The Impact of Data Science on the Healthcare Industry

Big data has amplified itself across several domains, from business to transportation, which has made us aware of how essential it is to daily life. Similar to how it has changed the medical industry, big data has also been structuring and mapping unstructured data to fundamentally alter how even the most basic health monitoring operations are carried out. It was for this that a staggering 3.5 billion US dollars were invested in digital health startups in 2017, enabling businesses to realise their goals of radically altering how the world views health. The article's later sections will discover data science's significance in the healthcare industry.

Importance of Data Science in the Healthcare Industry

The human body produces around 2 gigabytes of data daily, which consists of various things, such as heart rate, sugar levels, stress levels, and brain activity. We now have more sophisticated technologies, including Data Science, to manage and preserve enormous amounts of data. Using recorded data assists in monitoring patients' health. It is now possible to identify disease symptoms early, thanks to data science applications in healthcare. Due to the creation of numerous innovative instruments and technology, doctors can now remotely check their patients' illnesses. Before, medical officers and hospital management were unable to care for many patients simultaneously. Thus, as a result of inadequate care, the patient's health deteriorated. 

However, now the situation has changed as a result of data science applications in healthcare. Wearable gadgets that use data science and Machine Learning can alert clinicians to patients' medical concerns. Junior physicians, medical assistants, or nurses can be sent from the hospital administration to these patients' residences. 

For these patients, doctors can also set up a variety of diagnostic tools and equipment. These technology-based instruments can gather patient data like temperature, blood pressure, and other physiological measurements. Mobile application updates and notifications give clinicians access to real-time patient health information. They can then make a diagnosis and assist more junior medical professionals or nurses in providing patients with certain therapies at home. This is an example of how data science can use technology to improve medical care. 

Applications & Use Cases of Data Science in the Healthcare Sector

Medical imaging, drug research, genetics, predictive diagnosis, and many more areas of healthcare use Data Science. Each application of data science in healthcare is discussed with examples in the later sections of the article. 

Medical Imaging

The most significant application of data science in healthcare is medical imaging. There are imaging methods including X-rays, MRIs, and CT scans. These all represent the internal organ systems of the human body. 

Traditionally, medical professionals would manually inspect these photos for errors. Doctors, however, were unable to provide an accurate diagnosis since microscopic defects were frequently difficult to spot. 

Such minuscule abnormalities in scanned photos can now be found thanks to Deep Learning tools in data science. 

Genomics Data Science

The study of genetic sequencing and analysis is known as genomics. DNA and all of an organism's genes make up its genome. Since the Human Genome Project's completion, research has quickly expanded and permeated the fields of data science and big data. Prior to the availability of sophisticated computation, organisations devoted a lot of time and resources to studying gene sequences. However, it is now possible to investigate and gain insights from human genomes in much less time and at a lot lower cost thanks to improved data science tools and better healthcare data science. 

Examining genetic strands for flaws and anomalies is the main goal of research scientists. Researchers typically utilise data science to examine genomic sequences and look for a connection between the disease and the factors found there. Finding the appropriate drug also contributes to a greater comprehension of how a drug responds to a particular genetic problem in genomics research. 

Predictive Analytics

One of the most popular areas in health analytics is predictive analytics in the healthcare industry. A predictive model makes use of previous data in order to draw lessons from it, identify trends, and make precise forecasts. It makes insightful predictions by identifying correlations and links between symptoms, routines, and diseases. Predictive analytics in the healthcare industry are assisting in enhancing patient care, managing chronic diseases, and increasing the effectiveness of supply chains and pharmaceutical logistics. 

Predictive analytics is increasingly used in population health management. It is an evidence-based approach to disease prevention that concentrates on conditions that are common in society. Hospitals can use data science to forecast patient health deterioration and offer early diagnosis and treatment, lowering the likelihood of additional patient health deterioration. The logistic supply of hospitals and pharmaceutical departments can be tracked with the aid of predictive analytics in the healthcare industry. 

Monitoring Patient Health

Analytical techniques can be used by doctors to track a patient's calorie intake, blood pressure, and circadian cycle. With the use of wearable sensors and home devices, a doctor can keep an eye on a patient's health. For patients with chronic illnesses, several systems monitor their physical characteristics, follow their movements, and look for patterns in the data. Based on the patient's current state, it makes a prediction about whether they will experience a problem using real-time analytics. Additionally, it helps clinicians make the judgement calls required to care for distressed patients.

