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20 Examples of Big Data in Healthcare and Improvements It Brings

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The healthcare industry generates vast volumes of data daily. Collecting and analyzing it helps prevent future health issues, boost administrative tasks, and improve patient outcomes. As a result, healthcare vendors turn to software development companies with experience handling Big data in healthcare to find ways to bring forward everything the technology offers.

Big data analytics is currently booming. For example, Statista indicates the Big data market to be valued at $70 billion and is expected to reach $103 billion by 2027. In such a case, we need to have an in-depth look at the Big data healthcare phenomenon and explore 20 distinct instances of improvements it brings. 

The Essentials of Big Data in Healthcare 

Simply put, Big data in healthcare works with the volumes of information emerging due to digital technologies applied within the industry. The critical focus of the phenomenon is on new ways of collecting and processing patient data via medical records to improve the system’s functionality. Data analytics in healthcare is vital for healthcare vendors that scale and are massive in size. 

While we saw the prospects of the Big data market in general, there is a similar growth tendency for healthcare Big data analytics (see Fig.1).

Global healthcare big data analytics market
Figure 1. Global healthcare big data analytics market

Keeping that in mind, Big data and its analytics tools try redefining data usage in healthcare. If the technology is applied correctly, it promises various benefits for administrators and patients. In a broader scope, these benefits can even be generalized to population health. 

When understanding the nature of Big data in healthcare, one should know the sources of information that professionals can use to get better insights. Furthermore, with the ongoing digitalization, the number of sources is continually increasing. That provides the benefit of getting more valuable data and the challenge of handling a growing number of data sources. 

In general, with proper Big data analytics tools in healthcare, experts can extract and process information from the following sources:

  • Electronic Health Records (EHRs)
  • Patient portals
  • Clinical studies
  • Internet of Things (IoT) devices
  • Health databases
  • Government agencies
  • Billing records
  • Staffing schedules
  • Search engines
  • Scholarly and scientific journals

For years, healthcare professionals have been diving into the sources above to look for insights that can translate into improving administration and care. However, with Big data analytics in healthcare, the experts receive new opportunities to work with the given data sources. They can be prepared for any data inflow the digital world offers. 

Why Care about Big Data Analytics in Healthcare?

Nothing speaks better of the success or failure of a particular technology than the tangible benefits it brings. While various healthcare software companies can bring the best out of digitization, still, you need to know why you care about the instrument in the first place. 

In general, investment in analytics tools is the right decision. Companies that want to work with data effectively cannot afford to avoid using data analytics. Moreover, there is enough evidence to suggest analytics instruments will still be in demand in upcoming years (see Fig.2).

The U.S. advanced analytics market
Figure. 2 The U.S. advanced analytics market

By looking at particular benefits of Big data in healthcare, you will get an idea of the role of technology in the industry. The following advantages make the most impact.


Data Worldbank indicates that the current healthcare expenditures equal about 9.8% of the global GDP. With the global economy being approximately $104 trillion, simple arithmetic shows healthcare costs to be around $10 trillion. In turn, McKinsey states that in the U.S., healthcare expenses now take 17.6% of the nation’s GDP, which is about $600 billion. The critical issue is that the current expenses are much higher than the expected benchmark. It means cost reduction is one of the vital healthcare industry needs.  

Big data analytics in healthcare comes to the rescue. The only possible solution to the rising costs of healthcare is to offer sophisticated and intelligent data-driven approaches that can redefine the industry. Many healthcare providers realized that focusing on patient outcomes brings higher costs than traditional fee-for-service plans. 

The reason for data-related cost-efficiency is that with Big data tools, healthcare providers have a direct incentive to share patient data. Everything in this scenario has the means to improve patient outcomes and get financial incentives for that.

Eliminating Medical Errors

Medical errors are a huge issue. The scholarly evidence points out that about 100,000 patients die annually because of them in hospitals and clinics. In addition, medical errors cost the industry about $20 billion annually. Respectively, finding the tools to reduce their number is a direct way to save lives and cut costs. 

