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

BMC Medical Informatics and Decision Making

Rafael Gabriel-Sanchez¹  Manuel Ottaviano¹  Juan Francisco Merino-Torres²  Luca Chiovato³  Leif Groop⁴  Jose Verdú⁵  Jorge Posada⁵  Lidia Manero⁵  Konstantina Nikita⁶  Konstantia Zarkogianni⁶  Maria Teresa Arredondo⁷  Jorge Cancela⁷  Liss Hernanzez⁷  Giuseppe Fico⁷  Antonio Martinez-Millana⁸  Vicente Traver⁸  Arianna Dagliati⁹  Andrea Facchinetti^1,10  Claudio Cobelli^1,10  Lucia Sacchi^1,11  Riccardo Bellazzi^1,11 

[1] Asociación Española para el Desarrollo de la Epidemiología Clínica;Hospital La Fe;Istituti Clinico Scientifici Maugeri Hospital of Pavia;Lund University Diabetes Centre;Medtronic Ibérica;National Technical University of Athens;Universidad Politécnica de Madrid;Universidad Politécnica de Valencia;University of Manchester;University of Padova;University of Pavia;

关键词: Type 2 diabetes;    Computerized decision support systems;    Risk modelling;    Human centred design;    Multi-disciplinary approach;   

DOI  :  10.1186/s12911-019-0887-8

来源: DOAJ

【 摘 要 】

Abstract 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.

【 授权许可】

Unknown