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Systems Thinking

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Overview #

Systems thinking has emerged as a fundamental competency within health systems science, providing a framework for understanding and addressing the complexity inherent in modern healthcare delivery. This review examines the theoretical foundations, methodologies, tools, and applications of systems thinking in healthcare. Systems thinking recognizes healthcare as a complex adaptive system characterized by interconnections, feedback loops, emergence, and non-linear behaviors. We explore key systems thinking concepts including mental models, causal loop diagrams, leverage points, and system dynamics, along with their practical applications in quality improvement, patient safety, population health, and health policy. The integration of systems thinking into medical education through health systems science curricula represents a paradigm shift toward preparing healthcare professionals as “systems citizens” capable of leading transformative change in healthcare delivery.

Introduction #

Healthcare systems worldwide face unprecedented challenges characterized by rising costs, quality gaps, health inequities, and increasing complexity in care delivery [1]. Traditional reductionist approaches that focus on individual components in isolation have proven inadequate for addressing these multifaceted challenges [2]. Systems thinking offers an alternative paradigm that views healthcare as an interconnected whole, recognizing that system behavior emerges from the interactions and relationships between components rather than from the components themselves [3].

The recognition of systems thinking as essential to healthcare improvement gained momentum following influential publications such as the World Health Organization’s 2009 report “Systems Thinking for Health Systems Strengthening” and the Institute of Medicine’s “Crossing the Quality Chasm” [4,5]. More recently, systems thinking has been formalized as a core competency domain within health systems science (HSS), now recognized as the “third pillar” of medical education alongside basic and clinical sciences [6,7]. This integration reflects a fundamental shift in how healthcare professionals are trained to understand, navigate, and transform complex healthcare systems [8].

Defining Systems Thinking in Healthcare #

Systems thinking is fundamentally a mindset and approach that views systems through a holistic lens, focusing on how components are interconnected and how these interconnections give rise to system behavior [9]. Rather than examining isolated events or individual elements, systems thinking deliberately seeks patterns of behavior and the underlying systemic interrelationships responsible for these patterns [10].

In healthcare contexts, systems thinking is defined as an enterprise aimed at understanding how things are connected within healthcare systems and how interventions in one area ripple through the entire system, often producing unexpected or counterintuitive consequences [11]. This approach acknowledges healthcare as an open system constantly interacting with its environment and characterized by features including non-linearity, feedback mechanisms, emergence, self-organization, and adaptation [12].

The importance of systems thinking in healthcare stems from recognition that health systems are not merely complicated (having many parts) but genuinely complex—exhibiting behaviors that cannot be predicted by analyzing individual components [13]. This complexity arises from the diverse actors involved (patients, providers, administrators, policymakers), multiple levels of organization (from individual encounters to health systems to population health), and dynamic interactions that evolve over time [14].

Theoretical Foundations: Complex Adaptive Systems #

Healthcare organizations and health systems function as complex adaptive systems (CAS)—collections of individual agents with freedom to act in ways that are not always predictable, whose actions are interconnected such that one agent’s actions change the context for other agents [15]. Understanding healthcare through the CAS lens provides crucial insights into system behavior and transformation [16].

Core Principles of Complex Adaptive Systems

Non-linearity and Emergence: Healthcare outcomes often arise from non-linear interactions among system elements, where small changes can produce disproportionately large effects or large interventions may yield minimal impact [17]. Emergence refers to properties or behaviors that arise from interactions within the system but are not inherent in any individual component [18]. For example, patient safety culture emerges from interactions between individuals, teams, organizational structures, and external pressures, but cannot be attributed to any single element [19].

Self-Organization and Adaptation: CAS possess the capacity for self-organization, allowing adaptation without centralized control [20]. In healthcare, this manifests as clinicians and teams developing workarounds to system constraints, adapting protocols to local contexts, and creating informal networks to accomplish patient care goals [21]. This self-organizing capability represents both a strength (enabling flexibility and resilience) and a challenge (potentially undermining standardized processes) [22].

Feedback Loops: Feedback mechanisms—both reinforcing (amplifying) and balancing (stabilizing)—govern system dynamics [23]. In healthcare, reinforcing loops can drive improvement (e.g., better outcomes leading to increased confidence and further improvement) or deterioration (e.g., staff burnout leading to errors, which increase stress and further burnout) [24]. Balancing loops maintain system stability but can also create resistance to change [25].

Context Sensitivity and Path Dependence: CAS are sensitive to initial conditions and historical context—current system behavior depends not only on present circumstances but on the system’s history and trajectory [26]. In healthcare organizations, this explains why identical interventions may produce different results in different settings or at different times [27].

Core Concepts in Systems Thinking #

Interconnections and System Structure

The first fundamental characteristic of systems thinking involves recognizing and understanding interconnections and system structure [28]. Healthcare systems comprise multiple interrelated elements including people, processes, technologies, organizational structures, policies, and external environmental factors [29]. These elements connect through information flows, resource flows, authority relationships, and functional dependencies [30].

Understanding system structure requires mapping these interconnections to identify how changes in one area affect others. For instance, implementing electronic health records (EHRs) affects not only documentation but also clinical workflows, team communication, decision-making processes, billing systems, and patient-provider interactions [31]. Failure to appreciate these interconnections contributes to unintended consequences of well-intentioned interventions [32].

Feedback Loops and Causal Relationships

Feedback loops represent circular chains of cause and effect where system outputs circle back to influence inputs [33]. Two types of feedback loops characterize system behavior:

Reinforcing (Positive) Feedback Loops: These amplify changes, driving exponential growth or decline. In healthcare, reinforcing loops can drive quality improvement when successes build confidence and momentum, or can accelerate deterioration when problems cascade [34]. For example, hospital-acquired infections can create a reinforcing loop where infections increase length of stay, which increases exposure to pathogens, leading to more infections [35].

Balancing (Negative) Feedback Loops: These counteract changes, maintaining system stability or equilibrium. While stabilizing, balancing loops can also create resistance to improvement efforts [36]. For instance, efforts to reduce emergency department wait times may trigger a balancing loop where faster service increases patient volume, ultimately returning wait times toward baseline [37].

Causal loop diagrams (CLDs) provide a visual method for mapping these feedback relationships, enabling stakeholders to understand system dynamics and identify potential intervention points [38,39]. CLDs have been successfully applied to diverse healthcare challenges including obesity, maternal health, chronic disease management, and health system capacity [40].

Mental Models

Mental models are internal representations—assumptions, beliefs, and frameworks—that individuals use to understand and interact with systems [41]. In healthcare, clinicians, administrators, patients, and policymakers all maintain mental models about how healthcare works, what causes problems, and how to create improvement [42].

