All the right tools to assess the value of a technology
Decision-analytic modeling is a commonly used method in health technology assessments to assess the cost-effectiveness of a technology. This approach involves creating a model that simulates the potential outcomes and costs of using a particular technology, allowing decision makers to compare it to the current standard of care.
Traditional health technology assessments (HTAs) often take the form of a written report. With our interactive cloud software, you can easily demonstrate the value of a technology to stakeholders in different contexts. This allows you to show how the technology can benefit patients and healthcare organizations in a more dynamic and interactive way.
Using predictive modeling on existing patient data can improve the accuracy and relevance of decision models. This approach allows the model to better reflect real-world scenarios and provide more precise predictions of the potential outcomes and costs of using a particular technology.
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Tim Govers has a PhD in the use of decision models to make health care decisions for both the population and individual patients. After obtaining his PhD, he co-founded MedValue BV, where he analyzed the added value of medical products for a number of medical companies. In this role, he performed many model-based analyses of the value of diagnostic and prognostic tests.
In addition to his work for Medip Analytics, he currently also works as a Senior Scientist in Health Technology Assessment and an advisor on Medical Technologies for the Radboud University Medical Center.
Stan Wijn completed his PhD research on the analysis of the added value of medical interventions in subgroups of the population. He has developed various applications that are used to demonstrate the value of medical interventions to healthcare professionals, such as physicians.
In addition to this, he has also developed predictive algorithms to make predictions about treatment outcomes for patients. Stan's expertise includes software development, AI, statistics, and medical decision modeling.