building professional networks to support implementation of evidence-based mental health services
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Building Professional Networks to Support Implementation of Evidence-Based Mental Health Services. Funding : NIMH (R25 MH080916-01A2, T32 MH019117; F31 MH098478), VA (QUERI). Purpose. - PowerPoint PPT PresentationTRANSCRIPT
Building Professional Networks to Support Implementation of Evidence-Based Mental Health Services
Funding: NIMH (R25 MH080916-01A2, T32 MH019117; F31 MH098478), VA (QUERI)
Alicia BungerOhio State University
Byron PowellWashington University in St. Louis
Rochelle HansonMedical University of South Carolina
Nathan DooganOhio State University
Yiwen CaoOhio State University
Jerry DunnUniversity of Missouri-St. Louis
Purpose
Examine change in professional advice-seeking patterns among mental health clinicians participating in a learning collaborative for implementation.
Learning Collaborative Models• IHI’s Breakthrough Series
Learning Collaborative• Quality Improvement• Teams from multiple agencies
• Emphasizes shared learning• Stimulating interactions• Within & Across organizational
teams
• Are they effective? How?• Mixed evidence (Schouten et al, 2008)
• “Black Box” (Mittman, 2004)
• Expert Panel• Commitment
Preparatory Work
• In-Person Learning Sessions (3)• Plan-Do-Study-Act (PDSA) cycles
Active Learning
• Team Calls• Web Support• Quality Improvement Techniques
Supports
(IHI, 2003; Nadeem, et al, 2013)
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
3 Ways LCs May Build Networks:
Content Experts
Peers at Home
Agency
LC Peers at other
agencies
ClinicianIntra-Organizational Support
Technical info – knowledge/skill
Inter-organizational support, New ideas, Referrals
Opportunities for Interaction
• Learning Sessions
• Consultation• Calls
• Learning Sessions
• PDSAs
• Learning Sessions
• Group Calls
Do learning collaboratives “rewire” social networks in a way that supports implementation?
AIMS:1. Assess change in the composition of clinicians’
professional advice networks over the duration of a learning collaborative.
2. Examine how changes in clinician advice seeking patterns alter the structure of the regional network.
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
• Expert Panel• Commitment
Preparatory Work
• In-Person Learning Sessions (3)• Plan-Do-Study-Act (PDSA) cycles
Active Learning
• Team Calls• Web Support• Quality Improvement Techniques
Supports
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
• Expert Panel• Commitment
Preparatory Work
• In-Person Learning Sessions (3)• Plan-Do-Study-Act (PDSA) cycles
Active Learning
• Team Calls• Web Support• Quality Improvement Techniques
Supports
• Coaching Calls• On-Site Visits• Local Trainers• Rostering
Enhanced Features
MethodSample• 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)
Discipline Role ExperienceSocial Work 53% Sr. Leader 10% GT 5 yrs in field 65%
Counseling 28% Supervisor 22% LT 1 yr in job 43%
Psychology 12% Clinician 68%
MeasuresRank
Column A – Names
Column B - Communication
Who do you turn to for professional advice about youth with trauma histories? Please list their name and organization in order you would contact them.
In the past 6 months, how frequently have you communicated or been in contact with this person via in-person contact, telephone, or email? (Circle the most accurate number from the answer scale below for each person.)
Not Once1-2
times
About once/ month
About every 2 weeks
About once/ week
About daily
Many times daily
1.Name: Organization:
1 2 3 4 5 6 7
……
• Nominate up to 5 sources of professional advice• 422 Unique individuals nominated across both waves of data collection
Analysis1. 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 Stata 13 (Valente, 2010)
2. Compare Network Structure• Visualize • Network Descriptives (R - sna, igraph)
Content Experts
LC Peers at other
agenciesPrivate Practice
OtherPeers at Home
Agency
Ego-Net: Size of Professional Advice Networks
LS1 LS30%
20%
40%
60%
80%
100%
3.9 3.6
Ego-Net Size*
Num
ber o
f Nom
inat
ions
5
4
3
2
1
0
*t(131)=2.06, p<.05
LS1 LS30%
20%
40%
60%
80%
100%
0.05
0.72
0.030.06
0.15
OtherPrivate PracticePeers-Other AgenciesPeers-HomeExperts
Expo
sure
Ego-Net: Composition of Professional Advice Networks
LS1 LS30%
20%
40%
60%
80%
100%
0.050.20
0.72
0.66
0.03
0.050.06
0.030.150.07
OtherPrivate PracticePeers-Other AgenciesPeers-HomeExperts
Expo
sure
Ego-Net: Composition of Professional Advice Networks
**Experts:T(131)=6.60, p<.001
**Private Practice:T(131)=-3.24, p<.001
**Other:T(131)=-3.41, p<.001
Whole Network Structure – LS 1
Node = PersonN=422
Diamond = Faculty ExpertN=5
Line = Nomination/TieN=2487
Isolate = Person w/no tiesN=74
Components
Compare Network StructureLearning Session 1 Learning Session 3
Compare Network Structure
Learning Session 1 Learning Session 3N 422 422Isolates 74 177Density 0.014 0.013Centralization (in-degree) 0.097 .182Clustering (weighted) 0.293 .356
Compare Network Structure
Learning Session 1 Learning Session 3N 422 422Isolates 74 177Density 0.014 0.013Centralization (in-degree) 0.097 .182Clustering (weighted) 0.293 .356Reciprocity 0.164 0.227Weighted Reciprocity 0.188 0.246
Compare Network Structure
Learning Session 1 Learning Session 3N 422 422Isolates 74 177Density 0.014 0.013Centralization (in-degree) 0.097 .182Clustering (weighted) 0.293 .356Reciprocity 0.164 0.227Weighted Reciprocity 0.188 0.246Agency Homophily 89.08 77.72
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)
Summary of FindingsClinician-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
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.
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?
ReferencesAarons, 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.
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)