1. What is Complexity Science and Why Should I Care? Complexity Pre-Conference Workshop NAPCRG October 2007 Michael L. Parchman, MD MPH Associate Professor Department of Family & Community Medicine University of Texas HSC-San Antonio

2. Perspective  “The way you look at a thing determines what you see” Paul Thomas, MD AHRQ Annual PBRN Conference May 16, 2007

3. Goal  “What will I see if I take a complexity approach to research conducted in Primary Care Settings?

4. What is Complexity Science?  “…the study of complex adaptive systems – the patterns of relationships within them, how they are sustained, how they self- organize and how outcomes emerge.”  “…addresses aspects of living systems that are neglected or understated in traditional approaches.”  “…describes how systems actually behave rather than how they should behave.” Zimmerman B, Lindberg C, Plesk P. Edgeware: insights from complexity science for health care leaders.1998

5. A Story A Story  “Once upon a time there was a family medicine physician- researcher…”

6. AHRQ K08 Career Development Award 2002-2006  To become an expert in the conduct of research in primary care PBRNs using both quantitative and qualitative methods to study the quality of diabetes care  To use this expertise to understand the structures and processes of primary care with the goal of improving the quality of diabetes care in a way that is pragmatic for primary care practices

7. Primary Mentor  Jacqueline A. Pugh, MD, Department of Medicine  Director, Veteran’s Evidence-based Research Dissemination and Implementation Center (VERDICT)  VA HSR&D funded health services research center

8. Organizational Research  Knowledge Management  Clinical Micro- systems  VA grant for “external academic expert”: Reuben McDaniel, PhD.

9. Reuben’s Question:  “Why is it that health care organizations have so many smart people, yet perform so poorly?”

10. “Reuben School”

11. Sample of Readings  Kauffman, Stuart (1995). “The Origins of Life” in At Home in the Universe: The Search for the Laws of Self- organization and Complexity. Oxford University Press, Oxford, pp. 31-45  McDaniel, Reuben R., Jr., Jordan, Michelle E., & Fleeman, Bridgette F. (2003). Surprise, Surprise, Surprise! A Complexity Science View of the Unexpected. Health Care Management Review, 28(3), 266-278  Rivkin, J.W., & Siggelkow, N. (2002). Organizational sticking points on NK landscapes. Complexity, 7(5), 31- 43.  Levinthal, Daniel A., & Warglien, Massimo (1999). Landscape design: Designing for local action in complex worlds. Organization Science, 10 (3), 342-357

12. “Chaos” is NOT “Complexity”  Chaos Science Study of non-linear dynamic(NLD) systems comprised of agents who react rather than adapt or change: weather systems Study of non-linear dynamic(NLD) systems comprised of agents who react rather than adapt or change: weather systems  CAS Study of systems comprised of agents who learn and adapt; has NLD properties: human organizations Study of systems comprised of agents who learn and adapt; has NLD properties: human organizations

13. CAS: A Sub-Group of NLDS CAS Non-Linear Dynamic Systems

14. Complex Adaptive Systems  Every primary care office or clinic is a Complex Adaptive Systems (CAS)

15. Complex Adaptive Systems  Diverse agents who learn and adapt  Non-linear inter-dependencies/relationships  Co-evolution  Self-organization  Uncertainty/Emergence

16. A CAS Approach to Understanding Quality of Primary Care  How might knowledge of CAS properties inform how we approach research on quality of care in primary care?

17. Diverse Agents Who Learn  Traditional Approach People with specific skills, licenses, or degrees are interchangeable; Mindset that everyone does his or her own job. People with specific skills, licenses, or degrees are interchangeable; Mindset that everyone does his or her own job. Focus on knowledge & skills of the individual Focus on knowledge & skills of the individual  CAS Approach Interventions that improve diversity will improve ability of system to adapt to its environment Ability of a primary care team to LEARN is more important than knowledge of each individual;

