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The Perioperative Surgical Home

High-reliability or Ultra-safe Organization?

Huynh, Tinh T., MD*; Paiste, Juhan, MD, MBA, CPE; Black, Ian H., MD

International Anesthesiology Clinics: January 2019 - Volume 57 - Issue 1 - p 32–44
doi: 10.1097/AIA.0000000000000214
Review Articles

*Department of Anesthesiology and Pain Management, University of Texas Southwestern Medical School, Dallas, Texas

Department of Anesthesiology and Perioperative Medicine, University of Alabama School of Medicine, Birmingham, Alabama

Department of Anesthesiology and Perioperative Medicine, VA Eastern Colorado Health Care System, Denver, Colorado

The authors declare that they have nothing to disclose.

Address Correspondence to: Tinh T. Huynh, MD, 5323 Harry Hines Boulevard, Dallas, TX 75390. E-mail:

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Introduction: Complex Adaptive System (CAS) and the Perioperative Surgical Home (PSH)

Multifaceted organizations such as hospitals tend to form standardized practices with the intention to increase efficiency by limiting variation, maintaining constancy, and sustaining the status quo. Formal systems such as guidelines, protocols, machine processes, and rituals are established to construct predictable operations with the capacity to complete daily tasks with minimal error.1 Consequently, organizations attempt to become conventional factories as decision-making processes are geared toward achieving stability. When markets experience considerable instability and fluctuation, businesses often make decisions on the basis of their ability to achieve stability or an equilibrium state. In the current perioperative management literature, the possibility of a finite set of solutions in such an equilibrium state is implied as studies attempt to achieve optimal efficiency through conservative management of block allocation and daily scheduling.2,3

The presumption of equilibrium states is flawed because human organizations, especially large multifaceted ones, are dynamic.4 They are inevitably surrounded by instability through the impact of multiple factors including independent agents (decision makers), ancillary staff, interpersonal relationships, critical equipment, behavioral patterns, and external partners. Because of the presence of endless instability, behavior is unpredictable and responds in a way that is constantly readjusting. Over time, organizations tend to naturally cultivate an adapting configuration that prevents collapse, known as bounded instability. According to complexity theory, the best-run companies survive by operating at the edge of chaos or finding the perfect balance between stability and instability.5 Stacey affirms that “in order to produce creative, innovative, continually changeable behavior, systems must operate far from equilibrium where they are driven by negative and positive feedback from paradoxical states of stability and instability, predictability, and unpredictability.”6 Organizations are expected to demonstrate emergent behavior capable of constantly adapting to their ever-changing environments.7 Indeed, human organizations are CAS where individual agents in a group are in nonstop interaction and competition with one another, leading to emergent behavior that is nonlinear in nature.8

Why is this relevant to the PSH? Statistical data have shown that hospitals account for 32% of US health care expenditures.9 Perioperative services, the most costly process of any hospital, account for ∼27% of discharges and 52% of inpatient spending.10 Intuitively, perioperative care delivery embodies a significant opportunity for efficiency and productivity improvement. Hospital administrators, physicians, and nursing leadership have applied numerous management frameworks to improve hospital productivity and the quality of care. However, most health care administrators operate under the assumption that hospitals are fundamentally factories, neglecting the aforementioned concepts of complexity theory.11 Although the implementation of manufacturing-based improvement methods such as Lean and Six Sigma to hospitals12 has shown improvements in workflow processes, difficulties, and barriers remain. A hospital must be viewed not only as a conventional factory but also as a CAS.

In this review, we will argue that the PSH is a 2-tiered system composed of both factory-like processes and CASs. To advance this proposition, we need to incorporate CAS thinking into the development of new management strategies to address the shortcomings of factory-based redesign. First, we will discuss the fundamental differences between hospitals and factories. Then we will explain how both factory-like processes and CASs exist in the PSH and compare their characteristics. After this, we will propose what the focus of new “self-organization” improvement methods should be. Finally, we will link the confusion of viewing hospitals as purely factories to an institution’s inability to define and identify ultra-safe organizations (USO) and high-reliability organizations (HRO).

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Differences Between Hospitals and Factories

When comparing factories and hospitals, one can see the many distinctions on a rudimentary level. Typically, factories create economies of scale by maximizing their market and minimizing their range and effort on production. They do so by manufacturing products of low variability at high volume using extremely specialized assets such as assembly line methods and batch processing machinery. The domino effect is simple: a large market size combined with a narrow scope of activity leads to a cost-effective factory with a homogenous overhead structure and ultimately, a prevailing scale economy.

