TY - JOUR
T1 - A dashboard-based system for supporting diabetes care
AU - Dagliati, Arianna
AU - Sacchi, Lucia
AU - Tibollo, Valentina
AU - Cogni, Giulia
AU - Teliti, Marsida
AU - Martinez-Millana, Antonio
AU - Traver, Vicente
AU - Segagni, Daniele
AU - Posada, Jorge
AU - Ottaviano, Manuel
AU - Fico, Giuseppe
AU - Arredondo, Maria Teresa
AU - De Cata, Pasquale
AU - Chiovato, Luca
AU - Bellazzi, Riccardo
PY - 2018/5/1
Y1 - 2018/5/1
N2 - Objective To describe the development, as part of the European Union MOSAIC (Models and Simulation Techniques for Discovering Diabetes Influence Factors) project, of a dashboard-based system for the management of type 2 diabetes and assess its impact on clinical practice.
Methods The MOSAIC dashboard system is based on predictive modeling, longitudinal data analytics, and the reuse and integration of data from hospitals and public health repositories. Data are merged into an i2b2 data warehouse, which feeds a set of advanced temporal analytic models, including temporal abstractions, care-flow mining, drug exposure pattern detection, and risk-prediction models for type 2 diabetes complications. The dashboard has 2 components, designed for (1) clinical decision support during follow-up consultations and (2) outcome assessment on populations of interest. To assess the impact of the clinical decision support component, a pre-post study was conducted considering visit duration, number of screening examinations, and lifestyle interventions. A pilot sample of 700 Italian patients was investigated. Judgments on the outcome assessment component were obtained via focus groups with clinicians and health care managers.
Results The use of the decision support component in clinical activities produced a reduction in visit duration (P ≪ .01) and an increase in the number of screening exams for complications (P < .01). We also observed a relevant, although nonstatistically significant, increase in the proportion of patients receiving lifestyle interventions (from 69% to 77%). Regarding the outcome assessment component, focus groups highlighted the system’s capability of identifying and understanding the characteristics of patient subgroups treated at the center.
Conclusion Our study demonstrates that decision support tools based on the integration of multiple-source data and visual and predictive analytics do improve the management of a chronic disease such as type 2 diabetes by enacting a successful implementation of the learning health care system cycle.
AB - Objective To describe the development, as part of the European Union MOSAIC (Models and Simulation Techniques for Discovering Diabetes Influence Factors) project, of a dashboard-based system for the management of type 2 diabetes and assess its impact on clinical practice.
Methods The MOSAIC dashboard system is based on predictive modeling, longitudinal data analytics, and the reuse and integration of data from hospitals and public health repositories. Data are merged into an i2b2 data warehouse, which feeds a set of advanced temporal analytic models, including temporal abstractions, care-flow mining, drug exposure pattern detection, and risk-prediction models for type 2 diabetes complications. The dashboard has 2 components, designed for (1) clinical decision support during follow-up consultations and (2) outcome assessment on populations of interest. To assess the impact of the clinical decision support component, a pre-post study was conducted considering visit duration, number of screening examinations, and lifestyle interventions. A pilot sample of 700 Italian patients was investigated. Judgments on the outcome assessment component were obtained via focus groups with clinicians and health care managers.
Results The use of the decision support component in clinical activities produced a reduction in visit duration (P ≪ .01) and an increase in the number of screening exams for complications (P < .01). We also observed a relevant, although nonstatistically significant, increase in the proportion of patients receiving lifestyle interventions (from 69% to 77%). Regarding the outcome assessment component, focus groups highlighted the system’s capability of identifying and understanding the characteristics of patient subgroups treated at the center.
Conclusion Our study demonstrates that decision support tools based on the integration of multiple-source data and visual and predictive analytics do improve the management of a chronic disease such as type 2 diabetes by enacting a successful implementation of the learning health care system cycle.
KW - clinical decision support systems
KW - Data integration
KW - temporal data analytics
KW - type 2 diabetes
UR - https://www.scopus.com/pages/publications/85046552027
U2 - 10.1093/jamia/ocx159
DO - 10.1093/jamia/ocx159
M3 - Article
SN - 1067-5027
VL - 25
SP - 538
EP - 547
JO - Journal of the American Medical Informatics Association
JF - Journal of the American Medical Informatics Association
IS - 5
ER -
