Discover 10 Sure Fire Upstream Leader Competencies You Need to Respond to Systems Dynamics and Transforming Health Systems

Blip-Zip Executive Summary

Feeling overwhelmed by complex health challenges in your community? This article explores Systems Dynamics (SD), a powerful tool for upstream leaders, and empowers leaders to address the root causes of health issues, not just symptoms. Learn ten key strategies and upstream leadership competencies to create positive change!

Blip-Zip Takeaways

• Upstream Leadership: Shift your focus from treating symptoms to tackling root causes of health issues in your community.
• Systems Thinking: Understand the interconnectedness of factors that influence health. Food deserts, social isolation, and poverty are all players.
• Actionable Strategies: Learn effective interventions like community gardens, intergenerational programs, and early childhood education.

Key Words and Themes (#Hashtags)

#SystemsThinking #CommunityHealth #PublicHealth #UpstreamLeadership #HealthEquity #SocialDeterminantsofHealth #RootCauses #HolisticHealth

Introduction to Systems Dynamics and Need for the Upstream Leader

System Dynamics (SD) is an essential field that deals with the behavior of complex systems, such as health systems. It uses concepts like stocks, flows, feedback loops, and time delays to understand the nonlinear behavior of such systems over time. SD is crucial in the health and human services (HHS) sectors for improving operational aspects of health system capacity and delivery, analyzing policy options, and achieving consensus for action. It also helps leaders understand the different viewpoints of stakeholders and assess resources, effects, and upstream or preventable and sustainable solution options for organizations and communities.

This chapter emphasizes the critical role of using a sustainable approach to address the challenges faced by the health system and how SD is the most effective tool for tackling these problems.

Systems Dynamics Complexity: Bathtub, Stocks, And Flows

The United States is implementing various measures to reform its healthcare system and address its complexities. These measures include surveys, certifications, payments, and penalties, which create multiple feedback loops. However, these methods may result in unintended consequences such as disparities in access to care, poorer health outcomes, and increased costs.¹

To leverage the dynamics within complex health systems, policymakers must use scientific principles and systems thinking methods to achieve the best outcomes. Failing to manage complexity can result in delayed and counterintuitive outcomes. Complex systems’ behavior differs from mechanical systems because they interact nonlinearly and can produce unexpected results. Thus, it is essential to understand dynamic complexity to identify leverage points and avoid resistance to change.¹

Linear thinking, which focuses on access, quality, and cost in isolation, may need to be more effective for complex and nonlinear health systems. These systems include network components such as hospitals, clinics, nursing homes, rehabilitation units, patient homes, families, and patients. They interact nonlinearly on different levels, such as patient, family, health team, medical center, community, and government.

According to the World Health Organization (WHO), living conditions are the everyday environment where individuals live, play and work. Unintended consequences may arise when decisions are made linearly in complex systems, leading to adverse drug reactions, nosocomial infections, re-hospitalizations, and functional decline.

Therefore, it is vital to understand the dynamic complexity and identify leverage points to improve or avoid resistance to policies and change. By addressing these drivers, organizations can better manage and adapt to the changing needs of their communities.^2,3

The figure below illustrates how the health system and creating healthier communities work by using the bathtub, stocks, and flows.

Figure 1: Understanding How to Create Healthier Communities

Adverse conditions, influenced by social, economic, and physical factors, affect personal health and are often beyond an individual’s control.⁴. These conditions, such as hunger, war, environmental decay, homelessness, illiteracy, and injustice, hinder an individual’s ability to live and reach their full potential, all of which are linked to the social determinant of health.⁵

Health Systems at all Levels Contain Multiple Feedback Loops

Complex systems are formed by two main elements: positive and negative feedback loops. Understanding how these loops are linked together is crucial for comprehending complex systems.⁶  Most thinking relies on events, linear experiences, and an open-loop worldview.⁷ Stakeholder actions disrupt the system’s equilibrium, triggering reactions from other actors to restore stability. This limited explanation of situations is due to linear cause-effect relationships, which limit the explanation of successive events.^2,3,7-9

Figure 2 illustrates feedback loops and effects, with four boxes representing the health continuum. The far left-hand side shows healthy individuals, vulnerable to diseases due to risk or social situation, those with disease, not necessarily causing complications, and those with sickness and complications. The figure highlights the importance of understanding these loops and their effects.

