Complex Adaptive Systems in Healthcare and Medicine: An Overview

Introduction

Complex adaptive systems (CAS) are dynamic networks made up of many individual components, which all interact and influence one another in non-linear ways. These complex systems are common in our world—from the ecosystem, the human brain, the economy, and academia, to healthcare and medicine. (See Introduction to CAS)

Our goal in this lay summary is to review how complex system sciences have been utilized and examined by research in healthcare and medicine. The CAS framework has offered new insights and strategies to improve management of healthcare organizations, nursing practice, and acute and chronic care. The growing need to study complexity has also transitioned into the medical realm, where CAS frameworks have been used to model and tackle prevalent illnesses and diseases, including Alzheimer’s and cancer. To assist with this endeavour, assessment tools have been developed to measure complexity within a medical setting. It is out of complexity that we can find simplicity. CAS frameworks consider the influence and interactions of many agents in healthcare and medicine, and provide a more holistic picture of the field that is needed to boost discovery, collaboration and decision making.

Healthcare Organizations

Healthcare organizations (HCOs) play a major role in the healthcare system. HCOs include hospitals, clinics, laboratories, nursing homes, and many other organizations, whose goal is to provide care for the population. These organizations were previously described by researchers and scholars as simple systems, where a set number of parts interact to yield a predictable outcome. However, HCOs are far more complex and are built upon many layers of interacting elements including patients, physicians, nurses, researchers, managers and stakeholders. Thus, a complex adaptive system (CAS) model has been implemented in a variety of ways to cope with the complexity of HCOs.

In relation to managing HCOs, Anderson and McDaniel (2000) explored the intersectionality between complexity sciences and professionalism一the degree to which experts of a profession are consulted for important organizational decisions. By combining concepts associated with professionalism (expertise, values) and complexity (emergence, interactions, non-linearity, and dynamicity), a new mental model of health leadership was developed to improve management in HCOs. The model highlights 8 leadership tasks to improve management within HCOs: relationship building, loose coupling, complicating, diversifying, sense-making, learning, improvising, and thinking about the future (Anderson and McDaniel, 2000). Healthcare managers that adhere to the suggested model will help build better professional relationships, promote lifelong learning, and help HCOs adapt to the rapid and ever-changing technological and societal demands (Anderson and McDaniel, 2000). Ultimately, this is suggested to improve the overall clinical success of HCOs (Anderson and McDaniel, 2000).

Additionally, a CAS framework has also been used to improve areas of the healthcare system that were previously lacking. Tsasis et al. (2012) used a CAS framework to analyze the scarcity of integrated care in the healthcare system in Ontario. A focus group approach was undertaken to determine the problems and limitations of the current health system in Ontario based on shared personal experiences and observations of individuals. One of the major problems identified was the lack of integrated health in Ontario. A CAS framework allowed the researchers to identify weak ties and poor alignment among professionals and organizations as the most prevalent barriers preventing the development of integrated health in Ontario. Due to its potential, It was suggested that a CAS framework along with other theoretical frameworks, could be used to model and implement a fully integrated health system in Ontario.

The continuous implementation of CAS frameworks in a variety of settings has helped with the improvement of HCOs, ultimately enhancing care delivery in healthcare systems. By focusing on the complexity inherent in HCOs and taking a system approach, more problems can be identified and solved in adaptable and effective ways.

Nursing

While practicing, nurses must juggle a variety of relationships including patients, family members, other healthcare staff, etc., while also adapting to new policies and technologies. A complexity framework helps us understand intricacies in nursing, thereby, providing insight into ways nursing care can be improved.

Nursing was previously understood through a linear model, failing to account for the influence of a nurse’s experience, knowledge, and care involvement affected the delivery of high quality care (Cucolo & Perroca, 2014). Additionally, these studies failed to consider the impact of unexpected obstacles and demands that could limit nursing activities (Cucolo & Perroca, 2014). The use of a systems approach allowed researchers to infer the main factors affecting care: planning of care, qualification of the nursing staff, resources, and multi-professional interactions (Cucolo & Perroca, 2014). By establishing these factors, time shortages, limited resources, and excessive workload were indicated as the major obstacles that should be overcome to improve nursing care (Cucolo & Perroca, 2014). Furthermore, the information supplied by the systems approach is being used in the development of novel tools that will help guide nurses in clinical and managerial positions.

The quality of care within nursing homes has also benefited from a systems thinking approach. In the 2000s, despite clinical and regulatory efforts, poor quality of care was a huge problem within nursing homes. Researchers turned to a complexity framework to understand how open communication, decision making, and leadership styles related to self-organization and quality of care. Researchers identified the use of top-down and authoritative leadership styles by nursing managers, as the central barrier to care within nursing homes. These leadership styles discouraged open communication and decision making, lowered constructive feedback and trust between staff, and increased staff conflicts, which corresponded to decreased quality of care. (Andersen et al., 2003). Therefore, leadership styles which revolved around self-organization and connections and interactions between staff and residents were recommended to improve patient care (Andersen et al., 2003). This helped pave the way for transformational leadership, a highly-interconnected leadership style, that has been instrumental to the successful delivery of care within nursing (Poels et al., 2020; Lin et al., 2015).

