1% increase in PTE was correlated with just over an $11 decrease in cost per OR hour (p = 0.02, R2 = 0.76). No statistically significant results were found with hours per case, number of cases, or the average number of operating rooms running per day; however, there was a strong, positively associated correlation between hours per OR per day and PTE (p = 0.005, R2 = 0.89). Conclusions: The primary analysis in our current work illustrates that Provider Team Efficiency (PTE) is negatively correlated with OR cost per hour to a statistically significant degree associated with a loss of a fixed dollar amount. In the secondary analysis, while total operating rooms, total cases, and case duration did not appear to correlate with PTE, Hours per OR per Day had a strongly positive association. This has substantial ramifications for operating room practice management since Hours per OR per day- a much more readily available metric- can be used as a surrogate value for PTE in determining OR cost savings per hour.

Features| November 2022 Staffing and Efficiency in the OR Katherine Grichnik, MD, MS, FASE; Katherine Grichnik, MD, MS, FASE Search for other works by this author on: This Site PubMed Google Scholar Jay Mesrobian, MD, MBA, FASA; Jay Mesrobian, MD, MBA, FASA Search for other works by this author on: This Site PubMed Google Scholar Joseph W. Szokol, MD, JD, MBA; Joseph W. Szokol, MD, JD, MBA Search for other works by this author on: This Site PubMed Google Scholar Patricia Fogarty Mack, MD, FASA; Patricia Fogarty Mack, MD, FASA Search for other works by this author on: This Site PubMed Google Scholar Mitchell H. Tsai, MD, MMM, FASA, FAACD; Mitchell H. Tsai, MD, MMM, FASA, FAACD Search for other works by this author on: This Site PubMed Google Scholar Steven Schulman, MD, MHA, FASA; Steven Schulman, MD, MHA, FASA Search for other works by this author on: This Site PubMed Google Scholar Mark Edward Hudson, MD, MBA Mark Edward Hudson, MD, MBA Search for other works by this author on: This Site PubMed Google Scholar ASA Monitor November 2022, Vol. 86, 24–25. https://doi.org/10.1097/01.ASM.0000897352.16692.46 Views Icon Views Article contents Figures & tables Video Audio Supplementary Data Peer Review Share Icon Share Facebook Twitter LinkedIn MailTo Cite Icon Cite Get Permissions Search Site Citation Katherine Grichnik, Jay Mesrobian, Joseph W. Szokol, Patricia Fogarty Mack, Mitchell H. Tsai, Steven Schulman, Mark Edward Hudson; Staffing and Efficiency in the OR. ASA Monitor 2022; 86:24–25 doi: https://doi.org/10.1097/01.ASM.0000897352.16692.46 Download citation file: Ris (Zotero) Reference Manager EasyBib Bookends Mendeley Papers EndNote RefWorks BibTex toolbar search Search Dropdown Menu toolbar search search input Search input auto suggest filter your search All ContentAll PublicationsASA Monitor Search Advanced Search Topics: personnel staffing and scheduling The primary challenge facing anesthesiology practices nationwide is the shortage of anesthesia providers. While efforts to address provider supply are necessary, maximizing the capacity of our current teams to provide care is essential. This requires systems and processes that optimize the combined resource capability (hospital/facility and anesthesia) for surgical and nonoperating room anesthesia (NORA) care to achieve effectiveness and efficiency. While these efforts are aligned with hospital/facility goals and support the value proposition in anesthesia care delivery, in many cases, they will require significant operational management changes. This article explores ways to focus attention on improving the utilization of available anesthesia resources through thoughtful data-driven planning and management, thereby maximizing the overall capacity of anesthesia providers to be available for patient care in our current resource-restricted environment. Inefficient OR schedules frustrate health care providers and patients. Although efficiency will never reach 100%, it can be increased through standardizing clinical and... You do not currently have access to this content.

There is no gold standard criterion for the diagnosis of cystic fibrosis‐related liver disease (CFRLD) and there is uncertainty over its impact on the outcome of lung transplantation.

Background Northern England has been experiencing a persistent rise in the number of primary liver cancers, largely driven by an increasing incidence of hepatocellular carcinoma (HCC) secondary to alcohol-related liver disease and non-alcoholic fatty liver disease. Here we review the effect of the COVID-19 pandemic on primary liver cancer services and patients in our region. Objective To assess the impact of the COVID-19 pandemic on patients with newly diagnosed liver cancer in our region. Design We prospectively audited our service for the first year of the pandemic (March 2020–February 2021), comparing mode of presentation, disease stage, treatments and outcomes to a retrospective observational consecutive cohort immediately prepandemic (March 2019–February 2020). Results We observed a marked decrease in HCC referrals compared with previous years, falling from 190 confirmed new cases to 120 (37%). Symptomatic became the the most common mode of presentation, with fewer tumours detected by surveillance or incidentally (% surveillance/incidental/symptomatic; 34/42/24 prepandemic vs 27/33/40 in the pandemic, p=0.013). HCC tumour size was larger in the pandemic year (60±4.6 mm vs 48±2.6 mm, p=0.017), with a higher incidence of spontaneous tumour haemorrhage. The number of new cases of intrahepatic cholangiocarcinoma (ICC) fell only slightly, with symptomatic presentation typical. Patients received treatment appropriate for their cancer stage, with waiting times shorter for patients with HCC and unchanged for patients with ICC. Survival was associated with stage both before and during the pandemic. 9% acquired COVID-19 infection. Conclusion The pandemic-associated reduction in referred patients in our region was attributed to the disruption of routine healthcare. For those referred, treatments and survival were appropriate for their stage at presentation. Non-referred or missing patients are expected to present with more advanced disease, with poorer outcomes. While protective measures are necessary during the pandemic, we recommend routine healthcare services continue, with patients encouraged to engage.

