A dashboard-based system for supporting diabetes care

Research output: Contribution to journalArticle

  • External authors:
  • Lucia Sacchi
  • Valentina Tibollo
  • Giulia Cogni
  • Marsida Teliti
  • Antonio Martinez-Millana
  • Vicente Traver
  • Daniele Segagni
  • Jorge Posada
  • Manuel Ottaviano
  • Giuseppe Fico
  • Maria Teresa Arredondo
  • Pasquale De Cata
  • Luca Chiovato
  • Riccardo Bellazzi

Abstract

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.

Bibliographical metadata

Original languageEnglish
Pages (from-to)538-547
JournalJournal of the American Medical Informatics Association
Volume25
Issue number5
Early online date2 Feb 2018
DOIs
Publication statusPublished - 1 May 2018