Figure 2: How the Health System Should Work Using Policy Intervention and Leverage Points.

In the US, health spending heavily relies on the right-hand side, with 97% of all health spending going into these compartments. Policymaking should focus on the left-hand side, ensuring that the population is as far to the left as possible.⁵

Policy resistance is caused by the reaction of other balancing feedback loops to actions as the system tries to restore equilibrium or transform. From a systems thinking perspective, systems consist of interrelated feedback loops, where actions attempt to alter the balance of these loops.

Similarly, actions’ counterintuitive results result from an incomplete understanding of the purpose, structure, and benefits of the feedback loops present in a system.³ Two feedback loops include Reinforcing (positive) loops and Self-correcting (negative) loops.

Counterintuitive results of actions are due to an incomplete understanding of the structure of feedback loops in a system. There are two feedback loops: reinforcing (positive) loops and self-correcting (negative) loops. Reinforcing loops cause exponential growth or decline in the system while self-correcting loops resist the system towards equilibrium. If a system has many interacting feedback loops, it becomes impossible to predict its behavior.

Health is complex, with many inputs and outputs operating independently in overlapping feedback loops. All dynamics observed in systems are derived from shifts in feedback loop dominance as a system evolves.^10,11 over time. Actions can be interpreted as influences attempting to shift a balance of influence among the system’s feedback loops.

The system’s actual behavior depends on which loop is more robust, the positive or negative.⁶  For example, preventive diabetes services are not compensated, leading to more expensive services like emergency treatment and amputations.

Healthcare systems use different approaches to address the burden of disease. Some focus on one issue at a time, while others examine the entire system to identify patterns and behaviors. A systems dynamics approach uses reinforcing and balancing loops to understand the complex interplay between factors contributing to poor health.

There is less investment in social services in the US compared to other countries. The most vulnerable members of society, such as those who are economically and socially marginalized, disenfranchised, or oppressed, often bear the heaviest burden of illness. They are more susceptible to disease outbreaks and unhealthy habits, which can create a vicious cycle of harmful behaviors.

Leaders seeking to establish and maintain good health must identify its contributing conditions. This requires a systems thinking approach, which involves analyzing patterns and behaviors across the system. By understanding the factors contributing to poor health, we can prevent and address them more effectively.

Systems thinking is focused on reducing the prevalence of disease by targeting the most heavily affected individuals, particularly those who are economically and socially marginalized or oppressed. This group is more vulnerable to disease and unhealthy habits, which creates a vicious cycle of harmful behavior.

Systems thinking challenges leaders to identify conditions promoting good health and provides a more precise framework for understanding and preventing factors perpetuating health disparities. This approach works to reduce the prevalence of disease and promote healthy lifestyles.

Traditional epidemiological approaches often focus on individual issues, whereas systems thinking extends linear analyses by examining systemic patterns and behaviors across the system. The goal of systems thinking is to reduce the prevalence of disease by focusing on the heaviest burden of illness on economically and socially marginalized, disenfranchised, or oppressed individuals.

These individuals are more vulnerable to disease outbreaks and unhealthy habits, reinforcing a vicious cycle of unhealthy balancing loops. Systems thinking challenges leaders to identify conditions to create and sustain health, providing a more precise framework for understanding and preventing conditions that perpetuate health disparities. This approach works to reduce the prevalence of disease and promotes healthier lifestyles.⁵

Time Delays in Feedback Loops

Delays in causes and effects can make systems more complex and slow the learning process, hindering intervention to improve situations.^3,12,13. Regulations to control complex systems can deviate from desired outcomes or inhibit an ability to intervene to improve a particular situation.³ For example, the Australian healthcare system’s complexity is influenced by the use of disease protocols, economic levers, and stovepipe programs, which can lead to unintended consequences.¹⁴. Therefore, it is crucial to address these issues to ensure the health system’s success.