Objective:To describe survival of patients with hepatic encephalopathy (HE), up to 5 years after initiation of rifaximin-α (RFX) treatment.Design/Method:A retrospective, observational extension study within 9 National Health Service secondary/tertiary UK care centres. All patients had a clinical diagnosis of HE, were being treated with RFX and were included in the previous IMPRESS study which reported the 1-year experience. Demographics, clinical outcomes, selected cirrhosis-related complications, hospital admissions and attendances up to 5 years from RFX initiation were extracted from patient medical records and hospital electronic databases. The primary outcome measure was survival at 5 years post-initiation of RFX treatment.Results:The study included 138 patients. The survival rate at 5 years post-initiation of RFX was 35% (95% CI 28.2% to 44.4%) overall and 36% (95% CI 26.1% to 45.4%) for patients with alcohol-related liver disease. Median survival from RFX initiation was 2.8 years (95% CI 2.0 to 3.8; n=136). Among 48 patients alive at 5 years, 69% remained on RFX treatment at the end of the observation period, 74% reported no cirrhosis-related complications and 24% (9/37) had received a liver transplant. Between 1 and 5 years post-initiation, total numbers of liver-related emergency department visits, inpatient admissions, intensive care unit admissions and outpatient visits were 84, 194, 3 and 709, respectively; the liver-related 30-day readmission rate was 37%.Conclusion:Within UK clinical practice, RFX use in HE was associated with a 35% survival rate with high treatment adherence, 76% transplant-free survival rate, minimal healthcare resource and low rates of complications at 5 years post-initiation.

Introduction Transplantation is an established therapy for end-stage liver disease and there is great demand for donor organs. Psychosocial concerns are prevalent in patients undergoing liver transplant (OLT) assessment and this may impact on post-transplant outcomes. In the UK there is no current standardised method quantifying OLT candidates’ psychosocial risk. We investigated the use of a psychosocial risk assessment tool in assessing post-transplantation outcomes. Methods A psychosocial risk profile tool (mSIPAT) was retrospectively applied to all patients who underwent elective OLT assessment between January 2010 and December 2014. Electronic records were reviewed from the date of assessment and data was collected regarding assessment outcome, transplantation, late onset medical/psychiatric complications and mortality. Results 273 patients were identified during the 5-year time period. The median age of patients at the time of assessment was 57 years and 62.3% of patients were male. Of those assessed 168 patients were listed for transplant and 126 underwent OLT. The main indication for transplant was synthetic dysfunction (35.9%). 178 (65%) patients assessed were classed as meeting criteria for medium/high psychosocial risk (MPSR). MPSR patients when compared to low-risk patients (LPSR) were more likely to have an underlying alcohol related liver disease (p=0.005), were less likely to undergo transplantation (p<0.001) and were more likely to return to alcohol misuse post assessment (p<0.001). MPSR patients had a significantly higher all-cause mortality than LPSR patients (p=0.005). Patients who died scored significantly higher in the majority of the components of the mSIPAT. Undergoing transplantation (OR 0.10 (95%CI 0.06-0.20)) and concerns regarding financial support (OR 4.85 (95%CI 1.16-20.32) were factors independently associated with mortality when corrected for age and gender. Figure 1 illustrates the survival difference between those who classed as MPSR and LPSR. In those transplanted MPSR patients had a significantly higher mortality (p=0.037). 35 (27.7%) patients who underwent transplant died during the follow up period. Factors significantly associated with mortality post transplantation were recidivism (p=0.026) and concerns regarding social (p=0.002) and financial support (p=0.050). Conclusions Patients classed as MSPR had a higher all-cause mortality and an increased likelihood of recidivism. Using the mSiPAT as part of the elective OLT assessment can potentially identify both those at highest risk and areas on which to focus support in order to prevent poor outcomes.

