Every day scientists push the boundaries to discover new treatments. Treatments like antibiotics and vaccines that bring about profound changes in the world. But unfortunately many of these new treatments never pass the testing stage. The two main reasons for clinical trial failures are a steady decline in the number of patients recruited and failure to maintain patients in the trial. It is estimated that recruitment rates in clinical trials have dropped 20% since 2000, largely due to the fact that many patients lack knowledge about clinical trials that may be suited for their medical conditions.
· The necessity:
Patients with chronic diseases usually feel they are fighting an uphill battle against their depleting disease. Clinical trials provide patients with options that would otherwise not be part of their standard of care, and in doing so, they give patients a feeling of control over their disease and hope for the future. But the problems of recruitment and retention continue to plague researchers. In 2010 an estimated fifteen billion dollars was spent to enhance patient recruitment and continuation of clinical trials.
· The proposal:
We are proposing the development of a novel phone application and web-based software interconnected through a database that will match patients with cutting-edge experimental and investigational treatment options based on their medical conditions and proximity to clinical trial sites.
We plan to build a platform and software algorithm assembled on: geographic information system (GIS) data mining, neural network machine learning (artificial intelligence), Natural Language Processing (NLP), artificial intelligence, and HL7 FHIR standards to match patients’ medical records with clinical trial enrollment criteria.
We plan to create a platform that we hope will provide patients access to the most current and cutting-edge experimental and investigational treatment options and hopefully give back the control that their disease has taken from them. Our software will also help to increase the patient pool available to researchers, reduce the time spent on finding suitable candidates, and finally drive down the cost associated with conducting trials.
Table of Contents
Project outcme (30)
Business case description (59)
TRIAL on FHIR phases (project life cycle) (67)
Executive Summary (81)
About this Master's Thesis
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|Trial on FHIR ()||2017-11-24||