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What do healthcare professionals need to turn risk models for type 2 diabetes into usable computerized clinical decision support systems? Lessons learned from the MOSAIC project
BACKGROUND: To understand user needs, system requirements and organizational conditions towards successful design and adoption of Clinical Decision Support Systems for Type 2 Diabetes (T2D) care built on top of computerized risk models. METHODS: The holistic and evidence-based CEHRES Roadmap, used t...
Autores principales: | , , , , , , , , , , , , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
BioMed Central
2019
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6697904/ https://www.ncbi.nlm.nih.gov/pubmed/31419982 http://dx.doi.org/10.1186/s12911-019-0887-8 |
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author | Fico, Giuseppe Hernanzez, Liss Cancela, Jorge Dagliati, Arianna Sacchi, Lucia Martinez-Millana, Antonio Posada, Jorge Manero, Lidia Verdú, Jose Facchinetti, Andrea Ottaviano, Manuel Zarkogianni, Konstantia Nikita, Konstantina Groop, Leif Gabriel-Sanchez, Rafael Chiovato, Luca Traver, Vicente Merino-Torres, Juan Francisco Cobelli, Claudio Bellazzi, Riccardo Arredondo, Maria Teresa |
author_facet | Fico, Giuseppe Hernanzez, Liss Cancela, Jorge Dagliati, Arianna Sacchi, Lucia Martinez-Millana, Antonio Posada, Jorge Manero, Lidia Verdú, Jose Facchinetti, Andrea Ottaviano, Manuel Zarkogianni, Konstantia Nikita, Konstantina Groop, Leif Gabriel-Sanchez, Rafael Chiovato, Luca Traver, Vicente Merino-Torres, Juan Francisco Cobelli, Claudio Bellazzi, Riccardo Arredondo, Maria Teresa |
author_sort | Fico, Giuseppe |
collection | PubMed |
description | BACKGROUND: To understand user needs, system requirements and organizational conditions towards successful design and adoption of Clinical Decision Support Systems for Type 2 Diabetes (T2D) care built on top of computerized risk models. METHODS: The holistic and evidence-based CEHRES Roadmap, used to create eHealth solutions through participatory development approach, persuasive design techniques and business modelling, was adopted in the MOSAIC project to define the sequence of multidisciplinary methods organized in three phases, user needs, implementation and evaluation. The research was qualitative, the total number of participants was ninety, about five-seventeen involved in each round of experiment. RESULTS: Prediction models for the onset of T2D are built on clinical studies, while for T2D care are derived from healthcare registries. Accordingly, two set of DSSs were defined: the first, T2D Screening, introduces a novel routine; in the second case, T2D Care, DSSs can support managers at population level, and daily practitioners at individual level. In the user needs phase, T2D Screening and solution T2D Care at population level share similar priorities, as both deal with risk-stratification. End-users of T2D Screening and solution T2D Care at individual level prioritize easiness of use and satisfaction, while managers prefer the tools to be available every time and everywhere. In the implementation phase, three Use Cases were defined for T2D Screening, adapting the tool to different settings and granularity of information. Two Use Cases were defined around solutions T2D Care at population and T2D Care at individual, to be used in primary or secondary care. Suitable filtering options were equipped with “attractive” visual analytics to focus the attention of end-users on specific parameters and events. In the evaluation phase, good levels of user experience versus bad level of usability suggest that end-users of T2D Screening perceived the potential, but they are worried about complexity. Usability and user experience were above acceptable thresholds for T2D Care at population and T2D Care at individual. CONCLUSIONS: By using a holistic approach, we have been able to understand user needs, behaviours and interactions and give new insights in the definition of effective Decision Support Systems to deal with the complexity of T2D care. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (10.1186/s12911-019-0887-8) contains supplementary material, which is available to authorized users. |
format | Online Article Text |
id | pubmed-6697904 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2019 |
publisher | BioMed Central |
record_format | MEDLINE/PubMed |