Objective: We expand the application of cost frontiers and introduce a novel approach using qualitative multivariable financial analyses. Summary Background Data: With the creation of a 5 + 2-year fellowship program in July 2016, the Division of Vascular Surgery at the University of Vermont Medical Center altered the underlying operational structure of its inpatient services. Method: Using WiseOR (Palo Alto, CA), a web-based OR management data system, we extracted the operating room metrics before and after August 1, 2016 service for each 4-week period spanning from September 2015 to July 2017. The cost per minute modeled after Childers et al’s inpatient OR cost guidelines was multiplied by the after-hours utilization to determine variable cost. Zones with corresponding cutoffs were used to graphically represent cost efficiency trends. Results: Caseload/FTE for attending surgeons increased from 11.54 cases per month to 13.02 cases per month (P = 0.0771). Monthly variable costs/FTE increased from $540.2 to $1873 (P = 0.0138). Monthly revenue/FTE increased from $61,505 to $70,277 (P = 0.2639). Adjusted monthly reve-nue/FTE increased from $60,965 to $68,403 (P = 0.3374). Average monthly percent of adjusted revenue/FTE lost to variable costs increased from 0.85% to 2.77% (P = 0.0078). Adjusted monthly revenue/case/FTE remained the same from $5309 to $5319 (P = 0.9889). Conclusion: In summary, we demonstrate that multivariable cost (or performance) frontiers can track a net increase in profitability associated with fellowship implementation despite diminishing returns at higher caseloads.

Background: Biliary leaks and anastomotic strictures are common early anastomotic biliary complications (EABCs) following liver transplantation. However, there are no large multicentre studies investigating their clinical impact or risk factors. This study aimed to define the incidence, risk factors and impact of EABC. Methods: The NHS registry on adult liver transplantation between 2006 and 2017 was reviewed retrospectively. Adjusted regression models were used to assess predictors of EABC, and their impact on outcomes. Results: Analyses included 8304 liver transplant recipients. Patients with EABC (9.6 per cent) had prolonged hospitalization (23 versus 15days; P<0.001) and increased chance for readmission within the first year (56 versus 32 per cent; P<0.001). Patients with EABC had decreased estimated 5-year graft survival of 75.1 versus 84.5 per cent in those without EABC, and decreased 5-year patient survival of 76.9 versus 83.3 per cent; both P<0.001. Adjusted Cox regression revealed that EABCs have a significant and independent impact on graft survival (leak hazard ratio (HR) 1.344, P=0.015; stricture HR 1.513, P=0.002; leak plus stricture HR 1.526, P=0.036) and patient survival (leak HR 1.215, P=0.136, stricture HR 1.526, P=0.001; leak plus stricture HR 1.509; P=0.043). On adjusted logistic regression, risk factors for EABC included donation after circulatory death grafts, graft aberrant arterial anatomy, biliary anastomosis type, vascular anastomosis time and recipient model of end-stage liver disease. Conclusion: EABCs prolong hospital stay, increase readmission rates and are independent risk factors for graft loss and increased mortality. This study has identified factors that increase the likelihood of EABC occurrence; research into interventions to prevent EABCs in these at-risk groups is vital to improve liver transplantation outcomes.

cells and the associated fatty deposition inside the liver which lead to NAFLD as well as NASH . We also explored the effect of antiviral treatment, mainly Tenofovir (TDF) and Entecavir (ETV), on the renal function of CHB patients with T2DM. Results showed a significant decrease in glomerular filtration rate (eGFR) after taking antiviral medication, with median eGFR pre-treatment of 89 ml/ min/1.73m2 (IQR=14) for TDF and 84 ml/min/1.73m2 (IQR=19) for ETV, and latest eGFR of 74 ml/min/1.73m2 (IQR=19) for TDF, 70 ml/min/1.73m2 for ETV (IQR=20). Results also showed a marked increase in serum creatinine levels as well for both groups pre and post-treatment with pretreatment serum creatinine levels of 82 mmol/L (IQR=22) for TDF and 86 mmol/L (IQR=26) for ETV, and latest serum creatinine levels of 90 mmol/L for TDF (IQR= 21) and 95 mmol/L for ETV (IQR= 23). We then divided the levels of eGFR at different levels of HBA1C pre and post treatment with TDF for clinical significance, it showed a marked decrease in levels of eGFR especially in prediabetic groups, with pre-treatment median eGFR of 82 ml/min/1.73m2 to median eGFR of 64 ml/min/1.73m2 for post-treatment results. Another perspective that should be re-considered is the role of nurse-led virtual clinics in the management of chronic hepatitis B patients. Many liver services in the UK have started to adopt the concept of virtual clinics in the follow-up of chronic hepatitis B patients for their cost-effectiveness and better compliance . However, virtual clinics only deal with lab results and patient complaints for detection of disease flares and disease progression, but in term of screening and monitoring of metabolic risk factors such as increasing weight or elevation in blood pressure, this model can be considered ineffective. Conclusion The effect of