**4. Discussion**

This paper presents current challenges of designing clinical models and healthcare delivery services and presents a systems thinking approach to modeling healthcare delivery as an alternative framework to address the limitations presented in Section 1.2. An illustrative example of a clinical model, which embeds behavioral health services into primary care, was used to develop the system model. The remainder of this section highlights the advantages of system models over clinical models and the limitations of using such systems thinking models in healthcare delivery.

#### *4.1. Potential Advantages of Systems Thinking Based Modeling*

Systems thinking in healthcare delivery allows for five key advantages in system models that address the five challenges of designing clinical models, presented in Table 1. The five key advantages are presented in Table 2.


Advantage 1: System models are designed based on specified needs, rather than a specific diagnosis. The needs, also described as requirements in systems engineering, can come from patients with single or multiple diagnoses. Furthermore, since the system is designed to address specific needs, it becomes clearer to provide services that may help patients with multiple or complex diagnoses that have many different types of needs. The needs simply translate into a list of requirements that the system must be able to address.

Advantage 1 suggests that system models provide the ability over classic clinical models to describe and incorporate multiple patient needs, which need not be completely focused on a specific diagnosis. This is important and relevant since almost half of all people over the age of 45 have multiple chronic conditions [7].

Advantage 2: System models are inherently described at multiple levels and scales. Scope and scale are foundational concepts in systems thinking [77–79]. Diagraming a system at multiple levels is a core feature of system modeling [80,81]. Friedenthal et al. states, "An understandable model should include multiple levels of abstraction that represent different levels of detail but relate to one another" [80].

Advantage 2 suggests that system models provide the ability over classic clinical models to explain more clearly at the appropriate abstraction level the model details. This is critical for the multi-stakeholders that require different information from the model. For example, a high-level administrator would be interested in understanding the model of care implemented in their practice at the most abstract level, whereas a receptionist would need to clearly understand her tasks in detail. A classic clinical model does not provide the required information to all stakeholders.

Advantage 3: System models are described visually. Model-based systems engineering is by definition based on creating a *visual* model of the system. The focus on developing a model of the system is a shift from the traditional document based approach to systems engineering, where the emphasis is on producing and controlling documentation about the system [82]. The transition from the classic *text* document-based to *visual* model-based systems engineering occurred in the 1990s [83], while model-based approaches have been standard practice in electrical and mechanical design since the 1980s [80].

Advantage 3 suggests that a system model can be visually represented, whereas a typical clinical model is only described in narrative form. There are significant advantages to a visual representation. This includes the ability to see interconnections and interactions that may affect each other prior to implementation. For example, nurses suggesting a change to the method and type of data collected may not clearly pose an issue, but when checked in the system model would highlight how a specific data type is feeding into the data presented in a physicians dashboard.

Advantage 4: System models describe paths comprehensively. When modeling a system and specifically an activity, systems engineering methodology prescribes that all classes of inputs and outputs be described [78,79,82]. This ensures a comprehensive model and therefore allows the system to be quantitatively described. Situations which many clinical stakeholders may describe as having endless paths, are typically described in systems thinking by abstracting to generate a class of outputs representing a set of paths.

Advantage 4 allows system models to take into considerations paths that are typically ignored by clinicians because they believe they do not occur very often or they do not represent the focus of the model. It is critical to represent at least an abstraction of all outputs, since, when trying to understand problems in behavior, it is critical to ensure that all elements are included. This is an issue since recall abilities and perception of rates of occurrences of certain events may not be accurately recalled. For example, the role of the supporting psychiatrist in the CoCM is to have up to three clinical visits with a specific patient. Patient level data analysis, however, indicated many instances where a patient would see the psychiatrist for 10+ visits, indicating use of psychiatrists outside of the expected model.

Advantage 5: System models describe details at multiple levels, including implementation details [82]. Describing a model at multiple levels and scales to the very specific levels and scales leads to a comprehensive description that can be used for implementation. This is a natural conclusion given that engineering incorporates implementation as part of the engineering process [78,79].

Advantage 5 is critical in medicine. Engineering is naturally a field which develops and translates a solution as part of the same process. The medical research model, however, tends to follow a five-stage scheme of: T1 involves basic research, T2 involves pre-clinical research, T3 involves clinical research, T4 involves clinical implementation, and T5 involves implementation in the public health sphere [84]. Development separation creates significant delays in implementation and development, and does not take into consideration implementation science. This is an active concern of medical funding agencies [85].

#### *4.2. Limitations of a Systems Thinking Approach in Healthcare Delivery*

While the previous section presented several advantages of using systems thinking based modeling, it is also important to note possible limitations of a systems thinking approach in healthcare delivery. Three limitations have been identified and discussed below.

