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How health data management is used in clinical research

Clinical research has been around since the dawn of our species, in one form or another. Humans have always tested new remedies and worked to improve healthcare, even before we have a deep understanding of human health. Today, of course, the process is structured, controlled and very strict, in order to do no harm and to ensure that the new treatments actually work before they are sold to the general public.

Today, we have more tools than ever before to develop new treatments and care protocols. The data, for example, has allowed researchers to study trends and gain insights that help advance clinical research. Here’s what it looks like in practice.

The current state of clinical research

Luckily, we’ve gone beyond having someone eat a plant hoping it helps ease the pain. But what is the current state of clinical research? How do scientists test new drugs and treatments in the safest way possible?

For ethical clinical research, scientists are forced to conduct their research “in the real world” after taking important steps to ensure the safety of their interventions for volunteers. However, the real test comes through observational or clinical trials.

In a clinical trial, researchers follow four phases to test their intervention:

  1. Dose and minimize side effects
  2. Security assessment
  3. Comparison with old treatments
  4. Approval for large scale testing

Once a clinical trial has successfully passed these four phases, a treatment can be approved for sale.

How Big Data Makes Research Faster and More Efficient

Of course, the process of clinical research is long and complex. No one wants to put innocent volunteers at risk with under-tested therapy. But this lag has its drawbacks: It can slow down approval of new medical breakthroughs that could help countless people.

Big data and artificial intelligence, however, have the potential to dramatically improve the process. Being able to quickly sift through a large amount of health data and find relevant models has huge implications for the clinical research process.

Not only could this help researchers develop trial-ready therapies faster, but these tools could also help them find the right patients for their studies and place a magnifying glass on the process, allowing them to collect data on the response. patients. The data could even eliminate the need to recruit a control group, create “digital twins”.

The Benefits of Big Data, AI, and Enhanced Data Management for Clinical Research

It’s hard to overstate just how useful big data management could be in improving the clinical research process for a number of reasons. One of the biggest benefits of using data and AI in clinical research is speed, safe speed. Instead of “rushing” a drug to market, data can help spot errors, help researchers make smart, logical decisions, and quickly guide them through the process.

Using data reduces friction and bottlenecks in the process, which are often what hold things up. It is extremely useful for all types of research, but especially during a global health emergency, such as the COVID-19 pandemic.

How data solutions were used during the pandemic

Although the pandemic has devastated communities all over the world, it has ushered in some positive changes in medicine, including clinical trials. Researchers are starting to see new possibilities and to try decentralized trials, which allow volunteers to participate while isolating themselves, thanks to telehealth.

New healthcare data management solutions made these new protocols possible. Being able to track patient data remotely or ask them to voluntarily provide data is a game-changer, especially during a public health crisis. Intelligent data sharing can increase public knowledge of key health facts and improve treatment development for diseases like COVID-19.

Data management: improving the quality of research and healthcare

While data has enormous potential to advance clinical research, organizations don’t always manage their data well. Without proper management and sharing, data cannot be fully exploited and can turn out to be harmful. The data is factual, but there may be errors in collection and management.

That being said, health care quality management can make a huge difference at different levels of the medical industry. For example, some organizations have started using data to show how certain protocols can reduce costs and improve results. Researchers can also use the data to highlight a need for new treatments or protocols.

The bottom line is that in clinical research, data has the potential to provide us with revolutionary advancements faster than ever. With smart management, there is no limit to what these tools can accomplish for medicine and medical research, using the data we already collect.