How Does Data Science Drive Innovation In The Clinical Research Industry?

Introduction

As you all know, the digital revolution in healthcare is happening right around the globe. Every aspect of our lives has been flooded with data, and there is no end in sight. Over the past decade, the stream of data available to life science companies has increased dramatically from a trickle to a tidal wave. Every second, a massive amount of healthcare data is generated and processed for useful insights. Today, the healthcare industry accounts for over 30% of global data volume. As per Forbes, healthcare data is predicted to expand at a compound annual growth rate of 36 per cent by 2025.

Hence, as we move into the digital age, it’s more important than ever to understand how our healthcare system works and what needs improvement. This is where data science can play a key role in helping us move forward with health care reform. So in this article, we will see how data science is transforming the clinical industry. 

But first, let’s stick to the basics. 

Clinical Data Science – Overview 

Clinical data science is a new field of specialization in the healthcare industry. Within this domain, data scientists design, develop and apply new computational models to address major research problems. 

Clinical data management and research are the processes by which clinical information is stored, managed, shared and used in healthcare settings.

 

• Clinical data management encompasses all activities related to clinical information storage and retrieval, including its collection, management, sharing and use. 

• Clinical research involves the collection of scientific evidence in support of medical diagnosis or treatment strategies.

When data science is used in clinical data management and research, it provides a framework that enables accurate and efficient patient management. This is beneficial to patients as well as healthcare organizations as there is a reduction in medical errors. The essential part of any healthcare system is the clinical data, which includes patient records and clinical notes.

Furthermore, clinical data science has shown to be a fast-growing and lucrative occupation across fields such as medical informatics, health economics, drug development and medical imaging. So if you’re someone looking to make a career shift to data science, join the best data science certification course in Pune and upgrade your skills. 

Role of clinical Data scientists

Clinical data scientists use data science and analytics tools to answer questions about diseases and treatments and look for patterns in large databases that could help improve human health outcomes. The process starts by collecting clinical data from patients and their records, then analyzing it using statistical models or machine learning algorithms. This process can then be used to improve treatment outcomes or identify patterns that are relevant to disease prevention efforts.

Clinical data science techniques have been used in a wide array of applications, such as:

• Quality improvement 

• Clinical trials

• Drug development

• Regenerative medicine

Benefits of data science and analysis in clinical research:

1. More accurate clinical trials:

In a major pharmaceutical organization, thousands of active clinical studies with millions of datasets are possible. Effective data management and analysis are more important than ever before since there are so many data points. This can lead to costly errors and the loss of valuable time and resources for the whole clinical study when data is incorrectly managed.

Despite these options, many clinical studies still use conventional data collecting and verification techniques, like manually counting unused pills in bottles, faxing patient records, and tracking patients’ paper diaries to gauge medication adherence. These tasks frequently fall on the patient, who is more susceptible to forgetting or making mistakes.

Researchers will be able to spot important patterns and potential trial difficulties in real-time by combining digital data collection and applying cutting-edge technologies like data science.

2. Safer Manufacturing of Drugs: 

In the past, developing a new pharmaceutical medicine has been a difficult, lengthy procedure that heavily relied on human data processing and collection. The results of randomized clinical trials for novel medications can now be predicted thanks to new machine learning and statistical techniques that have enabled more efficient procedures. As a result, all concerned parties, including researchers, regulators, and patients, will benefit from more precise and timely estimations of the risks and benefits.

Researchers can develop better clinical trials that prevent major delays in a market launch by using more accurate estimates of the risk associated with drug development.

Another benefit of data science and analytics is expanding the selection criteria for patients. Researchers can better identify individuals who meet the inclusion and exclusion criteria when they can rapidly and precisely sort through variables such as patient characteristics, disease state, and genetics.

3. More Efficient trials:

Data science and analytics can boost the efficiency of research and clinical trials while helping decision-makers make better-informed decisions throughout the drug development process. The discovery of new potential candidate molecules that can be effectively turned into medications with a high degree of certainty will depend increasingly on the predictive modeling of drugs and biological processes.

Pharmaceutical businesses can instantly react to new clinical data insights by adopting big data and automation tools. They can also perform smaller tests with similar power or shorter trials to increase trial efficiency. These minor improvements add up quickly to minimize the trial period by months or even years.

Use Case 

Deep Learning: In clinical research, data science provides insights that lead to more effective therapies for patients. The most recent application of data science in clinical research is the use of deep learning models to predict the survival of lung cancer patients. 

These models perform as well as a panel of expert clinicians. As more deep learning models are developed and applied, they will provide key insights into diseases like cancer, which can inform clinical practice and treatment decisions, thereby increasing the benefits of clinical trials.

Future of Clinical Data Science

In the early phase, we will see a lot of changes in data types for future clinical data management and research efforts. Currently, most research groups have access to Electronic Health Records (EHRs) with clinical laboratory values, imaging reports, medication dosage, and medication dispensing records. 

In the upcoming era, there is plenty of potential to increase the sources of data and make appropriate use of data types, including sensors (sensors), personal activity/ health monitoring apps (personal data apps), social media logs, and environmental monitoring devices. Data generated from these external sources can be used to complement cognitive and behavioral information from EHRs and other medical devices – increasing its accuracy. This development requires computational data processing approaches such as Machine Learning(ML) to extract useful information based on machine-readable results.

Thus, there will be many opportunities to become a clinical data scientist. 

Bottom Line!

As you can see, with more information available in healthcare than ever before, there is an increasing need to address the potential risks and benefits of this new wealth of data. This is an important step towards a future where data science can enhance clinical decision support systems and contribute to cost-effective patient care.

This article discussed some data science concepts for clinical data management and research. I hope this article provides a clear idea about what data science is used in clinical data management and research. If you are interested in learning more about clinical data science or becoming a data scientist, visit the industry-accredited data science course in Pune.Gain practical training with the help of MAANG experts and launch a successful career as a clinical data scientist or analyst.