At Evidencio, we strongly believe that prediction models can complement human clinical decision-making and assist healthcare professionals in making patient-tailored evidence-based decisions. It is our mission to support the transition of these clinical prediction models from research into everyday medical practice.
To reach our goal, we are building a platform and research community which together provide a dynamic, high-quality, and transparent library filled with clinically relevant prediction models.
Evidencio aspires to:
Members of the online research community get full access to published models and the Evidencio platform.
The Evidencio Platform is intended for use in medical research, as well as every day medical practice. Online prediction models are made available through the Evidencio web-app, as well as through our API for use in third party applications, EHR's, and decision support tools.Evidencio's information security management system is ISO27001 and NEN7510 certified. Evidencio's quality management system is ISO13485 certified.
In the last few decades, the volume of scientific papers has increased exponentially. The resulting 'big data' pool houses an enormous potential, but is still only marginally used in medical practice. The World Health Organization (WHO) has acknowledged this disconnect between science and medical practice as the "know-do gap". Improvement of knowledge extraction, validation, and transferral is essential in order to utilize the untapped potential of medical databases such as PubMed and EMBASE. It was this insight that moved the developers of Evidencio to found the online research community Evidencio in 2015.
In addition to scientific papers, the number of published medical prediction models also steeply increased over the years. Prediction models are research-based tools that assist healthcare professionals in making evidence-based medical decisions and are a powerful way to translate scientific literature into medical practice. When appropriately developed and validated, prediction models have inherent advantages over human clinical decision making. Firstly, the statistical models can accommodate many more factors than the human brain is capable of taking into consideration. Secondly, if given identical data, a statistical model will always give the same result whereas human clinical judgment has been shown to result in both inconsistency and disparity, especially with less experienced clinicians. Thirdly, and perhaps most importantly, several prediction models have been shown to be more accurate than clinical judgment alone.
Prediction models can be applied in various settings, for example to describe the likelihood of the presence or absence of a certain condition, assist in determining patient prognosis, and help classify patients for treatment. The use of prediction models not only eliminates the distorting effect of subjectivity, but also sets the stage for true informed consent and shared decision-making. In addition, several clinical studies have shown that the use of prediction models leads to shorter hospital stays, fewer complications, cost savings, and a better allocation of scarce resources.
Please note: Although the added value of medical prediction models is obvious, these models are not a replacement for clinical judgment and should complement rather than supplant clinical opinion and intuition.
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