spelling | pubmed-66979042019-08-19 What do healthcare professionals need to turn risk models for type 2 diabetes into usable computerized clinical decision support systems? Lessons learned from the MOSAIC project Fico, Giuseppe Hernanzez, Liss Cancela, Jorge Dagliati, Arianna Sacchi, Lucia Martinez-Millana, Antonio Posada, Jorge Manero, Lidia Verdú, Jose Facchinetti, Andrea Ottaviano, Manuel Zarkogianni, Konstantia Nikita, Konstantina Groop, Leif Gabriel-Sanchez, Rafael Chiovato, Luca Traver, Vicente Merino-Torres, Juan Francisco Cobelli, Claudio Bellazzi, Riccardo Arredondo, Maria Teresa BMC Med Inform Decis Mak Research Article BACKGROUND: To understand user needs, system requirements and organizational conditions towards successful design and adoption of Clinical Decision Support Systems for Type 2 Diabetes (T2D) care built on top of computerized risk models. METHODS: The holistic and evidence-based CEHRES Roadmap, used to create eHealth solutions through participatory development approach, persuasive design techniques and business modelling, was adopted in the MOSAIC project to define the sequence of multidisciplinary methods organized in three phases, user needs, implementation and evaluation. The research was qualitative, the total number of participants was ninety, about five-seventeen involved in each round of experiment. RESULTS: Prediction models for the onset of T2D are built on clinical studies, while for T2D care are derived from healthcare registries. Accordingly, two set of DSSs were defined: the first, T2D Screening, introduces a novel routine; in the second case, T2D Care, DSSs can support managers at population level, and daily practitioners at individual level. In the user needs phase, T2D Screening and solution T2D Care at population level share similar priorities, as both deal with risk-stratification. End-users of T2D Screening and solution T2D Care at individual level prioritize easiness of use and satisfaction, while managers prefer the tools to be available every time and everywhere. In the implementation phase, three Use Cases were defined for T2D Screening, adapting the tool to different settings and granularity of information. Two Use Cases were defined around solutions T2D Care at population and T2D Care at individual, to be used in primary or secondary care. Suitable filtering options were equipped with “attractive” visual analytics to focus the attention of end-users on specific parameters and events. In the evaluation phase, good levels of user experience versus bad level of usability suggest that end-users of T2D Screening perceived the potential, but they are worried about complexity. Usability and user experience were above acceptable thresholds for T2D Care at population and T2D Care at individual. CONCLUSIONS: By using a holistic approach, we have been able to understand user needs, behaviours and interactions and give new insights in the definition of effective Decision Support Systems to deal with the complexity of T2D care. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (10.1186/s12911-019-0887-8) contains supplementary material, which is available to authorized users. BioMed Central 2019-08-16 /pmc/articles/PMC6697904/ /pubmed/31419982 http://dx.doi.org/10.1186/s12911-019-0887-8 Text en © The Author(s). 2019 Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. 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. |
spellingShingle | Research Article Fico, Giuseppe Hernanzez, Liss Cancela, Jorge Dagliati, Arianna Sacchi, Lucia Martinez-Millana, Antonio Posada, Jorge Manero, Lidia Verdú, Jose Facchinetti, Andrea Ottaviano, Manuel Zarkogianni, Konstantia Nikita, Konstantina Groop, Leif Gabriel-Sanchez, Rafael Chiovato, Luca Traver, Vicente Merino-Torres, Juan Francisco Cobelli, Claudio Bellazzi, Riccardo Arredondo, Maria Teresa What do healthcare professionals need to turn risk models for type 2 diabetes into usable computerized clinical decision support systems? Lessons learned from the MOSAIC project |
title | What do healthcare professionals need to turn risk models for type 2 diabetes into usable computerized clinical decision support systems? Lessons learned from the MOSAIC project |
title_full | What do healthcare professionals need to turn risk models for type 2 diabetes into usable computerized clinical decision support systems? Lessons learned from the MOSAIC project |
title_fullStr | What do healthcare professionals need to turn risk models for type 2 diabetes into usable computerized clinical decision support systems? Lessons learned from the MOSAIC project |
title_full_unstemmed | What do healthcare professionals need to turn risk models for type 2 diabetes into usable computerized clinical decision support systems? Lessons learned from the MOSAIC project |
title_short | What do healthcare professionals need to turn risk models for type 2 diabetes into usable computerized clinical decision support systems? Lessons learned from the MOSAIC project |
title_sort | what do healthcare professionals need to turn risk models for type 2 diabetes into usable computerized clinical decision support systems? lessons learned from the mosaic project |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6697904/ https://www.ncbi.nlm.nih.gov/pubmed/31419982 http://dx.doi.org/10.1186/s12911-019-0887-8 |
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