Unpacking the black box of improvement

• Autores: Ramaswamy, Rohit; Reed, Julie; Livesley, Nigel; Boguslavsky, Victor; Garcia Elorrio, Ezequiel; Sax, Sylvia; Houleymata, Diarra; Kimble, Leighann; Parry, Gareth
• Año de publicación: 2018
• Idioma: inglés
• Tipo de recurso: artículo
• Materia: CYNEFIN FRAMEWORK; EVALUATION COMPLEX SYSTEMS; IMPROVEMENT
• Institución: Consejo Nacional de Investigaciones Científicas y Técnicas

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.
Fil: Ramaswamy, Rohit. University of North Carolina; Estados Unidos
Fil: Reed, Julie. Nihr Clarch Northwest London; Estados Unidos
Fil: Livesley, Nigel. Institute for Healthcare Improvement; Estados Unidos
Fil: Boguslavsky, Victor. University Research Co; Estados Unidos
Fil: Garcia Elorrio, Ezequiel. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina. Instituto de Efectividad Clínica y Sanitaria; Argentina
Fil: Sax, Sylvia. University of Heidelberg; Alemania
Fil: Houleymata, Diarra. Applying Science to Strengthen and Improve Systems Project,; Malí
Fil: Kimble, Leighann. University Research Co; Estados Unidos
Fil: Parry, Gareth. Institute of Healthcare Improvement; Estados Unidos