Divergent mental models among stakeholders represent a significant barrier to system transformation [43]. For example, leaders may hold different mental models regarding population health—some focusing primarily on improving care for existing patients while others prioritize community-level health promotion [44]. Aligning mental models through dialogue, shared visioning, and collaborative learning is essential for coordinated action [45].

Systems thinking explicitly surfaces and examines mental models, recognizing that our assumptions shape what we perceive, how we interpret events, and what solutions we consider viable [46]. Techniques such as shared mental modeling help teams develop common understanding and more effectively navigate complexity [47].

Leverage Points

Leverage points are places within a system where small, strategic changes can produce significant, lasting impacts [48]. Donella Meadows’ seminal work identified twelve leverage points arranged by increasing effectiveness, from adjusting parameters (low leverage) to shifting paradigms and transcending paradigms (high leverage) [49].

In healthcare, high-leverage interventions often involve changing system goals, information flows, or the rules governing system behavior rather than simply adjusting resource levels or performance targets [50]. For example, shifting reimbursement from fee-for-service to value-based payment represents a change in system rules and goals, potentially altering incentives throughout the healthcare system [51].

Identifying leverage points requires understanding system structure and dynamics rather than focusing on obvious symptoms [52]. Systems thinking helps distinguish interventions that address root causes from those that merely treat symptoms, which often trigger balancing feedback that undermines improvement efforts [53].

Time Delays

Time delays between actions and consequences profoundly influence system behavior, often contributing to oscillations, overshooting, or policy resistance [54]. In healthcare, delays manifest in numerous ways: lag times between policy implementation and measurable outcomes, intervals between medical education and workforce deployment, and delays in diagnosing treatment effectiveness [55].

Delays complicate decision-making because actors may not immediately perceive consequences of their actions, leading to interventions that are poorly timed or excessive [56]. For instance, training programs responding to perceived workforce shortages may produce oversupply years later when new graduates enter practice, because planners failed to account for the multi-year training pipeline delay [57].

System Boundaries

Defining appropriate system boundaries—determining which elements to include within the system and which to treat as external environment—shapes analysis and intervention design [58]. In healthcare, boundary choices affect which stakeholders are engaged, what data are collected, and what solutions are considered [59].

Systems thinking recognizes that boundaries are often arbitrary and should be drawn pragmatically based on the problem being addressed [60]. Overly narrow boundaries risk missing important interactions, while excessively broad boundaries can make analysis unwieldy [61]. Effective systems thinking involves iteratively adjusting boundaries as understanding evolves [62].

Systems Thinking Tools and Methods #

Causal Loop Diagrams

Causal loop diagrams serve as qualitative systems thinking tools that visualize relationships between system variables and identify feedback loops [63]. CLDs use arrows to represent causal relationships, with positive (+) or negative (-) polarity indicating whether variables change in the same or opposite directions [64].

CLDs have been extensively applied in public health research to understand complex issues including obesity, tobacco use, maternal health, and infectious disease [65,66]. They can be developed through various approaches including literature synthesis, expert elicitation, stakeholder workshops (group model building), or combinations of these methods [67].

A scoping review of CLD use in public health found that CLDs help identify leverage points for system change, reveal unintended consequences of policies, and facilitate stakeholder dialogue around complex problems [68]. However, the review also noted variability in methods and limited guidance on best practices, particularly for low- and middle-income settings where primary data collection may be constrained [69].

System Dynamics Modeling

System dynamics extends causal loop diagrams through quantitative computer simulation models that track stocks (accumulations) and flows (rates of change) over time [70]. These models enable testing “what-if” scenarios, exploring policy options, and understanding long-term system behavior [71].

System dynamics has been applied to healthcare challenges including workforce planning, disease prevention, health services capacity, and resource allocation [72]. While powerful, system dynamics requires specialized expertise and significant data, potentially limiting accessibility [73]. Recent work has focused on making system dynamics more accessible to healthcare practitioners and policymakers [74].

Process Mapping and Network Analysis

Process mapping techniques visualize workflows and identify bottlenecks, redundancies, and disconnections in care delivery [75]. Network analysis examines relationships and information flows among actors, revealing informal structures that complement or contradict formal organizational charts [76].

These methods help teams understand how work actually happens (versus how it is designed to happen) and identify opportunities for redesign [77]. In healthcare safety applications, process mapping reveals latent conditions and system vulnerabilities that contribute to errors [78].

Group Model Building

Group model building engages stakeholders in collaborative modeling processes, building shared understanding while developing formal system models [79]. This participatory approach enhances model validity through diverse perspectives, builds buy-in for resulting insights, and develops systems thinking capacity among participants [80].

Scripts and facilitation guides support group model building sessions, enabling teams to elicit variables, construct causal loop diagrams, identify leverage points, and explore scenarios [81]. Group model building has been successfully applied to diverse healthcare challenges, demonstrating value for both model development and stakeholder engagement [82].

Applications in Healthcare Delivery and Improvement #

Quality Improvement and Patient Safety

Systems thinking fundamentally transforms approaches to quality improvement and patient safety by shifting focus from individual errors to system-level analysis [83]. Traditional approaches often blame individuals for mistakes, while systems thinking recognizes that errors typically emerge from interactions between system components rather than individual failings [84].

Safety-II frameworks, informed by systems thinking, emphasize understanding how systems create safety through human adaptation and resilience rather than solely focusing on failure prevention [85]. This perspective recognizes that healthcare work requires constant adaptation to unplanned circumstances, and these adaptations often prevent errors rather than cause them [86].

Systems thinking applications in patient safety include analyzing incidents through multiple system levels, identifying common causal pathways across events, and designing interventions that address root causes rather than proximate factors [87]. For example, medication errors often stem from system design issues including confusing labeling, inadequate information systems, or interruption-prone environments [88].

Population Health and Health Equity

Population health challenges exemplify wicked problems requiring systems approaches [89]. Issues such as chronic disease, health disparities, and social determinants of health involve multiple interacting factors across sectors including healthcare, education, housing, employment, and environment [90].

Systems thinking helps map these complex interactions, identify feedback loops perpetuating health inequities, and reveal high-leverage intervention points [91]. For instance, causal loop diagrams have illuminated how poverty, chronic stress, unhealthy behaviors, and disease mutually reinforce each other, suggesting that breaking reinforcing cycles requires multi-sector interventions [92].

Applications include analyzing maternal health systems, understanding drivers of obesity, examining mental health service integration, and evaluating community-based prevention programs [93,94]. Systems approaches emphasize that improving population health requires changing not just healthcare delivery but also underlying social and economic systems [95].

Health Policy and System Transformation

Health policy making increasingly incorporates systems thinking to anticipate unintended consequences, identify implementation barriers, and design policies aligned with system dynamics [96]. Traditional policy analysis often assumes linear cause-effect relationships and fails to account for feedback, delays, and adaptation [97].

