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Advanced Analytics in Clinical Research

Advanced analytics is playing a very critical role in helping the health and life- sciences industries evolve to meet the needs of the future. Combining and analyzing genomic, medical, and environmental data sources are sanctioning health care providers to get a more consummate picture of a patient’s health, the risk for disease, and lifestyle and circumstances so that they can recommend the right interventions at the right time.

Having a view of all our health care interactions in one place was the goal of the electronic health record. The dream was that a patient would not have to answer the same questions for each incipient medico or clinic because there would be consummate access to all our medical history. This has yet to materialize, even in the most connected and simplified health care settings.

To further complicate matters, much of the data that would be utilizable to medical providers aren’t concrete to patient health. Social determinants, including edification and environmental exposure, along with fitness tracking and other incipient data are not yet customarily used to drive better health outcomes.

In authenticity, most of today's analytic projects could be described by the graphic below. In this scenario, the EMR is a primary source of patient data, but it's coalesced with other third-party and proprietary data sets to arrive at an analytic data set that fortifies predictive modeling and AI.

Advanced Analytics interoperability occurs when germane patient data is assembled and distributed through one access point with one goal in mind: better outcomes. It sanctions the caregiver to better understand the patient; operations and financial leadership are better able to orchestrate; and it aligns the network of providers, both incipient and old, to the desiderata of the population they accommodate and the channels they operate.

The role of real-world data and real-world evidence in clinical research

Real-world data (RWD), including data from devices like the Apple Watch, holds an abundance of potential.  Researchers can utilize this data to better design and conduct clinical trials and answer questions previously thought infeasible.  Applying incipient, sophisticated analytical capabilities to RWD withal presents an opportunity to apply the results of our analyses to medical product development and approval.

Benefits of advanced analytics in healthcare

As with other industries, there's an incrementing interest in data-driven decisions throughout the health care system. As we visually perceive a growing need to collaborate among care providers, operational staff, executive leadership, and the incipient players in health care, there's a corresponding desideratum for data consistency and access. Convergence between health and the pharmaceutical industry, as well as an incrementation in retail-driven health care, engenders the desideratum to apportion data.

One simple benefit of this approach is medication. Interoperable health care provides a clinician with a precise and timely medication list without relying on patient recall. This information avails obviate the prescription of contraindicated medications, sanctions tracking through the pharmacy, and avails evade potential high-cost emergency care.

Another example is bringing data from home contrivances into the hospital to manage chronic conditions remotely, such as diabetes. Soothsaying the next patient intervention and applying preventative measures to avail keep them at home and well is a direct result of analytic interoperability.


In the advanced analytics era, healthcare is an important social foundation not only in terms of business but also in improving the equality of people’s lives.

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