Using Big Data in Healthcare: Analytics and Marketing

There is more and more data in the world, especially in medicine. As a result of regular activities, a huge stream of data is constantly generated. Most countries collect this information in archives and basements in the form of paper case histories, or in electronic form on the servers of modern clinics - but it is hardly ever used. Most people do not remember them until they are asked to do a study or if supervisors require verification of their qualifications. As in many other industries, healthcare providers need help in this matter.

What is Big Data in the Healthcare Industry?

In addition, to reduce treatment costs and forecast epidemics, medical analytics can be used to screen for diseases early. It can also streamline the quality of life in general, and introduce modern treatment methods into practice.

The use of Big Data analytics in healthcare has many positive examples. By digitizing everything and consolidating and analyzing it using specific technologies, big data represents a vast amount of information. Treatment models have changed, and many of these changes are due to the capabilities of Artificial Intelligence.

The average life expectancy is growing all over the world, which creates new challenges for modern methods of treatment. At the same time, healthcare costs are rising.

For example, in the United States costs have almost reached 18% of GDP

However, there is no direct link between rising costs and improving the quality of medical care and increasing life expectancy. In other words, healthcare costs are much higher than they should be, and they continue to rise.

It is clear that we need a more focused approach based on data, deeper analytics, and a change of thinking in this area. Many insurance companies are already moving from simple service payments (payment for expensive and sometimes unnecessary treatments and treatment of large numbers of patients) to plans that prioritize patient outcomes.

Finally, more and more clinicians are ready to use evidence-based treatment methods and make decisions relying on the results of large studies and clinical data, and not only on their academic knowledge, intuition and professional experience. This approach to treatment means that the demand for big data analytics in medical institutions is greater than ever before.

For the first time the term "Big Data" was used in 2008 by the American editor of the journal "Nature" Clifford Clinch.

“Big Data” is the explosive growth of information flows

To put it another way, Big Data is any data storage that exceeds 150 GB per day.

So, the comparative advantage of big data analytics in relation to traditional analytics is the speed of processing a large amount of data. This is because there is no need to sort information, and there is the possibility of analyzing incoming data in real time. On the contrary, traditional analytics require time-consuming processing, preliminary sorting and editing.

Big Data Characteristics: “7Vs”

The properties of big data are characterized as 7Vs:

The first three "Vs" cause the least questions. Indeed, who would argue that Big Data is primarily about volume?

The volume of data is growing exponentially: for example, aircraft annually generate 2.5 billion TB of data from sensors installed in engines. At the same time, the data is constantly updated, new ones are generated, and the speed of updating (Velocity is the second "V") is also important in order to consider them "large".

For example, almost 8 million requests for the Google search engine are made every minute around the world. The task of Big Data projects is to cope with the enormous speed at which data is being created and analyzed in real time.

The third "V" is Variety. This means that Big Data projects must include datasets in a variety of formats: structured and unstructured data, text, graphics, corporate mail or social media apps, up to video. Each of these data types requires different types of analysis and suitable tools.

Social media can help brand owners analyze customer sentiment. Sensory data will provide information about how the product is most often used to apply this knowledge to improve it.

Until recently, three "V"s were more than enough. But everything in the world is changing, including approaches to definition. Therefore, analysts have added four more "V" to avoid misunderstandings. So, Veracity, Variability, Visualization, Value were added to the definition. Let's consider each of these points.

Veracity. Of course, this characteristic is extremely critical, since any analysis will be completely useless if the data turns out to be unreliable. Moreover, it is imperative for us to make sure in advance that everything is OK with the data, because their inaccuracy can lead to incorrect decisions. The simplest example is contacts with false names and inaccurate contact information.

Variability is a new trend in the field of Big Data. Here we are talking about the fact that the meaning of the same data may vary depending on the context. For instance, the same words on Twitter may have different meanings and reflect different moods.

We must take into account all the nuances! In order to perform proper sentiment analysis, algorithms must be able to understand the context and be able to decipher the exact meaning of a word in that context.

Visualization is a necessary part of analysis, because it is visualization that makes big data accessible to human perception. Medical imaging is much more efficient and understandable for patients than spreadsheets and reports full of numbers and formulas. Of course, visualization within Big Data does not mean the construction of ordinary graphs or pie charts: complex graphs may be built that will include a lot of variable data, but they will still remain understandable and readable.

Value. Here we are talking about getting the most out of the results of digitization. What matters is how you will use this data. Moreover, whether you will be able to transform your public health organization into an organization that makes decisions based on data analysis.

However, these seven "Vs" are not enough to understand the essence of Big Data: we are talking about the fact that all these seven characteristics should be applied to a data-driven complex task, usually with several variables and a non-trivial condition.

The areas of big data implementation are diverse.

#1. Product development

Using data from social networks and the results of trial sales, a decision is made to launch a brand-new product on the market.

For example, Netflix created the series "House of Cards" based on big data analysis

#2. Predictive analytics

Algorithms allow you to identify factors that will help predict equipment failures.

#3. Machine learning

Big data makes it possible to train the machine, not to program it.

#4. Compliance with regulatory requirements

With the help of big data analytics, fraudulent activities can be quickly detected.

Banks and tax authorities are implementing such solutions.

Prerequisites For The Development of Big Data In The Patient Experience

PWC has identified global trends in the development of healthcare:

• Decrease in trust in institutions and technologies (patients require more information about the treatment process)
• And an aging population.

