Data science is facilitating every organization in improving their services to society. Nevertheless, healthcare is one such industry that receives gigantic help from Data Science in various platforms. By analyzing healthcare data, we can identify risk populations, design programs for healthcare inventions, and predict health trends of the future. As a whole, this proactive approach to data science leads to better health follow-ups with lower costs. RxDataScience is established to provide such data-driven solutions to healthcare and pharmaceutical companies with the help of various data science software. Larry Pickett, cofounder of the RxDataScience states that the vision of the organization is to improve patient lives by applying data science to healthcare.
Incorporation of Data Science in the Healthcare industry:
Studies say we generate 2 terabytes of data every day through our brain activity, heart rate, stress level, sugar, and many other things. With the advancement of technology and data science, we can manage such a huge amount of data and utilize it in tracking our health.
Data science applications have been instrumental in disease detection today at an early stage. Patients’ conditions can be monitored remotely with various data science tools and technologies.
Before the incorporation of data, managing large-scale data sets at hospitals was difficult to manage and this would create hindrances in providing the best services by doctors and healthcare institutions.
Now, you can keep updated and notified about patient’s exact health conditions with the use of machine learning and data science. Through wearable devices, patients at home can express their health conditions and seek help from hospitals.
Hospitals are installing various devices for the diagnosis of patients’ health conditions. These devices collect data about heart rate, temperature, blood pressure, sugar level, and so on. 24*7 notifications through various mobile applications keep doctors updated with real-time data.
This helps junior doctors and nurses to administer patient-specific treatments at hospitals and patients’ homes. In many ways, data science tools and technologies are improving healthcare services for patients.
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Various Applications of Data Science in Healthcare:
In the healthcare sector, data science now modifies the operation of healthcare, researches, and studies, and provides the best care to the patients. Below are some of those numerous applications of data science in healthcare:
- Predictive Analytics:
With the use of various data science tools and techniques, data scientists can analyze vast amounts of healthcare data consisting of patient records, medical histories, demographics, and hospital databases.
The result of data analysis gives us insights into understanding disease progression, high-risk patients, etc. The analysis report will suggest personalized treatment plans so that patients can benefit from the best services.
- Diagnosis and Prognosis:
Data science algorithms help us in the early detection of diseases with much better accuracy. With the analysis of medical images, lab results, and patient symptoms, we can create data-driven models. This is a great assistance for healthcare professionals in getting valuable insights and making data-driven diagnoses and prognoses.
- Development of Personalized Medicine:
Nowadays, personalized treatment plans are given to individual patients based on their unique requirements. For this, healthcare experts rely on analyzing data about patients’ characteristics, medical history, environmental influences, and lifestyle factors.
Based on the analytics results, and clinical trials, new medicines are introduced in the market. Machine learning algorithms now examine big databases to learn about molecular structures and genetic characteristics. This boosts the discovery of drugs and knowing their efficacy in the patient’s body.
- Health Monitoring:
Data science techniques are applied to monitor people’s health and wellness through various wearable devices like smartwatches, fitness trackers, and remote monitoring systems.
Such devices analyze data after collecting it from numerous patients. Doctors and healthcare professionals examine various health indicators like sleep patterns, physical activities, and heart rate to gain insights and predict the presence of abnormalities.
- Keeping Health Records:
A vast amount of electronic health records (HER) are produced every day by hospitals and other stakeholders of the healthcare industry. Data science helps us analyze large databases and extract insights on current trends etc. Such data science outputs help us develop advanced clinical decision support systems to recommend evidence-based treatment for every patient.
- Public Health Monitoring:
There are various categories of data collected from healthcare sectors. Social media data, surveillance data, environmental data, etc. provide us with valuable insights to monitor public health. We can predict the outbreak of a disease, or the spread of an infectious disease and implement the most effective interventions for the common public.
- Resource Optimization:
We know that data science tools and technologies are being employed in healthcare to improve operations, increase patient flow, and optimize healthcare resources. In terms of staffing, scheduling, cost reduction, and enhanced care, data science research is indispensable today.
