The GuideLine Implementability Appraisal (GLIA): development of an instrument to identify obstacles to guideline implementation.
BACKGROUND
Clinical practice guidelines are not uniformly successful in influencing clinicians' behaviour toward best practices. Implementability refers to a set of characteristics that predict ease of (and obstacles to) guideline implementation. Our objective is to develop and validate a tool for appraisal of implementability of clinical guidelines.


METHODS
Indicators of implementability were identified from the literature and used to create items and dimensions of the GuideLine Implementability Appraisal (GLIA). GLIA consists of 31 items, arranged into 10 dimensions. Questions from 9 of the 10 dimensions are applied individually to each recommendation of the guideline. Decidability and Executability are critical dimensions. Other dimensions are Global, Presentation and Formatting, Measurable Outcomes, Apparent Validity, Flexibility, Effect on Process of Care, Novelty/Innovation, and Computability. We conducted a series of validation activities, including validation of the construct of implementability, expert review of content for clarity, relevance, and comprehensiveness, and assessment of construct validity of the instrument. Finally, GLIA was applied to a draft guideline under development by national professional societies.


RESULTS
Evidence of content validity and preliminary support for construct validity were obtained. The GLIA proved to be useful in identifying barriers to implementation in the draft guideline and the guideline was revised accordingly.


CONCLUSIONS
GLIA may be useful to guideline developers who can apply the results to remedy defects in their guidelines. Likewise, guideline implementers may use GLIA to select implementable recommendations and to devise implementation strategies that address identified barriers. By aiding the design and operationalization of highly implementable guidelines, our goal is that application of GLIA may help to improve health outcomes, but further evaluation will be required to support this potential benefit.
Shiffman
Richard N
RN
Yale Center for Medical Informatics, Yale University School of Medicine, New Haven, CT, USA. richard.shiffman@yale.edu
Dixon
Jane
J
Brandt
Cynthia
C
Essaihi
Abdelwaheb
A
Hsiao
Allen
A
Michel
George
G
O'Connell
Ryan
R
D019985:Benchmarking / Q000379:methods*; D004059:Diffusion of Innovation; D005103:Expert Systems; D019983:Guideline Adherence; D006801:Humans; D017410:Practice Guidelines as Topic / Q000592:standards*; D011381:Programming Languages; D011785:Quality Assurance, Health Care; D011795:Surveys and Questionnaires