There have been numerous attempts to deal with the problem. Some providers improve the risk assessment instruments by introducing automation. Others use Big data analytics in healthcare. However, physicians and clinicians generally rely on evidence-based approaches when working with medical data. The problem is that processing vast amounts of data increases the chance of human error, leading to medical error. In such a case, Big data analytics in healthcare brings forward technological tools helping to eliminate the impact of human error.

Big data tools can alert professionals if some wrong medication was prescribed or some faulty clinical test occurred. Essentially, the phenomenon serves as a safety check mechanism, double-checking all the output presented by human agents to ensure the minimization of medical error. 

Optimal Staff Management

There is a growing problem with healthcare staff management. This issue was particularly visible when the COVID-19 pandemic struck. The research states the rising rates of health workers’ burnout because of the workload and inefficient staffing procedures. Strikingly, people who provide health services are also experiencing an increase in health problems due to work-related pressures. In such a context, optimization of staff management can be a lifesaver. 

For health workers to improve patient outcomes, they need to work in a facility with optimal schedules and proper general organization. Big data can help achieve that. Professionals can use predictive analytics in healthcare to collect and process real-time data measuring an organization’s staffing and personnel management performance. 

With the insights from the assessment, providers can alter their staffing schedules and ensure patients get the best care while health workers have an optimal workload. Big data in healthcare optimizes scheduling and helps reduce burnout among health professionals. 

Challenges with Healthcare Big Data Usage

With the benefits of Big data in healthcare, providers receive tools that help cut costs, reduce the number of medical errors, and optimize scheduling and personnel management. However, even when using the services of well-recognized software companies with expertise in Big data, there are still particular challenges one needs to keep in mind.

Data Storage and Management

To use the full potential of Big data, you need to handle the data storage first. In healthcare, vast volumes of data are spread across various sources stored and governed within hospitals and administrative departments. To manage these sources safely and to integrate them, you need a complex infrastructure intact. In addition, it ensures the proper degree of collaboration between providers and reduces data corruption due to inadequate data storage. 


After you have the infrastructure up and running, it is time to proceed with the data-sharing challenge. Providers often implement online reporting software based on distinct business intelligence methods to create a connection between relevant users. In the past, healthcare entirely relied on regression-based approaches. Big data in healthcare brings forward tools like Machine Learning (ML) and predictive analytics. These future-oriented instruments help create collaborative environments bringing reporting to another level. 

Security, Privacy, and Compliance

When working with patient data, security is always a top priority. Daily, hospitals deal with sensitive patient information. Considering the industry’s numerous data sources, there is a higher chance of data breaches for several reasons. To keep data protected, you need to have top-grade encryption tools and comply with all the related rules like HIPAA. Making software compliant is a step forward for a tool to succeed in healthcare

User Literacy

Working with data requires a certain degree of knowledge. If a healthcare professional does not have the proper training to handle Big data, one can corrupt some valuable information or increase the chance of data exposure. Remember that Big data is a technological instrument that users need to know how to handle. It means providers need to plan and implement data literacy training to ensure people with access to technology know how to work with it. 

Healthcare Data Usage Examples

With all the benefits and challenges of Big data analytics in healthcare in the open, it is time to have an in-depth look into particular examples. Big data can improve different aspects of the healthcare industry. The 20 examples mentioned further correspond to particular segments of the healthcare industry aiding from this technology.

  • Instances 1 to 5 cover administrative improvements brought by Big data. 
  • Examples 6 to 10 show Big data analytics’s power and its benefits. 
  • Instances 11 to 13 indicate how Big data can aid vital processes within the industry. 
  • Examples 14 to 16 illustrate better patient outcomes presented by Big data analytics.
  • Instances 17 to 20 indicate how it can benefit general population health trends

1. Predictions and Scheduling

Starting with the administrative and staffing segment, Big data can help solve the question of patient predictions used to improve staffing approaches. Equipped with proper healthcare software that utilize Big data methods, healthcare professionals can predict the number of patients coming to hospitals. Establishing correlating staffing and scheduling procedures is vital to ensure the best care provision and a balanced workload for health workers. 