Metabolic syndrome and its risk factors on chronic hepatitis B patients is yet to be unravelled. The results showed a considerable number of patients with metabolic risk factors in our cohort of patients. It also presented alarming results related to the number of overweight patients and the ones with uncontrolled Type 2 Diabetes. Our study also confirmed our hypothesis of a significant and strong correlation between the degree of liver inflammation which is represented in elevated ALT levels and an Increase in both BMI and HBA1C. Even with our large study population, and the robust and strict exclusion criteria that have been implemented to strengthen this study, some limitations have been considered during the interpretation of its results. However, providing this data can give a rationale for health promotion to reduce cardiovascular risk among chronic hepatitis B patients. Dietitian, as well as Diabetes experts, should be involved in the follow-up of overweight/obese patients with elevated ALT levels to prevent disease progression and its associated complications. Given the high number of patients with uncontrolled diabetes, consideration for re-choice of antiviral drugs should be considered given the diabetic nephropathy these patients may have as well as the adverse effect they can be having from antiviral medications. Clinicians should have a more active approach toward identifying and eliminating metabolic risk factors as well as cardiovascular risk in patients treated for chronic hepatitis B. Finally, comprehensive and longitudinal studies should be conducted for a better understanding of the extension of metabolic risk factors in our cohort and to accurately assess the associations between metabolic syndrome and chronic hepatitis B infection. REFERENCES 1. EASL. EASL 2017 Clinical Practice Guidelines on the management of hepatitis B virus infection. J Hepatol, v. 67, n. 2, p. 370-398, Aug 2017. ISSN 0168-8278. Disponível em: < http://dx.doi.org/10.1016/j.jhep.2017.03.021 >. 2. TERRAULT, N. A. et al. Update on prevention, diagnosis, and treatment of chronic hepatitis B: AASLD 2018 hepatitis B guidance. Hepatology, v. 67, n. 4, p. 15601599, 04 2018. ISSN 1527-3350. Disponível em: < https://www.ncbi.nlm.nih. gov/pubmed/29405329 >. 3. LIN, C. W. et al. Interactions of Hepatitis B Virus Infection with Nonalcoholic Fatty Liver Disease: Possible Mechanisms and Clinical Impact. Dig Dis Sci, v. 60, n. 12, p. 3513-24, Dec 2015. ISSN 0163-2116. Disponível em: < http://dx.doi.org/ 10.1007/s10620-015-3772-z >. 4. MAK, L. Y. et al. Association of adipokines with hepatic steatosis and fibrosis in chronic hepatitis B patients on long-term nucleoside analogue. Liver Int, Mar 25 2019. ISSN 1478-3223. Disponível em: < http://dx.doi.org/10.1111/liv.14104 >. 5. HEATHCOTE, E. J. Demography and presentation of chronic hepatitis B virus infection. Am J Med, v. 121, n. 12 Suppl, p. S3-11, Dec 2008. ISSN 0002-9343. Disponível em: < http://dx.doi.org/10.1016/j.amjmed.2008.09.024 >. 6. DIAS, A. Chronic hepatitis B infection in the immigrant communities of East London. 2014-02 2014. Disponível em: < https://qmro.qmul.ac.uk/xmlui/handle/ 123456789/8963 >. 7. PATI, G. K.; SINGH, S. P. Nonalcoholic Fatty Liver Disease in South Asia. Euroasian J Hepatogastroenterol, v. 6, n. 2, p. 154-62, Jul-Dec 2016. ISSN 22315047 (Print)2231-5128 (Electronic). Disponível em: < http://dx.doi.org/10.5005/jpjournals-10018-1189 >. 8. LONARDO, A. et al. Nonalcoholic fatty liver disease: Evolving paradigms. World J Gastroenterol, v. 23, n. 36, p. 6571-92, Sep 28 2017. ISSN 1007-9327 (Print) 2219-2840 (Electronic). Disponível em: < http://dx.doi.org/10.3748/wjg.v23. i36.6571 >. 9. CORRIGALL, D. et al. PTH-086|Virtual hepatitis B clinics significantly improve cost and clinical effectiveness. 2018-06-01 2018. Disponível em: < https://gut.bmj. com/content/67/Suppl_1/A121.2 >.

A nursing strike represents an operational disruption from normal perioperative services. The impact a nursing strike has on a perioperative system has not been well studied. The purpose of this study was to evaluate the realized and unrealized financial and operational impacts a nursing strike has on perioperative services. This was a retrospective analysis conducted at an academic, rural medical center during a nursing strike. During the nursing strike, the hospital implemented measures to maintain patient safety and continue normal operations. Several operating room (OR) management and clinical productivity metrics were used to evaluate the realized and unrealized financial and operational impacts. First Case Start Delay times increased (+69%), Scheduling Error decreased (-42%), Clinical Productivity decreased (-6%), Total ASA units decreased (-44%), Productivity per Attending decreased (-55%), estimated missed revenue of $864,118, and missed expected profit of $432,059. The changes in overall OR workflow resulted in additional expenses, loss of individual productivity and concomitant lost revenue from cancelled cases. Some operational metrics improved. Tactical planning to maintain clinical operations during a strike must involve clinical directors and anesthesiology administrators. The primary directive should balance patient safety against long-term implications and account for unrealized costs.