Systems thinking reveals how policies interact with existing system structures and incentives [98]. For example, analysis of dialysis policy in Thailand using causal loop diagrams identified how short-term fixes to capacity constraints unintentionally increased long-term demand, creating system strain [99]. System archetypes (common patterns of system behavior) helped policymakers understand dynamics and identify more effective interventions [100].

Applications span diverse policy areas including healthcare financing, workforce planning, service delivery models, and public health interventions [101]. Systems approaches support more realistic policy assessment, accounting for implementation complexity and context [102].

Healthcare Workforce and Human Resources

Systems thinking applications in healthcare human resource management examine workforce as a dynamic system shaped by multiple interacting factors including recruitment, training, retention, workload, workplace culture, and external labor market conditions [103]. Viewing workforce through a systems lens reveals feedback loops that traditional HR approaches miss [104].

For example, understaffing creates increased workload and stress, leading to burnout and turnover, which further worsens understaffing—a reinforcing loop [105]. System dynamics models help forecast workforce needs accounting for pipeline delays, examine retention strategies, and evaluate policy options [106].

A recent scoping review of systems thinking in healthcare HRM identified applications at macro (national/regional), meso (organizational), and micro (unit) levels, demonstrating value for workforce planning, policy development, and service coordination [107]. Most studies employed soft systems modeling methods, though opportunities exist for incorporating quantitative system dynamics approaches [108].

Integration into Health Systems Science Education #

Health Systems Science as the Third Pillar

Health systems science has emerged as the third foundational pillar of medical education, complementing basic sciences and clinical sciences [109,110]. HSS encompasses competency domains including healthcare structures and processes, health system improvement, value-based care, population and public health, health policy and economics, social determinants of health, and clinical informatics [111]. Systems thinking serves as the integrating framework linking these domains [112].

The HSS framework addresses growing recognition that traditional medical education inadequately prepares physicians for contemporary healthcare challenges [113]. Graduates must understand not only disease pathophysiology and treatment but also how to navigate complex healthcare systems, lead improvement efforts, work in interprofessional teams, address health inequities, and practice high-value care [114].

Core Systems Thinking Competencies

Systems thinking competencies within HSS include [115,116]:

  • Recognizing healthcare as a complex adaptive system with emergent properties
  • Understanding interconnections, feedback loops, and unintended consequences
  • Applying systems analysis tools including process mapping and causal loop diagrams
  • Identifying leverage points for system improvement
  • Recognizing how mental models shape perception and action
  • Engaging stakeholders across system boundaries
  • Analyzing problems from multiple perspectives and system levels
  • Appreciating time delays and long-term system dynamics

Educational Approaches and Implementation

HSS curricula employ diverse pedagogical strategies including didactic instruction, experiential learning, simulation, quality improvement projects, and reflective practice [117]. Effective systems thinking education requires moving beyond conceptual knowledge to practical application in authentic healthcare contexts [118].

Experiential learning is particularly critical for developing systems thinking skills [119]. Medical students participating in quality improvement projects, analyzing real system problems, or engaging with community health initiatives develop deeper understanding of system dynamics than through classroom instruction alone [120]. These experiences help students develop professional identity as “systems citizens” responsible for system stewardship [121].

Challenges to HSS implementation include competing curricular priorities, limited faculty expertise in systems thinking, and student perceptions that HSS is less important than traditional basic and clinical sciences [122]. Addressing these barriers requires faculty development, protected curricular time, assessment strategies demonstrating HSS importance, and institutional commitment [123].

Assessment and Outcomes

Assessing systems thinking competencies presents methodological challenges given the cognitive and applied nature of these skills [124]. Assessment approaches include written examinations testing systems concepts, portfolio-based evaluation of improvement projects, direct observation of system analysis skills, and reflective exercises examining mental models [125].

Emerging evidence suggests HSS education improves learner ability to analyze system problems, identify improvement opportunities, and engage in interprofessional collaboration [126]. However, longer-term outcomes research is needed to determine impact on practice behaviors and patient outcomes [127]. The field is developing more robust assessment tools and conducting longitudinal studies tracking graduates into practice [128].

Challenges and Future Directions #

Implementation Challenges

Despite growing interest, systems thinking remains underutilized in healthcare practice, particularly in low- and middle-income countries [129]. Barriers include limited awareness of systems approaches, perception that systems thinking requires sophisticated technical methods, lack of practical guidance, resource constraints, and organizational cultures focused on reactive problem-solving rather than proactive system design [130,131].

Additional challenges include the “messiness” of real-world systems that resist neat analysis, difficulty achieving stakeholder alignment, and tension between systems approaches emphasizing emergence and adaptation versus traditional management emphasizing control and prediction [132]. Power dynamics and vested interests may resist systems changes that threaten established arrangements [133].

Methodological Considerations

Systems thinking’s strength—embracing complexity—also presents methodological challenges [134]. System boundaries require subjective choices, models necessarily simplify reality, and validation proves difficult when systems are constantly evolving [135]. Balancing rigor with accessibility remains an ongoing tension [136].

The field continues debating relationships between qualitative and quantitative systems methods and optimal combinations for different applications [137]. While quantitative system dynamics offers predictive power, qualitative approaches may be more accessible and better suited for stakeholder engagement [138]. Pragmatic pluralism—combining methods based on purpose and context—appears most promising [139].

Integration with Implementation Science

Recent work has explored synergies between systems thinking and implementation science [140]. Systems science offers methods for designing interventions accounting for complexity, while implementation science provides frameworks for successful adoption and sustained use [141]. Integration could strengthen both fields, though relatively few studies have combined these approaches [142].

Systems thinking can inform implementation by illuminating contextual factors, anticipating barriers, identifying leverage points, and adapting interventions to local system dynamics [143]. Conversely, implementation frameworks can guide translation of systems insights into practical change strategies [144].

Future Research Priorities

Priority areas for advancing systems thinking in healthcare include:

  1. Developing accessible tools and training that bridge theory and practice [145]
  2. Building evidence base linking systems approaches to improved outcomes [146]
  3. Expanding applications in low- and middle-income settings [147]
  4. Enhancing integration with implementation science and other improvement methodologies [148]
  5. Addressing equity and power dynamics in systems change efforts [149]
  6. Advancing education assessment and longitudinal outcomes research [150]
  7. Fostering learning health systems that embed systems thinking in routine practice [151]

Conclusion #

Systems thinking represents a fundamental paradigm shift in healthcare—from reductionist, linear approaches toward holistic understanding of healthcare as a complex adaptive system. Through concepts including interconnections, feedback loops, emergence, mental models, and leverage points, systems thinking provides frameworks for understanding system behavior and designing effective interventions.