There is a report offered by Intel showing how French healthcare providers effectively used Big Data in healthcare to have daily and even hourly predictions on the part of patient inflow. Experts managed to analyze ten years’ worth of hospital admissions to develop a prediction model anticipating future admissions based on past experiences. Researchers used Big Data analytics in healthcare to explore relevant patterns. These were exposed through ML tools, the technology presenting the most accurate prediction algorithms. 

2. Personnel Management

Without an engaged workforce, it is impossible to provide high-quality care. However, as we showed before, health workers are currently facing massive rates of burnout and cannot find the proper work-life balance due to inadequate staffing and scheduling. In such a case, Big data in healthcare can help streamline various administrative activities. 

The technology ensures the right degree of personnel fluidity. It entails having staff distributed in the areas requiring the most attention. It is about avoiding overcrowded or understaffed departments. That can be achieved through Big data analytics tools. Professionals collect and analyze data on peak admission periods and evaluate the recent cases of staff overcrowding.

Big data researchers can present predictive models guiding staffing and scheduling decisions within the scope of personnel management. With the technology, hospitals know when to attribute additional workforce to peak sectors and when to withdraw personnel from segments on the brink of overcrowding. 

3. Learning and Development

Forbes indicates that learning and development among health workers play a vital role in ensuring innovation adoption. In addition, one can say that innovation delivered through software products is key to success in any given industry. In a healthcare environment, having particular skills and knowledge can be the difference between a patient’s life and death. Respectively, learning is a direct way to boost patient outcomes.

When digressing the point, you can see that learning and development are aspects that need to be a part of a broader organizational capability. For a hospital to run at optimum capacity, it is crucial to keep track of performance and understand what underperforms. 

Big data analytics in healthcare come with tools that help track down performance. It grants an objective view of who needs additional training and what departments can benefit from learning and development first. Healthcare professionals should also evolve if a healthcare organization evolves toward better patient outcomes. 

4. Electronic Health Records (EHRs)

Handling EHRs is one of the parts of having smooth administrative tasks. The tool has been there for years and is still proven to have a certain degree of demand (see Fig. 3). 

The U.S. electronic health records market
Figure 3. The U.S. electronic health records market

Often, if a healthcare provider has a well-developed EHR platform, one can be confident of partial success. In turn, EHRs can be considered the most widespread adoption of Big data in healthcare. That is because various patient data aspects, such as their medical history and demographics, are stored across the organization’s infrastructure. In addition, with data being stored collectively, healthcare professionals can directly access it from different places.

While EHR has proved to serve well to healthcare, there are massive concerns regarding ways of its adoption. The evidence offered by HITECH research states that while about 94% of hospitals in the U.S. managed to adopt the system, many nations worldwide struggle to use the technology universally. At this point, one can expect new EHR alternatives to emerge. These use the power of Big data analytics in healthcare and learn from the past mistakes linked to implementation-based challenges. 

5. Patient Engagement

With a well-designed telehealth solution, you can boost patient engagement significantly. That will make care delivery more accessible and more seamless. Besides, with the advent of IoT technologies, many people are willing to use smart wearable devices to track different health indicators for disease prevention.

All this vital data can be used to develop better healthcare solutions. However, to access trackable data, patients must be engaged and provide it to physicians. Patients willing to monitor their health are a gold mine for practitioners. In such a case, Big data analytics in healthcare coupled with IoT and various types of healthcare software is an excellent opportunity to improve disease prevention by making patients more engaged in their health monitoring. 

6. Telemedicine

The COVID-19 pandemic forced many industries to adopt models with a significant focus on remote operations and processes. And healthcare was not an exception. In such a case, telemedicine proved to be the next big thing. It emerged at the right time and the right place. For providers, knowing how to develop a good telemedicine platform now represents a direct way toward better care provision. In addition, there is evidence portraying the positive trend with telemedicine (see Fig. 4).

North America telemedicine market
Figure 4. North America telemedicine market

Thanks to the tool, physicians can provide treatment tailored to particular patient needs. In this context, telemedicine is necessarily applied with Big data analytics in healthcare. With predictive analytics, professionals can anticipate acute medical events and offer timely treatment. All these are possible due to health information collected with IoT devices, delivered to physicians through HIPAA-compliant telemedicine platforms, and processed by Big data analytics tools. 