Monitoring and Preventing Disease

Data Science is essential in order to track patients' health and notify the appropriate actions to be done to avert potential diseases, To detect chronic diseases ahead of their time, Data Scientists employ sophisticated predictive analytical methods in the healthcare industry. 

Due to their ineligibility, diseases are frequently not identified at an early stage in extreme instances. This has a detrimental effect on both the patient's health and the associated financial costs. As a result, data science in healthcare has a big impact on maximising healthcare spending. 

Artificial Intelligence has frequently contributed significantly to the early diagnosis of diseases. Researchers at the University of Campinas in Brazil have developed an AI framework to identify the Zika virus using metabolic indicators. 

Virtual Assistance

A comprehensive virtual platform that helps patients by using disease predictive data modelling has been developed by data scientists. These platforms allow patients to submit their symptoms and, depending on their confidence level, receive information and insight about the numerous potential diseases. Patients with neurodegenerative disorders like Alzheimer's and psychological conditions like depression and anxiety can use virtual applications to help them with daily tasks thanks to data science applications in healthcare. One well-known example of a virtual assistant is Ada, a Berlin-based business that diagnoses illnesses based on the user's symptoms. Woebot is a chatbot created by Stanford University that offers therapy to people with depression. 

Automatic Disease Detection

An average person produces 2 terabytes of data every day. Thanks to advancements in technology, we can now get the majority of it, including data on heart rate, sleep patterns, blood glucose levels, stress levels, and even brain activity. Scientists are advancing the field of health monitoring because they have access to such a quantity of health data. 

Machine learning algorithms can be used to identify and monitor more prevalent disorders like heart or respiratory ailments. By capturing and tracking heart rate and breathing patterns, technology can identify even the smallest changes in a patient's health markers and anticipate impending diseases. Although abrupt cardiac arrest kills 600,000 Americans annually, being able to anticipate the problem and send out alerts that could save countless lives. 

Developing Effective Drugs

New issues with the human body frequently arise with the gradual boom in the global population. A lack of a nutritious diet, ongoing anxiety, pollution, physical ailments, or other things may contribute to this scenario. It is currently difficult for medical research institutes to find drugs or vaccinations for diseases in a timely manner. Millions of test cases may be necessary because scientists need to comprehend the features of the causal agent in order to develop a formula for a drug. After discovering the formula, the researchers must test it once again. 

The data of millions of test cases used to be analysed over a period of 10 to 12 years. However, it has become a much simpler process with the aid of numerous data science applications in healthcare. In a matter of weeks or months, data from millions of test cases can be produced. Through data analysis, it assists in establishing a drug's efficacy. A successful vaccine or medication can therefore be made public in less time than a year. Data science applications in healthcare and machine learning enable this. The research and development fields in the pharmaceutical industry have been revolutionised by both. The application of data science in genomics will then be discussed after that. 

Future Trends in Data Science for the Healthcare Industry

The four major elements that are causing the healthcare sector to boom because of the differences that data science has made globally include,

• Technological advancements
• Digitalization is required
• High cost of care
• The necessity of handling a huge population

The healthcare sector is likewise dealing with issues with cost-effectiveness and technological adoption. Electronic health records (EHR) and patient portals are only two examples of the different technologies that healthcare organisations have used. Despite this, these technologies haven't been able to live up to their claims because of their high costs, challenging deployment procedures, a lack of system interoperability, etc. 

The development of artificial intelligence (AI) and machine learning (ML) technology will determine the direction of data science in the healthcare industry. It is hardly unexpected that these two technologies are making their way into the healthcare sector given how they are already revolutionising many other sectors, including retail and banking. 

Conclusion

Applications of data science in healthcare are already advantageous to society, and there is no doubt that these applications will become even more valuable in the future. It will advance the medical field. Patients will gain a distinctive experience and superior care, and doctors will be well-served. 

Long-term goals for self-management, better patient care, and therapy can be realised with the aid of big data. Real-time predictive analytics from data science can be utilised to understand various disease processes and provide patient-centred care. It will help advance epidemiological research, personalised medicine, and other fields of science for researchers. On the other hand, the ability to generalise forecast accuracy depends heavily on the effective integration of data from several sources.