Agents may take actions without considering the consequences, leading to convergent systems needing more time to absorb the effects and respond adequately. For instance, the Helping Families in Mental Health Crisis Act, part of the 2016 21st Century Cures Act, was a significant mental health reform bill aimed at increasing psychiatric hospital beds and enhancing treatment for young patients.¹⁵

The typical decision-making approach, characterized by the single-decision Open Loop View in Figure 3, results in delaying or oscillating behavior, leading to systems overshooting or lagging behind equilibrium.

Figure 3: Linear Versus Systems Thinking And Decision-Making

Effective decision-making becomes more prominent when delays are unobservable, making it difficult to make informed decisions.^12,13. For example, pay-for-performance payment models may encourage aggressive treatment without considering life expectancy or adverse effects.

However, these models have not significantly impacted mortality¹⁶ or Medicare spending.¹⁷ Clinical practice guidelines designed to improve care quality have not reduced socioeconomic disparities in diabetes treatment and could increase education costs for patients with multiple chronic conditions.¹⁸

Nonlinear Relationships

Complexity can sometimes be unpredictable when there are nonlinear relationships in a system. This is because an action taken may not produce a proportional response. As a result, feedback loops that control the system can shift dominance from one to another. The balance of power among these loops determines the system’s response, which can lead to unexpected consequences.

The current reimbursement system based on fees discourages sharing responsibility for patient care among organizations. This payment system only covers one service at a time or in isolation, which is a problem in the health system. Care is divided into different parts, with little attention given to patient handoffs, communication, coordination across organizations, and continuity. Recognizing the nonlinear interactions of components is critical to the success of a complex system.¹⁹

Effect of Dynamic Complexity on Decision-Making

Human decision-making could perform better in complex situations for two main reasons. The principle of “bounded rationality” by Herbert Simon states that humans have two bounds of rationality: limited information processing capabilities and cognitive skills and memory limitations. These limitations lead to flawed mental models and a focus on reduced information, simplifying cause-effect maps. Secondly, humans cannot work out the consequences of their actions, requiring formal modeling as a learning catalyst to improve decision-making performance.⁸

The misperception of feedback also drives differing responses. The principle of “bounded rationality” applies to all types of decision-making but is amplified in dynamic situations. Experiments have shown that human performance decreases dramatically in the presence of high levels of dynamic complexity, especially when subjects have considerable experience or receive incentives. This behavior is more evident when subjects have significant expertise or receive rewards.

The “misperception of feedback” hypothesis suggests that humans ignore feedback, do not appreciate time delays between actions and consequences, and are insensitive to the nonlinearity between a system’s elements evolving. Overcoming these different bounds of rationality is crucial for effective double-loop learning to occur in time.²⁰

System Dynamics in Action

System Dynamics (SD) is a methodology developed by Professor Jay Forrester at the Massachusetts Institute of Technology’s Sloan School of Management in the late 1950s. This methodology focuses on applying feedback, control, and management principles to the dynamics of social systems. SD assumes that the internal structures of a system drive its behavior, and delays and nonlinearities are what influence it.

SD aims to model and predict complex systems’ responses to decisions, identify leverage points, and redesign structures to eliminate undesirable behavior. The intervention process is divided into phases.²

A purpose and a defined problem or undesirable behavior are needed to create an SD model. The different variables are described as models and graphical representations of their behavior over time. The factors believed to cause the behavior are identified, and relationships between them are described in causal loop diagrams (CLDs). A dynamic hypothesis is used to explain how the structure of a system causes observed behavior.

A decision-making process is described to determine how agents in the system transform information into decisions. It is important to note that this conceptual, qualitative intervention phase should be conducted by more than SD experts. Involving those involved in the system or problematic situations helps capture better mental models with first-hand knowledge about the problem’s causes.²¹

Model-building involves creating a structure using CLDs and a computer-based behavioral model to illustrate stocks and flows. The relationship between them is defined, and a link between variables and dynamic behaviors is established. This quantitative phase generates insights. Software programs have made SD modeling accessible to individuals without solid computational backgrounds.

