Big Data in Healthcare: benefits, use cases, and main challenges

Big Data in Healthcare: benefits, use cases, and main challenges

Healthcare, an aspect of the medical industry, can be traced back to its origins many years ago with some popular yet questionable methods. As for the present time, our understanding and development has led modern doctors to actively use solutions on the basis of such high-tech concepts as telemedicine, augmented reality, artificial intelligence, and, among other things, big data. 

As for the last concept – big data – in the healthcare sector, it’s generally innovative. After all, solutions based on big data and used by both doctors and patients, can save lives, improve the processes of treatment and diagnostics, reduce the likelihood of errors, automatize the workflows, and in general, help improve the quality of medical workers on a daily basis.

It should be noted that the most recent flash of the demand for big data as a concept for the development of software in the healthcare sector, appeared due to the COVID-19 pandemic, when the number of patients increased dramatically, and the traditional scripts of the interaction of medical staff with people became ineffective.

That’s why we decided to study in detail all aspects of big data usage in the healthcare industry – both positive and negative.

Big Data Market Overview: Healthcare Sector

If we consider statistics, we see that the relevance of big data in the context of use in health care will grow (moreover, this industry will occur much more rapidly than in such extensive niches as banking and marketing). In particular, it’s expected that in 2025, the annual increase in the volume of big data in healthcare will be in excess of 35%. 

As for earlier forecasts, this year, experts argue that the global capitalization of big data in the healthcare sector will exceed $ 34 billion, and the main investors of such software solutions (in particular, digital medical cards and smart diagnostic apps) will be representatives of North America. From the point of view of profitability, big data innovations will allow companies to save more than $ 400 billion annually.

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Major Sources of Big Data in Healthcare

Big data in healthcare solutions can have very different resources. These can be medical records of patients, IoT devices that record the vital signs of patients, local databases of pharmacy warehouses, etc. At the same time, the data obtained is often unstructured and diverse, so it is important for developers who deal with them to choose the correct tools for their ordering and fast processing.

If these tools are chosen correctly, the quality of medical care improves significantly: doctor's appointments and diagnostics become faster, medical centers and suppliers of medical preparations and medical equipment save their costs due to end-to-end automation.

To be more precise, let's list the specific benefits of using big data in healthcare:

  • minimizing the likelihood of medical errors;
  • reducing the risk of epidemics;
  • implementation of effective preventive measures;
  • prognostic activity;
  • early diagnosis;
  • providing a personalized approach;
  • risk assessment;
  • preventive measures for patients with suicidal tendencies;
  • the invention of new approaches to the treatment of patients (including surgery) and new medications;
  • cost reduction;
  • partial transition to automation of work processes in medical centers. 

Big Data Use Cases for Healthcare Sector

Let's look at particular applications of the big data concept in the healthcare sector.

Diagnosis and treatment

Big data, combined with artificial intelligence and machine learning, revolutionized the field of disease diagnosis a number of years ago. In particular, solutions based on these technologies are able to diagnose diseases at an early stage, when even the most experienced doctors are unable to do so. As for the treatment of diseases, specialized big data solutions can select the optimal drugs and their dosage, based on an individual analysis of patients.


Big data can provide invaluable help in the field of prognosis, in particular when it comes to predicting the outcomes of the disease of specific patients. Also, predictive applications are able to assess the risks of developing the current state of patients, as well as the risks associated with epidemics. Obviously, such software solutions will be extremely useful at the present time, during the COVID-19 pandemic.

Monitoring of vital signs

Wearable devices and, in general, IoT systems, together with big data solutions, can provide round-the-clock monitoring of the vital signs of patients without the need for the participation of medical personnel. In general, such solutions can significantly help both in emergency medical care and in the diagnosis and prescription of medications.

Healthcare of population

In the long term, big data solutions help raise public awareness of healthy lifestyles and existing diseases. Thus, such software can prevent epidemics, help in preventive measures and reduce the number of patients, as well as accelerate the response of the population to the symptoms of dangerous diseases.


Big data, being an integral part of telemedicine, is able to distance not only a standard doctor's appointment but also more serious events, such as operations. As for less complicated services, telemedicine significantly increases patient satisfaction, eliminating the problems of waiting for an appointment, accepting patients in serious condition, as well as the high cost of medical services.


Even experienced doctors are not always able to correctly assess the state of the patient's organs from ultrasound, CT, or MRI. In turn, big data solutions that use machine learning can simplify the diagnostic procedure and make it more accurate.