Although factory-like processes exist inside hospitals, a hospital is not identical to a factory in terms of scope, operations, and production. A hospital’s “market size” (catchment area) is bounded by distance and its “products” (medical and surgical treatments) are highly variable and involve versatile assets and skilled labor tailored to individual cases. These fundamental characteristics limit a hospital’s ability to achieve economies of scale like a factory. Mahajan et al13 clearly stated that “a hospital is not like a factory in that its scope of activity is too broad, its overhead structure is too multipurpose, and the volume of any single product is too low to enable it to operate like an assembly line or a batch-processing line.”

Further, hospitals have all the components of a CAS. Unlike factories, which are designed using a “top-down” approach, CASs are formed through a “bottom-up” progression. The key characteristic of every CAS is its capacity for self-organization, a bottom-up process whereby complex processes (or behaviors) emerge at multiple levels from lower-level interactions. A CAS is formed through nonlinear interactions rather than rigid planning and it does not respond as predictably to process mapping and redesign as factories. Instead, it can generate unpredictable and potentially aberrant responses to the external stress of factory-based remodeling, and counter the efforts of redesign.

CAS concepts have been mentioned in the health care literature related to disciplines in nursing, public health, trauma, emergency care, telemedicine, and many others.14–20 Although we do believe that hospitals are self-organizing CASs, we recognize that there are processes within health care systems where manufacturing-based redesign strategies to enhance performance (eg, Lean, Six Sigma) are applicable and effective. With the US health care system shifting toward a value-based payment model, the need for new management techniques to improve cost-efficiency becomes more pressing, especially in perioperative care. Yet, the current manufacturing-based redesign methods have yet to yield true bottom-line cost savings.21 Similarly, little has been done to establish improvement methods based on CAS principles. “Self-organization”–based methods are needed to improve performance where factory-based methods fail. To maximize performance, scale efficiencies, and improve the clinical process, hospital administrators must have the ability to distinguish between CASs and factory-like processes and utilize appropriate strategies.

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Factory-like Processes in the PSH

Lean, a manufacturing-based redesign method, originated with Toyota and revolutionized the car industry by exploiting scrupulous standardization in their production lines.22 Conceptually, perioperative procedures could be analogous to a car production line and standardization of perioperative care could result in processes with less error and higher quality. Although this theory has been inconsistent in the real world, manufacturing-based improvement methods are certainly beneficial to improving factory-like processes in perioperative care. To better understand how factory-like processes exist in the PSH, the following classic types of manufacturing processes must be reviewed:

  • Assembly line—produces large quantities of items using workers and/or machines that perform specific tasks to build parts of a product as it moves down a production line to its completion (eg, car industry, toy industry).23
  • Batch processing—essentially a scaled-down version of an assembly line; products are produced in groups instead of in continuous streams.
  • Continuous flow process—produces or process materials without interruption (eg, petroleum refinery, power stations).
  • Job shop—produces small batches of a variety of custom products and requires a unique setup and sequencing of process steps for each product.

Today, most manufacturers start as job shops and eventually grow into the other manufacturing processes as volume permits. Job shops give entrepreneurs the opportunity to make a variety of products to capture different markets of customer demand. When customers demand repeat jobs, and volumes increase, the job shop will ultimately group machines by compartments to process batches of like jobs for efficiency purposes.24–26 The PSH closely resembles the above-mentioned maturation of the job shop for several reasons. First, perioperative processes produce a variety of custom services (medical and surgical treatments) on a daily basis. Second, the demand for repeat jobs in the perioperative setting is natural because of the human prevalence of certain diseases. Finally, the growth of volume provides an opportunity to develop standardized processes for efficiency, similar to batch processes and assembly lines.

The standardization of each high-volume perioperative job occurs within a “focused factory.” Introduced by Wickham Skinner from the Harvard Business School, this concept states that:

A factory that focuses on a narrow product mix for a particular market niche will outperform the conventional plant, which attempts a broader mission. Because its equipment, supporting systems, and procedures can concentrate on a limited task for one set of customers, its costs and especially its overhead are likely to be lower than those of the conventional plant. But, more important, such a plant can become a competitive weapon because its entire apparatus is focused to accomplish the particular manufacturing task demanded by the company’s overall strategy and marketing objective.27

Hospitals have applied the focused factory model to low-variance jobs in perioperative care delivery.28 Here, these processes are factory like because of the presence of the following common characteristics:

  • Homogenous patient population—medically homogenous patients with little to no comorbidities experience little variance in care delivery, permitting specialization and scalability of processes.
  • High volume—repeat exposure and experience creates an opportunity for the development of standardization to reduce process variation.
  • Well-established practices—the features described above forge an environment for continued progression of evidence-based protocols and standard care.

Standard surgical cases that meet all 3 of these conditions include appendectomy, hysterectomy, total joint replacement, hernia repair, aortic valve replacement, carotid endarterectomy, and routine coronary artery bypass graft, without significant comorbidities, among others.