A significant part of the population is aging and making more and more demands on the local health infrastructure and social institutions.

According to the UN forecast, the population aged 65 and over will reach about 1.5 billion people by 2050

In addition, at the moment there is an increase in user activity on the Internet, which has become a full-fledged prerequisite for the active development of Big Data management.

The psychology of modern people is built on the principle:

"I don’t book an appointment, I complain on social networks"

A potential patient will first ask for advice on the Internet, and then (perhaps) see a doctor. With the help of big data analytical tools, it would be possible to analyze the texts of such messages and identify probable outbreaks of viruses.

The Most Popular Technologies in Medicine

Systems that allow you to work with Big Data are a unique tool for solving healthcare problems, because they help you analyze large data sources about patients, especially:

• The emergence and course of diseases
• The effect of drugs
• Pandemics
• Effective methods of treatment
• Genomics

In addition, the use of tools for working with large data is especially relevant in healthcare due to the growth of patient requests for quality of service.

Our ability to analyze healthcare big data will contribute to personalized healthcare, improved diagnostics, and prevention of epidemics. We can also combat insurance fraud by providing more efficient treatment.

The table above presents information about modern technology in demand in various industries. The survey was conducted online and was attended by representatives of the healthcare sector and customers of medical services. The rating of each technology was calculated as the percentage of respondents’ votes who indicated it as one of the most popular.

The table shows that 65% of respondents chose big data analytics in healthcare as the most popular technology. A precise example of big data in healthcare is all the information about the genetic characteristics of the body, which is hundreds of GB per person.

Benefits of Big Data

Big data technologies help simplify some processes in healthcare. For example, with the help of healthcare data analytics, the quality of clinical trials is improving. In addition, among the main advantages of using such technologies we can distinguish:

• The ability to independently control your health and perform data sharing via medical devices
• Simplification of the decision-making process on the diagnosis of the patient due to computer health information analysis
• The transition from traditional clinical decisions to improved methods with the storage of accumulated experience

It is noteworthy that the huge potential of big data is evidence of the attitude of doctors themselves towards change.

Big Data Application Examples

Electronic Health Records

Electronic Health Records (EHR) worldwide is a system that stores all possible records of the patient's condition, in all areas of medicine, throughout the patient's life. It is connected to almost 94% of clinics.

According to McKinsey, this helped improve the results of treatment of cardiovascular diseases. It also brought about $1 billion in savings by reducing the number of visits to doctors and laboratory examinations on account of telemedicine. Europe also has a centralized European system of medical records.

Real-time Analytics (Real-time Alerting)

Real-time analytics help doctors through the decision-making system to correctly diagnose and prescribe treatments, as well as receive alerts from wearable devices.

The doctor will be able to adjust treatment after receiving an alert if, for example, the patient's blood pressure reaches an alarming level. Chronic disease treatment plans are developed based on GPS data from trackers.

Medical decision-making systems

Data scientists have developed these systems to help doctors make informed decisions within seconds and improve patient care. The upcoming tools will also be able to predict the risk of diabetes and other diseases. To do this, laboratories collect millions of patient records through EHR.

The Cancer Moonshot

This program is an ambitious project to accelerate progress in cancer treatment. A combination of oncology sequencing research from hospitals, universities, and non-profit organizations is generated through this process. Biobanks allow researchers access to all tumor samples and can provide information about how certain mutations and proteins interact with different types of treatments.

“All of Us”

"All of Us" is a research platform organized by the NIH. Its goal is to collect and process millions of patient data. Among these data are electronic medical records, social and behavioral characteristics of people and information about the environment over the past few years. In turn, this makes it possible to provide a person with high-quality treatment faster and reduce the number of repeated examinations.

Big Data in Healthcare Marketing

Marketing professionals now have the benefit of using Big Data as a tool that not only assists them in their work, but also predicts results. For example, using data science, you can display ads only to an audience interested in the product, based on the real-time bidding (RTB) model.

In healthcare marketing, leveraging big data enables more tailored patient outreach and enhances engagement strategies. Platforms like DataCamp provide essential learning resources for professionals aiming to deepen their proficiency in data management and interpretation. Through comprehensive courses, users can develop skills that drive effective decision-making by analyzing large datasets which improve service delivery and operational efficiencies.

Big data allows marketers to get to know their clients and attract a new target audience. It also allows them to assess patient satisfaction, apply creative ways to increase their loyalty and implement projects that will be in demand.

Why it is profitable to use big data technologies:

• Easy to plan
• Projects are launched faster
• Easy to attract the target audience
• You can improve service at a lower cost
• You can make the right strategic decisions.

Modern PMS has a powerful Analytics module that works as a decision-support tool. A variety of sources, such as EHR, billing, ads, bookings, and so on, are connected to these tools, so you can analyze all the necessary data.

Final Thoughts

The above examples show that it is important to expand the scope of application of medical data. This will optimize the time and material resources spent on developing novel approaches to treatment, and improve the quality of training of medical workers. The use of big data is the key to the development of preventive measures in healthcare.

Wrapping up, Big Data can inspire the emergence of progressive ideas, their rapid implementation and adaptation. Healthcare needs to catch up with other industries that have already moved from standard regression-based methods to more future-oriented ones, such as intelligent analytics, machine learning and graph analytics. Business owners and healthcare professionals need to collect and utilize medical data carefully.