MedTech
Article | July 20, 2022
Introduction
The medical and life-science industries are experiencing a robust transformation with the increasing prevalence of various types of diseases, including infectious diseases, chronic disorders, and acute conditions around the world. As a result, a significant rise in demand for more effective therapeutic drugs and bionics is being witnessed, leading to a swift increase in the number of clinical trials.
For a successful trial, it is important for biotech companies to ensure the data submitted to regulatory bodies regarding clinical trials is accurate, reliable, and definitive from an ethical point of view. A reliable clinical trial management system plays a vital role in collecting, monitoring, and managing clinical data. The availability of high-quality clinical data also helps clinical research institutions make efficient treatment decisions and provide proper patient care.
Hence, a number of biotech companies and research organizations are focusing on leveraging innovative clinical trial management solutions to handle a large amount of data, particularly in multi-center trials, and generate reliable, high-quality, and statistically sound data from clinical trials. However, selecting the most appropriate and reliable clinical trial management system is vital for the clinical trial's success. Let's see some of the steps that will assist these firms in choosing the right CTMS.
Key Steps for Selecting Right Biotech Clinical Trial Management System
Prioritize Study Needs
Considering and prioritizing study needs is a crucial step in choosing the most reliable clinical trial management system for biotech companies. Prioritizing helps them to identify a solution that improves the study's quality and removes uncertainty for researchers when faced with difficult choices.
Hence, biotech and life-science organizations should choose a clinical trial system that is simple to use, well-organized, and suitably designed to minimize the number of clicks required to complete a task.
Select CTMS with Multiple Integrations
Integrated clinical trial management systems provide the best value for the companies’ funds as they guarantee the smooth functioning of research protocols. In addition, integrations are necessary to fully understand the importance and advantages of clinical trial management software for ensuring smooth transitions between site management and data collection.
Biotech and clinical research should look for CTMS platforms that can integrate with electronic medical record (EMR) platforms and clinical research process content (CRPC) billing grids. This will allow them to use the same billing designations and ensure compliance while minimizing the need for duplicate processes.
Ensure System Compliance and Security
Clinical research organizations need to adhere to a plethora of complex regulations in order to ensure compliance with one of the most challenging environments of principles, which is information security and privacy. Security and system compliance are vital aspects of choosing the right CTMS solutions for biotech firms as they assist in building trust and form a part of the system’s duties.
While selecting CTMS systems, it is essential for companies engaged in clinical research to ensure that these platforms are able to configure both, group and individual permissions, along with having a data backup and recovery plan for hosted systems. This will allow companies to assess the privacy and security implications of research and anticipate complications that may arise in each phase of the project.
Assess the Scalability
Choosing a scalable CTMS that can accommodate various types of fluctuations and expansions enables biotech and clinical firms to quickly adapt to fast-changing trends and demand spikes while reducing maintenance costs and enhancing user agility.
As scalability also means secure and expanded data storage, these businesses should instead use SaaS solutions than manually manage an ever-growing collection of hard drives. The right CTMS ensures accommodating the firm’s availability requirements without incurring the capital costs associated with expanding a physical infrastructure.
The Closing Thought
A well-executed and successful clinical trial involves multiple stages and processes. Several quality controls and stringent adherence to regulations are essential for the steps, along with efficient cross-departmental processes and procedures. Incorporating the right CTMS paves the way for paperless data collection, regulatory filing, and fiscal management tools for biotech researchers and administrative personnel.
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MedTech
Article | September 22, 2022
Biotechnology is a vast discipline of biology that employs diverse biological systems to create solutions that can significantly alter the ways in which they operate across various domains. That said, biotechnology is not a new notion. It has existed for millennia, with ancient civilizations using its earliest incarnations to cultivate crops and create alcoholic beverages.
Today, the biotechnology industry has developed by leaps and bounds and has amassed a vast quantity of scientific data through study and research. Given the importance of data in the biotechnology business, it is not difficult to understand why biotech companies utilize data analytics.
Modern data analytics tools have made it possible for researchers in the biotech industry to build predictive analytics models and gain knowledge about the most efficient approaches to accomplish their desired goals and objectives. Data analytics is increasingly being adopted by biotech businesses to better understand their industry and foresee any problems down the road.
How is Data Analytics Revolutionizing Fields in Biotechnology?
Today's business and scientific fields greatly benefit from data. Without the analysis of vast information libraries that provide new insights and enable new innovations, no industry can really advance. Being highly reliant on big data analytics, biotech is not an exception in this regard.