In this context, the application of factory-based rationale to improve performance is effective. The conceptual frameworks of factory-based improvement methods have been applied in various hospitals and integrated into today’s standard management practice.29 Its applicability to standard, high-volume, low-risk surgery is undeniable, and it has served as the basis for the past 2 decades of health care optimization and cost reduction. However, its usefulness in complex, low-volume, high-risk surgery remains questionable.

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CAS in the PSH

Current research in the perioperative management literature has applied linear analysis techniques.30,31 Conventional mathematical modeling techniques are fundamental to the development of manufacturing-based redesign and well suited for fixed environments with predictable production processes. However applying manufacturing-based redesign to perioperative services fails to account for the instability and unpredictability present in complex, high-risk cases.

Complexity theory can be applied to the perioperative model to address this inadequacy. High-variant cases have the following characteristics of CASs:

  • CASs are comprised of independent agents. With high-variant processes in the PSH, control is highly dispersed among agents (eg, surgeons, anesthesiologist, nurses, technicians, assistants, and ancillary staff).
  • CASs are in constant conflict. Each agent possesses a unique hierarchical position with different responsibilities and objectives. Because of this heterogeneity, agents are likely to be in conflict and must adapt to each other’s behaviors (eg, all providers must understand, cooperate, and adapt to each other’s practices to provide optimal care).
  • CASs have many levels of organization. Communication and interaction occur on all levels, with agents at lower levels serving as building blocks for agents at higher levels. These building blocks are constantly reorganizing as they acquire more knowledge (eg, providers are able to enhance their skillset and work more efficiently as a collective as they gain more experience).
  • CASs have the capacity to self-organize. Because agents are intelligent, they learn through experience and are able to facilitate systemic adaptive behavior patterns over time. As a result, CASs can anticipate change (eg, with enough experience and exposure, providers will develop a system that is well prepared for the complications and variance of high-risk patient experience).32

Hospital administrators should consider these inherent attributes when managing complex, high-risk patients in the PSH.

Clinical management requires high levels of collaborative autonomy and the capacity to learn adaptive behavior. High-variance jobs possess the following differing characteristics compared with low-variance lines:

  • Heterogenous patient population—medically heterogeneous patients with significant comorbidities experience unpredictable variance in care delivery, prohibiting specialization and scalability of processes.
  • Low volume—the lack of repeat exposure and experience prevents the development of standardization to reduce process variation.
  • Unestablished practices—the features described above make it difficult to foster robust evidence-based protocols and standard care.

Surgical cases that meet all 3 of these conditions are typically high-risk operations that carry a mortality rate of 5% or more.

The high mortality rate can be attributed to various factors including the nature of the surgery and the patient’s physiological status.33–36 To understand the shortcomings of solely applying factory-based redesign to improve performance in the PSH, one must appreciate the differences between CASs and factory-like processes. A comparison of the characteristics of these processes is shown in Table 1.

Table 1

Table 1

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The Focus of Self-organization Methods

The differences between the 2 processes can be appreciated when examining the degree of autonomy present in their individual agents. In factory-like processes, autonomy is minimal outside of the rules and regulations of standardization. The low level of autonomy allows organizations to optimize efficiency by way of manufacturing-based redesign. Even when optimized, the structure of factory-like processes is mostly linear and with little variability. Again, factory-like processes have low variable costs and are naturally scalable because of their repetitive and predictable nature.

By contrast, CAS agents have high levels of autonomy, independent of the system in which they operate. The structure of a CAS is multilevel, dynamic, and nonlinear in nature. The high level of autonomy makes it difficult for organizations to manage with manufacturing-based redesign. Because CASs constantly self-organize, they have a low degree of repetition and high variable costs, making them a challenge to scale. With these differences in mind, the need for “self-organization”–based improvement methods is clear. The question is, “What will be the central focus of self-organization methods?” Mahajan et al13 cleverly stated, “Factory-like processes can be optimized for efficiency while CASs adapt; therefore, the relevant measure of their performance is their agility defined as the speed and ease with which they adapt and learn as context changes.” Restated, the central focus of “self-organization” methods should focus on maximizing this agility.

The agility of a system depends heavily on systems behaviors, which can be broken down into 2 types: generative (collaboration and cooperation) and antagonistic (competition and conflict). Systems behaviors result from the interactions between agents, each with their own responsibilities and objectives. In such a heterogenous environment, agent goals are prone to clash, compete, and conflict. Levels of cooperation and collaboration can vary depending on each agent’s ability to find common goals with one another and adapt. Clearly, generative behavior relies on the balance of agents’ mutual and conflicting goals. The key to optimizing generative behavior is to create an environment that maximizes mutual goals while minimizing conflicting ones. When the capacity for generative behavior is optimized, agility is optimized.

