Big Data For Healthcare In 2023: Applications and Benefits

Telemedicine, medical imaging, electronic health records, robots, and other advancements have helped the healthcare sector get to where it is now with the help of modern technologies, like big data - an industry-changing tech that allows the healthcare sector lowers costs, increases productivity, and helps save lives.

Global pandemic breakout has sped up innovation and uptake of digital technology, particularly big data and big data analytics. However, it has also highlighted a number of flaws in the healthcare sector. In this article we will discuss how bag data can benefit the healthcare industry and outline the most common applications of big data for healthcare.

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Overview Of Big Data In Healthcare

The IDC analysis predicts that the healthcare sector would experience bigger growth in big data than other sectors like manufacturing, financial services, or media. Through 2025, a 36% compound annual growth rate (CAGR) is predicted for the healthcare data.

At a CAGR of 22.07%, the worldwide big data market for the healthcare sector is anticipated to reach $34.27 billion by 2022. By 2024, it is anticipated that the big data analytics market would be worth more than $68.03 billion globally, mostly due to sustained North American investments in workforce management programs, practice management software, and electronic health records. According to McKinsey & Company's analysis, big data in healthcare can help Americans save between $300 billion and $450 billion annually.

Big data is used in many areas of modern digital healthcare - from wearable technology and search engine server logs to electronic health records. These areas constantly generate large amounts of data that can be leveraged to improve business practices and patient care. Knowing how to use this data effectively is crucial. All parties involved in the healthcare system, including healthcare organizations (HCOs), patients, medical professionals, pharmaceutical makers, etc., can benefit from proper data analysis software. Big data can help improve patient care and, subsequently, their health, increase the accuracy of medical testing, hospitals can reduce the costs and increase operational efficiency.

Here is a list of possible advantages of using big data in digital healthcare:

  • reduce human error

  • prevention of global diseases

  • effective and fast models of disease transmission

  • early-stage diagnostics of diseases

  • more precise therapy

  • Individualized patient care

  • accurate treatment cost estimations

  • accurate estimations of treatment side effects

  • locating and supporting patients at high risk

  • Self-harm and suicide prevention

  • quick development of new drugs and treatments

  • higher standard of personnel management

  • improved client services

  • reduced monetary spending

 Use Cases Of Big Data In Healthcare

Big data analysis provides healthcare professionals with previously unattainable insights. Every stage of the healthcare cycle, from medical research to patient experience and outcome, uses big data.


Big data allows medical professionals to greatly increase the speed of diagnosis. The typical in-hospital checkup takes a long time and a lot of effort on the part of the doctor who has to talk to the patient, examine them, and match their symptoms to a known disease, and in cases where the symptoms are complex doctors conduct scientific paper research and consult with peers. Big data, however, offers a more effective method of patient diagnosis. The patient data can simply be gathered by doctors and entered into an algorithm that will provide the most likely diagnosis in a matter of minutes, providing a comprehensive list of tests that will help avoid taking unnecessary tests. Additionally, computer vision is commonly employed in diagnostics, for instance, in the examination of the retina with the goal of spotting irregularities early and preventing diseases.

Modeling and forecasting outcomes

Big data and predictive analytics help medical professionals make better clinical decisions. In medicine, prognostic modeling is frequently employed for a variety of reasons. Some models are intended to forecast future results of illnesses and/or therapies. Others concentrate on finding patients who might be vulnerable to the emergence of a specific illness. Additionally, there are models that predict how diseases will spread across the populace. Predictive modeling, for instance, has been effectively used in several nations to detect undiagnosed diabetes, estimate the COVID-19 pandemic's development, and predict survival following in-hospital cardiopulmonary resuscitation.

Real-time monitoring of patient vitals

One of the most important trends in healthcare technology is the usage of wearables and other IoT devices, which healthcare technology companies currently produce in sufficient quantities. They can automatically gather data on your blood pressure, temperature, oxygen saturation, heart rate, pulse, blood sugar level, and other vital signs. As a result, they do away with the necessity for patients to travel to the providers or pick it up themselves. These gadgets produce a ton of useful data that can aid medical professionals in diagnosing and treating patients.

Treating difficult diseases

In order to determine which treatment plans have the highest rates of success, data gathered from patients on various treatment modalities can be examined for trends and patterns. This is crucial in the fight against serious diseases including cancer, AIDS, multiple sclerosis, etc.

Population health

Big data contributes to bettering people's lives. It offers the ability to foresee infectious disease epidemics and stop them from spreading. When facing prior pandemics, healthcare professionals did not have access to big data techniques. Big data assists in enhancing epidemic surveillance and appropriate response. Big data and data analytics are being used by nations all over the world to offer real-time statistics, monitor the virus's spread, and forecast the effects of different epidemics.

Preventive care

Disease prevention is preferable to disease treatment for patients, hospitals, and insurance companies. It is in the interest of healthcare professionals that their patients maintain good health and avoid hospitals. Big data makes it possible to estimate a person's likelihood of becoming ill based on their habits and to spot early warning signals of serious illness.


Big data is extremely important in telemedicine. For instance, surgeons can operate on patients despite physically being kilometers away thanks to robots and high-speed real-time data. Big data is essential for initial diagnosis, remote patient monitoring, and virtual nursing support in addition to robot-assisted surgery. The lives of doctors and patients are made easier by telemedicine and big data:

  • Patients don't have to stand in lines

  • Medical professionals don't waste time on pointless consultations or paperwork

  • Patients may be consulted at any time and anywhere thus avoiding re-hospitalization

  • Clinicians can anticipate acute medical events in advance and stop patient conditions from getting worse

  • telemedicine lowers costs and boosts service quality.