The use of complexity sciences within the nursing discipline has provided unique insights that have been overlooked by linear and non-dynamic methodological frameworks. Complexity sciences helps organize the complex nature of nursing, allowing researchers to propose lasting changes that will benefit nurses, patients, and the broader healthcare system.

Acute and Chronic Medical Care

The care a patient receives, from acute treatment in hospital to the continuous management of one’s condition outside of the hospital, involves interactions between a number of players in the healthcare system. Patients may receive care from various departments, different professionals in healthcare, and perhaps even social services. The work of these individual agents is connected and influences the overall health and well-being of patients. Thus, a CAS approach that takes into account the dynamic and non-linear nature of acute and chronic patient care is vital.

A CAS approach has shown to be more appropriate for characterizing patient care in emergency rooms. Traditionally, a patients’ journey in the emergency room was explained by a 3 step linear model where they are first categorized, then diagnosed and treated, and finally discharged (Nugus et al., 2010). However, it became obvious in Nugus et al.’s study (2010) that to accomplish these steps, clinicians needed to regularly communicate with various healthcare personnel (e.g. family physicians, specialists in other hospital departments, the manager of hospital bed spaces, social service agents etc.). Consequently, to better understand patient care, researchers suggested that emergency departments should be represented as a CAS, a complex network of different healthcare providers who interact and react to each other (Nugus et al., 2010). In the future, the researchers suggest implementation of a CAS framework when examining patient care in other hospital departments and generate potential methods for improving the quality of patient care (Nugus et al., 2010).

Another domain that can be assessed using a CAS framework is medicine management. Medicine management following a patient’s hospital discharge is an important process in ensuring optimal clinical outcome. Many factors can affect this process such as patients’ own social networks and their interactions with healthcare providers. Researchers mapped the social and healthcare networks of patients and discovered that each patient has unique networks which influence medicine management (Fylan et al., 2019). This emphasizes the need to focus on the social interactions of patients beyond the hospital in order to ensure proper healing. Examining medicine management using CAS has revealed that current care is not tailored to the unique needs of each patient (Fylan et al., 2019). This is a nuance that is not captured by linear models of healthcare, which are too simple to accurately account for all the complexities of this system. Future steps for healthcare policy can involve more research on how to provide a more patient-focused approach.

Is there a benefit to applying a CAS framework to patient care instead of a linear model that emphasizes predictability? As suggested by Lykeum et al. (2007), health outcomes can be improved. Their study assessed 32 different educational programs for Type II diabetes patients and investigated whether characteristics of CAS (e.g. self-organization, individuals learning from each other, etc.) were taken into consideration in planning and implementation of the programs. It was found that interventions with more CAS characteristics; for instance, those where participant suggestions shaped the educational material, were associated with better patient health outcomes for Type II diabetes patients (Lykeum et al., 2007). Taking a systems approach to examining medical care helps clinicians understand which components of patient educational programs are conducive to improved health. CAS, then, proves to be a better way to think about patient care in clinical settings. Nonetheless, more research should be done in other clinical settings using CAS theories to improve the care of patients and health outcomes.

Medical Diagnosis/Research

The linear approach to medical diagnosis has worked for simple illnesses like bacterial or viral infections, since physicians can determine the cause of illness then administer medication to mitigate that. However this approach is not effective in treating chronic and complex diseases (e.g. cancer, neurodegenerative conditions, etc.). To account for the various complexity contributing factors of chronic diseases (lifestyle habits, genetic predispositions, etc.) a systems approach is needed which considers the interplay of variables contributing to disease.

Neurodegenerative conditions like Alzheimer’s have taken a serious toll on healthcare systems worldwide due to immense prevalence and disease variability in patients. Failure to effectively manage these conditions has cost the US healthcare system over $200 billion annually, a number that is expected to rise to $1.1 trillion in 2050 (Institute for Neurodegenerative Diseases, 2010). To mitigate this, clinical approaches must aim to deconstruct and understand the unique, individual complexities underlying the disease (lifestyle factors, genetic predisposition, etc.). Bredesen(2017) applied this complexity lens to the treatment of 10 Alzheimer’s patients. Clinicians assessed the network of metabolic markers and conducted a genome-wide analysis in all patients. From this, the physicians were able to infer which aberrant pathways were causing the disease and provide targeted and personalized medications for improved clinical outcome. Out of the 10 patients involved, 9 demonstrated sustainable and significant reversal of cognitive decline, highlighting the potential efficacy of implementing complex systems approaches to medical diagnosis and treatment (Bredesen, 2017).