The integration of systems thinking into health systems science reflects recognition that tomorrow’s healthcare professionals must not only master clinical science but also understand how to navigate and transform complex systems. As healthcare confronts mounting challenges including rising costs, persistent quality gaps, workforce shortages, and health inequities, systems thinking offers essential competencies for leading meaningful change.

While implementation challenges persist, growing bodies of literature, educational programs, and practical applications demonstrate systems thinking’s value. Future progress requires continued work bridging theory and practice, building evidence for effectiveness, developing accessible tools, and fostering organizational cultures that embrace complexity rather than seeking to eliminate it. By cultivating systems thinking capabilities among healthcare professionals and organizations, the field can advance toward truly integrated, adaptive, and equitable health systems.

References #

[1] Gonzalo JD, Haidet P, Papp KK, et al. Health Systems Science in Medical Education: Unifying the Components to Catalyze Transformation. Academic Medicine. 2020;95(9):1362-1372. https://doi.org/10.1097/ACM.0000000000003400

[2] Carey G, Malbon E, Carey N, Joyce A, Crammond B, Carey A. Systems science and systems thinking for public health: a systematic review of the field. BMJ Open. 2015;5(12):e009002. https://doi.org/10.1136/bmjopen-2015-009002

[3] Adam T. Advancing the application of systems thinking in health. Health Research Policy and Systems. 2014;12:50. https://doi.org/10.1186/1478-4505-12-50

[4] De Savigny D, Adam T. Systems Thinking for Health Systems Strengthening. Geneva: World Health Organization; 2009.

[5] Institute of Medicine. Crossing the Quality Chasm: A New Health System for the 21st Century. Washington, DC: National Academy Press; 2001. https://doi.org/10.17226/10027

[6] Skochelak SE, Hawkins RE. Health Systems Science. Philadelphia, PA: Elsevier; 2017.

[7] Lomis K, Amiel JM, Ryan MS, et al. Implementing an Accelerated Change in Medical Education Consortium: Innovations in Health Systems Science Education. Academic Medicine. 2017;92(7):966-973. https://doi.org/10.1097/ACM.0000000000001507

[8] Borkan J, Gonzalo JD, Wolpaw DR, et al. Health systems science education: The new post-Flexner professionalism for the 21st century. Medical Teacher. 2021;43(sup2):S25-S31. https://doi.org/10.1080/0142159X.2021.1924366

[9] Peters DH. The application of systems thinking in health: why use systems thinking? Health Research Policy and Systems. 2014;12:51. https://doi.org/10.1186/1478-4505-12-51

[10] Meadows DH. Thinking in Systems: A Primer. White River Junction, VT: Chelsea Green Publishing; 2008.

[11] Trochim WM, Cabrera DA, Milstein B, Gallagher RS, Leischow SJ. Practical challenges of systems thinking and modeling in public health. American Journal of Public Health. 2006;96(3):538-546. https://doi.org/10.2105/AJPH.2005.066001

[12] Sturmberg JP, Martin CM. Complexity and Health: Yesterday’s Traditions, Tomorrow’s Future. Journal of Evaluation in Clinical Practice. 2009;15(3):543-548. https://doi.org/10.1111/j.1365-2753.2009.01163.x

[13] Plsek PE, Greenhalgh T. Complexity science: The challenge of complexity in health care. BMJ. 2001;323(7313):625-628. https://doi.org/10.1136/bmj.323.7313.625

[14] Willis CD, Riley BL, Stockton L, et al. Scaling up complex interventions: insights from a realist synthesis. Health Research Policy and Systems. 2016;14:88. https://doi.org/10.1186/s12961-016-0158-4

[15] Plsek PE, Wilson T. Complexity, leadership, and management in healthcare organisations. BMJ. 2001;323(7315):746-749. https://doi.org/10.1136/bmj.323.7315.746

[16] Anderson RA, Crabtree BF, Steele DJ, McDaniel RR Jr. Case study research: the view from complexity science. Qualitative Health Research. 2005;15(5):669-685. https://doi.org/10.1177/1049732305275208

[17] Braithwaite J, Churruca K, Long JC, Ellis LA, Herkes J. When complexity science meets implementation science: a theoretical and empirical analysis of systems change. BMC Medicine. 2018;16:63. https://doi.org/10.1186/s12916-018-1057-z

[18] Heylighen F. The Science of Self-Organization and Adaptivity. In: The Encyclopedia of Life Support Systems. Oxford, UK: Eolss Publishers; 2001.

[19] Ellis LA, Churruca K, Clay-Williams R, et al. Patterns of resilience: A scoping review and bibliometric analysis of resilient health care. Safety Science. 2019;118:241-257. https://doi.org/10.1016/j.ssci.2019.04.044

[20] Kauffman SA. The Origins of Order: Self-Organization and Selection in Evolution. New York: Oxford University Press; 1993.

[21] Tucker AL, Edmondson AC. Why hospitals don’t learn from failures: organizational and psychological dynamics that inhibit system change. California Management Review. 2003;45(2):55-72. https://doi.org/10.2307/41166165

[22] Debono DS, Greenfield D, Travaglia JF, et al. Nurses’ workarounds in acute healthcare settings: a scoping review. BMC Health Services Research. 2013;13:175. https://doi.org/10.1186/1472-6963-13-175

[23] Sterman JD. Business Dynamics: Systems Thinking and Modeling for a Complex World. Boston: Irwin/McGraw-Hill; 2000.

[24] Shanafelt TD, Boone S, Tan L, et al. Burnout and satisfaction with work-life balance among US physicians relative to the general US population. Archives of Internal Medicine. 2012;172(18):1377-1385. https://doi.org/10.1001/archinternmed.2012.3199

[25] Senge PM. The Fifth Discipline: The Art and Practice of the Learning Organization. New York: Doubleday; 2006.

[26] Paina L, Peters DH. Understanding pathways for scaling up health services through the lens of complex adaptive systems. Health Policy and Planning. 2012;27(5):365-373. https://doi.org/10.1093/heapol/czr054

[27] May CR, Johnson M, Finch T. Implementation, context and complexity. Implementation Science. 2016;11:141. https://doi.org/10.1186/s13012-016-0506-3

[28] Cianelli C, Mulemi DC, Mulundo D, et al. Development of the Systems Thinking for Health Actions framework: a literature review and a case study. BMJ Global Health. 2023;8(3):e010815. https://doi.org/10.1136/bmjgh-2022-010815

[29] Sheikh K, Gilson L, Agyepong IA, Hanson K, Ssengooba F, Bennett S. Building the field of health policy and systems research: framing the questions. PLOS Medicine. 2011;8(8):e1001073. https://doi.org/10.1371/journal.pmed.1001073

[30] Atun R. Health systems, systems thinking and innovation. Health Policy and Planning. 2012;27(suppl 4):iv4-iv8. https://doi.org/10.1093/heapol/czs088

[31] Smith SW, Koppel R. Healthcare information technology’s relativity problems: a typology of how patients’ physical reality, clinicians’ mental models, and healthcare information technology differ. Journal of the American Medical Informatics Association. 2014;21(1):117-131. https://doi.org/10.1136/amiajnl-2012-001419

[32] Ash JS, Sittig DF, Poon EG, Guappone K, Campbell E, Dykstra RH. The extent and importance of unintended consequences related to computerized provider order entry. Journal of the American Medical Informatics Association. 2007;14(4):415-423. https://doi.org/10.1197/jamia.M2373

[33] Kim DH, Anderson V. Systems Archetype Basics: From Story to Structure. Waltham, MA: Pegasus Communications; 1998.