7. Cloud Computing and Alerting

Real-time patient alerting is the instrument that can save lives. Many healthcare organizations use Clinical Decision Support (CDS) tools to analyze medical data simultaneously. That helps health practitioners to make more accurate patient-related decisions. The method described above works best when patients are in the hospital, which enables professionals to collect all the necessary data. 

However, due to the emergence of Big data in healthcare, the case of patient alerting is changing. Physicians can collect health data in real-time with people now having wearable and smart devices. After that, the data is sent to the cloud, where it is safely stored and managed

With data being available to all the relevant parties and stored in one place, various health indicators can be examined by multiple professionals. Such real-time alerting leads to better decision-making and can help with individual and general population health. For instance, platforms like Glooko and Omada Health use cloud computing coupled with Big data to help people with diabetes track their health indicators to anticipate any worsening of their condition. 

8. Predictive Analytics 

Based on AI and ML, predictive analytics in healthcare is the next-generation method of anticipating specific outcomes based on vast volumes of data analyzed with neural networks mimicking human-type reasoning. These outputs are the most accurate and include many factors boosting their precision. 

To illustrate, initiatives like Optum Labs managed to collect data points on more than 150 million people while being HIPAA-compliant. As a result, predictive analytics is accurate, and it helps healthcare professionals access the range of data physicians of the past could only dream of.

Big data analytics in healthcare can help deal with various pressing health concerns on the global level. For instance, it can help diabetes patients to receive treatment tailored to each person’s specific case. With 415 million diabetes patients worldwide, predictive analytics is the instrument that can make a difference. 

9. Medical Imaging

Medical imaging is one of the critical sources of patient data. With proper healthcare software tools, providers can get their hands on an invaluable source of information speaking volumes on patient health. With the medical imaging market booming, one can expect more and more data to be produced by different medical imaging instruments like CT, MRI, and X-Ray (see Fig. 5).

Medical imaging market size by product
Figure 5. Medical imaging market size by product

An estimated 600 million medical imaging procedures are performed annually in the United States alone. Therefore, a vast amount of data can be processed to get valuable insights. At this point, Big data analytics in healthcare offers all the needed tools. With Big-style data technologies like optical character recognition (OCR), healthcare professionals can seamlessly turn images into readable data, and predictive analytics algorithms can later analyze the information and turn it into insights improving patient outcomes. 

In addition, medical imaging processing coupled with upcoming mHealth solutions can be considered a key to preventive healthcare. 

10. Prevent Human Error

The human error factor is responsible for about $29 billion in losses and often can lead to preventable patient deaths. Human error is most visible when analytics is performed. For instance, a person collects information, analyzes it, and forgets about certain variables. Thus, the results of such analysis are corrupted and later translate into wrong drug prescriptions or inadequate treatment plans.

When AI and ML come forward, there is no place for human error left. Companies often use Big data analytics to identify areas potentially affected by such errors. Therefore, AI and ML take on processes like analyzing, parsing, dissecting, and drilling down vast data volumes without human input. That drastically reduces the chance of human error corrupting the analytics process. This report indicates a 66% reduction in data errors when AI is used. 

11. Strategic Planning

When it comes to informed strategic planning, Big data analytics provides insights healthcare administrators and leaders can use to change their decision-making process on a broader scale. For instance, check-up results, appointments, and admissions across different demographics and locations can be analyzed to determine which factors need to be improved to boost patient engagement. It means with good appointment management software at hand, providers can get data that help redefine their approach to care provision. 

Healthcare providers are using the data from Google instruments to change their approaches to care provision. For example, a researcher from Emory University used Google Cloud to develop algorithms analyzing more than 70 factors to predict sepsis in patients. Staggeringly, their predictions offered an 85% accuracy and played a crucial role in how practitioners changed their planning and preparation for the onset of sepsis across the chosen population. As a result, Big data analytics in healthcare make strategic planning more effective.

