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Rugged landscapes: complexity and implementation science
BACKGROUND: Mis-implementation—defined as failure to successfully implement and continue evidence-based programs—is widespread in public health practice. Yet the causes of this phenomenon are poorly understood. METHODS: We develop an agent-based computational model to explore how complexity hinders...
Autores principales: | , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
BioMed Central
2020
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7523395/ https://www.ncbi.nlm.nih.gov/pubmed/32993756 http://dx.doi.org/10.1186/s13012-020-01028-5 |
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author | Ornstein, Joseph T. Hammond, Ross A. Padek, Margaret Mazzucca, Stephanie Brownson, Ross C. |
author_facet | Ornstein, Joseph T. Hammond, Ross A. Padek, Margaret Mazzucca, Stephanie Brownson, Ross C. |
author_sort | Ornstein, Joseph T. |
collection | PubMed |
description | BACKGROUND: Mis-implementation—defined as failure to successfully implement and continue evidence-based programs—is widespread in public health practice. Yet the causes of this phenomenon are poorly understood. METHODS: We develop an agent-based computational model to explore how complexity hinders effective implementation. The model is adapted from the evolutionary biology literature and incorporates three distinct complexities faced in public health practice: dimensionality, ruggedness, and context-specificity. Agents in the model attempt to solve problems using one of three approaches—Plan-Do-Study-Act (PDSA), evidence-based interventions (EBIs), and evidence-based decision-making (EBDM). RESULTS: The model demonstrates that the most effective approach to implementation and quality improvement depends on the underlying nature of the problem. Rugged problems are best approached with a combination of PDSA and EBI. Context-specific problems are best approached with EBDM. CONCLUSIONS: The model’s results emphasize the importance of adapting one’s approach to the characteristics of the problem at hand. Evidence-based decision-making (EBDM), which combines evidence from multiple independent sources with on-the-ground local knowledge, is a particularly potent strategy for implementation and quality improvement. |
format | Online Article Text |
id | pubmed-7523395 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2020 |
publisher | BioMed Central |
record_format | MEDLINE/PubMed |
spelling | pubmed-75233952020-09-30 Rugged landscapes: complexity and implementation science Ornstein, Joseph T. Hammond, Ross A. Padek, Margaret Mazzucca, Stephanie Brownson, Ross C. Implement Sci Methodology BACKGROUND: Mis-implementation—defined as failure to successfully implement and continue evidence-based programs—is widespread in public health practice. Yet the causes of this phenomenon are poorly understood. METHODS: We develop an agent-based computational model to explore how complexity hinders effective implementation. The model is adapted from the evolutionary biology literature and incorporates three distinct complexities faced in public health practice: dimensionality, ruggedness, and context-specificity. Agents in the model attempt to solve problems using one of three approaches—Plan-Do-Study-Act (PDSA), evidence-based interventions (EBIs), and evidence-based decision-making (EBDM). RESULTS: The model demonstrates that the most effective approach to implementation and quality improvement depends on the underlying nature of the problem. Rugged problems are best approached with a combination of PDSA and EBI. Context-specific problems are best approached with EBDM. CONCLUSIONS: The model’s results emphasize the importance of adapting one’s approach to the characteristics of the problem at hand. Evidence-based decision-making (EBDM), which combines evidence from multiple independent sources with on-the-ground local knowledge, is a particularly potent strategy for implementation and quality improvement. BioMed Central 2020-09-29 /pmc/articles/PMC7523395/ /pubmed/32993756 http://dx.doi.org/10.1186/s13012-020-01028-5 Text en © The Author(s) 2020 Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated in a credit line to the data. |
spellingShingle | Methodology Ornstein, Joseph T. Hammond, Ross A. Padek, Margaret Mazzucca, Stephanie Brownson, Ross C. Rugged landscapes: complexity and implementation science |
title | Rugged landscapes: complexity and implementation science |
title_full | Rugged landscapes: complexity and implementation science |
title_fullStr | Rugged landscapes: complexity and implementation science |
title_full_unstemmed | Rugged landscapes: complexity and implementation science |
title_short | Rugged landscapes: complexity and implementation science |
title_sort | rugged landscapes: complexity and implementation science |
topic | Methodology |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7523395/ https://www.ncbi.nlm.nih.gov/pubmed/32993756 http://dx.doi.org/10.1186/s13012-020-01028-5 |
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