Building Professional Networks to Support Implementation of Evidence-Based Mental Health Services

1. Building Professional Networks to Support Implementation of Evidence-Based Mental Health Services Funding: NIMH (R25 MH080916-01A2, T32 MH019117; F31 MH098478), VA (QUERI)

2. Purpose Examine change in professional advice-seeking patterns among mental health clinicians participating in a learning collaborative for implementation.

3. Learning Collaborative Models • IHI’s Breakthrough Series Learning Collaborative • Quality Improvement • Teams from multiple agencies • Emphasizes shared learning • Stimulating interactions • Within& Acrossorganizational teams • Are they effective? How? • Mixed evidence (Schouten et al, 2008) • “Black Box” (Mittman, 2004) (IHI, 2003; Nadeem, et al, 2013)

4. Social Networks and Implementation • LCs may support implementation by building social networks within and across participating agency teams. • Networks are conduits for technical information and social support

5. 3 Ways LCs May Build Networks: Technical info – knowledge/skill Intra-Organizational Support Clinician Inter-organizational support, New ideas, Referrals

6. Do learning collaboratives “rewire” social networks in a way that supports implementation? AIMS: • Assess change in the composition of clinicians’ professional advice networks over the duration of a learning collaborative. • Examine how changes in clinician advice seeking patterns alter the structure of the regional network.

7. Study Setting • $2 million regional initiative to implement TF-CBT funded through a county-based tax levy • 32 Children’s behavioral health agencies • Community-based trainers, certified by NCTSN as TF-CBT therapists

8. Study Setting • $2 million regional initiative to implement TF-CBT funded through a county-based tax levy • 32 Children’s behavioral health agencies • Community-based trainers, certified by NCTSN as TF-CBT therapists • Enhanced Learning Collaborative Model

9. Method Sample • 132 participants from 32 agencies (with pre & post data) • 90% of Learning Collaborative completers Data Collection • Surveys administered in-person during 1st & 3rd learning sessions (est. 10 months apart)

10. Measures • Nominate up to 5 sources of professional advice • 422 Unique individuals nominated across both waves of data collection

11. Content Experts Analysis 1. Compare Composition of Professional Advice Networks • Clinician Ego-Network at LS1 and LS3 • Calculate and compare Exposure (% of Ego-net) using paired samples t-test in Stata13 (Valente, 2010) 2. Compare Network Structure • Visualize • Network Descriptives (R - sna, igraph) • Peers at Home Agency • LC Peers at other agencies Private Practice Other

12. Ego-Net: Size of Professional Advice Networks *t(131)=2.06, p<.05

13. Ego-Net: Composition of Professional Advice Networks

14. Ego-Net: Composition of Professional Advice Networks **Other: T(131)=-3.41, p<.001 **Private Practice: T(131)=-3.24, p<.001 **Experts: T(131)=6.60, p<.001

15. Whole Network Structure – LS 1 Diamond = Faculty Expert N=5 Node = Person N=422 Isolate = Person w/no ties N=74 Line = Nomination/Tie N=2487 Components

16. Compare Network Structure Learning Session 1 Learning Session 3

17. Compare Network Structure

18. Compare Network Structure

19. Compare Network Structure

20. Limitations • Generalizeability • 1 region • No comparison/control • Was the LC responsible for making net change? • What elements of the LC?? • Measurement validity • Self-report measures • Drop-Out/Missing data • Some participated in only one wave of data collection • Drop-Out • Opt-Out • Snow-Out (winter weather during one LS)

21. Summary of Findings Clinician-Level • Clinicians rely on colleagues at their home agency • Exposure to faculty experts increased • Slight reduction in exposure to external sources of advice (perhaps because of coaching+consultation) Whole Network • Centralization around Faculty Experts • Reciprocity

22. Implications • For Learning Collaborative Organizers • Provide additional opportunities for participants to network across organizational boundaries. • For Policy Makers and Administrators • Benefits of local experts/knowledge leaders for scale-up initiatives. • Potential for sustainment? • Integration of local service delivery system (in terms of advice sharing) • Small changes at the individual clinician-level can translate to big changes at the systems-level.

23. Future Research Questions: • Why do professional advice ties change? • LC Components: LS? Coaching? LS + Coaching? • Network dynamics? Readiness for implementation? Supportive climate? • Do professional advice networks have a role in implementation success? • What is the relationship between ego-net composition, position in the network, etc. with implementation fidelity? Treatment outcomes? • Why do some clinicians/organizations remain disconnected? • Initial Readiness? • Innovation-values fit?

24. Contactinformation Alicia Bunger Bunger.5@osu.edu

25. References Aarons, GA, Hurlburt, M, & Horwitz, SM. (2011). Advancing a Conceptual Model of Evidence-Based Practice Implementation in Public Service Sectors. Administration and policy in mental health, 38(1), 4–23. Damschroder, LJ, Aron, DC, Keith, RE., …(2009). Fostering implementation of health services research findings into practice: a consolidated framework for advancing implementation science. Implementation science, 4, 50. IHI (2003). The Breakthrough Series: IHI’s Collaborative Model for Achieving Breakthrough Improvement. Cambridge, MA. Retrieved from http://www.ihi.org/IHI/Results/WhitePapers/ Mittman, BS. (2004). Creating the Evidence Base for Quality Improvement Collaboratives. Ann Intern Med, 140(11), 897–901. Nadeem, E, Olin, SS, Hill, LC, Hoagwood, KE, & Horwitz, SM. (2013). Understanding the components of quality improvement collaboratives: a systematic literature review. The Milbank quarterly, 91(2), 354–94. Powell, BJ, McMillen, JC, Proctor, EK … (2011). A Compilation of Strategies for Implementing Clinical Innovations in Health and Mental Health. Medical care research and review, 69(2), 123–157. Schouten, LMT, Hulscher, MEJ, van Everdingen, JJE, …(2008). Evidence for the impact of quality improvement collaboratives: systematic review. BMJ (Clinical research ed.), 336(7659), 1491–4. Valente, TW (2010). Social networks and health: models, methods, and applications. Oxford University Press.

26. Acknowledgements • Missouri Academy of Child Trauma Studies (MoACTS) at the Child Advocacy Center of Greater St. Louis (UMSL). • NIMH • Postdoctoral Traineeship (T32 MH019117) sponsored by UNC-CH & Duke (Bunger) • Predoctoral traineeship (F31 MH098478) (Powell) • NIMH/VA • Implementation Research Institute (R25 MH080916-01A2) (WUSTL) (Bunger & Hanson) • Doris Duke Charitable Foundation • Fellowship for the Promotion of Child Well-Being (Powell)