Hospital administrators should strive to promote the generative behavior of agents through new management methods. For example, highly specialized teams designed to handle high-variant cases can facilitate the following generative behaviors:

  • Understanding what people do. By being specialized, agents experience constancy and are able to analyze each other under different contexts consistently. Over time, agents will know what other agents do and why they do what they do. This creates an environment where agents will take minimal action accordingly to optimize performance.
  • Reinforcing integrators. An integrator is any person who has the power and desire to encourage cooperation. Agents in specialized teams have the luxury of familiarity. Working with the same agents on a daily basis fosters an environment that is less rigid and empowers individuals to express personal judgment and exercise organizational power.
  • Increasing the total quantity of power. This refers to increasing the autonomy of people in an organization. When teams are specialized, agents are able to build trust and gain a mutual understanding of the importance of each other’s roles. This results in increased autonomy among agents and improved adaptive behavior.
  • Increasing reciprocity. This refers to creating a space where mutual success is total success. The constancy experienced by specialized teams enhances the connections between agents and allows them to recognize how their individual inputs affect collective output. Agents in turn are more cooperative and dependent on each other’s judgment for success.
  • Extending the shadow of the future. This refers to improving our knowledge of the future to improve current decisions. Specialized teams inherently streamline perioperative processes because they allow individual agents to take responsibility for any adverse events occurring from the time of admission to discharge. As a result, agents will be able to better understand the impact of their actions on patient outcomes and adjust accordingly, creating tighter feedback loops. Ultimately, this leads to better decision-making by agents and self-organization as a system.37
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HRO Versus USO

Exclusively applying manufacturing-based improvement methods to the PSH is insufficient because it fails to account for the fact that the PSH is a 2-tiered system dealing with factory-based processes and CASs. The confusion behind this practice can be traced back to an institution’s inability to understand the differences between USO and HRO. Indeed, the current literature and organizational policies have commonly used these terms interchangeably despite major differences in characteristics and cultures such as safety guidelines and complication rates.38,39 Essentially, the underlying distinction between these 2 types of organizations is their preference to limit production to maintain a certain level of safety.40

In USOs, safety is of utmost importance. Production is limited to maintain a certain standard of safety. Physicians in USOs work in low-risk environments. Examples of ultra-safe processes include common cases that require elective surgery (eg, inguinal hernia, cataract surgery, mastectomy). These processes typically have boundary conditions (eg, no significant comorbidities, normal preoperative evaluation tests) to ensure that a certain risk profile is not exceeded. In addition to safety prioritization, USOs are also characterized by delineated areas of expertise, rule-based behavior, consistent recruitment of patients, limited complexity, and the presence of equivalent actors, all of which are applicable to factory-like processes.

In HROs, production, within safety boundaries that may be suboptimal, is the priority. Here, the highest level of safety is achieved without compromising the production mandated. Physicians in HROs work in high-risk environments. It is important to note that there is a quantifiable tradeoff in adverse event rates and safety in HROs compared with USOs. This event rate differs by at least an order of magnitude, with events occurring at 1 per 105 exposures in HROs and 1 per 106 exposures in USOs.41 Examples of high-reliability processes include cases that require emergent surgery (eg, unstable acute abdomen, ruptured abdominal aortic aneurysm). In these situations, production is required and treatment is provided with the highest level of safety possible. In addition, HROs are characterized by deference to expertise of individual experts, knowledge-based behavior, unstable recruitment of patients, complexity, and the commitment to resilience.42 All of these characteristics apply to CASs.

Understanding the differences between USOs and HROs is crucial to recognizing their connection to factory-like processes and CASs. A comparison of the characteristics of USOs and HROs is shown in Table 2. Amalberti et al41 elaborated on the concept of 2-tiered hospital systems by demonstrating that health care delivery consists of both highly reliable and ultra-safe processes. Institutions must be willing to abandon historical and cultural precedents when adopting tools from other industries if they want to be successful. To optimize perioperative care delivery, we believe that perioperative management must cater to a 2-tiered system. By identifying processes as USOs or HROs, institutions are also identifying processes as factory-like or CASs, respectively. This differentiation should allow institutions to harness and apply appropriate management methods (eg, manufacturing based vs. self-organization based).

Table 2

Table 2

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Many institutions have adopted concepts such as the PSH43,44 and Enhanced Recovery After Surgery45,46 to enhance perioperative care with the mentality that hospitals function like factories. Consequently, industrial engineering and manufacturing-derived improvement methods such as Six Sigma and Lean have been applied to perioperative care delivery to optimize productivity and quality of care. These redesign efforts have only been partially effective because they fail to account for the fact that the PSH is a 2-tiered system composed of factory-like processes and CASs. Hospital administrators and physicians must understand the fundamental differences between these 2 processes as each process requires a different methodology for optimization—manufacturing-based improvement methods for factory-based processes and “self-organization”–based improvement methods for CASs. Until the need to incorporate these concepts is recognized, the PSH and health care will continue to operate at suboptimal levels.

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