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Top 5 Big Data Use Cases in Healthcare

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28th Aug, 2023
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Top 5 Big Data Use Cases in Healthcare

Thanks to improved healthcare services, today, the average human lifespan has increased to a great extent. While this is a commendable milestone for humankind, it also poses lots of new and diverse challenges for health care providers (HCPs). They face increasing amounts of challenges in delivering healthcare services to patients. This is where Big Data comes in the scenario. 

Big Data in healthcare pertains to the massive amounts of healthcare data gathered from multiple sources such as pharmaceutical research, electronic health records (EHRs), healthcare wearables, medical imaging, genomic sequencing, and other such processes. The digitization of healthcare information and the increase in demand for value-based care are the primary reasons behind the rapid rise in Big Data in healthcare. As the ever-increasing pile of healthcare data continues to pose new challenges for HCPs, it calls for the adoption of Big Data technologies and tools that can efficiently collect, store, and analyze large datasets to deliver actionable insights.

Rise of Big Data in Healthcare

The adoption of big data use cases in healthcare has been quite slow compared to other industries (manufacturing, BFSI, logistics, etc.) due to reasons like the sensitivity of private healthcare data, security issues, and budget constraints, among other things. However, a report by the International Data Corporation (IDC) sponsored by Seagate Technology maintains that Big Data is likely to grow faster in healthcare than in sectors like media, manufacturing, or financial services. Furthermore, estimates suggest that healthcare data will grow at a CAGR of 36% all through till 2025.

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Currently, 2 primary trends have encouraged the adoption of big data use cases in healthcare.

  • The first push came from the transition from the ‘pay-for-service’ model (it offers financial incentives to HCPs and caregivers for delivering healthcare services) to a ‘value-based care’ model (it rewards HCPs and caregivers according to the overall health of their patient population). This transition has been possible because of the ability of Big Data Analytics to measure and track the health of the patients. 
  • The second trend is where HCPs and medical professionals leverage using  Big Data Analytics to deliver evidence-based information that promises to boost the efficiencies of healthcare delivery while simultaneously increasing our understanding of the best healthcare practices.

Bottomline – adopting big data use cases in healthcare can potentially transform the healthcare industry for the better. It is not only allowing HCPs to deliver superior treatments, diagnosis, and care experiences, but it is also lowering healthcare costs, thereby making healthcare services accessible to the mass.


Applications of Big Data in Healthcare

  1. Health Tracking

Along with the Internet of Things (IoT), Big Data Analytics is revolutionizing how healthcare statistics and vitals are tracked. While wearables and fitness devices can already detect heart rate, sleep patterns, distance walked, etc., innovations in this front can now monitor one’s blood pressure, glucose levels, pulse, and much more. These technologies are allowing people to take charge of their health. 

  1. Episode Analytics

HCPs are always struggling with offering quality healthcare services at marginalized costs. Episode Analytics and Big Data tools are helping solve this dilemma by allowing HCPs to understand their performance, to identify the areas that offer scope for improvement, and to redesign their care delivery system. Together, all of this helps to optimize the processes as well as reduce the costs.

  1. Fraud detection and prevention 

Big Data Analytics and tools come in very handy to detect and prevent fraud and human errors. These can validate the patient data, analyze his/her medical history, and point out any out of place errors in prescriptions, wrong medicines, wrong dosage, and other minor human mistakes, thereby saving lives.

  1. Real-time alerts

Big Data tech allows HCPs and medical professionals to analyze data in real-time and perform accurate diagnoses. For instance, Clinical Decision Support (CDS) software can analyze medical data on-spot, thereby offering crucial medical advice to healthcare practitioners as they diagnose patients and write prescriptions. This helps save a lot of time.

  1. Telemedicine 

Thanks to Big Data technologies, we are now able to make full use of Telemedicine. It allows HCPs and medical practitioners to deliver remote diagnosis and clinical services to patients, saving them both time and money. 

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Big Data Analytics for Disease Diagnosis and Prediction

Big data analytics has emerged as a powerful tool in healthcare for disease diagnosis and prediction. With the exponential growth in healthcare data, including electronic health records, medical imaging, genomic information, and patient-generated data, the potential to extract valuable insights has increased significantly. Here are some key aspects of how big data analytics is transforming disease diagnosis and prediction in healthcare:

1. Early Detection and Diagnosis

Big data use cases in healthcare enable healthcare providers to analyze large datasets from diverse sources, helping identify patterns, trends, and anomalies that may indicate early signs of diseases. By analyzing patient data, including vital signs, lab results, and lifestyle information, healthcare professionals can identify high-risk individuals and intervene proactively, leading to early diagnosis and timely treatment.

