Clinical trials

Clinical trials require human participants for medical procedures. The purpose is to test the effectiveness of a newly introduced health treatment, its ability to function better than other treatment plans, and the possible side effects of products and plans. 

So, trials are essential in medical practice. Aside from providing medical practitioners with a comprehensive blueprint on tackling health issues, they inform healthy solutions for a particular diagnosis.

In all, technology has contributed significantly to carrying out trials. It's more evident in data analytics. Using analytics, clinicians can generate predictive clinical results and click here to make compliant SDTM datasets.  

Continue reading this post to learn how analytics shapes the clinical trial process.

What Is A Clinical Trial?

Clinical trial refers to a medical research study conducted on living organisms. This research is to meet particular medical purposes, which include:

  • Devising a treatment plan for diagnosis
  • Developing new drugs and vaccines for the treatment
  • Creating new or advanced surgical procedures
  • Forming new therapies for human health

In short, conducting clinical trials are ways medical researchers discover safe and better ways to:

  • Administer treatments
  • Screen certain diseases
  • Improve test and treatment techniques
  • Examine disease prevention strategies
  • Discover treatments and vaccines for new viruses and diseases

Thus, trials are essential to human survival, as they provide solutions to new and existing medical problems.

What Are The Phases/ Processes Of Clinical Trials?

Before a clinical trial is safe and healthy for human treatment, it undergoes phases of investigation. This assessment is done to determine whether it fits into a treatment plan or technique for diseases. 

The different phases of clinical trials include:

  • PHASE 1—treatment screening test for safety: This is the first stage in a clinical trial. Here, the focus is to determine whether a treatment will be safe for humans and its distribution process within the body. At this stage, the trial is conducted only with a small number of healthy volunteers or patients with deteriorating health conditions that have no proven solution.
  • PHASE 2—determining the treatment effectiveness: After the initial safety of the clinical drug or procedure is selected, the next phase involves testing to evaluate the effectiveness of the drugs on patients and for which patients the trial treatment is effective. This stage requires a large number of trial participants.
  • PHASE 3—confirmation of treatment safety and effectiveness: At the final stage of the trial, it has become clear the functionality of the treatment and how safe it is for disease treatment. This phase also involves devising strategies for more participants in the trial process. 

How Analytics Is Changing Clinical Trial Processes 

Today, clinicians carry out clinical trials by using artificial intelligence models and standard datasets that enable the seamless collection of data in real time. Using analytics, they can quickly identify health issues and patterns and implement solution-based strategies. 

Below are other ways analytics is changing the clinical industry:

  • Helps Identify The Right Cohorts Of Patients For Clinical Research And Participation: At the core of clinical trials is patient participation. In fact, there's no trial without volunteer patients. But choosing the right patient cohorts can be tricky without analytics. Patient analytics provides a comprehensive analysis of the ideal patients by using their data for relevance. Relevant patient participation is a relevant clinical study.
  • Helps Determine Long-Term Side-Effects Of Drugs: No clinician is 100% definite about the side effects of drugs, especially during trials. They cannot categorically say how long the results would continue and to what extent. But with analytics, they can leverage data and insight to make accurate predictions of drug effects, if not precisely.
  • Helps Boost Patient Satisfaction And Product Profitability: Clinical analytics provides detailed insight into treatment pathways, patient care, and drug effectiveness. Through the provisions, clinicians can boost patient satisfaction, improve product marketability, and drive product sales.
  • Helps With Clinical Forecasting And Disease Progression: What happens in the next few years in clinical studies can be predicted through analytics. Moreover, it helps to evaluate how clinical drugs progress from manufacturing logistics to patient consumption. With improved insights come effective clinical progression and drug management measures.
  • Helps Improve Drug Cost-Effectiveness: One of the issues facing clinical trials is the expensiveness of drugs. Many drugs are costly, making it difficult for patients to access health benefits. But with improved drug management and clinical marketing due to analytics, patients can get medications at reduced costs.

Conclusion

Indeed, clinical trials in the 21st century are advancing past what they used to be. With technology, such as clinical analytics, problems become more predictive and easier to resolve. Furthermore, clinicians can now leverage insights to transform clinical trial processes and make the entire research faster, safer, and better for patient consumption.