**1. Introduction**

Growing healthcare costs have drawn significant attention to the healthcare delivery system and its fragile and fragmented nature [1]. Similarly, the growing burden of illness and its impact on individuals, families, and society has led to a concerted effort towards addressing the needs of patients (i.e., focusing on person-centered care). The consequences of the growing burden of illness compounded by an increasingly expensive healthcare delivery system place grave consequences on our economy and way of life.

National Academy of Medicine Reports continue to highlight the need to improve healthcare delivery [2,3]. This includes designing healthcare systems that address current needs of patients and can be implemented and disseminated across varying healthcare system environments.

#### *1.1. The Changing Needs of Patients: From Treating Acute to Chronic Conditions*

Acute conditions, namely infectious diseases and traumatic injury, dominated the medical problems of the 19th and early 20th century. In response, the development of the biomedical model addressed these problems by focusing on the body as a machine [4] and therefore disease as the consequence of breakdown in the machine. This reductionist approach to the physical body analogy led to dividing the healthcare delivery system into departments based on discrete service types (e.g., cardiology, endocrinology, podiatry).

Healthcare needs have significantly shifted from treating primarily acute conditions to treating primarily chronic conditions. Chronic conditions now make up over 78% of total healthcare costs in the United States [5]. Furthermore, expenditures for patients with multiple chronic conditions are up to seven times as much as patients with only one chronic condition [6]. This is a significant population given that over half (51.7%) of all Americans have at least one chronic condition and almost one third (31.5%) of all Americans have multiple chronic conditions [7]. This problem increases dramatically with age where almost half (50%) of all people aged 45–64, and 80% of those 65 and over, have multiple chronic conditions [7].

While chronic conditions are typically described by their long-term disease duration [8–11], the complexity that arises from the condition is not to be underestimated. Chronic conditions are particularly complex in that they tend to involve multiple factors with multiple interactions between them [12]. These conditions are described as having a complex, multiple, and co-occurring nature. These conditions can be primarily physical (e.g., diabetes and obesity), physical and behavioral (e.g., cancer and depression), or mental and behavioral (e.g., substance use and mental health).

Increasing patient needs associated with chronic conditions have led many healthcare systems—motivated by both cost and quality—to focus on providing holistic care. Studies have shown improvement in patient health outcomes and reduced system costs when services are restructured to focus on patient-oriented experiences and needs [13,14]. The recognition of such improvements has led to an increasing interest in providing single-point services, classically provided by different departments or healthcare delivery systems (e.g., primary care and behavioral health, palliative care and cancer).

#### *1.2. The Current Healthcare Delivery System and Challenges of Conventional Clinical Modeling*

The healthcare delivery system organically developed to address acute conditions. The characteristics of chronic conditions present several new healthcare delivery challenges [15,16]. Namely, continuing to deliver care well after the individual has left the healthcare facility, deeply understanding the health state of the individual, managing individualized health outcomes, and coordinating numerous practitioners representing many medical specialties [15].

Now that healthcare systems recognize the need to provide services tailored to patients with chronic diseases, healthcare uses classic clinical constructs typically used in medicine to design such services. Current clinical methods and tools to generate evidence-based models and implementing them present five key challenges. These challenges have been identified by the author based on the literature, discussions with many different types of clinicians from different training backgrounds (e.g., physicians, nurses, medical assistants, etc.) and specialities (e.g., primary care, psychiatry, palliative care, emergency medicine, etc.). These challenges are presented in Table 1 and described in detail below.

**Table 1.** Challenges in designing clinical models.


Challenge 1: Clinical models are typically designed based on a single-diagnoses. The medical approach for generating evidence-based models, treatments, and protocols rests on the current gold standard of testing them using randomized clinical trials (RCTs). RCTs have very strict inclusion criteria, meaning that they test using a homogeneous cohort of patients. Consequently, patients with multiple and complex conditions are specifically excluded, leading to limited generalizability for patients with multiple or complex conditions.

Challenge 2: Clinical models are typically described at a high-level of abstraction with a focus on personnel (i.e., human personnel are one type of resource in the healthcare system). In doing so, clinical models do not define the needed functions, but instead describe the type of provider that should be performing these functions. Describing the model based on the type of provider is problematic for three reasons.