[34] Hovmand PS. Community Based System Dynamics. New York: Springer; 2014. https://doi.org/10.1007/978-1-4614-8763-0

[35] Magill SS, Edwards JR, Bamberg W, et al. Multistate point-prevalence survey of health care-associated infections. New England Journal of Medicine. 2014;370(13):1198-1208. https://doi.org/10.1056/NEJMoa1306801

[36] Forrester JW. Counterintuitive behavior of social systems. Technology Review. 1971;73(3):52-68.

[37] Asplin BR, Magid DJ, Rhodes KV, Solberg LI, Lurie N, Camargo CA Jr. A conceptual model of emergency department crowding. Annals of Emergency Medicine. 2003;42(2):173-180. https://doi.org/10.1067/mem.2003.302

[38] Jalali MS, Rahmandad H, Bullock SL, Lee-Kwan SH, Gittelsohn J, Ammerman A. Dynamics of intervention adoption, implementation, and maintenance inside organizations: The case of an obesity prevention initiative. Social Science & Medicine. 2019;224:67-76. https://doi.org/10.1016/j.socscimed.2018.12.021

[39] Ansah JP, Matchar DB, Koh V, Schoenenberger L. Mapping the dynamic complexity of chronic disease care in Singapore: using group model building in knowledge elicitation. Systems Research and Behavioral Science. 2018;35(6):759-775. https://doi.org/10.1002/sres.2517

[40] Cassidy R, Singh NS, Schiratti PR, et al. How to do (or not to do)… using causal loop diagrams for health system research in low and middle-income settings. Health Policy and Planning. 2022;37(10):1328-1336. https://doi.org/10.1093/heapol/czac064

[41] Jones NA, Ross H, Lynam T, Perez P, Leitch A. Mental models: an interdisciplinary synthesis of theory and methods. Ecology and Society. 2011;16(1):46. https://doi.org/10.5751/ES-03802-160146

[42] Glasgow RE, Vinson C, Chambers D, Khoury MJ, Kaplan RM, Hunter C. National Institutes of Health approaches to dissemination and implementation science: current and future directions. American Journal of Public Health. 2012;102(7):1274-1281. https://doi.org/10.2105/AJPH.2012.300755

[43] Rouse WB, Serban N. Understanding and managing the complexity of healthcare. MIT Press; 2014.

[44] Singh R, Chamberlain N, Peckens J, et al. An organizational case study of mental models among health system leaders during early-stage implementation of a population health approach. Journal of Healthcare Leadership. 2024;16:327-341. https://doi.org/10.2147/JHL.S466208

[45] Doyle JK, Ford DN. Mental models concepts for system dynamics research. System Dynamics Review. 1998;14(1):3-29. https://doi.org/10.1002/(SICI)1099-1727(199821)14:1<3::AID-SDR140>3.0.CO;2-K

[46] Meadows DH, Robinson JM. The Electronic Oracle: Computer Models and Social Decisions. Chichester, UK: Wiley; 1985.

[47] Schieffer MF, Kilgore IS, Meyers E, Fouquier AM. Visualized shared mental modeling: an adapted practical framework for interdisciplinary teams solving complex problems in healthcare. BMC Health Services Research. 2025;25:153. https://doi.org/10.1186/s12913-025-13588-7

[48] Meadows D. Leverage Points: Places to Intervene in a System. Hartland, VT: The Sustainability Institute; 1999.

[49] Fischer J, Riechers M. A leverage points perspective on sustainability. People and Nature. 2019;1(1):115-120. https://doi.org/10.1002/pan3.13

[50] Abson DJ, Fischer J, Leventon J, et al. Leverage points for sustainability transformation. Ambio. 2017;46(1):30-39. https://doi.org/10.1007/s13280-016-0800-y

[51] Porter ME, Teisberg EO. Redefining Health Care: Creating Value-Based Competition on Results. Boston: Harvard Business School Press; 2006.

[52] Kim DH. Systems Archetypes I: Diagnosing Systemic Issues and Designing High-Leverage Interventions. Waltham, MA: Pegasus Communications; 1992.

[53] Stroh DP. Systems Thinking for Social Change. White River Junction, VT: Chelsea Green Publishing; 2015.

[54] Richardson GP. Feedback Thought in Social Science and Systems Theory. Philadelphia: University of Pennsylvania Press; 1991.

[55] Sterman JD. Learning from evidence in a complex world. American Journal of Public Health. 2006;96(3):505-514. https://doi.org/10.2105/AJPH.2005.066043

[56] Diehl E, Sterman JD. Effects of feedback complexity on dynamic decision making. Organizational Behavior and Human Decision Processes. 1995;62(2):198-215. https://doi.org/10.1006/obhd.1995.1043

[57] Barber P, López-Valcárcel BG. Forecasting the need for medical specialists in Spain: application of a system dynamics model. Human Resources for Health. 2010;8:24. https://doi.org/10.1186/1478-4491-8-24

[58] Midgley G. Systemic Intervention: Philosophy, Methodology, and Practice. New York: Kluwer Academic/Plenum Publishers; 2000.

[59] Ulrich W. Critical Heuristics of Social Planning: A New Approach to Practical Philosophy. Bern: Haupt; 1983.

[60] Checkland P, Poulter J. Learning for Action: A Short Definitive Account of Soft Systems Methodology and its Use for Practitioners, Teachers and Students. Chichester, UK: Wiley; 2006.

[61] Jackson MC. Systems Thinking: Creative Holism for Managers. Chichester, UK: Wiley; 2003.

[62] Reynolds M, Holwell S. Systems Approaches to Managing Change: A Practical Guide. London: Springer; 2010. https://doi.org/10.1007/978-1-84882-809-4

[63] Sterman JD. System dynamics modeling: Tools for learning in a complex world. California Management Review. 2001;43(4):8-25. https://doi.org/10.2307/41166098

[64] Richardson GP, Pugh AL III. Introduction to System Dynamics Modeling with Dynamo. Cambridge, MA: MIT Press; 1981.