18. Non-Linear Relationships  Traditional Approach Improving knowledge & skills of individual has a linear relationship with performance Improving knowledge & skills of individual has a linear relationship with performance Input = Output Input = Output  CAS Approach Changes in the dynamics of relationships/communi- cation between agents will be more likely to change performance than interventions that focus on single agents

19. Self-Organization  Traditional Approach Organization can be dictated from outside of the system. Organization can be dictated from outside of the system. Hierarchical control / command is possible within the organization. Hierarchical control / command is possible within the organization.  CAS Approach Organization cannot be “imposed from above.” Agents on the primary care team are active participants in the process of creating, adapting & co-evolving Results in self- organization of the team

20. Co-evolution  Traditional approach Interventions can be moved from one system to another without regard for local conditions. Interventions can be moved from one system to another without regard for local conditions.  CAS Approach Each clinic is unique, based on the way it has co-evolved with its local environment and how agents have co- evolved with each other. Interventions may not easily translate from one clinic to another.

21. Uncertainty/Emergence  Traditional Approach Uncertainty can be reduced to zero through planning and procedures that will have predictable effects. Uncertainty can be reduced to zero through planning and procedures that will have predictable effects. Primary Care research: we can reduce uncertainty by collecting more information Primary Care research: we can reduce uncertainty by collecting more information  Complexity Approach Uncertainty and surprise are inherent in primary care clinics. It is not possible to make this disappear with planning or additional data collection. Performance is an emergent property of the Primary Care CAS.

22. Important CAS Concepts  Fitness Landscapes  Attractors

23. Fitness Landscapes  Stuart Kauffman At Home in the Universe Capacity of an organism to break down a protein Capacity of an organism to break down a protein  Agents climb toward “fitness peaks”  Peaks bestow considerable survival advantages

24. Implications for Primary Care Research  Fitness landscapes Internal Internal External External  Dimension/determinants Finances Finances Patient Satisfaction Patient Satisfaction Quality of care Quality of care Mission, Values and Culture of the Practice Mission, Values and Culture of the Practice

25. Practice “Readiness to Change?”  Attributes of the fitness landscape upon which the agents in the practice co-evolve?  “Change” is a unpredictable emergent outcome of this non-linear co- evolution?

26. Attractors  Non-linear Dynamics and feedback  Boundary of possible states of system  Transient movement away from attractor  “Relaxation time” back into the attractor

27. Phase Space Plots  One method of characterizing an attractor within a non-linear dynamic system  A method to transform a series of values over time into an object in space (embedding)  Certain properties of the data are easier to determine form the phase space set than the original sequence of values in time

28. Time Series X(t) Y(t) Z(t) embedding Hilltop Family Practice RVUs Quality Satisfaction 0360 DAYS

29. Phase Space X(t) Z(t) phase space set Y(t)

30. Implications of a CAS Approach for Primary Care Research  How we ask the research question  Methods used to answer the question

31. Research Questions: Traditional Approach  Isolate and Intervene  Find “THE ANSWER”  If we can predict, then we understand  Reduce uncertainty in prediction by collecting more data

32. Research Questions: CAS Approach  Rather than predict: try to understand the observed phenomenon  Teenagers are a CAS on the edge of chaos Uncertainty & Surprise Uncertainty & Surprise Learn & adapt Learn & adapt Provide a supportive “fitness landscape” Provide a supportive “fitness landscape”

33. Implications for the Research Question #1  Instead of asking: “Is physician knowledge related to the likelihood of starting insulin for a persistently elevated A1c?”  We should ask: What rules/schemata do patients and physicians follow during each encounter, over a series of encounters, that might help us understand why there is a long delay before insulin is initiated? What rules/schemata do patients and physicians follow during each encounter, over a series of encounters, that might help us understand why there is a long delay before insulin is initiated?

34. A CAS Perspective on Question #1  Instead of “does A cause B” we recognize that the system where A(patient) and B(physician) reside is evolving over time  The first question does NOT recognize that it evolves, the second question does.

