Health Economics and Outcomes Research (HEOR) is an area of economic analyses that deals with the studies and mathematical models (simulations) based on available clinical, epidemiological, economic data and published evidence to inform rational and evidence based decision making. HEOR deals with allocation of funds for healthcare purchasing,  evaluations of the value for money of healthcare technologies, drugs and medical devices in order to inform budgeting and change in clinical practice decisions. Healthcare payers (government, insurance companies, hospitals) operating under constrained budgets are always seeking to maximise the output (value, health, procedures) subject to limited financial resources and other constraints (technical, time, legal, management changes).

Health Economics evaluations and Real World Evidence (RWE) studies take disaggregated data from multiple sources to produce estimates of cost-effectiveness and budget impact based on expected clinical outcomes projected over a specific time horizon with Markov model. Other types of health economic models, such as budget impact model can be used to produce a more precise projection of expected costs over the 5 year time horizon.

Value of data processing innovations in HEOR modelling

In this article we describe technology innovations for seamless data updates of health economics models. We also explore the benefits of real-time dynamic data linked to HEOR cost effectiveness and budget impact models.

The question is how fast the value for money cost-effectiveness modelled estimates become outdated given new market dynamics and evolution of published evidence? And what is the extent of monetary losses due to delayed health economics models updates and related healthcare purchasing decisions?

At least every year a health economics model has to be updated with new cost data to produce actual estimates for the new fiscal year. While it is quite straightforward to update the model with new price data, spotting and related integration of new epidemiological or clinical evidence, including the one published by other manufacturers and HEOR research centers might not be that easy and fast. Very often multiple specialists, opinion leaders and organisations (industry, academia, consulting) are involved in economic evaluations of blockbuster pharmaceutical and medical device innovations. There is an anticipated time lag of about 1 to 3 years, between acceptance of new evidence, related updates of global cost-effectiveness models and decisions taken by a healthcare payer.

With new technology innovations HEOR models can automatically fetch data from a variety of sources and databases to produce real time – actual value for money cost-effectiveness estimates, hence preventing monetary losses related to a stay on current standard of care due to late cost-effectiveness assessments and updates of modeled projections.

Researchers putting up HEOR models based on evidence and data synthesis require a single environment that connects all dots together: data, models, and reports to produce dynamics estimates of cost-effectiveness of drugs and healthcare technologies.

The anticipated monetary, health and societal losses due to delayed cost-effectiveness and budget impact models update are enormous. Governments and healthcare purchasers spend millions to manage different health conditions with available portfolio of healthcare technologies. Can digital innovation prevent these losses ? At least to some extent, by connecting live input data feeds to economic models and providing a single ecosystem for review of changes made to default base case scenario by various activities and data owners. Live data feeds and automated data update suggestions could significantly reduce the time required to make an update to the HEOR model and to make the final decision.

HEOR models fed by dynamic input data could also help to spot inefficiencies and areas for improvement by delivering smart suggestions related to switch to a different drug/therapy, all based on cost-effectiveness league tables.

Digital Health Outcomes (DHO) provides custom software solutions and  integration of live data from databases into health economics and budget impact models. DHO designs the overall information architecture and finds technical ways to stream data from multiple sources into interactive web based HEOR  models. To better understand the value of health economics models migration to web or iPad formats, please see our recent blog article.