The Major Applications of Big Data in Healthcare

Data without analytics is equivalent to trash. Most healthcare facilities are sitting on tons of data that were never utilized for the correct reasons.  For instance, clinicians with access to huge data sets may be able to recognize the precursors of a dangerous sickness before it manifests. In this blog, we will talk about similar use cases of big data analytics that can enhance the workflows of every healthcare facility.

But before that lets understand a bit about Big Data.

The Promise of 3Vs by Big Data 

The three V's of big data. Velocity, Volume and Variety

The three Vs—volume, velocity, and variety—are crucial to comprehending how big data may be measured and how different it is from traditional data. 

  • Volume: Volume is a crucial idea to understand when it comes to large data. Many corporations and industries use a lot of data, maybe because they have a lot of customers or because they feed AI with data. This includes the intelligent appliances in our homes that are always absorbing information from their environment or services like Uber, which has millions of users at any given moment and adds a ton of data to the mix. Let’s look at some real-world instances, like Facebook, which keeps pictures, to help better understand this. It’s estimated that Facebook has a staggering 250 billion photographs in its archives. This doesn’t even account for things like Facebook postings, which are estimated to number 2.5 trillion (and that is only from 2016 onwards). 
  • Velocity: Velocity in the context of big data refers to the rate of data inflow. Using the earlier Facebook example, 900 million photos are posted by users every single day, despite the social media giant’s 250 billion image storage capacity. This massive volume of data needs to be processed, filed, and retrieved every day. Sensor data is another instance of velocity. There will be an increasing number of connected sensors as the Internet of Things grows rapidly. Effectively, this will result in practically constant data transmission. 
  • Variety: Variety is the third area of big data. When talking about big data diversity, it means that the data might vary greatly from one application to another, with a large portion of it also being unstructured data. As in the past, all the data may not necessarily fit neatly into one database application. Emails provide a good illustration of big data’s diversity. Since each message has a unique destination, time stamp, potential attachments, and text that is unique, no two messages are ever the same. Emails are a type of data that, like audio recordings, films, and images, tends to be exceedingly diverse and unstructured. 

Want to discover the immense possibilities of Big Data?

Connect with us

Where in Healthcare is Big Data used? 

  • Big Data Healthcare application on Patient care: This is a dashboard that collects patient data in a central platform. It is designed to enhance provider service and treatment accuracy. For instance, measures on bed occupancy rates provide a window into where resources may be needed, and tracking missed or canceled appointments will provide senior executives with the information they need to lower expensive patient no-shows. 
  • Big Data in Healthcare Applied on a Hospital Dashboard: This is a dashboard that collects Hospital data from every division like attendance, specialty bifurcation, average wait time, the total number of patients, and many more.  

Top 17 Applications of Big Data in Healthcare 

The applications of big data in healthcare can be broadly mapped into 17 different directions.

  • Patient predictions for improved staffing
  • Use in Electronic Health Records
  • Real-time Alerting
  • Enhancing Patient Engagement
  • Help in preventing opioid abuse
  • Informed strategic Planning
  • Big data in cancerresearchPredictive analysis
  • Reduce Fraud and Enhance Security
  • Telemedicine
  • Medical Imaging and Big Data
  • Preventing Unnecessary ER Visits
  • Smart Staffing & Personnel Management
  • Learning & Development for Healthcare Professionals
  • Advanced Risk & Disease Control
  • Reducing cost
  • Better staffing & management

Let’s take a deeper look at all these applications and understand exactly how big data is doing it.

