More than Complicated, Healthcare Delivery is Complex, Adaptive, and Evolving

Ellen S Deutsch, MD, MS, FACS, FAAP, CPPS
Editor, Pennsylvania Patient Safety Advisory
Medical Director, Pennsylvania Patient Safety Authority

Introduction

Although great progress has been made¹, many challenges remain² as we strive to provide the safest and best healthcare for our patients. Why has this quest been so challenging?

It may help to recognize that healthcare delivery is not just complicated, it is a complex adaptive system that evolves over time.^3-4 At the “person” level, complex adaptive systems include networks of agents—such as patients, healthcare providers, support personnel, and administrators—who constantly act and who interact with each other.^3-4 Because their actions are interconnected, the actions of one agent change the context for other agents, so relationships and circumstances are dynamic and fluid.^3-5 Agents, as individuals or as members of teams, may act in unpredictable ways.^4-5 Interactions in healthcare delivery at the person level further intersect with multiple interactions at additional levels. Patients and care providers interface within the context of collaborations among the entire care team, the healthcare division or unit, the department, and the organization. These interactions are further influenced by, and, in turn, influence, the regional and national healthcare delivery systems, which includes payers and regulators, as well as community or societal desires and expectations. Expectations are affected by the evolution of resources and abilities. Motivations, incentives, rewards, and penalties are influenced at all levels. Consistent with the properties of complex adaptive systems, control is dispersed and decentralized.^3-4,6

Traditional processes, such as root cause analysis or the 5 Whys and Ishikawa diagrams,⁷ often seek to dissect and deconstruct events to understand, and potentially improve, each of the components of successful outcomes. Conventional reductionist scientific thinking assumes that we will eventually figure it all out and resolve all the unresolved issues; complexity theory is comfortable with and even values the inherent tension between different parts of the system.^4-5 Although it is important to ensure that each component of the healthcare delivery system functions well, it is also important to understand that each healthcare decision, each intervention, and each interaction occurs within a larger context.

Compilation of socio-technical models adapted from Vincent, Taylor-Adams and Stanhope; Carayon et al.¹⁰;  Harrison, Koppel and Bar-Lev¹¹; and Sittig and Singh.¹²

Systems thinking may help to bridge the gap between analysis of the deconstructed parts of a system and synthesis of the parts into a complex whole greater than the sum of its parts.⁸ The constructs of socio-technical systems, in particular, can be used to negotiate the interplay between the parts and the whole. Several authors have described socio-technical systems and identified components that interact and impact healthcare delivery. The general concepts of healthcare socio-technical systems are viewed through lenses that overlap but are not identical. The Figure presents a multi-faceted compilation of models developed by Vincent, Carayon, and Harrison and their respective colleagues and Sittig and Singh,^9-12 that demonstrates the potential for innumerable multi-directional interactions. Healthcare delivery socio-technical systems in Pennsylvania incorporate multiple diverse interactions between patients and providers, technology and tasks, and organizations and the larger healthcare-delivery environment. Social, technical, and other factors influence each other as well as impacting patient care.¹⁰

A dynamic tension exists between analyzing and optimizing each component of a complex adaptive socio-technical system and understanding that properties such as safety, which emerge from the synthesis of these components, are not completely understandable or predictable. Individuals, or organizations, prioritize their improvement efforts by their needs, goals, and resources. The inherent paradoxes are inevitable and challenging but also offer opportunities for creative problem solving and advances as we strive to provide the safest and best healthcare possible for our patients.

Notes

1. U.S. Department of Health and Human Services. Efforts to improve patient safety result in 1.3 million fewer patient harms, 50,000 lives saved and $12 billion in health spending avoided [press release online]. [updated 2014 Dec 2; cited 2016 Feb 9]. http://www.hhs.gov/about/news/2014/12/02/efforts-improve-patient-safety-result-1-3-million-fewer-patient-harms-50000-lives-saved-and-12-billion-in-health-spending-avoided.html#
2. Kuehn BM. Patient safety still lagging: advocates call for national patient safety monitoring board. JAMA 2014 Sep 3;312(9):879-80.
3. Vincent C. Patient safety. 2nd ed. Great Britain: Wiley Blackwell; 2010.
4. Braithwaite J, Wears RL, Hollnagel E. Resilient health care: turning patient safety on its head. Int J Qual Health Care 2015 Oct;27(5):418-20.
5. Plsek PE, Greenhalgh T. Complexity science. The challenge of complexity in health care. BMJ 2001;323:625-28.
6. Dekker S. Drift into failure. From hunting broken components to understanding complex systems. Ashgate: Farnham, Surrey, United Kingdom; 2011.
7. Barnard C. Performance improvement basics: a resource guide for healthcare managers. 2nd ed. HCPro: Danvers, MA; 2009.
8. Blanchard BS, Fabrycky WJ. Systems engineering and analysis. 5th ed. Prentice Hall: Boston; 2011.
9. Vincent C, Taylor-Adams S, Stanhope N. Framework for analysing risk and safety in clinical medicine. BMJ Apr 1998;316(7138);1154-57.
10. Carayon P, Schoofs Hundt A, Karsh B-T, et al. Work system design for patient safety: the SEIPS model. Qual Saf Health Care 2006;15(Suppl 1):i50-8.
11. Harrison MI, Koppel R, Bar-Lev S. Unintended consequences of information technologies in health care – an interactive sociotechnical analysis. J Am Med Inform Assoc 2007;14:542-9.
12. Sittig DF, Singh H. A new sociotechnical model for studying health information technology in complex adaptive healthcare systems. Qual Saf Health Care 2010;19(Suppl 3):i68-i74.