Cancer is a leading cause of death worldwide according to the World Health Organization (2020). Many papers, such as the one by Schwab and Pienta (1996), describe cancer as a CAS, where tumor cells can quickly adapt to new surroundings and dynamically interact with many different cells (e.g. immune cells). The need to view cancer as a CAS where factors such as genetics, biochemical pathways, and the immune system intersect and interact with one another is becoming increasingly valued and validated with emerging literature (Derbal, 2018). Embodying this view when creating computerized mathematical models has improved our ability to predict patient response to a chemotherapy. For instance, the model by Bagchee-Clark et al. (2020) evaluates how patients’ responses to chosen cancer drugs are affected by not only each gene product known to have an impact, but also every other gene or gene product that those known gene products interact or are in the same biochemical pathways with. By factoring in the genetic complexities underlying patient response to those drugs, researchers improved their ability to predict patient sensitivity and resistance to medication. This study highlights the potential benefits of applying CAS concepts into cancer related medical research in the future.

Our emergence into truly 21st century medicine will require a modification of our approach to medical diagnosis and research. Our questions of “what is the disease/treatment?” must transform into “why do people have this condition?”, and this can only be accomplished by adequate consideration of the underlying complexity of those diseases. A systems medicine approach that comprehensively considers disease factors (genetics, lifestyle habits, clinical team-work) will help in developing effective, targeted and personalized medicine that will improve the health of all people worldwide.

Assessing complexity in medicine

Complexity has been a growing manifestation of medical practice and there has been an increasing need to adequately model and measure that complexity. With all the moving parts and variables inherent in medicine (lifestyle factors, family history, patient-physician cultural barriers and so on), the execution of sound medical practice relies on a complexity measurement model that adequately accounts for the various contributing factors.

Islam, Weir, and Del Fiol (2015) have tried to create a comprehensive measurement model of clinical complexity, with a focus on task and patient complexity in healthcare. The researchers surveyed existing healthcare and non-healthcare complexity measurement models. With this, they devised a validated complexity measurement tool, by comparison with clinical transcripts and constant modification of identified complexity contributing factors (CCFs) to achieve high measurement reliability. Of the 49 CCFs initially compiled, 13 clinical task CCFs (e.g. decision conflict, time pressure) and 11 patient CCFs (e.g. poverty & low social support, older age) were deemed pertinent and can be used in real-life situations to measure and deal with complexity in medicine.

There have been other attempts in the field to characterize and elucidate dimensions contributing to complexity in medicine. Tommasi et al. (2017) assessed clinical and caring complexity in healthcare by classifying internal medicine patients according to a validated index of clinical complexity (ICC) and cumulative illness rating scale (CIRS). Complexity contributing factors like the number of daily medications and time to perform standard nursing tasks were measured in patients. Significant differences were found between increasingly complicated and less complex patient health. Although these indices may be used for effective allocation of human resources by providing an estimate of expected workload, the researchers note that no single index can fully and accurately characterize the complexity of internal medicine patients (Tommasi et al., 2017).

This means that increasingly reliable complexity measurement tools are needed to streamline healthcare delivery, especially with more clinically complex patients. Turner (2011) admits that this process of generating a complexity measurement model is complex in itself, requiring a comprehensive consideration of many contributing factors, some of which are not accessible through electronic medical records. For instance, substance abuse and psychiatric disease are inconsistently recorded on medical records, while factors like pain, functional status, and engagement with care contribute to overall patient complexity but are rarely documented in medical records, adding to the difficulty of this process (Turner, 2011).

In an age where the prevalence of chronic diseases and comorbidities are rising in both adults and children, measuring complexity in medicine poses serious benefits. Robust measurement tools enable efficient allocation of clinical effort, appropriate mobilizing of healthcare and non-healthcare resources, and increased accuracy of complicated clinical decisions. Widespread integration and constant reiteration of complexity measurement models will be valuable in furthering our understanding of complexity in medicine while equipping practitioners with the tools to address that complexity.

Conclusion

Various medical settings (e.g. hospitals, nursing homes, etc.), healthcare fields (e.g. nursing, internal medicine), and approaches to complicated medical conditions ( e.g. Alzheimer’s, cancer), have been examined using a complexity lens. This approach highlights the complex interactions involved between important agents at play, and has led to significant improvements in how we understand their roles within healthcare. That newfound knowledge has proven to be useful for identifying factors that were previously unaccounted for. The use of CAS has enabled the development of better management models, increased efficiency within healthcare teams, improved delivery of truly patient-centered care, and advancements in personalized medicine. There are many avenues for future research using CAS in areas such as resource allocation in patient care, medical diagnosis, and expanding insights in other fields of healthcare and medicine. As Stephen Hawking had mentioned “...the next [21st] century will be the century of complexity.” Thus, in order to tackle the growing intricacies found in healthcare, medicine, and the broader society, we must work with a complexity framework in mind.

Personal Thoughts

Evidently, the use of CAS in healthcare and medical research has provided countless new insights and applications that have improved care in a variety of healthcare organizations and fields. Yet, research utilizing a complexity lens is scarce in literature. During our review process, we recognized that the current studies and reviews do not adequately explain how to implement CAS into research. Without proper guidance, CAS can be overwhelming and difficult to understand, thereby deterring researchers from applying complexity sciences to their work. Therefore, we urge the need for simplified overviews that will accommodate and guide non-expert audiences in working within complexity.

References

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Page History

Created by Balogun. T., Cao. J., Wang. J., Yi. J., & Yu. T., Dec. 4, 2020 (Medical Sciences 4300F CEL)