Before using a model for policy analysis, it is crucial to build confidence or validate it. Models replicate reality, and a satisfactory level of replication improves decision-making.^2,9 Once validated, models can be used for various purposes, such as testing health market policies, exploring what-if scenarios, or optimizing sub-structures. The model serves as a base for determining policies or structural changes, ultimately enhancing decision-making in the system.

System Dynamics Modeling For Health Systems

Health systems are complex and subject to counterintuitive behavior and policy resistance, leading to disappointing results in current policies to improve their performance. Despite a small fraction of the government budget allocated to health, results have yet to match expectations. SD modeling can effectively address these concerns and improve health system performance by effectively dealing with strategic and tactical problems involving aggregate patient and resource flows.²²

Model-building involves creating a structure using CLDs and a computer-based behavioral model to illustrate stocks and flows. The relationship between them is defined, and a link between variables and dynamic behaviors is established. This quantitative phase generates insights. Software programs have made SD modeling accessible to individuals without solid computational backgrounds.

A model called system dynamics was used to improve stroke care units. Figure 4 summarizes how a team of professionals created Clinical Decision Trees utilizing this model to describe how stroke care processes can benefit patients. This model helps capture cause-and-effect relationships that are not fully understood by linear models.

Figure 4: Illustration of How Causal Loop Diagrams Are Used For Mapping Stroke Care Processes.

The method of conceptualizing stroke care involves discussing key variables and processes, experimenting with models, and fostering dialogue to improve care delivery. System Dynamics (SD) modeling is crucial for improving health system performance ²³ Often, health system decisions involve delays at all levels, leading to gaps in supply and demand, which can cause problems in care management. A formal modeling approach like SD can be adopted for better problem representation and analysis.²⁴

SD modeling and simulation can help understand the complexity of health systems, leading to improved operational aspects and better community outcomes. ²⁵  Analyzing variables like health team motivation, fatigue, responses to incentives, and psychological safety can lead to healthier outcomes and reliable patient care. SD can create safer patient care environments, mitigate unsafe nursing care, and accommodate variables like nurses’ role in patient safe²⁶

The Power of Upstream Leadership

Imagine a community struggling with high rates of obesity. It’s easy to blame unhealthy habits, but what if there’s more to the story? Maybe there’s a lack of fresh food options (food deserts), limited access to safe parks for exercise, or even a stressful work environment that makes healthy choices harder. This is where systems dynamics come in. It’s like understanding all the connected parts of a machine – in this case, the factors that influence health in a community. By addressing these dynamics, upstream leaders can make a real difference!

Here’s a breakdown of how systems dynamics play out in everyday situations, along with some smart strategies upstream leaders can use to address them:

Community Health & Human Services Systems Dynamics	Upstream Leadership Interventions	Essential Competencies for Upstream Leaders
Food Deserts: Limited access to healthy food choices leads to poor nutrition and health problems.	Support Community Gardens: Help communities grow their own fresh produce. Partner with Grocery Stores: Encourage them to locate in underserved areas.	Collaboration: Work with community members and organizations to find solutions. Problem-Solving: Think creatively to address complex challenges.
Social Isolation: Loneliness and lack of social connections can worsen mental and physical health.	Community Cohesion Programs: Create opportunities for people to connect, like intergenerational activities or social clubs. Volunteer Networks: Build a network to combat loneliness, especially among seniors.	Community Engagement: Listen to community needs and concerns. Empathy: Understand the struggles people face.
Intergenerational Poverty: When families struggle financially across generations, it limits access to healthcare, education, and healthy living environments.	Early Childhood Education: Invest in quality childcare and educational programs to break the cycle. Affordable Housing Initiatives: Advocate for safe and affordable housing options.	Strategic Planning: Develop long-term plans that address root causes. Equity Focus: Ensure interventions benefit everyone, especially marginalized populations.
Inadequate Public Transportation: Limited mobility can restrict access to healthcare, jobs, and healthy food options.	Public Transit Expansion: Advocate for more affordable and accessible public transportation routes. Transportation Assistance Programs: Support ride-sharing or carpool programs.	Data Analysis: Use data to identify areas with limited transportation access. Advocacy: Champion policies that promote accessible transportation.
Fragmented Mental Health Services: Lack of coordinated mental health services can lead to treatment gaps and poorer outcomes.	Integrated Care Models: Advocate for connecting primary care providers with mental health professionals. Mental Health Awareness Campaigns: Reduce stigma and encourage help-seeking behaviors.	Change Management: Navigate resistance to new models of care delivery. Communication: Explain the benefits of integrated care to all stakeholders.