12. Supply Chain Management

If a healthcare organization has a fragmented supply chain, it will have an adverse effect on all aspects of care provision. Luckily, with Big data analytics, there is a great chance to improve supply chain effectiveness. There is evidence indicating that data-driven decisions brought by predictive analytics can help hospitals save up to $9.9 million in supply chain costs annually. 

With analytics tools brought by Big data, providers can make more informed decisions in terms of price negotiations and product ordering. Thus, the data-driven approach grants insights that medical institutions can effectively utilize to deal with supply chain management. One should also remember that smooth supply management has a direct effect on patient outcomes. And it can avoid delays affecting treatment plans. 

13. Security and Fraud-Prevention

We will show how expertise in Big data services is a direct way to make your processes more secure and protected from any fraud. It matters because the healthcare fraud analytics market is booming (see Fig.6). 

Healthcare fraud analytics market
Figure 6. Healthcare fraud analytics market

It means that companies understand the value of the technology and know the damages fraud and lack of security bring. With an average data breach costing about $9.42 million and more than 93% of healthcare organizations experiencing a data breach, the problem appears to be pressing. In most cases, the reason behind a data breach is that patient data is considered a valuable commodity on the black market.

In turn, for a healthcare organization, a data breach is incredibly costly because it can lead to litigation and audits. Many providers now use predictive analytics in healthcare to detect the most vulnerable areas and anticipate data breaches. Essentially, these algorithms keep track of all the traffic and can detect inconsistencies and potentially fraudulent behaviors. 

Moreover, Big data brings forward new encryption standards and innovative firewalls that use AI-powered tools to apply bank-grade security into healthcare. This scholarly piece indicates that effective fraud-prevention measures can save up to $2.6 billion annually. Cybersecurity is something no company can afford to avoid. In this context, preventing fraud by boosting security helps save money and avoid issues linked to exposure to sensitive patient data. 

14. Emergency Room (ER) Visits

The Department of Health indicates about 46% of preventable ER visits in the State of Rhode Island. In New York, the percentage can go up to 74%. It means many people might have avoided going to the ER. In turn, millions in costs could have been avoided. Some reports suggest avoidable ER visits are responsible for $32 billion annually. 

How can ER visits be prevented with Big data? Hospitals can be integrated with Big-style data platforms sharing patient data across departments. In this case, the ER professionals know whether a patient has already undergone tests in other medical organizations, whether a patient has been assigned to other hospitals, and whether a patient has already received treatment plans from different healthcare vendors. 

With the abovementioned knowledge, ER professionals can avoid unnecessary tasks or reduce the required input. There are already such platforms up and running. There are tools like PreManage ED already helping healthcare organizations avoid preventable ER visits. 

15. Therapies and Innovations

Bringing innovation to care provision is a direct way to boost patient outcomes. If providers develop innovative therapies that prove their worth in practice, healthcare organizations get their hands on instruments that can help millions of individuals. 

Big data analytics in healthcare have the most potential in realms like drug discovery. By analyzing historical data and tracking information in real-time, predictive algorithms help boost the speed of the drug development cycle. Thus, patients can receive the innovative medication they need sooner. 

In addition, with existing data visualization techniques, it is much easier to make sense of the output offered by Big data predictive analytics in healthcare. Thus, Big data helps bring forward innovative drugs and develop new therapies to help practitioners save lives more effectively. 

16. Drug Prescription

The evidence shows how Big data can help improve the drug prescription process. Numerous prescription errors can adversely impact patient outcomes (see Fig. 7).

Common types of prescription errors
Figure 7. Common types of prescription errors

Each error mentioned above can cost lives. However, with Big data analytics in healthcare, practitioners can avoid them. To do that, it is vital to process vast volumes of data.

For instance, this research indicated how drug prescriptions could be improved through processing patient data. The study explored millions of patient records and developed an algorithm illustrating which populations are most vulnerable to becoming addicted to a particular medication. At this point, with such insights at hand, practitioners can prescribe drugs that people will be less likely to be addicted to. 

17. Early Disease Detection

It is no secret that having the ability to detect diseases at the early stages of their development leads to higher chances of tackling the condition. Dealing with early disease prevention is one of the significant areas linked to Big data and propagated by healthcare software tools. 