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Future of Data Science Applications in the Healthcare Industry:
We have seen the tremendous impacts of data science in the healthcare industry across the globe. The contribution is vast no doubt, but now we will ponder over where this development is progressing in the coming years. The future of data science concerns mainly four factors in healthcare. They are-
- Technical innovation
- Digitalization of healthcare services
- Costing of treatment
- Population address
Much is left in acquiring error-less technical outputs from data-driven devices and technologies. We can access technologies like electronic health records (EHR) but are not satisfied with their performances.
Healthcare organizations are also using various patient portals and technical devices, but in very few cases have received reliable outputs. Even, the cost of such devices increases the expenditure of treatment.
Their complex implementation process has created barriers to adopting such technologies. The interoperability among all systems of the healthcare sector is another challenge of data science.
However, the future of the healthcare industry is in the contribution of artificial intelligence and machine learning and proper implementation of these will be impactful.
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About the Organization- RxDataScience:
RxDataScience is a multidisciplinary team of members from fields like IT, computer science, data science, scientific research, and the medical profession. The dedicated team members contribute their perspectives and expertise to improve healthcare for all.
Such exceptional professionals provide unique data-driven solutions to our problems. The result of this is an innovative software solution at a low cost with an effectiveness of care.
The healthy work culture of the company has sprouted innovation and creativity from time to time along with the work-life balance of the employees. There are plenty of career development opportunities through job rotation, stretch assignments, and promotions.
The company organizes several events and conferences for the same purpose. The company’s ultimate ambition is to provide advanced data-centric solutions to healthcare, pharmaceutical, and biotech companies.
To achieve this aim, RxDataScience fundamentally relies on advanced data science and data analytics specifically. It’s a well-known fact that our day-to-day lives are being directly affected by data science and its tremendous impacts.
The improvement of computing literacy has been crucial in shifting this paradigm. In this context, it is high time to accept the fact that data science holds the power to improve the quality of healthcare and patient lives.
With the advancement of cloud architecture and data computing, the management of exponential data has become economically and technically feasible. This is manifested across the healthcare industry and life sciences.
From the development of precise medicine advice along with innovative and new-age therapies like RNA, CAR-T, gene therapy, and biologics to digital health initiatives, data science is offering new opportunities for the betterment of patient lives.
This would have been completely unprecedented 10 years ago. Billions of data points are collected today to generate meaningful insights after the prolonged process of data analysis and this result is used in building the treatment pathway.
Thus, both during treatment and years after the completion of treatment, RxDataScience remains instrumental in assessing the effectiveness of data science in treatment, adverse situations, and cure.
RxDataScience Works at Four Main Levels. They are-
- Quantitative Systems Pharmacology (QSP)
- Manufacturing and regulatory
- Supply chain
- Commercial
With This Workflow, RXDataScience Focuses Its Services on the Following Sectors of Life Sciences and Healthcare:
- Biotech
- Bio-pharma
- Med Tech/Devices
- Payers and providers
RxDataScience believes that the unique challenges created in the Life Sciences and Healthcare organization need to be addressed specifically. The data science experts together with other domain experts try to support the industry in their data analytics journey.
This teamwork creates a tailored approach to different difficult situations with some customer-focused solutions. The methodology followed by the team is asset-based consultation and services that leverage the RxDS platform and products. These kinds of services drive long-term value to their clients.
For the internal research of RxDataScience, they require data of different categories from various sources. These include Pharma data, mortality data, business applications, survey data, hospital data, EMR Data, Genomics data, Claims data, disease registries, pharmacy registries, logistic distribution, social media data, manufacturing quality, and wearable.
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Different Platforms of Operation:
RxDataScience is a software solution Indian Non-Government company with an authorized capital of Rs. 50 Lakhs. This is a major business in India with active operations. The company is registered in the Tamil Nadu Registrar’s office.
The business ranges from basic reporting to advanced data-driven actions. To meet the requirements of the customers, the company blends machine learning, artificial intelligence, and some other data science tools and provides quick solutions to complex problems.