  1. Patient Predictions for Improved Staffing: Big data analytics allow healthcare facilities to predict patient inflow accurately, enabling optimized staff planning and resource allocation. Predictive models analyze historical admission rates and seasonal trends to anticipate future demands, helping hospitals adjust staffing levels dynamically. This proactive approach reduces waiting times, avoids understaffing or overstaffing, and ensures high-quality patient care.
  2. Use in Electronic Health Records (EHRs): The biggest and most widespread usage of big data in healthcare is in EHRs. These EHRs have a digital profile that includes information about their background, health history, allergies, lab test results, etc. The records are accessible to providers from every secure network. As it is a digital file providers can edit and modify the data without the pressure of increased paperwork. These EHRs come with other small but really important features like Real-Time alerting utilizing CDS(Clinical Decision Support) software. 
  3. Real-Time Alerting: Big data systems integrate with healthcare monitoring devices to track patient vitals and other health indicators continuously. These systems analyze the data in real time and can trigger alerts for healthcare providers when abnormalities are detected. This immediate notification allows for prompt intervention, which is critical in preventing severe health events and improving patient safety.
  4. Enhancing Patient Engagement: By leveraging data from wearable devices and health apps, big data empowers patients to take an active role in managing their health. Analytics help in providing personalized feedback, health tips, and lifestyle recommendations based on the user’s health data. This not only fosters greater patient engagement but also encourages adherence to prescribed health regimens and preventive measures.
  5. Help in preventing opioid abuse in the US: Interacting with those deemed to be “high risk” and keeping them from becoming addicted to drugs is a tricky task. Big data in healthcare helps in strategically storing the required information from e-prescriptions into the state PDMP database and also the EHRs. Consequently, this helps in giving real-time alerts in scenarios of Opioid overuse. Learn how a custom PDMP database can help you flag the potential cases of opioid overuse.
  6. Informed Strategic Planning: Data-driven insights are invaluable for healthcare administrators for strategic planning and decision-making. Big data analyzes patterns in service usage, patient outcomes, and operational efficiency to guide investments in technology, facilities, and personnel. This strategic planning helps in enhancing service delivery, cutting costs, and improving patient satisfaction and outcomes.
  7. Big Data in Cancer Research: In cancer research, big data analytics processes vast datasets from genomic studies, clinical trials, and patient records to identify potential treatment pathways and understand disease mechanisms. This accelerates the pace of research and aids in developing targeted therapies that improve survival rates and reduce side effects.
  8. Predictive Analytics: Predictive analytics in healthcare uses algorithms and machine learning to forecast future health events based on patient histories and population health data. This can predict disease outbreaks, hospital readmissions, and patient deterioration before they occur, allowing healthcare providers to deploy preemptive measures to avoid adverse outcomes.
  9. Reduce Fraud and Enhance Security: Big data applications significantly reduce healthcare fraud by analyzing billing and claims data to detect irregular patterns and suspicious activities. Additionally, data encryption and robust access controls ensure the protection of sensitive patient information, thereby enhancing overall data security.
  10. Telemedicine: Big data supports telemedicine by enabling more accurate diagnoses and broader accessibility to healthcare services, especially in remote and underserved areas. By collecting and analyzing health data, telemedicine platforms can offer personalized care recommendations, monitor chronic conditions, and provide timely medical consultations remotely.
  11. Medical Imaging and Big Data: The integration of big data with medical imaging technology enhances the ability to process and analyze complex imaging data quickly. This leads to more precise diagnoses, better tracking of disease progression, and more tailored treatment plans, which significantly improve patient outcomes in fields such as oncology and neurology.
  12. Preventing Unnecessary ER Visits: By utilizing big data to analyze patient records and real-time health status, healthcare providers can manage chronic conditions more effectively and make informed decisions about the necessity of emergency care. This reduces unnecessary ER visits, alleviating pressure on emergency services and reducing healthcare costs.
  13. Smart Staffing & Personnel Management: Advanced analytics optimize healthcare staffing by predicting peak times and patient loads, allowing hospitals to allocate their resources more effectively. This ensures that patient care is timely and efficient while maintaining high satisfaction levels among staff due to better workload management.
  14. Learning & Development for Healthcare Professionals: Big data drives personalized learning and development for healthcare professionals by identifying skill gaps and tracking performance improvements over time. Tailored training programs based on big data analytics enhance clinical skills, adapt to new healthcare technologies, and improve patient care practices.
  15. Advanced Risk & Disease Control: Utilizing big data for disease surveillance and risk management helps in early detection and control of outbreaks, chronic disease management, and health promotion campaigns. Predictive models analyze health trends to implement effective preventive measures, improving public health outcomes and reducing the burden on healthcare systems.
  16. Reducing cost: According to a report by Mckinsey, after a steady increase of 20 years, healthcare expenses now represent 17.6 percent of the US GDP, which is nearly $600 billion more than the expected benchmark. We obviously require some insightful, data-driven thinking in this regard. Additionally, current incentives are changing. Many insurance companies are moving away from fee-for-service plans, which encourage the use of expensive and occasionally unnecessary treatments as well as the quick treatment of large numbers of patients, and toward plans that place a higher priority on patient outcomes. Well, in earlier plans, healthcare providers weren’t directly encouraged to share patient information, which made it more difficult to harness the potential of analytics. They have a financial incentive to share data that can be used to improve patients’ lives while lowering costs for insurance companies now that more of them are paid based on patient outcomes.
  17. Better staffing & management: Patient care will deteriorate, service levels will decline, and errors will occur without a cohesive, motivated workforce. But you can streamline your staff administration duties in a variety of crucial areas with the help of big data solutions in the healthcare industry. Medical facilities that are already overburdened with patient care might streamline patient care by using the correct HR analytics powered by big data to estimate operating room demand and manage staffing. 