First, healthcare delivery systems have organized and structured their departments based on a reductionist view of the body into physical components of organ systems (e.g., cardiology, neurology, dermatology)—in other words, based on system form. This is the same mental construct used to develop clinical models. While this is exactly why there is a need for systems thinking, it is also a limitation in that the personnel in this field are not trained to think from a systems perspective. This may make systems thinking harder for healthcare personnel to grasp and understand. Systems thinking is not currently part of mainstream medical school curriculum. However, the importance of systems thinking in medicine and public health is evident in literature [86,87]. Furthermore, the Council on Education for Public Health (CEPH) (www.ceph.org) which provides accreditation to Masters of Public Health (MPH) programs and schools has now included "Apply systems thinking tools to a public health issue" as one of the foundational competencies expected of students when they complete a public health accredited degree. While systems thinking education and consequently knowledge in the healthcare field is limited, it is slowly being addressed and integrated into medical and public health education.

Second, introducing systems thinking to the healthcare field, especially to model current care, requires bringing in systems engineering personnel into the healthcare field. Although the importance of systems engineering in medicine has been presented in several high impact reports such as the President's Council of Advisors on Science and Technology [88] and the National Academy of Sciences [89], there still exists a limited number of systems engineers entering medicine relative to other fields. This is primarily because, at this early stage, there are limited systems engineering positions in medicine and healthcare delivery. The defense sector currently attracts a significant portion of systems engineering graduates.

Third, the current fee-for-service paymen<sup>t</sup> models in healthcare have forced clinical practices to increase throughput of patients, leaving the system with very little space to innovate, or add any new functionalities such as systems thinking and systems modeling. The fee-for-service system creates incentives for operations research focused on increasing throughput—moving patients faster through a poorly designed system. Systems thinking and system modeling take time from the already very fast pace and full schedule load of clinicians and personnel in healthcare. While there is much evidence to sugges<sup>t</sup> that systems thinking could help alleviate some of the time-related issues by ensuring that processes are performed in an efficient manner (1) relative to how they are needed by the patient, (2) relative to the operations of the healthcare delivery system, and (3) relative to the use and need of other fellow clinicians across the healthcare delivery system in space (i.e., different department) and time (i.e., one month later), current fee-for-service paymen<sup>t</sup> models pose a limitation.

#### **5. Conclusions and Future Directions**

In conclusion, 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. An illustrative example taking a clinical model and describing it as a system model was presented based on the literature available and implementing an integrated behavioral health model of care into primary care at a local hospital. The developed system model alleviates many of the described clinical modeling limitations, by describing the healthcare delivery system from a systems perspective, in which system form, system function, and their allocation were described at multiple levels of detail. This allowed the model to be described at varying levels, including implementation-level details, from a patient-perspective. Such a description also facilitates the ability to evaluate and quantify the system at any of the levels. The process of developing the model was also just as useful as the model. It helped the team "see" things they didn't otherwise see, especially related to the work of co-workers and how an individual's work process can drastically affect a downstream co-worker work flow. This process in the described case example allowed the team to make process changes that improved both organizational and patient outcomes.

The culture and current work environment in healthcare delivery systems is a fast-paced environment, which does not typically reward organizations to slow down and self-assess and develop such clinical models. The typical fee-for-service paymen<sup>t</sup> models pose a limitation to the translation of this work since they incentivize high patient throughput over patient satisfaction and health outcomes. Luckily, in many organizations, the patient voice, patient needs and outcomes are so highly regarded and assessed that organizations are trying to accommodate and develop these new healthcare services and models regardless of current paymen<sup>t</sup> models. These frameworks can be used as a roadmap for organizations to develop services and models themselves, or to translate these services and models to their organizations using a more clearly described and enumerated model described at many levels of detail. Developing these models not only helps support new healthcare delivery services, but they also address many patient needs for integrated services and an integrated system experience. This work highlights the need to increase systems trained thinkers in healthcare and systems education in clinical and public health training and degree programs.

Future work will utilize the described modeling methodology and framework to enumerate both the healthcare delivery system and individual patient trajectories. This includes the use of this model in its enumerated form to address the generally high no-show rates seen for this service. This is not atypical for behavioral health and psychiatry visits, but did sugges<sup>t</sup> room for improvement. Furthermore, designing quantifiable models (i.e., allowing for the evaluation of the system model) is often requested by high-level administration assessing their clinical services.

**Funding:** This research received no external funding.

**Acknowledgments:** The authors would like to thank the behavioral health design and implementation team members for their discussion and feedback. We would also like to thank Amber E. Barnato for her input and comments on this manuscript.

**Conflicts of Interest:** The author declares no conflict of interest.
