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E-consult implementation: lessons learned using consolidated framework for implementation research.

Haverhals LM, Sayre G, Helfrich CD, Battaglia C, Aron D, Stevenson LD, Kirsh S, Ho M, Lowery J. E-consult implementation: lessons learned using consolidated framework for implementation research. The American journal of managed care. 2015 Dec 1; 21(12):e640-7.

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Abstract:

OBJECTIVES: In 2011, the Veterans Health Administration (VHA) implemented electronic consults (e-consults) as an alternative to in-person specialty visits to improve access and reduce travel for veterans. We conducted an evaluation to understand variation in the use of the new e-consult mechanism and the causes of variable implementation, guided by the Consolidated Framework for Implementation Research (CFIR). STUDY DESIGN: Qualitative case studies of 3 high- and 5 low-implementation e-consult pilot sites. Participants included e-consult site leaders, primary care providers, specialists, and support staff identified using a modified snowball sample. METHODS: We used a 3-step approach, with a structured survey of e-consult site leaders to identify key constructs, based on the CFIR. We then conducted open-ended interviews, focused on key constructs, with all participants. Finally, we produced structured, site-level ratings of CFIR constructs and compared them between high- and low-implementation sites. RESULTS: Site leaders identified 14 initial constructs. We conducted 37 interviews, from which 4 CFIR constructs distinguished high implementation e-consult sites: compatibility, networks and communications, training, and access to knowledge and information. For example, illustrating compatibility, a specialist at a high-implementation site reported that the site changed the order of consult options so that all specialties listed e-consults first to maintain consistency. High-implementation sites also exhibited greater agreement on constructs. CONCLUSIONS: By using the CFIR to analyze results, we facilitate future synthesis with other findings, and we better identify common patterns of implementation determinants common across settings.





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