IEEE INTERNATIONAL SYSTEMS CONFERENCE 1

The Application of Model-Based SystemsEngineering to the Practice of Clinical Medicine

Inas S. Khayal, Amro M. Farid

Abstract—Humanity is currently facing an unprecedentedchronic disease burden. Healthcare needs have significantlyshifted from treating acute to treating chronic conditions. Chronicdiseases tend to involve multiple factors with complex interactionsbetween them evidenced by the continually growing medicalknowledge base. The health profession requires the ability tomanage this rapidly deepening knowledge base to assimilate thelessons from research and clinical care experience by system-atically capturing, assessing and translating it into the highestlevel of reliable care. A more systems approach to practicingmedicine exists and is referred to as functional medicine. Ittakes into account the many subsystems in the human body andtheir many interactions. Although the science behind treating thepatient as a system exists, the application of systems tools andtechniques have not been utilized. It is only natural to begin toformalize the systems thinking using the established tools fromthe systems engineering field. Specifically, this paper is the first tocontribute to the need for systems tools in the practice of clinicalmedicine and includes an example application of model-basedsystems engineering to clinical medicine.

Keywords: engineering systems, model-based systems en-gineering, managing knowledge complexity, improving healthoutcomes

I. INTRODUCTION

Humanity is currently facing an unprecedented diseaseburden. Although science and technology have allowed forsignificant advancements in health treatments, these treatmentstend to be either 1.) curing, in many cases, of acute conditionsor 2.) managing, at best, in cases of chronic conditions.

Healthcare needs have significantly shifted from treatingacute conditions to treating chronic conditions. Given that78% of total healthcare costs in the United States are due tochronic disease [?], clinical medicine needs to evolve and shiftto specifically address the growing chronic disease epidemic.

Healthcare needs have significantly shifted from treatingacute conditions to treating chronic conditions. Chronic condi-tions, unlike acute conditions, are particularly complex in thatthey tend to involve multiple factors with multiple interactionsbetween them [1]. It is now established that combating chronicdisease requires treating the patient more holistically [1]–[11].This is currently a challenge given that the science of clinicalmedicine is fundamentally reductionist [1]. Many domains

I. S. Khayal is with the Faculty of the Dartmouth Institute of Health Policy& Clinical Practice and The Population Health Collaboratory @ D-H, GeiselSchool of Medicine at Dartmouth, Dartmouth College, Lebanon, NH 03766,USA.

A. M. Farid is with Faculty of Thayer School of Engineering at Dartmouth,14 Engineering Dr, Hanover, NH 03755, USA and a Research Affiliate at theMIT Department of Mechanical Engineering, Cambridge, MA

within the health field believe that the limit of reductionistthinking has been achieved and to understand the whole systemone must start to apply systems thinking [1], [2], [4]–[6]. Theparadigm shift to a more whole patient focus where the patientis treated as a system exists and is called functional medicine[7].

Functional medicine is a systems approach to practicingmedicine. It takes into account the many subsystems in thehuman body and their many interactions. Although the sciencebehind treating the patient as a system exists, the applicationof systems tools and techniques have not been utilized. It isonly natural to begin to formalize the systems thinking usingthe established tools from the systems engineering field.

This paper first describes in Section II the backgroundconcepts of systems engineering, systems thinking in thehealth field and functional medicine and in Section III showsan example application of model-based systems engineeringtools to clinical medicine.

II. BACKGROUND CONCEPTS

This section serves to introduce Section II-A describing thebasic concepts of systems engineering related to complexitymanagement in this work, Section II-B discussing the twohealth areas that have incorporated systems thinking andSection II-C includes a brief description of a systems approachto the practice of medicine: Functional Medicine.

A. Systems Engineerings as Complexity Management

Systems engineering has emerged as an approach to syn-thesize and design large complex systems [12], [13]. Thatsaid, the process is also highly analytical in that it encouragesthe development of multiple complementary models to view,describe and communicate the system at varying levels ofdetail, abstraction and complexity.

The pertinent background concepts needed to understandsystems engineering application to clinical medicine include:

Concept 1: system function, form, and their allocationConcept 2: scale and scopeConcept 3: system properties (e.g. emergence, robust-ness, modularity, etc.)

These concepts can be applied to clinical medicine by utilizingthe latest tools and techniques such as SysML.