[65] Allender S, Owen B, Kuhlberg J, et al. A community based systems diagram of obesity causes. PLOS ONE. 2015;10(7):e0129683. https://doi.org/10.1371/journal.pone.0129683

[66] Rutter H, Savona N, Glonti K, et al. The need for a complex systems model of evidence for public health. The Lancet. 2017;390(10112):2602-2604. https://doi.org/10.1016/S0140-6736(17)31267-9

[67] Hovmand PS, Andersen DF, Rouwette E, Richardson GP, Rux K, Calhoun A. Group model-building ‘scripts’ as a collaborative planning tool. Systems Research and Behavioral Science. 2012;29(2):179-193. https://doi.org/10.1002/sres.2105

[68] Nobles J, Wheeler K, Nasser M, et al. Diverse approaches to creating and using causal loop diagrams in public health research: recommendations from a scoping review. Public Health Reviews. 2021;42:1604352. https://doi.org/10.3389/phrs.2021.1604352

[69] Ivanovic T, Hill E, Mutch A, Ling R, Cassidy R. Understanding the context for maternal health: making visible the structural vulnerabilities underlying maternal deaths in South Sudan using a causal loop diagram. Conflict and Health. 2021;15:83. https://doi.org/10.1186/s13031-021-00417-y

[70] Homer JB, Hirsch GB. System dynamics modeling for public health: background and opportunities. American Journal of Public Health. 2006;96(3):452-458. https://doi.org/10.2105/AJPH.2005.062059

[71] Hirsch G, Homer J, Evans E, Zielinski A. A System Dynamics Model for Planning Cardiovascular Disease Interventions. American Journal of Public Health. 2010;100(4):616-622. https://doi.org/10.2105/AJPH.2009.159434

[72] Marshall DA, Burgos-Liz L, IJzerman MJ, et al. Applying dynamic simulation modeling methods in health care delivery research—the SIMULATE checklist: report of the ISPOR simulation modeling emerging good practices task force. Value in Health. 2015;18(1):5-16. https://doi.org/10.1016/j.jval.2014.12.001

[73] Rahmandad H, Sterman JD. Reporting guidelines for simulation-based research in social sciences. System Dynamics Review. 2012;28(4):396-411. https://doi.org/10.1002/sdr.1481

[74] Botwright S, Teerawattananon Y, Yongphiphatwong N, et al. Understanding healthcare demand and supply through causal loop diagrams and system archetypes: policy implications for kidney replacement therapy in Thailand. BMC Medicine. 2025;23:99. https://doi.org/10.1186/s12916-025-04054-6

[75] Jun GT, Ward J, Morris Z, Clarkson J. Health care process modelling: which method when? International Journal for Quality in Health Care. 2009;21(3):214-224. https://doi.org/10.1093/intqhc/mzp016

[76] Chambers D, Wilson P, Thompson C, Harden M. Social network analysis in healthcare settings: a systematic scoping review. PLOS ONE. 2012;7(8):e41911. https://doi.org/10.1371/journal.pone.0041911

[77] Trebble TM, Hansi N, Hydes T, Smith MA, Baker M. Process mapping the patient journey: an introduction. BMJ. 2010;341:c4078. https://doi.org/10.1136/bmj.c4078

[78] Vincent C, Taylor-Adams S, Stanhope N. Framework for analysing risk and safety in clinical medicine. BMJ. 1998;316(7138):1154-1157. https://doi.org/10.1136/bmj.316.7138.1154

[79] Vennix JAM. Group Model Building: Facilitating Team Learning Using System Dynamics. Chichester, UK: Wiley; 1996.

[80] Andersen DF, Richardson GP. Scripts for group model building. System Dynamics Review. 1997;13(2):107-129. https://doi.org/10.1002/(SICI)1099-1727(199722)13:2<107::AID-SDR120>3.0.CO;2-7

[81] Scott RJ, Cavana RY, Cameron D. Recent evidence on the effectiveness of group model building. European Journal of Operational Research. 2016;249(3):908-918. https://doi.org/10.1016/j.ejor.2015.06.078

[82] Brennan LK, Sabounchi NS, Kemner AL, Hovmand P. Systems Thinking in 49 Communities Related to Healthy Eating, Active Living, and Childhood Obesity. Journal of Public Health Management and Practice. 2015;21(Suppl 3):S55-S69. https://doi.org/10.1097/PHH.0000000000000248

[83] Hollnagel E, Wears RL, Braithwaite J. From Safety-I to Safety-II: A White Paper. University of Southern Denmark; 2015.

[84] Reason J. Human error: models and management. BMJ. 2000;320(7237):768-770. https://doi.org/10.1136/bmj.320.7237.768

[85] Braithwaite J, Wears RL, Hollnagel E. Resilient health care: turning patient safety on its head. International Journal for Quality in Health Care. 2015;27(5):418-420. https://doi.org/10.1093/intqhc/mzv063

[86] Sujan M, Spurgeon P, Inada-Kim M, et al. Clinical handover within the emergency care pathway and the potential risks of clinical handover failure (ECHO): primary research. Health Services and Delivery Research. 2014;2(5). https://doi.org/10.3310/hsdr02050

[87] Stewart D, Johnston B, McConnachie A, et al. Development and application of ‘systems thinking’ principles for quality improvement. BMJ Open Quality. 2020;9(1):e000714. https://doi.org/10.1136/bmjoq-2019-000714

[88] Institute of Medicine. Preventing Medication Errors. Washington, DC: The National Academies Press; 2007. https://doi.org/10.17226/11623

[89] Rittel HWJ, Webber MM. Dilemmas in a general theory of planning. Policy Sciences. 1973;4(2):155-169. https://doi.org/10.1007/BF01405730

[90] Braveman P, Gottlieb L. The social determinants of health: it’s time to consider the causes of the causes. Public Health Reports. 2014;129(Suppl 2):19-31. https://doi.org/10.1177/00333549141291S206

[91] McGlashan J, Hayward J, Brown A, et al. Comparing complex perspectives on obesity drivers: action-driven communities and evidence-oriented experts. Obesity Science & Practice. 2018;4(6):575-581. https://doi.org/10.1002/osp4.306

[92] Orton L, Halliday E, Collins M, Egan M, Lewis S, Ponsford R. Putting context centre stage: evidence from a systems evaluation of an area based empowerment initiative in England. Critical Public Health. 2017;27(4):477-489. https://doi.org/10.1080/09581596.2016.1250868

[93] Bolton KA, Kremer P, Gibbs L, et al. Combining systems thinking approaches and implementation science constructs within community-based prevention: a systematic review. Health Research Policy and Systems. 2023;21:82. https://doi.org/10.1186/s12961-023-01023-4

[94] Van Dijk WH. Applying systems theory to global mental health. Global Mental Health. 2025;12:e5. https://doi.org/10.1017/gmh.2024.144