It can be difficult and time-consuming to process and analyze imaging data from CT, MRI, or PET scans. However, big data analytics can automate radiologists' image reading processes. In order to assist medical professionals with the diagnosis, algorithms can recognize specific patterns in the images and translate them into numbers. Therefore, medical professionals have the option to create image datasets and employ computer vision and data science tools for speedy analysis.

Electronic Health Records 

One of the largest sources of big data in the healthcare industry is electronic health records (EHRs). They have already been used by many HCOs. The HITECH study indicates that 94% of US hospitals are using EHRs. EHRs provide patients and physicians with a thorough understanding of a patient's medical background. Records are accessible to providers from the public and private sectors and are shared via secure information systems. Doctors can make modifications over time without having to fill out paperwork or worry about data replication. EHRs can also track medications to see if a patient has been following a doctor's recommendations or send alerts and reminders when a patient needs a new blood test.


Big data and data analytics can aid in the detection and prevention of fraud. It is feasible to spot alterations in network activity or any other behavior that points to a cyberattack and take action to obstruct destructive actions.

Hospital management

The management of hospitals depends on big data. In addition to greatly lowering costs, it can enhance hospital operations. By using data-driven analytics, you may, for instance, forecast when you might need people in specific departments during busy times while redistributing qualified personnel to other areas at down times. Additionally, by monitoring overall employee performance, hospitals may use healthcare data analysis to learn who and when needs support or training.

However, a comprehensive plan is needed to apply a big data solution in the healthcare industry. Either you can create your own solution or buy a premade system. The most important thing is to be crystal clear about your needs and objectives.

Challenges Of Big Data In Healthcare

Big Data has the potential to significantly alter the healthcare industry. By avoiding illnesses, predicting medical outcomes, and lowering medical errors, it can save people's lives. It can also lower healthcare costs and raise the standard of care. But not every healthcare provider has integrated big data into routine tasks. A recent PwC poll found that 95% of healthcare CEOs are looking into new methods to use and manage big data, but just 36% have made any progress in doing so. What then are the primary impediments to the widespread use of big data in healthcare? Let's examine some of the most important issues:

  • Data integration and storage

The big data ecosystem was developed specifically to address issues with absorbing and storing large amounts of exceedingly heterogeneous data. The issue of storing many types of healthcare data, like photographs, document files, exports from outdated RDBMS systems, and so forth, can be resolved using a concept called a data lake.

  • Data standardization

However, it is feasible to standardize a range of data from data lakes into some structured form as data warehouses. Healthcare is known for a number of standards applied to the data.

  • Data quality

AI and ML algorithms require accurate input data free of duplication and errors in order to generate credible insights. Doctors could misidentify a patient or recommend the incorrect treatment if the quality is inadequate. To enhance the quality of the data, HCOs should work on data governance and master data management systems. All incremental processes should have automated checks put in place, and data cleansing and preparation should receive special attention.

  • Data mining

When it comes to data mining issues, big data ecosystem data exploration tools that are frequently employed in business intelligence are very helpful. Additionally, data scientists and engineers could support data mining in the healthcare industry.

  • Data sharing

Sharing healthcare data between different businesses is one of the major problems because there isn't any standardization. Furthermore, such sensitive data calls for strong privacy measures. Data must be communicated in a timely and accurate manner during public health emergencies, including the COVID-19 pandemic.

  • Data visualization

Projects using big data in healthcare need to be highly visible. As a result, continual business/report dashboards, operational dashboards, and real-time monitoring are crucial. However, because it necessitates specialized equipment and knowledge, visualizing health data is a challenge in the healthcare sector.

  • Scalability

Some enterprise data warehouse systems that are often used in the healthcare industry only handle vertical scales and lack horizontal scalability. Such scaling concerns can be resolved by moving to massively parallel processing (MPP) data warehouses or the big data ecosystem.

  • Security and privacy of data

Security is a major issue in the healthcare industry. The storage and sharing of sensitive data is strictly restricted in the highly regulated healthcare sector. Nevertheless, there are numerous instances of data breaches and leaks. Setting up the relevant configurations, performing frequent audits, assessing risks, and educating staff on security best practices are therefore essential.

  • Integrating legacy systems with the big data ecosystem

It is crucial to determine whether legacy systems can be integrated into the new pipeline and continue to perform some functions or if they need to be completely updated to meet the big data ecosystem in order to obtain future cost and performance benefits. Middleware buses are used when rewriting is not an option, which happens frequently in healthcare due to rules and software certifications.

  • Lack of big data skills

Big data in the healthcare industry is extensive and varied. It is difficult to gather, purify, process, manage, and analyze such enormous volumes of data. These and many other problems with big data and big data analytics in healthcare can be readily resolved, though, if you have a trustworthy partner at your side.

Your Trusted Healthcare Software Development Partner

If you are looking for a software development partner to help you implement a big data solution in the healthcare sector, we at Businessware Technologies can help you do just that:

  1. Businessware Technologies is a reliable custom software development vendor: it has been recognised as one of the top software development companies by Clutch and Manifest, it is a Top Rated Plus agency Upwork, and has received local awards for its excellent work
  2. A team of over 70 highly skilled software engineers with extensive experience in developing complex software for both startups and Fortune 500 companies
  3. Deep expertise in modern machine larning technologies and approaches to system development, like data science, AI and machine learning, OpenCV, Python, Tesseract, and many more
  4. Businessware Technologies is a Microsoft Gold Certified partner
  5. Businessware Technologies is compliant with GDPR, ISO 9001, ISO 27001 standards
  6. Businessware Technologies works with Fortune 500 companies and has had decades-long relationships with most of its clients
  7. Businessware Technologies has proven to be a reliable custom software outsourcing partner by having an excellent track record in software development backed by an extensive portfolio of successful projects.