BACKGROUND: Benchmarking group surgical anesthesia productivity continues to be an important but challenging goal for anesthesiology groups. Benchmarking is important because it provides objective data to evaluate staffing needs and costs, identify potential operating room management decisions that could reduce costs or improve efficiency, and support ongoing negotiations and discussions with health system leadership. Unfortunately, good and meaningful benchmarking data are not readily available. Therefore, a survey of academic anesthesiology departments was done to provide current benchmarking data. METHODS: A survey of members of the Society of Academic Associations of Anesthesiology and Perioperative Medicine (SAAAPM) was performed. The survey collected data by facility and included type of facility, number and type of staff and anesthetizing sites each weekday, and the billed American Society of Anesthesiologists (ASA) units and number of cases over 12 months. The facility types included academic medical center (AMC), community hospital (Community), children's hospital (Children), and ambulatory surgical center (ASC). All anesthesia care billed using ASA units were included, except for obstetric anesthesia. Any care not billed or billed using relative value units (RVUs) were excluded. Percentage of nonoperating room anesthetizing sites, staffing ratio, and surgical anesthesia productivity measurements "per case" and "per site" were calculated. RESULTS: Of the 135 society members, 63 submitted complete surveys for 140 facilities (69 AMC, 26 Community, 7 Children, and 38 ASC). In the survey, overall median productivity for AMC and Children was similar (12,592 and 12,364 total ASA units per anesthetizing site), while the ASC had the lowest median overall productivity (8911 total ASA units per anesthetizing site). By size of facility, in the survey, the smaller facilities (<10 sites, ASC or non-ASC) had lower median overall productivity as compared to larger facilities. For AMC and Children, >20% of anesthetizing sites were nonoperating room anesthetizing sites. Anesthesiology residents worked primarily in AMC and Children. In ASC and Community, residents worked only in 18% and 35% of facilities, respectively. More than half the AMCs reported at least 1 break certified nurse anesthetist (CRNA) each day. CONCLUSIONS: To make data-driven decisions on clinical productivity, anesthesiology leaders need to be able to make meaningful comparisons at the facility level. For a group that provides care in multiple facilities, one can make internal comparisons among facilities and follow measurements over time. It is valuable for leaders to also be compare their facilities with industry-wide measurements, in other words, benchmark their facilities. These results provide benchmarking data for academic anesthesiology departments.

THE CURRENT PARADIGM: THE FAILURE OF REDUCTIONISM Managers are not confronted with problems that are independent of each other, but with dynamic situations that consist of complex systems of changing problems that interact with each other. I call such situations messes…managers do not solve problems, they manage messes. —Ackoff1 Health care delivery and its role in the broader economic system is increasingly complex.2 Complexity theory may serve as an ideal platform for operating room (OR) management. OR governance structures have been modeled after administrative leadership structures, in which management decisions are centered on planning, budgeting, organizing, staffing, and problem-solving. These initiatives are all directed at creating predictable, stable processes. In truth, a perioperative system represents an ecosystem of surgeons, nurses, anesthesia health care providers, and patients, interconnected to influence the delivery of surgical care.3 For many health care organizations, perioperative services consume a relatively large share of resources and generate significant revenue streams. Traditionally, OR management decisions are subdivided into 3 categories: strategic, tactical, and operational (Figure 1).3 Strategic decisions focus on establishing a niche or equilibrium in the environment and offer a long-term perspective. Strategic analyses usually aim to identify contextual “boundaries” established by institutional constraints, the local and regional environment, and the organizational mission. Conversely, tactical decisions focus on the utilization of current resources in the near future. Strategic decisions may include building a new service line or expanding an ambulatory surgery center so hospital administrators can expand market share or create operational efficiencies.4 Tactical decisions, in contrast, focus on block allocations, the perioperative nurse skill set needed for the surgical workload, and necessary anesthesia services.Figure 1.: Traditional framework for operating room management decisions showing how strategic and tactical decisions inform operations over time in a unidirectional fashion.Each year, as we attempt to manage the health care system with a series of rules and regulations, the health care system in the United States continues to create tremendous inefficiencies, leading to little progress toward reducing the cost of care. Reeves et al5 recently argued that taming complexity requires the following efforts: creating a modular structure; using simple, common operating principles; embedding a bias for change; relaxing control; optimizing globally; and fixing, repairing, and pruning. Presumably, by adopting the above framework, perioperative managers should be able to manage the perioperative services with better-informed strategic, tactical, and operational processes. Today, much of the current literature is based on the assumption that ORs resemble manufacturing plants. Here, lean manufacturing and Six Sigma approaches are used to reduce variability in clinical processes and minimize inefficiencies to create value.6 With this analytic or reductionist approach, the complex parts are reduced to smaller components. However, we believe that this decomposition usually leads to a loss of information, especially when it comes to downstream consequences, both financial and operational.7 In many respects, these efforts resemble the short-sighted “lean” management style of the past, of a culture that lasts because it works at the present time, even though