Below is a list of the platforms on which level RxDataScience extensively works:
- Augmented Intelligent Insights:
This is an integrated platform with a building block artificial intelligence approach. This is used to solve business challenges with artificial intelligence and machine learning. For insights generation, prediction, and data-driven solutions, Ai2 enables efficiency.
- BioPharma Analytics Platform:
This platform is built upon an agile data lake and generates strategic and real-time insights. This is an integrated platform that operates through different areas of the company. Commercial, finance, HR, Medical Affairs, and product launching are some of the areas of its application.
- R&D Optimization:
Machine learning, along with a high-performance computational engine optimizes this engine. With this, you can run data models on millions of virtual patients.
- Quantitative Systems Pharmacology Accelerator:
This high-performance computing platform with coding optimization helps in scaling virtual patients’ clinical trials.
- Patient Finder:
The patient-finding algorithms of RxDataScience enable you to market your products to customers or patients. This event identifies patients with health issues and patients required for clinical trials.
- IoT Platform:
This platform helps us combine static data with streaming data collected from wearable devices. This works in preventing adverse events and real-time analysis.
- RYDAL:
RDAL or rapid development advanced analytics lab is leveraged to test the feasibility of innovation. Before presenting the final project, this is used to accelerate new ideas.
Know About RxDataScience Products and Solutions:
They leverage an array of open-source solutions to build comprehensive machine learning, artificial intelligence, and other data analytics tools created for large enterprise organizations. At RxDataScience, they have built an in-house technology to provide rapid and iterative front-end solutions.
These are again backed by some NoSQL and data mining platforms. All of these along with AppBuilder enable RxDataScience to deliver quick solutions to complex problems.
Over the last century, RxDataScience has been consistent in managing business-facing issues from a wide arena of healthcare disciplines.
They have already addressed issues like real-world evidence data analysis, scientific computing, commercial market research, Data Lake for the pharma ecosystem, etc. with their pharma experience.
At present, RxDataScience collaborates with some of the biggest names in the industry to provide extensive services.
For their services, RxDataScience relies on various cloud-based and on-premises NoSQL and SQL-dependent database solutions. Apart from that, the company utilizes machine learning software like Python, and R, and more advanced CUDA-based deep learning solutions.
Besides data mining and machine learning, the RxDataScience advanced solutions draw upon high-performance computing and quantitative models developed by Matlab and Mathematica.
Such models support operations related to simulation and optimization across hundreds of cloud-based servers.
RxDataScience has developed a medical device data analysis platform to acquire and analyze large-scale streaming sensors and the Internet of Things (IoT).
This can ingest around 50TB of data and give real-time analytical research of scientific measurements. As an on-demand application, Vidyut is available here for organizations to scale.
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Contact Information of RxDataScience:
The RxDataScience, America has offices in North Carolina and New York; and you can visit the official website to know the exact address of the India office which is in Tamil Nadu.
Current Status of RxDataScience:
Recently the company has been overtaken by an integrated biopharmaceutical solutions organization called Syneos Health. As a result, the healthcare-focused data science company RxDataScience will accelerate better technology-based solutions for the total lifecycle of a product.
From lab to life, data management, data analytics, and data management will perform better services to biopharma customers.
This acquisition will boost end-to-end services with behavioral and therapeutic efficiencies with the fruits of data science and Artificial Intelligence.
Now, advanced enterprise analytics, and deep knowledge of data management and engineering are being reflected in improved customer performances and commercialization of products.
How to Become Eligible for the Data Scientist Position at RxDataScience:
The job of a data scientist requires an integration of various skills and experience. If you want to grab a lucrative job at a prestigious organization, here is a recommended guideline you can follow:
- Grow a solid foundation in statistics and mathematics:
With a comprehensive understanding of mathematics and statistics, and a focus on linear algebra, probability, and calculus you will be eligible to address the underpinnings of a data science job.
