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Artículo

Unpacking the black box of improvement

Ramaswamy, Rohit; Reed, Julie; Livesley, Nigel; Boguslavsky, Victor; Garcia Elorrio, EzequielIcon ; Sax, Sylvia; Houleymata, Diarra; Kimble, Leighann; Parry, Gareth
Fecha de publicación: 04/2018
Editorial: Oxford University Press
Revista: International Journal For Quality In Health Care
ISSN: 1353-4505
Idioma: Inglés
Tipo de recurso: Artículo publicado
Clasificación temática:
Otras Ciencias de la Salud

Resumen

During the Salzburg Global Seminar Session 565-Better Health Care: How do we learn about improvement, participants discussed the need to unpack the black box of improvement. The black box refers to the fact that when quality improvement interventions are described or evaluated, there is a tendency to assume a simple, linear path between the intervention and the outcomes it yields. It is also assumed that it is enough to evaluate the results without understanding the process of by which the improvement took place. However, quality improvement interventions are complex, nonlinear and evolve in response to local settings. To accurately assess the effectiveness of quality improvement and disseminate the learning, there must be a greater understanding of the complexity of quality improvement work. To remain consistent with the language used in Salzburg, we refer to this as unpacking the black box of improvement. To illustrate the complexity of improvement, this article introduces four quality improvement case studies. In unpacking the black box, we present and demonstrate how Cynefin framework from complexity theory can be used to categorize and evaluate quality improvement interventions. Many quality improvement projects are implemented in complex contexts, necessitating an approach defined as probesense- respond. In this approach, teams experiment, learn and adapt their changes to their local setting. Quality improvement professionals intuitively use the probe-sense-respond approach in their work but document and evaluate their projects using language for simple or complicated' contexts, rather than the complex contexts in which they work. As a result, evaluations tend to ask 'How can we attribute outcomes to the intervention, rather than 'What were the adaptations that took place. By unpacking the black box of improvement, improvers can more accurately document and describe their interventions, allowing evaluators to ask the right questions and more adequately evaluate quality improvement interventions.
Palabras clave: CYNEFIN FRAMEWORK , EVALUATION COMPLEX SYSTEMS , IMPROVEMENT
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info:eu-repo/semantics/openAccess Excepto donde se diga explícitamente, este item se publica bajo la siguiente descripción: Creative Commons Attribution-NonCommercial 2.5 Unported (CC BY-NC 2.5)
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URI: http://hdl.handle.net/11336/99498
URL: https://academic.oup.com/intqhc/article/30/suppl_1/15/4860379
DOI: http://dx.doi.org/10.1093/intqhc/mzy009
URL: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5909642
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Citación
Ramaswamy, Rohit; Reed, Julie; Livesley, Nigel; Boguslavsky, Victor; Garcia Elorrio, Ezequiel; et al.; Unpacking the black box of improvement; Oxford University Press; International Journal For Quality In Health Care; 30; 4-2018; 15-19
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