With the emergence of IoT and instruments like video calling used in telemedicine, practitioners received new sources of information for onset disease prevention. It does not matter whether a professional look for cancer or multiple sclerosis – detecting the condition early on is vital for staying ahead of the disease. 

Companies are utilizing Big data analytics in healthcare to help practitioners with detection. For instance, Prognos Health is an AI-powered platform utilizing Big data approaches to grant access to multi-source diagnostic data. Using this instrument helps physicians detect diseases early by accessing up-to-date data illustrating corresponding symptoms directed toward certain conditions to develop. 

There is also PeraHealth, the company standing behind the so-called Rothman index. It is a scoring system for measuring a patient’s health. It includes data from EHRs, IoT, and lab results. The overall score helps detect diseases, even when the slightest of symptoms are showing. 

18. Cancer 

The World Cancer Research Fund indicates that there are an estimated 18.1 million cancer cases around the world. Helping those people is often considered the top priority because of the mortality rates the condition brings. At this point, Big data in healthcare can help boost cancer treatments’ effectiveness. Starting from telehealth solutions improving access to care and to predictive analytics, helping to detect early stages of cancer. 

To illustrate, one should mention the Cancer Moonshot program. It was developed as a tool for gathering and processing vast volumes of data linked to cancer. Moreover, medical researchers tapped into the program to develop new treatment plans. The key was to determine which therapies had the highest recovery rates. 

With all the data collected and analyzed with Big data tools, researchers saw which hospitals worldwide had the most effective cancer treatments. It allowed the adoption of similar approaches on a large scale, which improved the total cancer recovery rates, reaching up to 50%. As a result, simple data analysis showed the best-performing cancer treatments and allowed practitioners to use them on a broader scale.

19. Opioid

The opioid crisis is one of the most pressing issues in healthcare. In the U.S. alone, thousands of people perish due to the problem (see Fig. 8). 

National drug-involved overdose deaths
Figure 8. National drug-involved overdose deaths

The main concern coming with such a crisis is all about preventable overdose and drug misuse deaths. In addition, PEW estimates the economic tool for the phenomenon to be about $35 billion in healthcare spending annually. Finding new ways to tackle the issue can save lives and money. How can Big data analytics in healthcare help?

The instrument can aid by identifying certain risk factors for opioid abuse or drug misuse. To illustrate, there is a case of Blue Cross Blue Shield organizations that started using predictive analytics to determine particular risk factors and predict drug abuse. They managed to identify a staggering 742 factors offering accurate predictions on whether a particular population is at high risk of being exposed to opioid abuse. 

Keeping that in mind, practitioners can use findings offered by Blue Cross Blue Shield and engage in new analytical studies to be a step ahead of drug abuse. Equipped with valuable data, they can make more appropriate drug prescriptions and consider factors fueling the issue. 

20. Suicide

Similarly to the opioid crisis, Big data analytics can help with suicide prevention measures. The evidence shows the number of suicide deaths is almost comparable to the number of deaths from alcohol abuse (see Fig. 9). 

Annual deaths from alcohol, drugs, and suicide in the United States
Figure 9. Annual deaths from alcohol, drugs, and suicide in the United States

Using Big data analytics in healthcare, researchers and practitioners develop new methods of predicting suicide risk. For instance, Kaiser Permanente offered a study showing how predictive algorithms can help flag behaviors indicating a higher risk of suicide among particular study groups. Essentially, the experts managed to find out the risk factors indicating whether a person is likely to commit suicide or not. This evidence can effectively prioritize treatment and therapy for in-risk groups and help reduce suicide deaths.  

Final Thoughts

All in all, the 20 specific examples mentioned above prove Big data analytics in healthcare have the potential to change the industry for the greater good of everyone involved. Companies working with Big data in healthcare will always have clients. With the demand for Big data services rising, the ones that manage to apply the technology early on will be reaping benefits and getting a competitive advantage. With all being said, one can add that Big data analytics in healthcare appears to be an excellent opportunity to save lives while making profits.

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