35. How do Birds Flock?: An Emergent Outcome  Rules/Schemata of Agents: Don’t fly too close Don’t fly too close Aim for center of flock Aim for center of flock Fly in same direction Fly in same direction Match speed Match speed  Agent-based models  Computer simulation: “Boids”

36. Implications for the Research Question #2  Instead of asking: “will intervention A improve the quality of diabetes care better than intervention B”  We should ask: What are the characteristic patterns of the non-linear interactions (relationships, communication) among agents that help us understand why intervention A worked better in Clinic X, and intervention B worked better in Clinic Y. What are the characteristic patterns of the non-linear interactions (relationships, communication) among agents that help us understand why intervention A worked better in Clinic X, and intervention B worked better in Clinic Y.

37. CAS Implications  If we acknowledge that relationships in the system are non-linear and reciprocal, then instead of asking the first question, you should ask the second question  Highly likely that relationships are unstable, so rather than just look for stable patterns at one point in time, need to observe the dynamics over time.

38. Implication of CAS Theory for Primary Care Research Methods  Uncertainty and Surprise  Dynamics and evolution  Diversity of research methods  Diversity on the research team

39. Expect the Unexpected  Uncertainty  Surprise  Emergence

40. Uncertainty is Uncomfortable!  Multi-Method  Share insights/findings frequently with colleagues  Increase diversity on the research team  Adapt methods used in other disciplines for study of CASs Agent Based Models Agent Based Models Catastrophe Cusp Models Catastrophe Cusp Models  Research Design is a VERB

41. “Straight-Line-itis”  Turn off your SPSS  Observe phenomena of interest over time and look for dynamic patterns.  Increase frequency and depth of observations and measurements

42. CAS Resources  Books  Articles  Professional Society

43. Recommended Books Also: Zimmerman B, Lindberg C, Plesk P. Edgeware: Complexity and Health Care. VHA Irving, TX1998

44. Recommended Articles  Stroebel CK, et al. How complexity science can inform a reflective process for improvement in primary care practices. J Quality Patient Safety. 2005;31:438-446  Miller WL et al. Practice Jazz: Understanding Variation in Family Practices using complexity science. J Fam Pract 2001;50:872-878  Anderson P. Complexity theory and organizational science. Org Science 1999;10:216-232.  Agar MH. Drugmart: Heroin epidemics as complex adaptive systems. Complexity 2002;44-52  Agar M. We have met the other and we’re all non-linear: Ethnography as a non-linear dynamic system. Complexity 2004;10:16-24

46. The 3M Approach to PBRN Research  “Mental Models Matter”  How will you approach your research through the lens of complexity science?

47. THANKS! Acknowledgments  AHRQ K08 Career Development Award Kay Anderson Kay Anderson  Jacqueline Pugh, MD  Reuben McDaniel, PhD Holly Lanham Holly Lanham Michelle Jordan Michelle Jordan  VERDICT Investigators Nedal Arar Bob Badgett Laurel Copeland Val Lawrence Luci Leykum Eric Mortensen Polly Noel Mary Jo Pugh Marcos Restrepo Chen-Pin Wang John Zeber

49. Phase Space Plot of Sequential Visits: Quality and Visit Length Length of Visit (minutes) Length of Visit (minutes) Clinics with no EMR

50. Phase Space Plot of Sequential Visits: Quality and Visit Length Clinics with an EMR Length of Visit (minutes) Length of Visit (minutes)

51. Results  Encounter dynamics demonstrate periodic orbit attractors.  The location of the attractor was different for clinics with an EMR compared to those without an EMR.  EMR changes practice landscape such that fitness peaks are different for practices with an without an EMR

52. CAS in Primary Care  Miller WL, Crabtree BF, McDaniel RR, Stange KC. Understanding change in primary care practice using complexity theory. J Fam Pract 1998;46:369-376.  Miller WL, McDaniel RR, Crabtree BF, Stange KC. Practice jazz: understanding variation in family practices using complexity science. J Fam Pract 2001;50:872-878  Crabtree BF. Primary care: uncertainty and surprise. In: Uncertainty and surprise in complex systems. Eds: McDaniel RR, Driebe DJ. Springer. New York 2006. pp123-129