*First*, identifying a function based on the type of provider is no longer as informative as it used to be. Typically, clinical medicine names the type of provider in a manner that alludes to their functions (e.g., a surgeon performs surgery). This was possible because classic Doctor of Medicine (MD) education, training, and certification processes provide a clear description of scope of work for such a personnel. There are now many additional trainings, certifications, licenses, and bodies of knowledge that are not encompassed in the classic training and medical degree (e.g., providing palliative care, providing behavioral health care, providing opioid treatments). There is also a critical phenomenon occurring in medicine. Some of the fastest growing resources in healthcare are non-MD personnel [17]. While many of these non-MD clinicians (e.g., nurses, medical assistants, behavioral specialists, social workers) also have education programs and certifications, their experiences and continued training allow them to practice with a wider scope of work and provide higher levels of clinical care. For example, using the term "nurse" only describes the most minimal functions that a nurse can provide based on a nursing degree. However, there are nurses that provide specialized nursing support for complex palliative care, complex medication management, opioid treatment, and addiction recovery, to name a few.

*Second*, new integrated services may bring together personnel from across-departments, but it is important to understand that they tend to bring significantly different clinical language, culture, and operational practices. Not specifically addressing scope of work or tasks of each personnel introduces many possibilities for misunderstanding and allows the behavioral dynamics of the team to be reduced to individual personalities. Bringing together human resources from different departments or systems requires the explicit description of not only individual scope of work, but also dyads and the aggregate team scope of work.

*Third*, some integrated services may describe individual resource functions or tasks, but functions performed by multiple resources are rarely specifically described as to when, how, and where they are to occur. Furthermore, key functions required for team success are not well defined and, if defined, not allocated the appropriate value (i.e., value in terms of time to perform a task or paymen<sup>t</sup> for a task). For example, curbside consults (i.e., when a treating physician seeks information or advice for patient care in an informal face-to-face discussion) of primary care physicians with integrated behavioral health specialists are described as a key element of the collaborative care model in order to help identify the best decisions for patient care needs. It also serves as a teaching and educational moment for human resources in the system. However, it is an underutilized function in real-world implementation because it is left to occur in an ad hoc manner with no design to facilitate, encourage, or monitor when or how it occurs.

Challenge 3: Clinical models are typically described and presented primarily using text-based toolkits [18] with minimal visualizations. Neuroscience has shown that images are processed in as little as 13 ms [19], while integration of processes that allow for word recognition takes 200 ms [20]. Specifically relevant to healthcare, Tien et al. state "Constructing and communicating a mental image common to a team of, say, clinicians and nurses could facilitate collaboration and could lead to more effective decision-making at all levels, from operational to tactical to strategic. Nevertheless, cognitive

facilitation is especially necessary in operational settings which are under high stress" [21]. Visual representations have the ability to relieve much of the cognitive burden of reading, comprehending, translating, and processing verbal materials in a fast paced clinical environment. Not having visual models translates to a minimal ability to first, relay the clinical model sufficiently and thoroughly when attempting to ge<sup>t</sup> buy-in from a clinical team for implementation and second, implement the model in an easy and time and resource efficient manner.

Challenge 4: Clinical models are typically described by the most expected paths, rather than a comprehensive list of possible paths. Justification to only model expected or typical paths are two-fold. *First*, it is assumed that being comprehensive distracts from the core model with unnecessary information. Not being comprehensive translates to not noticing or classifying any deviations from the expected path. This allows clinical decision making biases to persist unseen, a significant problem in healthcare [22,23]. Therefore, modeling comprehensively is key to identifying and reducing problematic variations in clinical practice due to clinician decision-making biases.

*Second*, decision paths are described from the providers' perspective rather than the patients' perspectives. While there have been significant efforts to shift the discussion of clinical decision-making from the clinician to a shared-decision between the patient and clinician [24,25], the focus of shared-decision making is made at specific times rather than for every healthcare system interaction with the patient. Taking into account patient choice at each level of the modeling allows for the explicit elucidation of patient drop-out and non-compliance. This allows for the quantification of not only services provided, but to which types of patients and with what outcomes.

Challenge 5: Clinical models are typically described with minimal to no specificity of implementation-level details. This is particularly evident where details are needed at the mid- to most-specific detail-level description of the model. While healthcare environments vary and it may be best to leave certain details to the implementer, it is critical to be able to specifically describe the aspect of the tested model, which yields the success outcomes claimed by the model. This helps to inform implementers of the critical and more optional components of the tested clinical model.

#### *1.3. Paper Contribution—Systems Thinking Approach to Tackle Current Clinical Modeling Challenges*

This paper presents and uses systems thinking and systems engineering principles and tools as an alternative strategy to thinking about and designing clinical system models and healthcare services to alleviate many of the current healthcare clinical modeling design challenges. This allows current clinical models to be described as system models with *multi-level detail* and *quantification*, currently limited in clinical models. Systems thinking as a process also produces transparency and invites collaboration and understanding across all involved stakeholders. In doing so, stakeholders gain appreciation for the complexity across the healthcare system and insights as to how their own behavior affects patients, other healthcare personnel, and the healthcare delivery system.