Electronic health records

The introduction of electronic health records (EHR) eliminates all the trivial problems associated with their paper prototypes: outdated data, data loss and leakage, lack of centralization, etc. In general, this solution has taken root in countries with developed economies - in particular, in the United States, only 6% of hospitals have not implemented EHR yet.

Workflow automation

Big data-based software solutions can streamline routine workflows in hospitals, reducing the burden on employees. This is especially useful during peak hours when it’s difficult to rationally prioritize the examination of critically ill patients without competent assistance.

Business analytics

In addition to the benefits for patients and staff, medical centers can benefit from big data solutions in terms of evaluating and predicting business opportunities. In particular, an analysis of the increase in the incidence of morbidity in certain regions may indicate the prospects for opening new hospitals.


What Problems Can Arise When Using Big Data in the Healthcare Sector?

It is clear that big data has a great potential to revolutionize the healthcare sector. For example, with its help, doctors can track both general trends in population health indicators, and specific ones related to the progress of the treatment process for a particular patient. Anyway, modern technologies in the field of medicine cannot be ignored, because in the long term they will be able to save millions of lives and improve the health of people in general. Let's look at some problems of using big data in the healthcare sector.

Large volumes of unstructured data 

The concept of big data has always meant not only its large volumes but also its heterogeneity, which can be difficult to deal with in practice. To solve this problem, you can use a data lake. With it, you can structure data in different formats: images, records from old databases, text files, etc.

Data formatting  

A similar solution can be found in the problem of data standardization. In such a case, the data lake will help bring it into some general format so that your application can handle it correctly.

Poor quality of data 

The data coming from different sources must exclude duplicates and empty records in order for your application to process them. Otherwise, they can lead to mistreatment when it comes to complex diagnostic solutions based on artificial intelligence and machine learning. In this case, algorithms that regularly check and clean up unstructured data work well.

Data collection

As for data mining, it is sometimes very difficult for them to choose the right research tools. To solve this problem, your team will need a data science expert who specializes specifically in the healthcare niche.

Data exchange

Due to the lack of standardization, the exchange of data between individual authorities can be difficult. There may also be security gaps. To avoid this, it is important to provide a single exchange standard and compliance with the security policies that apply in your location.

Data visualization

Big data almost always needs some kind of visualization, as it is often intended for analytical reporting. That's why you might want to design dashboards that also need to be provided with an intuitive interface to lower the entry barrier for people with basic PC proficiency.


In some cases, big data solutions allow only vertical scaling - increasing the number of available computing resources by increasing the capacity of servers. At the same time, horizontal scaling is impossible for certain reasons. To make this option also available, it makes sense to use massively parallel processing (MPP) storage.


Health care, like other government sectors, is strictly regulated by local and sometimes international legislation. Therefore, before you release your software to the public, you have to make sure that it meets the standards for privacy and security of user data. That is why it is important not only to ensure secure data storage and transfer but also to regularly audit the network in order to assess the risks of possible data leakage.

Integration with third-party solutions

Another problem that developers often face is the incompatibility of their product with third-party solutions needed for the operation of a particular medical center (in addition, they often turn out to be outdated). Of course, as an option, you can always create an update of these incompatible products, but unfortunately, this approach is not always cost-effective. This is where middleware buses come to the rescue.

Lack of work experience for staff members

And finally, the most common problem with big data software is the inability of medical center staff to interact with it. This is not surprising, because big data implies the presence of complex algorithms. This is why even organized data that is intelligently visualized often remains incomprehensible to people with basic knowledge of digital devices. If you want to prevent this, choose a software development service provider that specializes not only in big data but also in creating user-friendly interfaces.

How We Can Help Make Your Big Data Project for Healthcare Sector

Our team creates medical software, of the highest quality, which fully complies with state and international standards. As a result, our solutions minimize the risks specific to the healthcare sector and simplify interactions between patients, doctors, pharmacists, and other representatives of this niche. 

Thus, S-PRO helps medical centers and startups implement technological innovations in everyday workflows, boost the customer experience, and manage user data efficiently and securely. 

In particular, here are the benefits our clients get:

  • cutting-edge technologies to deliver better customer care in the healthcare sector; 
  • the turn-key approach which helps to automate workflows;
  • the reduction of risks and errors associated with diagnosis, treatment prescription, as well as the interaction between representatives of the healthcare sector.

If you are looking for a dedicated team to take on your project and make it cost-effective, contact us now!


CEO, Co-founder. Innovative and commercially-minded Creative Technologist. Solid experience in product & market needs analysis, business strategy development & team leadership.
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