1) Concept 1: System Function, Form, and their Allocation:One of the most fundamental ideas in systems engineering isthat a system has 1.) one or more functions, 2.) a physicalform or structure, and 3.) their allocation as the mappingbetween the former to the latter. In the human body, there

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exists high level functions (e.g. Assimilation (digestion &absorption), Defense and Repair, Energy Regulation, Elimi-nation, Transport, Communication). As these functions occurthey evolve an individual’s health “state”. These functionsmay be performed by specific primary high level structuresof multiple-organ systems (e.g. Digestive System, ImmuneSystem, Cardiovascular System). Their mutual allocation ormapping is typically found in the knowledge base of medicinedescribing the functions of different organs and specific cellswithin them. This knowledge base is continually growing insize, detail, and complexity based on the continued researchof scientists and experience of clinicians, ultimately providinga new big data application.

The complexity of systems, arrises from the elemental sizeof a system. Systems with more than 7n where n ≥ 3 areconsidered fundamentally complex [14]. These elements canbe parts of the system form or the system function. Thehuman body is therefore complex not just by virtue of itstrillions of cells but also by virtue of its many functions.Secondly, systems may also be classified as complex becausethese elements of function and form may interact. Finally,complexity arises when there is not a 1-to-1 exclusive mappingbetween elements of function and form.

2) Concept 2: Scale and Scope: Given the complexityof the human body, it is important to discuss the conceptsof system scale and scope in the context of complexitymanagement. The structure of the human body is organizedinto several (length) scales. In anatomy and physiology this istypically referred to as “Levels of Organizations”. In systemsengineering terminology, there is the scale of the human bodyas a whole, followed by the 2nd highest level consisting oforgan systems (e.g. nervous system), followed by individualorgans (e.g. the brain), followed by organ structures (e.g. thecerebellum). The function of the human body is organized intoseveral temporal scales ranging from long term effects (e.g.aging) to fleeting transients (e.g. neuron firing). Naturally, thecomplexity of a system grows with the number of scales underconsideration. Therefore, it is often important to restrict thescope of study to a certain length or temporal scale in formor function; potentially leading to reductionist approaches. Incontrast, systems tools facilitate the integration of multiplelength or temporal scales so as to give both broad holisticviews with lower resolution as well as detailed views at higherresolution. Consequently an individual’s health state can alsoviewed at varying levels of granularity associated with scaleand scope.

3) Concept 3: System Properties: Complex interconnectedsystems exhibit system properties which would not neces-sarily have appeared from understanding the individual partsalone. For systems engineering, these are often called “life-cycle properties” and often take a grammatical form endingin “ility” (e.g. flexibility, reconfigurability) [13], [14]. Thefunctional medicine literature (to be discussed in the nextsection) has paid particular attention to three such systemproperties namely: emergence, robustness, and modularity [7].Emergence refers to the properties such as larger entities,patterns, and/or regularities that arise through interactions ofsmaller entities that themselves do not exhibit such proper-

ties and cannot be predicted from an understanding of theindividual parts. This is primarily why medicine has startedto recognize that a reductionist approach is not providinga realistic picture of some of the manifestations that canarise in people’s overall health [1], [15]. Robustness is theability of a systems to maintain itself in the face of changingenvironmental conditions. Modularity refers to a system thatis comprised of functional units working together to producean outcome that cannot be produced by any of the unitsworking independently [12], [16]. The more complex a systemis, the more these properties manifest themselves in seeminglyunpredictable ways.

4) SysML as a Complexity Management Tool: The systemsengineering field has developed many tools and techniquesto build and manage complex systems in defense, aerospace,and infrastructure. These are specifically intended to man-age systems with many form and function elements, theirinteractions and their mapping. In recent years, the SystemsModeling Language (SysML) [17], [18] has emerged as a defacto standard of systems engineering process [13]. It is agraphical modeling language that facilitates the description ofsystem function, form, and their allocation. It also supportsparametric descriptions which may be used to develop quanti-tative models in coherent and well-organized ways. Therefore,SysML is chosen as an appropriate analytical tool in this workto model the complex, interconnected, and hetero-functionalnature of human health with the ability to handle this big dataapplication.