there is evidence, as we will show, of its inferiority and unsustainability. Previously, Mahajan et al8 noted that perioperative systems behave like complex adaptive systems.9 For example, there are numerous expectations regarding block management, case scheduling, management of add-ons and emergent cases, and individual patient variability. This inherent variability occurs not only at a single institution but also across institutions. Further, complex adaptive systems are characterized by Volatility, Uncertainty, Complexity, and Ambiguity (VUCA; “volatility” represents the systems propensity for sudden and dramatic change; “uncertainty” speaks to the ability, or inability, to predict the incident, frequency, amplitude, or timing of the change event; “complexity” encompasses the multitude of factors, control nodes, and confounding variables that creates a “chaotic,” dynamic, every evolving landscape; and “ambiguity” represents the “fog of war” that robs one’s ability to clearly delineate the relationships, causes, and effects, leading to a potential for assumptions and biases that color decision-making and blur judgment).9 VUCA represents a framework that encompasses the multiple and diverse user interactions in any complex system to seek innovations in a dynamic environment and evaluate these through deliberate transitioning processes. The diversity of activities, the myriad of equipment, persisting uncertainty, patient and provider vulnerability, and modes of delivery are emblematic of the complexity of perioperative systems. Below, we review how perioperative services behave like complex systems and then apply the lens of complex adaptive systems to understand how to harness the power of complexity. PREDICTABILITY IN AN UNPREDICTABLE WORLD: THINKING IN SYSTEMS Every day, in every OR across the country, various teams of anesthesiologists, nurses, and surgeons decide where, when, and how cases are accommodated and completed, using mostly “rules of thumb” or intuition.9 Most health care systems try to maximize OR utilization rates and maintain high throughput to maximize revenue streams.10 Therefore, operational decisions made on the day of surgery attempt to minimize variability from the perioperative processes.4 Ironically, even though anesthesiologists expect variability in the way patients respond to treatments, anesthesia clinical directors are taken aback and often challenged to understand “illogical responses” of the perioperative system they work within.11 Within any perioperative system, a “stock” (referring to the previously mentioned “manufacturing plant” analogy) can be represented by block allocations. For anesthesia clinical directors, the ability to manage OR throughput is through inflow to and outflow from the OR. Although block allocations may change over time, the dynamic responsiveness inherent in any complex system exists because of many feedback loops.12 These feedback loops, whether positive or negative, occur when human agents disrupt the system by implementing and executing strategic, tactical, and operational decisions. By acting as open systems, block allocations respond to external input and self-evolving feedback loops that are often changing and unpredictable.12 In contrast to fixed rules and regulations, health care organizations are natural systems that are continually changing, and the agents within these systems are constantly adapting to these changes.13 As nonlinear systems, output from a complex system is not proportional to the input generated, as it would be in complicated systems.14 Returning to the previous block allocations example, surgeons may use block allocations to maintain access to perioperative services (Figure 2). Unfortunately, this self-serving mentality increases inefficiency at the systems level and makes it difficult for OR managers to appropriately manage variability in the daily workload. A specific surgeon’s “backlog” is only one of many inputs into the system. However, many other services have patient backlogs, and patients entering perioperative services can come from the emergency room, the inpatient setting, and intensive care units. Simply focusing on patient backlog assumes that the linear model presented above captures the complexity of a perioperative system, limiting the opportunity for predictive load balancing and staffing adjustments to mitigate overutilized time.Figure 2.: A bathtub is analogous to the block allocations of the OR (Figure 1) and represents the time necessary for a surgeon or surgical service to operate despite the changing demands and workload. The major service lines create demand. Release times represent a surgeon’s or a surgical service’s ability to control the system “stock,” and the patient backlog is a consequence of the release time for block allocations from the surgeon’s perspective. Presumably, many surgeons continue to use release time (ie, the point in time when future OR block allocations are made available to the other surgical services) as a method to protect their own clinical operations. However, the perioperative service system is a nested system; the outflows are directly affected by patient access to rehab facilities, inpatient settings, and the clinic. OR indicates operating room.Often, the least obvious part of the system is its function or purpose, which is also often the most crucial determinant of the system’s behavior.14 Consequently, most organizations actively and intentionally create 2 conflicting organizational models: one supports stability, as defined by organizational structure with roles/responsibilities and a governance model that addresses decisions with broad scope and impact; and the other supports agility (eg, interdisciplinary teams focused on decisions that are narrow in scope or exploratory in nature and new processes that address evolving changes in the environment).15 Again, feedback loops are nonlinear within any perioperative system, and unexpected consequences might begin to show up as first-case start delays, longer turnover times, or lack of surgical equipment.12 By understanding the role of positive and negative feedback loops, a perioperative service should be able to design a system that strives to maintain an equilibrium. In terms of a surgical service’s block allocations, it should be recognized that the equilibrium is merely a distribution at a specific point