[95] Kindig D, Stoddart G. What is population health? American Journal of Public Health. 2003;93(3):380-383. https://doi.org/10.2105/ajph.93.3.380

[96] Silburn A. Systems thinking in public health policy development. Frontiers in Health Services. 2025;5:1555284. https://doi.org/10.3389/frhs.2025.1555284

[97] Sanderson I. Evaluation, policy learning and evidence-based policy making. Public Administration. 2002;80(1):1-22. https://doi.org/10.1111/1467-9299.00292

[98] Agyepong IA, Kodua A, Adjei S, Adam T. When ‘solutions of yesterday become problems of today’: crisis-ridden decision making in a complex adaptive system (CAS)—the Additional Duty Hours Allowance in Ghana. Health Policy and Planning. 2012;27(suppl 4):iv20-iv31. https://doi.org/10.1093/heapol/czs083

[99] Botwright S, Teerawattananon Y, Yongphiphatwong N, et al. Understanding healthcare demand and supply through causal loop diagrams and system archetypes: policy implications for kidney replacement therapy in Thailand. BMC Medicine. 2025;23(1):99. https://doi.org/10.1186/s12916-025-04054-6

[100] Braun W. The System Archetypes. The Systems Thinker. 2002;13(5):2-6.

[101] Lich KH, Ginexi EM, Osgood ND, Mabry PL. A call to address complexity in prevention science research. Prevention Science. 2013;14(3):279-289. https://doi.org/10.1007/s11121-012-0285-2

[102] Skivington K, Matthews L, Simpson SA, et al. A new framework for developing and evaluating complex interventions: update of Medical Research Council guidance. BMJ. 2021;374:n2061. https://doi.org/10.1136/bmj.n2061

[103] Rahmaningtyas D, Santana-Gallego M, Permatasari H. Systems Thinking and Human Resource Management in Healthcare: A Scoping Review of Core Applications Across Health System Levels. Systems. 2024;13(11):1001. https://doi.org/10.3390/systems13111001

[104] Pruitt Z, Emechebe N, Quast T, et al. Expenditure reductions associated with a social service referral program. Population Health Management. 2018;21(5):359-365. https://doi.org/10.1089/pop.2017.0199

[105] Dieleman M, Gerretsen B, van der Wilt GJ. Human resource management interventions to improve health workers’ performance in low and middle income countries: a realist review. Health Research Policy and Systems. 2009;7:7. https://doi.org/10.1186/1478-4505-7-7

[106] Ishikawa T, Ohba H, Yokooka Y, Nakamura K, Ogasawara K. Forecasting the absolute and relative shortage of physicians in Japan using a system dynamics model approach. Human Resources for Health. 2013;11:41. https://doi.org/10.1186/1478-4491-11-41

[107] Rahmaningtyas D, Santana-Gallego M, Permatasari H. Systems Thinking and Human Resource Management in Healthcare: A Scoping Review of Core Applications Across Health System Levels. Systems. 2024;13(11):1001. https://doi.org/10.3390/systems13111001

[108] Willis CD, Riley BL, Lewis D, et al. Knowledge synthesis of system dynamics models to inform evidence-informed decision-making in public health. Implementation Science Communications. 2020;1:32. https://doi.org/10.1186/s43058-020-00016-7

[109] Gonzalo JD, Haidet P, Papp KK, et al. Health Systems Science in Medical Education: Unifying the Components to Catalyze Transformation. Academic Medicine. 2020;95(9):1362-1372. https://doi.org/10.1097/ACM.0000000000003400

[110] Skochelak SE. A Decade of Reports Calling for Change in Medical Education: What Do They Say? Academic Medicine. 2010;85(9 Suppl):S26-S33. https://doi.org/10.1097/ACM.0b013e3181f12e34

[111] American Medical Association. Health Systems Science. Chicago, IL: American Medical Association; 2023.

[112] Lomis K, Amiel JM, Ryan MS, et al. Implementing an Accelerated Change in Medical Education Consortium: Innovations in Health Systems Science Education. Academic Medicine. 2017;92(7):966-973. https://doi.org/10.1097/ACM.0000000000001507

[113] Frenk J, Chen L, Bhutta ZA, et al. Health professionals for a new century: transforming education to strengthen health systems in an interdependent world. The Lancet. 2010;376(9756):1923-1958. https://doi.org/10.1016/S0140-6736(10)61854-5

[114] Lucey CR, Thibault GE, Ten Cate O. Competency-based, time-variable education in the health professions: crossroads. Academic Medicine. 2018;93(3S):S1-S5. https://doi.org/10.1097/ACM.0000000000002080

[115] Gonzalo JD, Chang A, Wolpaw DR. New educator roles for health systems science: implications of new physician competencies for U.S. medical school faculty. Academic Medicine. 2019;94(4):501-506. https://doi.org/10.1097/ACM.0000000000002552

[116] Englander R, Cameron T, Ballard AJ, Dodge J, Bull J, Aschenbrener CA. Toward a common taxonomy of competency domains for the health professions and competencies for physicians. Academic Medicine. 2013;88(8):1088-1094. https://doi.org/10.1097/ACM.0b013e31829a3b2b

[117] Prober CG, Khan S. Medical education reimagined: a call to action. Academic Medicine. 2013;88(10):1407-1410. https://doi.org/10.1097/ACM.0b013e3182a368bd

[118] Kolb DA. Experiential Learning: Experience as the Source of Learning and Development. Englewood Cliffs, NJ: Prentice Hall; 1984.

[119] Armstrong EG, Barsion SJ. Using an outcomes-logic-model approach to evaluate a faculty development program for medical educators. Academic Medicine. 2013;88(8):1161-1166. https://doi.org/10.1097/ACM.0b013e31829a3ce5

[120] Ogrinc G, Headrick LA, Mutha S, Coleman MT, O’Donnell J, Miles PV. A framework for teaching medical students and residents about practice-based learning and improvement, synthesized from a literature review. Academic Medicine. 2003;78(7):748-756. https://doi.org/10.1097/00001888-200307000-00019

[121] Borkan J, Gonzalo JD, Wolpaw DR, et al. Health systems science education: The new post-Flexner professionalism for the 21st century. Medical Teacher. 2021;43(sup2):S25-S31. https://doi.org/10.1080/0142159X.2021.1924366

[122] Gonzalo JD, Graaf D, Johannes B, Blatt B, Wolpaw DR. Adding value to the health care system: identifying value-added systems roles for medical students. Academic Medicine. 2017;92(9):1267-1272. https://doi.org/10.1097/ACM.0000000000001662

[123] Gonzalo JD, Dekhtyar M, Hawkins RE, Wolpaw DR. How can medical schools build a curriculum that enhances health systems science? Academic Medicine. 2017;92(12):1616-1622. https://doi.org/10.1097/ACM.0000000000001896