B. Systems Thinking in the Health Field

Similar to most of the sciences, the health field is based onreductionist thinking. Systems thinking tools and techniqueshave only recently gained strides within the biological andhealth fields. The domains where systems tools have beenapplied to the health field include 1.) Healthcare SystemsEngineering and 2.) Systems Biology.

Healthcare Systems Engineering can be viewed as a healthapplication of industrial engineering and operations research.Specific applications generally focus on improving hospitaloutpatient flow [19], [20], improving emergency room opera-tions [21] and improving patient safety [22].

Systems Biology has a goal of understanding complexbiological processes in sufficient detail to allow for the de-velopment of a computational model. This model would thenallow for the simulation of system behavior thus elucidatingsystem function [23]. This can be viewed as applying systemstheory at the cellular and sub-cellular level, one of the smallerphysical scales.

There have been suggestions of extending Systems Biologytheory to a higher physical scale, sometimes referred to asSystems Medicine [6], [24]–[29]. The term has been used in afew articles, but the meaning it represents varies significantly.The idea though is trying to apply systems thinking to a higherphysical scale than cells. However, this has generally been intheory rather than with practical methods.

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C. Functional Medicine: A Systems Approach to the Practiceof Medicine

Functional medicine is a systems approach to medicinethat is not a separate discipline or specialty; rather, it is anapproach to clinical care that is patient-centered, personalized,and grounded in the science of clinical medicine [7], utiliz-ing diagnostic tools and analytical evaluating mechanisms ofdisease [7], [30]. It addresses chronic conditions by treatingthe individual as a system of complex interactions of multipleorgan systems and multiple physiological and biochemicalpathways with internal genetic predispositions and externalenvironmental influences [7]. Functional medicine practition-ers have published on chronic diseases discussing them inan interconnected and systemic fashion. The foundation offunctional medicine stands on the identification of the primaryfunctions/processes of the body, which through the systemicinterconnectedness in the body manifest as dysfunction.

The goal of applying systems tools to clinical medicine isto manage the complexity of medical knowledge to effectivelyimprove patient care and health outcomes at lower costs. TheInstitute of Medicine’s 2010 report [31] states that “Pervasiveinefficiencies, an inability to manage a rapidly deepeningclinical knowledge base, and a reward system poorly focusedon key patient needs, all hinder improvements in the safetyand quality of care... Achieving higher quality care at lowercost will require fundamental commitments to the incentives,culture, and leadership that foster continuous learning, as thelessons from research and each care experience are systemat-ically captured, assessed, and translated into reliable care.”

III. APPLICATION OF SYSML TO FUNCTIONAL MEDICINE:AN EXAMPLE

In this section, the Model-Based Systems Engineeringtool SysML is utilized to translate aspects of the functionalmedicine text to produce graphical models. A small yetinterconnected example of the beginning stages of digestionis chosen. This will keep the example manageable yet en-lightening as a first example of translating medical knowledgeinto graphical models. Section III-A describes the semantics ofSysML and finally Section III-B describes the SysML model.

The text translated for the SysML example is from Chapter24 (Digestive, Absorptive and Microbiological Imbalances)of the Textbook of Functional Medicine pages 327-328 [7].An example of digestion was chosen given that visits forGI distress of one kind or another account for significanthealthcare visits [32]–[34].

A. SysML Semantics

Figure 1 describes the semantics needed to describe SysMLfor the model developed in Section III-B but not detailedenough as to serve as a complete reference for SysML. Othersources generally referred to are referenced here [17], [18].

B. SysML Model

The SysML model developed includes 5 figures: 1 blockdiagram (structure), 1 primary activity diagram (function) with2 sub-activity diagrams and an allocation diagram. Fig. 2

System Form System Function

SysML Block Definition Diagram SysML Activity Diagram

block type: system block

subsystem blockexternal block

association

directed association

directed composition

system boundary

Control Flow (controller)

Object Flow ("something" flows)

JoinFork

input pin to Action A

output pin to Action B

Decision

embedded Activity Diagram

Fig. 1. SysML Semantics for the SysML Block Definition Diagram (SystemForm) and the SysML Activity Diagram (System Function).

shows the structure with the block diagrams consisting of thedefined system: digestive system (defined in this example asthe mouth to the stomach), the subsystems (mouth, esophagusand stomach), the external components that interact with thesystem (lower GI/intestines, central nervous system, bodyphysical state, mental state, enteric nervous system and thenormal system) and the human body block they all belongto. Fig. 3 shows the functions within the primary activitydiagram. Fig. 4 shows a deeper level activity diagram of theaction Masticate from within the primary activity diagram,while fig. 5 shows the deeper level activity diagram of theaction Store & Mix from within the primary activity diagram.Additionally, fig. 6 shows the allocation of function (theactivities) to the form (the structures). The allocation clearlyshows functions, within the form system, occurring and/oraffecting far reaching external structure blocks.