in time. Ultimately, the astute operating room managers should understand the downstream impact of tactical and operational decisions in the context of maintaining an equilibrium that is in alignment with hospital strategy as delineated by executive leadership. ORGANIZATIONAL AGILITY: REAL-LIFE APPLICATIONS Inevitably, clinical directors are confronted with new technology, operational demands, and ever-changing regulatory and financial forces. They must manage a complicated set of rules, policies, and processes impacting clinical delivery in a complex patient ecosystem.13 We will elaborate on the earlier mentioned complexity-taming efforts defined by Reeves et al,5 applying them to real-world scenarios where the consequences of isolated strategic, tactical, and operational decisions have larger ramifications. In contrast, we hope to demonstrate that it is possible to move perioperative services toward self-governing, transparent processes that seek equilibrium. Strategic Versus Tactical At many institutions, there is a lack of congruency when it comes to the implementation of strategic decisions and the allocation of tactical resources. When the University of Vermont Medical Center instituted a minimally invasive surgical service, the Division of Urology led the strategic and operational changes necessary to get the initiative off the ground. The Division of Urology worked closely with the Department of Anesthesiology and the perioperative nurses to communicate the anesthetic and procedural considerations for the patient. However, the Division of Urology elected to put the robotic unit in an OR that had traditionally served as the dominant block allocation for the service. Although the strategic plan was appropriate, tactical errors appeared soon after implementation of the new technology. The tactical decision to place the new equipment, used by multiple services, in a room used by a single division, coupled with an operational metric of defining block time by room rather than service, resulted in the unintended consequence of the Division of Urology working outside of their block allocations any time another service needed the robotic equipment. This corruption of utilization data had a domino effect on other services as well, resulting in an inability to accurately track and manage to clinical needs. In August 2013, the University of Vermont Medical Center was more careful the next time when the Division of Vascular Surgery created a hybrid OR unit. The redesign involved an OR that had traditionally been the daily block allocation for the Division of Vascular Surgery. Here, the tactical committee asked the Chief of Vascular Surgery: Who should control the block allocations: the committee or the division? The surgeon requested that the division manage the block allocations for the hybrid OR (modular structure). To prevent the tactical errors associated with implementation of the robotic unit, all surgical services requesting time for the hybrid OR were required to swap block allocations with the Division of Vascular Surgery (an example of simple, common OR principles). From a complex systems perspective, management of block allocations for the hybrid unit did not necessitate a rigid bureaucratic structure, but rather depended on relaxing central control and giving more responsibility to end users. Finally, this move to a “floating” block utilization facilitated the revalidation of block utilization and helped the Operating Room Block Utilization Committee recognized the need for additional block time for the Division of Urology (an example of fixing, repairing, and pruning). When “managing” a complex perioperative service, embracing complexity means relaxing control, pushing the time and resources to the individuals on the front line, and embracing change that facilitates systems evolution that supports the institutional mission. Tactical Versus Operational Tactical decisions in OR management focus on the allocation of block time and are usually made 3–6 months in advance.4 Block allocations are predicated on the total surgical demand for a future date and the anticipated variability in the workload for the perioperative system. Managing this anticipated workload is a function of the tactical block allocations and staffing patterns. Subsequently, many institutions develop rules on first-case start delays, establish minimums for block utilization rates, and optimize release times. At the University of Pittsburgh Medical Center, the perioperative services implemented a strategic initiative to globally optimize staffing allocation and utilization for all the surgical sites across the network. In January 2017, individual anesthetizing location site limits were set at for each surgical facility based on the 90th percentile of rooms running for the previous quarter, establishing a system-wide cap on locations at 208 as an initial step in controlling maximum anesthetizing locations across the system. This process has evolved to include adjustments based on expected provider efficiency, surgical time divided by total staffed time, with a stepwise reduction in defined total system-wide anesthetizing location to a limit of 194 beginning in February of 2019. To actively manage case volume and labor resources within this cap, 3 days before the day of surgery, the Central Scheduling Department sends an electronic announcement to all sites when the total number of expected anesthetizing locations exceeds the defined system cap (another example of using simple, common operating principles). By embedding a bias for change, the individual surgical facilities work in a collaborative manner with the primary tactical goal of consolidating the schedule down to within the total site allocation limits. For example, surgeons with first-case starts with no case to follow condense their case lists into 1 site. Subsequently, the workforce necessary for the final distribution of anesthetizing locations is finalized and deployed for distribution across the system the day before surgery. Again, tactical decisions may mitigate, but not necessarily solve operational issues. It is the role of clinical directors to minimize the implications and consequences of these decisions on the day of surgery. In this type of organizational framework, frontline employees are empowered to identify and address emerging problems and opportunities