[124] Warm EJ, Mathis BR, Held JD, et al. Entrustment and mapping of observable practice activities for resident assessment. Journal of General Internal Medicine. 2014;29(8):1177-1182. https://doi.org/10.1007/s11606-014-2801-5

[125] Tekian A, Watling CJ, Roberts TE, Steinert Y, Norcini J. Qualitative and quantitative feedback in the context of competency-based education. Medical Teacher. 2017;39(12):1245-1249. https://doi.org/10.1080/0142159X.2017.1372564

[126] Santen SA, Hamstra SJ, Yamazaki K, et al. Assessing the transition of training in health systems science from undergraduate to graduate medical education. Journal of Graduate Medical Education. 2021;13(3):404-410. https://doi.org/10.4300/JGME-D-20-01268.1

[127] Gonzalo JD, Wolpaw DR, Lehman E, Chuang CH. Patient-centered interprofessional collaborative care: factors associated with bedside interprofessional rounds. Journal of General Internal Medicine. 2014;29(7):1040-1047. https://doi.org/10.1007/s11606-014-2817-x

[128] Starr SR, Agrwal N, Bryan MJ, et al. Science of health care delivery: an innovation in undergraduate medical education to meet society’s needs. Mayo Clinic Proceedings: Innovations, Quality & Outcomes. 2017;1(1):117-129. https://doi.org/10.1016/j.mayocpiqo.2017.01.002

[129] Chughtai AA, Blanchet K. Systems thinking in public health: a bibliometric analysis. Health Policy and Planning. 2017;32(5):733-740. https://doi.org/10.1093/heapol/czw170

[130] Adam T, de Savigny D. Systems thinking for strengthening health systems in LMICs: need for a paradigm shift. Health Policy and Planning. 2012;27(suppl 4):iv1-iv3. https://doi.org/10.1093/heapol/czs084

[131] Swanson RC, Cattaneo A, Bradley E, et al. Rethinking health systems strengthening: key systems thinking tools and strategies for transformational change. Health Policy and Planning. 2012;27(suppl 4):iv54-iv61. https://doi.org/10.1093/heapol/czs090

[132] Braithwaite J, Churruca K, Ellis LA, et al. Complexity Science in Healthcare – Aspirations, Approaches, Applications and Accomplishments: A White Paper. Sydney: Australian Institute of Health Innovation, Macquarie University; 2017.

[133] Greenhalgh T, Papoutsi C. Studying complexity in health services research: desperately seeking an overdue paradigm shift. BMC Medicine. 2018;16:95. https://doi.org/10.1186/s12916-018-1089-4

[134] Eoyang GH, Holladay RJ. Adaptive Action: Leveraging Uncertainty in Your Organization. Stanford, CA: Stanford University Press; 2013.

[135] Lane DC, Oliva R. The greater whole: Towards a synthesis of system dynamics and soft systems methodology. European Journal of Operational Research. 1998;107(1):214-235. https://doi.org/10.1016/S0377-2217(97)00205-1

[136] Rouwette EAJA, Vennix JAM, van Mullekom T. Group model building effectiveness: a review of assessment studies. System Dynamics Review. 2002;18(1):5-45. https://doi.org/10.1002/sdr.229

[137] Leischow SJ, Best A, Trochim WM, et al. Systems thinking to improve the public’s health. American Journal of Preventive Medicine. 2008;35(2 Suppl):S196-S203. https://doi.org/10.1016/j.amepre.2008.05.014

[138] Barros VV, Oppenheimer ML, Santos PAG, et al. Patient-Centric Paradigm: A Systems Thinking Approach to Enhance Healthcare. Healthcare. 2025;13(3):213. https://doi.org/10.3390/healthcare13030213

[139] Mingers J. Multimethodology—mixing and matching methods. In: Rosenhead J, Mingers J, eds. Rational Analysis for a Problematic World Revisited. Chichester, UK: Wiley; 2001:289-309.

[140] Nilsen P, Wallerstedt B, Behm L, Ahlström G. Towards evidence-based palliative care in nursing homes in Sweden: a qualitative study informed by the organizational readiness to change theory. Implementation Science. 2018;13:1. https://doi.org/10.1186/s13012-017-0699-0

[141] Bolton KA, Kremer P, Gibbs L, et al. Combining systems thinking approaches and implementation science constructs within community-based prevention: a systematic review. Health Research Policy and Systems. 2023;21:82. https://doi.org/10.1186/s12961-023-01023-4

[142] Kerner J, Rimer B, Emmons K. Dissemination research and research dissemination: how can we close the gap? Health Psychology. 2005;24(5):443-446. https://doi.org/10.1037/0278-6133.24.5.443

[143] Hawe P, Shiell A, Riley T. Theorising interventions as events in systems. American Journal of Community Psychology. 2009;43(3-4):267-276. https://doi.org/10.1007/s10464-009-9229-9

[144] Damschroder LJ, Aron DC, Keith RE, Kirsh SR, Alexander JA, Lowery JC. Fostering implementation of health services research findings into practice: a consolidated framework for advancing implementation science. Implementation Science. 2009;4:50. https://doi.org/10.1186/1748-5908-4-50

[145] Rwashana AS, Williams DW, Neema S. System dynamics approach to immunization healthcare issues in developing countries: a case study of Uganda. Health Informatics Journal. 2009;15(2):95-107. https://doi.org/10.1177/1460458209102971

[146] Thompson DS, Fazio X, Kustra E, Patrick L, Stanley D. Scoping review of complexity theory in health services research. BMC Health Services Research. 2016;16:87. https://doi.org/10.1186/s12913-016-1343-4

[147] Mutale W, Vardhan MS, Kachemba A, et al. Applied systems thinking: unlocking theory, evidence and practice for health policy and systems research. Health Research Policy and Systems. 2021;19(Suppl 3):122. https://doi.org/10.1186/s12961-021-00758-w

[148] Proctor E, Silmere H, Raghavan R, et al. Outcomes for implementation research: conceptual distinctions, measurement challenges, and research agenda. Administration and Policy in Mental Health. 2011;38(2):65-76. https://doi.org/10.1007/s10488-010-0319-7

[149] Baum F, MacDougall C, Smith D. Participatory action research. Journal of Epidemiology and Community Health. 2006;60(10):854-857. https://doi.org/10.1136/jech.2004.028662

[150] Kirkpatrick JD, Kirkpatrick WK. Kirkpatrick’s Four Levels of Training Evaluation. Alexandria, VA: ATD Press; 2016.

[151] Friedman CP, Rubin JC, Sullivan KJ. Toward an Information Infrastructure for Global Health Improvement. Yearbook of Medical Informatics. 2017;26(1):16-23. https://doi.org/10.15265/IY-2017-004

Updated on December 11, 2025