Fig. 2. The block definition diagram consists of the defined system: digestivesystem (defined in this example as the mouth to the stomach), the subsystems(mouth, esophagus and stomach) and the external components that interactwith the system (lower GI tract/intestines, central nervous system, bodyphysical state, mental state, enteric nervous system and the normal system).

IV. DISCUSSION

This paper presents the need for system tools in the practiceof clinical medicine with the goal of managing knowledgecomplexity to effectively improve patient care and health out-comes. An example of the model-based systems engineering

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Fig. 3. The primary activity diagram describes the function or action ofdigestion. This includes 3 primary actions: Masticate, Transport and Store &Mix with several external parameters, inputs and an output to this activity.

Fig. 4. The secondary activity diagram describes more specifically thefunction masticate. This involves several inputs, several activity parameterswith an output to the function masticate.

tool SysML was utilized to translate functional medicine textto produce visual graphical models. To our knowledge this isthe first paper applying systems engineering tools to clinicalmedicine at the patient level. At the sub-cellular level, anothersystem engineering modeling language, OPM, has been ap-plied [35]. However, OPM is primarily intended for conceptualmodeling rather than complex informatic descriptions whichare necessary to incorporate the large bodies of informationand knowledge. With this example in place, the discussion canreturn to the systems thinking concepts introduced in SectionII.

Systems thinking allows for a practice of medicine thattruly addresses chronic conditions, from both structure andfunction, rather than “re-working” or “re-applying” of acutecare models.

While some of these clinically-applied systems thinkingconcepts are similar to those found in Systems Biology,there are two critical distinctions between the typical tech-niques used in Systems Biology and the model-based systemsengineering approach proposed here. First, the model-basedsystems engineering method is fundamentally a top-downapproach. It fixes the scope of the model and grows its size asone drills down further and adds greater detail. In contrast,the Systems Biology approach is intrinsically a bottom-upapproach. It begins with one or more more relatively detailed“building-block” models (e.g. translations, transcriptions of

Fig. 5. The secondary activity diagram describing in more detail the functionStore & Mix. This involves several inputs, several activity parameters and anoutput to Store & Mix.

Fig. 6. The allocation matrix includes form on the vertical scale and functionon the horizontal scale. The form include the system components followed bythe external components. The function include 3 primary activity figures witheach separated to include the inputs/outputs, the external factors affecting theactivities and the internal activities.

RNA, proteins) and then grows its size outward in scope to in-clude interactions between these smaller models. Some wouldargue that Systems Biology is better categorized as a middle-out approach [36]. Nevertheless, the approach “is based onconceptualizing insight at whichever level there is a goodunderstanding of data and processes, and on then incorporatinggreater levels of structural and functional integration [37].Therefore, the two approaches have very different approachesto model development.

The second distinction has to do with the fundamentaldifference in data used to build and develop these models.In the clinically-applied model-based systems engineering ap-proach, the data comes from mining patient histories, records,and case studies. The fidelity of this model is driven by theexisting medical science as it is already published and hence

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the translation of medical text used as an example in this paper.Systems biology, in contrast, is biologically focused and usesdata from wet lab experiments or first principles of the naturalsciences. Although systems thinking at the patient level isnot novel, this paper begins to formalize the systems thinkingusing the established tools from the systems engineering field.

V. CONCLUSION & FUTURE WORK

In conclusion, this paper presents the need for system toolsin the practice of clinal medicine with the goal of managingknowledge complexity to effectively improve patient care andhealth outcomes. To our knowledge, this is the first paperpresenting the utilization of system tools and providing anexample at the patient level. Specifically, the example modeldemonstrated how model-based systems engineering tools canbe utilized for clinical medicine, ultimately giving a new bigdata application. This presents opportunities for collaborationbetween clinical stakeholders, research stakeholders, systemsengineers and patients with the goal of developing these toolsto be utilized within clinical medicine.

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