and exploit any opportunity in real time. In agile organizations, individual teams focus on small parts of a complex adaptive system and work with a list of organizational priorities.15 The responsibility of the upper-level managers is to relax control, shift decision-making farther down the line, and create the urgency to continue refining the process to align operations with the mission and values of the organization. Operational Versus Strategic Each day, operational decisions mitigate or amplify deficiencies in tactical processes up until the day of surgery.4 In 2009, a private ophthalmology group at the University of Vermont Medical Center decided to leave the ambulatory center and build their own surgical center. With this move, the surgical volume at the ambulatory center dropped from 5000 to 3000 cases annually. Faced with a 40% drop in surgical volumes, the organization should revisit its tactical allocations and shorten the staffed hours to match the historical workload (an example of fixing, repairing, and pruning).4 However, in this instance, the institution bewilderingly embarked on an operational efficiency project to improve turnover times at the ambulatory center. The literature is strewn with similar misconceptions of time savings.16,17 Shortening turnover times, an operational metric, to satisfy a strategic initiative will not mitigate a tactical issue. The most important aspect of achieving system efficiency is that, in dynamic, complex systems, optimization of the system must supersede optimization of any individual node.18 Unfortunately, there are ample instances of perioperative decisions made to optimize individual operational metrics for an individual or service line that carries the most clout.19 By embracing complexity, perioperative services can transform and innovate processes at strategic, tactical, and operational levels for perioperative services. As surgeons, nurses, and anesthesiologists gain experience and accumulate systemic knowledge of their administrative and operational systems, they need to work as teams and build “processes that balance freedom and control.”20 Ultimately, a clinical director must have a true understanding of the system’s competing goals, embed a bias for change by strengthening the decision-making power of those working in the system, and remove the organizational obstacles that prevent a perioperative service from adapting to changing conditions and demands. CONCLUSIONS Perfect prediction is only possible under 2 conditions: when nothing changes and when the behavior of a system occurs in “accordance with deterministic causal laws, and that we know perfectly these laws.”1 Managing a complex adaptive system requires the foresight to recognize the limitations of reductionist approaches. The single-minded drive for efficiency does little to create true innovation. Perioperative leaders need to create modular governance structures; develop simple operating procedures; simultaneously invest the resources necessary to create change and relax control; and recognize that in globally optimizing dynamic, complex systems, optimization of the system must supersede the optimization of any individual node or metric. In closing, we need to “design and manage systems so that they can effectively serve their own purposes, the purposes of their parts, and those of the larger systems.”1 DISCLOSURES Name: Mitchell H. Tsai, MD, MMM, FASA, FAACD. Contribution: The author helped create, prepare, and edit the manuscript. Name: Stephen J. Kimatian, MD. Contribution: The author helped create, prepare, and edit the manuscript. Name: James R. Duguay, MD. Contribution: The author helped create, prepare, and edit the manuscript. Name: Mohan R. Tanniru, PhD. Contribution: The author helped create, prepare, and edit the manuscript. Name: Elie Sarraf, MDCM. Contribution: The author helped prepare and edit the manuscript. Name: Mark E. Hudson, MD, MBA. Contribution: The author helped prepare and edit the manuscript. This manuscript was handled by: Nancy Borkowski, DBA, CPA, FACHE, FHFMA.

Background & Aims: Autoimmune liver disease (AILD) is thought to result from a complex interplay between genetics and the environment. Studies to date have focussed on primary biliary cholangitis (PBC) and demonstrated higher disease prevalence in more urban, polluted, and socially deprived areas. This study utilises a large cohort of patients with PBC and primary sclerosing cholangitis (PSC) to investigate potential environmental contributors to disease and to explore whether the geo-epidemiology of PBC and PSC are disease-specific or pertain to cholestatic AILD in general. Methods: All adult patients with PBC and PSC in a tightly defined geographical area within the UK were identified. Point- and area-based analyses and structural equation modelling (SEM) were used to investigate for disease clustering and examine for relationships between prevalence, distribution of environmental contaminants, and socio-economic status. Results: We identified 2,150 patients with PBC and 472 with PSC. Significant spatial clustering was seen for each disease. A high prevalence of PBC was found in urban, post-industrial areas with a strong coal-mining heritage and increased environmental cadmium levels, whereas a high PSC prevalence was found in rural areas and inversely associated with social deprivation. Conclusions: This study demonstrates spatial clustering of PBC and PSC and adds to our understanding of potential environmental co-variates for both diseases. Disease clustering, within the same geographical area but over different scales, is confirmed for each disease with distinct risk profiles identified and associations with separate putative environmental factors and socio-economic status. This suggests that different triggers and alternative pathways determine phenotypic expression of autoimmunity in the affected population. Co-variate analysis points towards the existence of specific disease triggers. Lay summary: This study looked for potential environmental triggers in patients with primary biliary cholangitis (PBC) and primary sclerosing cholangitis (PSC) living in the north-east of England and north Cumbria. We found that PBC was more common in urban areas with a history of coal mining and high levels of cadmium whereas PSC was more common in rural areas with lower levels of social deprivation.