INTERACTIVE HEALTH ECONOMIC MODELS CONFIGURED WITH GOOGLE ANALYTICS. DATA AND METRICS APPLICABLE FOR A HEALTHCARE RESEARCH
Author(s)
Topachevskyi O1, Siabro V2, Volovyk A2
1Digital Health Outcomes, Brussels, Belgium, 2Digital Health Outcomes, Kiev, Ukraine
Presentation Documents
Budget impact health economics models are often transformed into Web/iPad applications in order to improve model transparency, end user experience and communication of modeled outcomes. Google Analytics (GA) campaigns configured for health economics models provide metrics and insights that are useful for a healthcare research. GA allows setting up of custom campaigns dedicated to track specific events, usage metrics and detailed profiles of target audiences (payers, healthcare professionals) of economic models. Other important and useful information relates to specific user behavior patters within the model, user acquisition channels, geography, time spend on particular pages and other metrics. This information allows to better understand payers perceptions and general interest to the particular parts of the economic value story. When GA is paired with embedded questioners or interface elements designed to record reaction, the service may provide data and insights required to continuously refine and upgrade an economic model to reflect real world payers perceptions. GA events is a powerful tracking tool also allowing to understand what data was changed and what were the input values entered by decision makers in a particular setting. GA also provide means to collect direct medical cost data during payer facing. Client-server software architecture of interactive economic models enable integration of different analytics web services. The majority of modern analytics services provide API for integration of necessary functionality in web based or standalone interactive health economics models.
Conference/Value in Health Info
2016-10, ISPOR Europe 2016, Vienna, Austria
Value in Health, Vol. 19, No. 7 (November 2016)
Code
PRM225
Topic
Methodological & Statistical Research
Topic Subcategory
Confounding, Selection Bias Correction, Causal Inference
Disease
Multiple Diseases