The Challenges of Implementing Big Data in Healthcare

Implementing Big Data in healthcare comes with its own set of unique challenges. Lets take a look at those.

  1. Data Privacy and Security: The biggest concern is protecting patient confidentiality as healthcare data often includes sensitive personal information. Ensuring security and compliance with laws like HIPAA in the U.S. requires sophisticated encryption methods and secure data practices, which can be complex and costly to implement.
  2. Data Integration: Integrating heterogeneous data from various sources—like electronic health records, medical imaging, genomic data, and patient-generated data from wearables—poses significant challenges. This data often varies in format, quality, and completeness, requiring robust integration tools and strategies to create a unified view that can be effectively analyzed.Here is a case study of us solving an integration issue between hospitals and medical imaging labs. 
  3. Data Quality and Accuracy: Poor data quality can lead to incorrect conclusions, which can be particularly critical in healthcare settings where decisions directly impact patient outcomes. Ensuring the accuracy, completeness, and consistency of healthcare data is imperative, yet challenging due to the vast amounts of unstructured data generated.
  4. Infrastructure and Costs: Building and maintaining the infrastructure necessary for big data analytics involves significant investment in terms of both technology and human resources. High-performance servers, secure storage solutions, and advanced analytics software are required, which can be prohibitively expensive for some healthcare providers.
  5. Skill Shortages: There is a pressing need for skilled data scientists and healthcare IT professionals who understand both the technical aspects of big data analytics and the unique needs of the healthcare industry. The shortage of such professionals can hinder the effective implementation and utilization of big data solutions.
  6. Regulatory and Ethical Issues: Navigating the regulatory landscape of healthcare data usage and ensuring ethical handling of patient information adds another layer of complexity. Each country has its own regulations and standards, which can complicate cross-border data integration and usage.

Addressing these challenges requires not only technological solutions but also strategic planning and ongoing management to fully harness the potential of big data in improving healthcare outcomes. An experienced solution partner can solve all these challenges for you.

Think Big (Data), Think Nalashaa 

Big data in healthcare has enormous potential to improve patient outcomes and support more effective and efficient healthcare delivery.  

However, the effective use of big data in healthcare requires a deep understanding of data and the ability to process, analyze, and visualize the information in meaningful ways. Nalashaa’s team of experts has extensive experience in the field of big data and healthcare IT services. We can help organizations harness the power of big data to support their goals and initiatives. 

 Whether you’re looking to improve population health management, enhance patient outcomes, or gain insights into disease patterns, Nalashaa can help you.  Connect with us at 

The following two tabs change content below.
Mitrajit Das

Mitrajit Das

A writer with a keen interest in the Healthcare domain and B2B content marketing. He enjoys writing and creating pieces around the latest Healthcare IT trends using the simplest of words.
Mitrajit Das

Latest posts by Mitrajit Das (see all)

2 thoughts on “The Major Applications of Big Data in Healthcare

  1. Hey there! I hope you wouldn’t mind if I share this article with my cousin so he can find the right manager to assist him. He has a lot of health forms from his colleagues to compile and analyze. Anyway, kudos for showing us that we’d be able to minimize operational expenses by applying big data in healthcare management.

  2. Your blog post was a valuable resource on the topic. I found the content to be informative and thoughtfully structured. I appreciated the clear explanations and the practical applications you discussed.

Leave a Reply

Your email address will not be published. Required fields are marked *