With the tools and methods that help scientists systematize their findings and speed up their research for better and safer results, data analytics is making deeper inroads into the biotechnology industry. It is emerging as a crucial link between knowledge and information and is extensively being used for purposes other than just examining the information that is already available. The following are a few of the cutting-edge biotechnology applications of data analytics
Genomics and Disease Treatment
Pharmaceutical Drug Discovery
Drug Recycling and Safety
Agriculture and Agri-products
Environmental Damage Mitigation
Data Analytics Possibilities in Biotechnology
With data analytics becoming an integral part of how biotech businesses operate, biotechnologists and related stakeholders need to understand its emergence and crucial role.
Data analytics has opened new frontiers in the realm of biotechnology. Thanks to developments in data analytics, research and development activities that once took years may now be accomplished in a matter of months. Also, now scientists have access to biological, social, and environmental insights that can be exploited to create more effective and sustainable products.
By understanding the importance of data-related tools and techniques applications, biotech companies are aiming to invest in the popularizing technology to stay updated in the fast-paced biotechnology industry.
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Research
Article | July 11, 2022
Artificial intelligence has grasped the foundation in biotech. It can have the most innovative impact on biotechnology. AI has already established its presence in our day-to-day life. AI has made the existence of self-driving cars possible. Likewise, the benefits and quality that it can contribute to biotech can also be felt.
With AI, bio technicians will be able to enhance virtual screening, overlook preliminary datasets from clinics, and decipher an enormous amount of information. It can also help in improving the medication process by gathering and analyzing every bit of information.
The Significance of AI in Biotechnology
In the past few years, the application of artificial intelligence in the biotechnology industry has shifted from being sci-fi to sci-fact. A vast number of biotech companies like Deep Genomics are adopting AI for making data-driven decisions and use analytics tools to work efficiently.
Unlike the AI robots in sci-fi that are ready to take over the world. AI designed for biotech has been designed to solve certain problems or complete a bunch of tasks by using automated algorithms.
The aim of AI technology for biotech is to collect insights along with hidden patterns from large amounts of data. All the different industries of biotech including agriculture, animal, medical, industrial, and bioinformatics are gradually being affected by artificial intelligence.
Moreover, the biotech industry is realizing that AI enables them some of the important strength to their business, including:
Expanding accessibility
Cost-effectiveness
Critical predictions
Efficient decision-making
Research centers like PwC have also estimated output of $15.7 trillion by 2030 solely with AI contribution in industries. A survey revealed that about 44% of life science experts are using AI for R&D activities, as well.
Use of AI in Biotechnology
Altering Biomedical and Clinical Data
So far the most developed use of AI is its ability to read voluminous data records and interpret them. It can prove to be a life-save for bio technicians who would have to examine that much data from research publications by themselves for the validation of their hypothesis.
With the help of AI, clinical studies of patients will also become easier as all the examination reports and prescriptions will be stored in one place for cross-reference. Furthermore, it will also help in blending and fetching data into usable formats for analysis.
Test Result Prediction
Through trial and error, AI along with machine learning can help in predicting the response of the patient to certain drugs to provide more effective outcomes.
Drug Design & Discovery
AI plays a vital role whether it’s designing a new molecule or identifying new biological targets. It helps in identifying and validating drugs. It reduces the cost and time spent on the entire drug trial process and reaches the market.
Personalized Medications for Rare Diseases
With the combination of body scan results, patients’ body and analytics, AI can also help in detecting dangerous diseases at an early stage.
Improving Process of Manufacturing
To improve the process of manufacturing in biotechnology, AI offers a wide range of opportunities. It controls quality, reduces wastage, improves useability, and minimizes the designing time.
Moving Towards AI-Enhanced Biotech Future
Ever since the concept of artificial intelligence has arrived, being curious by nature, humans have started working towards achieving this goal. It has been growing at a fast pace while showing unbelievable growth and achievements at times. In comparison to the traditional methods used in the biotechnology industry, AI-based methods seem more reliable and accurate. In the upcoming years, it will show its success by improving the quality of health people have. You can also develop your AI-based application or know more about it by taking IT consultations.
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Article | April 17, 2020
With everything that's going on with the COVID-19 pandemic, many healthcare companies have grabbed plenty of spotlight during these challenging times. At the same time, a number of otherwise promising businesses have slipped under the radar. That's especially true for small-cap biotech stocks that aren't actively involved in developing tests, vaccines or treatments for COVID-19. Vaccine developers, protective equipment producers, and healthcare service providers are all attracting plenty of attention during this pandemic, but there are just as many promising biotech stocks that aren't involved in these areas. Here are two such companies that you might have missed, but they deserve a spot on your watch list.
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