- Programming Proficiency:
Among the required educational knowledge for a data scientist position, you have to enhance your knowledge and application of computer programming languages such as R, Python, TensorFlow, PyTorch, etc.
Healthcare-specific libraries and different frameworks are designed to make your data-related job like data manipulation, modeling, data analysis, etc. fast and smooth.
- Healthcare Domain Knowledge:
Before applying for the job, you should prepare yourself with knowledge of the healthcare industry. Different terminology, data sources, data regulations, electronic health records, clinical trials, medical coding systems, healthcare analytics, etc. information will give your job profile another edge.
You should advance your knowledge with health informatics and biomedical informatics etc. specialized knowledge.
- Practical Skill Acquisition:
The job of a data scientist needs to have prior hands-on experience in real-life problems. You need to be immersed in the healthcare projects that entail your analytical and data science skills.
You can also work with publicly accessible data from the healthcare domain and gather research experiences. By undertaking hands-on collaborating tasks, you will develop practical skills and knowledge of data science.
Continuous learning through real-life experience is the key to becoming a successful data scientist in the healthcare industry. You should keep yourself updated about new trends, and industry reports, and keep in touch with professional organizations.
Maintaining a commitment to consistent gradation of your knowledge and skills will reward you with a successful career as a data scientist in a healthcare organization.
Due to the widespread demand for data scientists, many traditional and professional institutes are coming up with courses on specialized data science.
An online certification from a reputed educational organization like IIM SKILLS will provide you with comprehensive guidance for building a dynamic career in healthcare data science.
Frequently Asked Questions:
1) Who is the CEO of RxDataScience India?
Sayee Nataranjan is the CEO as well as co-founder of RxDataScience India Private Limited. Several directors, Vice presidents, and other important personalities together perform the management of the reputed company.
2) Describe Some Applications of Artificial Intelligence in the Healthcare Industry:
Artificial intelligence is now incorporated into the healthcare industry and this brings a revolution in drug discovery and medicine development, improved care towards patients, medical simulation, diagnosis of hitherto unknown diseases, medical imaging, research, personalized treatment, and medical chatbots.
AI has been instrumental in breaking the barrier to brain disease treatment.
3) Write About Some Data Science Tools That Are Used in the Healthcare Sector.
Data science tools are used to find valuable insights from healthcare databases and lead to new inventions. Nowadays, data science tools are being extensively used to interpret the current scenario of the industry and predict future trends. Such tools are-
This is open-source software used in almost every healthcare organization. This is a parallel data processing tool used to analyze data from multiple sources or channels. As a distributed file system, this tool helps us analyze data from various nodes.
- Statistical Analysis System
Most healthcare organizations implement this data science tool because of its attractive representation of data analysis results. As healthcare professionals can view the data insights in attractive visual form, they can focus on the problems they need to work on.
This tool assesses large healthcare datasets minutely and then presents the most beneficial results in a filtered way.
4) Which Healthcare Organizations Are Recruiting Data Scientists?
Almost every healthcare company is seeking data scientists to run data-driven programs and enhance outcomes. They harness the healthcare efficacy of the company with data science technologies and improve patients’ lives with healthcare innovations.
The demand for expert data scientists is extending beyond pharmaceutical organizations and reaching research institutes, insurance companies, hospitals, and technology firms.
You can visit the career pages of the following companies to explore their job openings and eligibility criteria for applications.
To know more about the specific requirements of these healthcare organizations, you should attend industry events and maintain communication with potential employers.
- UnitedHealth Group
- Cigna
- Roche Holding AG
- Pfizer
- Johnson & Johnson
- NovaSignal
- Eli Lilly and Company
- AstraZeneca
- Anthem, Inc.
Conclusion:
Data science provides practical insights to healthcare services about strategy making and decision formation. To gather a comprehensive picture of the health care customers or patients and the clinicians, the application of data science by RxDataScience has opened up new avenues.
This article has tried to present before you the current state and prospects of data science in healthcare with an emphasis on the techniques used by RxDataScience for leveraging the benefits of data science in healthcare.