## *1.4. Paper Outline*

The background, in Section 2, will first describe a systems thinking approach to modeling healthcare delivery. This includes a description of the domains applying systems thinking to the health field and a systems thinking approach to healthcare delivery. Section 3 includes an illustrative example of taking a clinical model, called the Collaborative Care Model (CoCM) and developing a system model. This includes a description of the Collaborative Care Model, the methodology for developing the system model, and a detailed description of the developed system model. Section 4 includes a discussion of advantages and limitations of systems thinking in modeling and designing healthcare delivery services and models. Finally, Section 5 ends with the paper's conclusions.

#### **2. Systems Thinking Approach to Modeling Healthcare Delivery**

The health field, similar to most of the sciences, is based on reductionist thinking [12], breaking things down into their components and examining each of the pieces separately. On the opposite end

of reductionist thinking is systems thinking. Systems thinking is based on examining the full system, its pieces, and interconnections to understand the system. The idea of systems thinking has been used in many fields and actually does not have a very clear definition. This special issue states that "Systems thinking can be broadly considered the activity of thinking applied in a systems context, forming a basis for fundamental approaches to several systems disciplines, including systems engineering, systems science, and system dynamics".

#### *2.1. Domains Applying Systems Thinking to the Health Field*

Systems thinking and systems engineering methods and tools have been used as exemplars across the health field. This section, however, focuses on the fields that have emerged that draw significantly from systems thinking [26]. These include Systems Biology and Healthcare Systems Engineering.

Systems Biology can be broadly viewed as a convergence of molecular biology and systems theory where the focus shifts to understanding the system structure and dynamics rather than the static connections of the components [27–37]. One of the goals of systems biology is to understand a complex biological process in sufficient detail to allow for the building of a computational model. This model would then allow for the simulation of system behavior, thus elucidating system function [38]. This can be viewed as applying systems theory at the cellular and sub-cellular level, one of the smaller physical scales.

Healthcare Systems Engineering is a relatively new field that applies systems theory and systems engineering tools to healthcare delivery primarily in acute care (e.g., intensive care unit (ICU), emergency department (ED)). This field can be viewed as an application of industrial engineering and operations research to health [39]. It is primarily focused on informing administrative stakeholder decision-making based on computational optimization of time and cost [39]. It is primarily focused on quantitatively representing the system in order to use optimization techniques for applications ranging from scheduling [40–45], reducing errors [46], improving hospital outpatient flow [47,48], improving emergency room operations [49], and improving patient safety [50].

This section presented the two primary domains specifically focused on using systems thinking tools and methods. It is worth noting that many applications of system tools (e.g., system dynamics [51], social network analysis [52], and agent-based simulation [53]) have been used across the health field to glean insights. It is beyond the scope of this paper to describe all such applications.

#### *2.2. Systems Thinking for Healthcare Delivery*

Next, a formal description of healthcare delivery as a system is described based on systems thinking principles that specifically addresses both acute and chronic conditions [15]. It begins with describing a system in the most abstract terms, its characterization by its system function, system form, and the allocation of function to form, called the system concept. This section highlights the application of systems thinking to developing a system model representation of personalized healthcare delivery and managed individual health outcomes [15].

## 2.2.1. System Function

The healthcare delivery system is composed of processes representing system function (i.e., the function of a system). Four types of processes have been previously defined in the literature [15] based on merging two concepts: the clinical diagnostic framework of measure, decide, and treat [54] and engineering systems functional type classifications of transform and transport [55]. The clinical diagnostic framework first examines the patient's complaint or concern (measure), second, decides on the cause of the issue or how to proceed next (decide), and third applies a treatment regiment (treat or transform) [54]. The healthcare delivery system function is thus represented as the union of the following four processes: *Transformation Process*: A physical process that transforms the operand: specifically the internal health state of the individual (i.e., treatment of condition, disease or disorder); *Decision Process*: A cyber(non-physical)-physical process occurring between a healthcare

system resource and the operand: the individual, which generates a decision on how to proceed next with the healthcare delivery system; *Measurement Process*: A cyber-physical process that converts a physical property of the operand into a cyber, (i.e., non-physical, informatic) property to ascertain health state of the individual; and *Transportation Process*: A physical process that moves individuals between healthcare resources (e.g., bring individual to emergency department, move individual from operating to recovery room).
