TY - GEN
T1 - A data-driven decision-support tool for population health policies
AU - Chorev, Michal
AU - Shpigelman, Lavi
AU - Bak, Peter
AU - Yaeli, Avi
AU - Michael, Edwin
AU - Goldschmidt, Ya'ara
N1 - Publisher Copyright:
© 2017 International Medical Informatics Association (IMIA) and IOS Press.
PY - 2017/1/1
Y1 - 2017/1/1
N2 - Epidemiological models are key tools in assessing intervention policies for population health management. Statistical models, fitted with survey or health system data, can be combined with lab and field studies to provide reliable predictions of future population-level disease dynamics distributions and the effects of interventions. All too often, however, the end result of epidemiological modeling and cost-effectiveness studies is in the form of a report or journal paper. These are inherently limited in their coverage of locations, policy options, and derived outcome measures. Here, we describe a tool to support population health policy planning. The tool allows users to explore simulations of various policies, to view and compare interventions spanning multiple variables, time points, and locations. The design's modular architecture, and data representation separate the modeling methods, the outcome measures calculations, and the visualizations, making each component easily replaceable. These advantages make it extremely versatile and suitable for multiple uses.
AB - Epidemiological models are key tools in assessing intervention policies for population health management. Statistical models, fitted with survey or health system data, can be combined with lab and field studies to provide reliable predictions of future population-level disease dynamics distributions and the effects of interventions. All too often, however, the end result of epidemiological modeling and cost-effectiveness studies is in the form of a report or journal paper. These are inherently limited in their coverage of locations, policy options, and derived outcome measures. Here, we describe a tool to support population health policy planning. The tool allows users to explore simulations of various policies, to view and compare interventions spanning multiple variables, time points, and locations. The design's modular architecture, and data representation separate the modeling methods, the outcome measures calculations, and the visualizations, making each component easily replaceable. These advantages make it extremely versatile and suitable for multiple uses.
KW - Computer-assisted decision making
KW - Statistical models
KW - Stochastic processes
UR - https://www.scopus.com/pages/publications/85040516340
U2 - 10.3233/978-1-61499-830-3-332
DO - 10.3233/978-1-61499-830-3-332
M3 - Conference contribution
C2 - 29295110
AN - SCOPUS:85040516340
T3 - Studies in Health Technology and Informatics
SP - 332
EP - 336
BT - MEDINFO 2017
A2 - Gundlapalli, Adi V.
A2 - Marie-Christine, Jaulent
A2 - Dongsheng, Zhao
PB - IOS Press BV
T2 - 16th World Congress of Medical and Health Informatics: Precision Healthcare through Informatics, MedInfo 2017
Y2 - 21 August 2017 through 25 August 2017
ER -