By understanding systems dynamics and employing upstream leadership strategies, we can create a ripple effect of positive change in our communities. It’s about working together to address the root causes of health issues, not just treating the symptoms.

So, let’s get creative, collaborate, and build a healthier future for everyone!

Summary and Conclusion

This chapter highlights the critical role of Systematic Design (SD) in promoting sustainability within health systems. It strongly emphasizes the need for a systematic approach that considers societal needs, financial constraints, and environmental expectations to improve community health. Traditional techniques are less effective than SD modeling, which offers benefits such as stakeholder involvement, improved interaction in policy development, knowledge sharing, non-threatening questioning of assumptions, group learning, and the ability to arrive at sustainable solutions for complex problems.

Given the growing attention to sustainability in health services delivery and public health.³ SD must become a widely used technique in health settings.  By understanding the interconnected factors influencing health outcomes, you can move beyond treating symptoms and tackle root causes in your community.

Ready to become a game-changer in your field? Check out the practical questions, resources, and assessment tools.

Deep Dive Discussion Questions

1. Reflect on your community. What are some of the biggest health challenges facing your community? Can you identify any social determinants of health that contribute to these challenges (e.g., poverty, food deserts, social isolation)?
2. Reimagine your leadership style. How can you leverage a systems thinking approach to identify leverage points for positive change?
3. Developing Upstream Strategies: Based on the leadership competencies discussed, what strategies can you implement as an upstream leader?
4. Identify potential partners. Who are the key stakeholders you can collaborate with to address these challenges (e.g., community organizations, policymakers, healthcare providers)?
5. Plan for the future. What long-term strategies can you implement to create lasting positive change in your community? How will you measure the success of your interventions?
6. Embrace continuous learning. What resources can you utilize to stay up-to-date on the latest advancements in Systems Thinking and Upstream Leadership?

Professional Development and Learning Activities

1. Conduct a community health needs assessment. Partner with local organizations to gather data and identify the most pressing health concerns in your community.
2. Develop a causal loop diagram (CLD). Choose a specific health challenge and map out the interconnected factors that contribute to it. This will help you visualize the system and identify potential intervention points.
3. Design a pilot program. Develop a small-scale intervention to address a specific health issue in your community. Evaluate the program’s effectiveness and make adjustments as needed.
4. Identify potential partners. Connect with organizations working on similar issues in your community to explore collaboration opportunities.
5. Develop a logic model. This visual tool will help you map out the relationships between your intervention activities, outputs, and desired outcomes.
6. Evaluate existing upstream interventions. Research successful upstream programs implemented in other communities and identify elements that could be adapted for your context.

References and Resources

1. The Public Health Foundation: (https://www.phf.org/) – A non-profit organization working to improve public health in the US.
2. The de Beaumont Foundation: (https://debeaumont.org/) – Supports innovative approaches to improving population health.
3. Causal Loop Diagrams: A Powerful Tool for Thinking About Complexity: by Pugh III, Alexander L. – [A guide to using causal loop diagrams for problem-solving] https://www.researchgate.net/publication/364305919_CAUSAL_LOOP_DIAGRAMS_A_tool_for_visualizing_the_system_structure_resulting_in_emergent_system_behaviour
4. Accelerating systems thinking in health: Perspectives from the region of the Americas, 2023, https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10060521/

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