Innovative Prediction Tools with Real-World Data
healthcaretechoutlook

Innovative Prediction Tools with Real-World Data

By Healthcare Tech Outlook | Friday, February 15, 2019

Healthcare AnalyticsBig Data is a big name, too often confusing and misused, but it must never be ignored that in recent years, the data available to users has become more diverse and complex than ever before, both structured and unstructured, proven from non-traditional sources. These data contain much valuable business information, some of which are evident and some are hidden in various interdependencies. This information will be sliced and diced to provide a wide range of reports using traditional analytical tools from data mining to data visualization. However, it is evident that the reports become as quickly and graphically appealing as possible, and there is a growing gap between the needs of business users and their value.

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Data mining is important because it supports data analysts and data scientists in the process of acquiring trends and relationships. Incorporate data collection, informed decisions are helpful. Previously, there were no scientific instruments available in their search to guide prospectors. Sites for past finds were the beginning of what was a shattered dream for many. This has now become the 21st century Gold Rush, due to the unprecedented demand for patient entry into the clinical trials. Recent independent surveys indicate that only 10-17 percent of clinical studies are complete in time and, since only three percent of cancer patients choose to participate in a clinical trial, patient recruitment in drug development research remains a problematic and expensive pain area.

The first step in the trial design of the tests is a complicated process, which requires careful attention and care. Minimizing trial costs through the choice of high-performance sites is crucial. This includes higher randomization rates but reduces screen failure rates while maintaining high levels of protocol compliance. When achieved, this helps to increase statistical power by decreasing variability for investigator-assessed endpoints.

Clinical studies sites have historically been selected by choosing from known sites and several sponsors have competed for the same asset. The LabCorp database of de-identified laboratory test results including LabCorp patient service center locations can, fortunately, be linked to other real-world data sources including disease management and prevention rates in the Centers for Disease Control and Prevention. The analysis of spatial clusters enables promoters to take into account the impact of RWD-based inclusion/exclusion criteria. This can be used to increase site identity and selection criteria through increased knowledge of the incidence of illness and protocol-evaluable patient densities, irrespective of past direct interaction with researchers.

Data mining supports the detection of relevant patterns in the database, using different approaches and algorithms to analyze and predict future trends based on current and historical data. These data comprise structured information from the ERP system or unstructured data, such as information on social media, industry trends, political and global policy, agricultural, and weather patterns.

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