2. Predictive Analytics for Patient Outcomes

Through predictive analytics, big data helps healthcare institutions anticipate patient outcomes, treatment responses, and disease progression. By employing machine learning algorithms on vast amounts of patient data, healthcare providers can create predictive models that estimate the likelihood of specific outcomes based on individual patient characteristics, historical data, and treatment options.

3. Precision Medicine

Big data analytics is critical in advancing precision medicine, tailoring medical treatments to individual patients based on their genetic makeup, lifestyle, and other relevant factors. Analyzing massive genomic datasets allows researchers and clinicians to identify genetic markers associated with specific diseases and determine personalized treatment strategies that offer individual patients the highest chances of success.

4. Real-time Data Monitoring

Big data analytics enables real-time patient data monitoring, offering healthcare professionals a continuous and comprehensive view of a patient’s health status. This real-time data access facilitates prompt detection of any alarming changes, allowing for timely interventions and reducing the risk of complications.

5. Disease Outbreak Prediction and Management

In public health, big data use cases in healthcare assists in disease outbreak prediction and management. By analyzing data from various sources, including social media, surveillance systems, and patient records, public health authorities can identify and respond to potential outbreaks more swiftly and effectively, helping to control the spread of infectious diseases.

6. Drug Discovery and Development

Big data analytics is accelerating drug discovery and development processes in the pharmaceutical industry. Researchers can identify potential drug targets, predict drug efficacy, and optimize treatment regimens by analyzing vast datasets, including molecular information, clinical trial results, and drug interactions.

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Enhancing Personalized Medicine through Big Data Insights

Big data use cases in healthcare insights have significantly advanced personalized medicine, tailoring medical treatments to individual patients based on their unique characteristics. Here’s how big data insights are enhancing personalized medicine:

1. Patient Profiling and Risk Stratification

Big data analytics allows healthcare providers to create detailed patient profiles by analyzing vast patient data, including medical history, genetic information, lifestyle factors, and treatment outcomes. These profiles enable risk stratification, identifying patients at higher risk for specific diseases or adverse treatment reactions. Healthcare professionals can develop personalized prevention plans and treatment approaches by understanding individual patient risk factors.

2. Genomics and Precision Medicine

Big data analysis of genomic data is crucial in advancing precision medicine. Researchers can identify genetic variations associated with certain diseases or drug responses by analyzing large-scale genomic datasets. This information helps develop targeted therapies that are more likely effective and reduce the risk of adverse reactions.

3. Treatment Response Prediction

Big data analytics leverages machine learning algorithms to analyze patient data and predict individual treatment responses. By considering genetic markers, clinical history, and lifestyle factors, healthcare providers can determine the most suitable treatment options for each patient, increasing the chances of successful outcomes.

4. Real-time Monitoring and Wearable Devices

With the proliferation of wearable devices and IoT-enabled healthcare solutions, big data use cases in healthcare insights enable real-time monitoring of patient’s health parameters. Continuous data collection and analysis provide healthcare professionals with up-to-date information about patients’ conditions, facilitating timely adjustments to treatment plans based on their evolving health status.

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Wrapping up

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In the future, the healthcare sector will see a lot more of Big Data applications that will revolutionize the healthcare industry one step at a time. Not only will Big Data help streamline the delivery of healthcare services, but it will also allow HCPs to enhance their competitive advantage through smart business solutions.

If you are interested to know more about Big Data, check out our Advanced Certificate Programme in Big Data from IIIT Bangalore.

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Frequently Asked Questions (FAQs)

1What is meant by structured and unstructured data?

Big Data consists of high velocity, huge volume and an enormous variety of data. This data can be of three types – structured data, unstructured data and semi-structured data. Structured data is basically data that is properly formatted and organized into a repository that is essentially a relational database. Elements of structured data can be used effectively for analysis without needing any reformatting. Unstructured data is not formatted or organized in any way. It lacks any predefined data model and is not suitable for conventional relational databases. Semi-structured data contains elements that are somewhat organized and can be used after partial processing, for example, XML data.

2What kind of data is Big Data?

Big Data essentially consists of massive and complex sets of data which are of a wide variety and increasing volumes and arrive at substantial velocity. These data sets are so enormous that conventional applications designed for data processing are not enough to manage them. But the greatest value or power of Big Data is that it contains insights that can help address a variety of business concerns and help in generating higher profits. In the present times, Big Data has assumed capital significance and can derive great benefits to companies, thereby helping them achieve tremendous efficiency in terms of services and products.

3Can Big Data be used by schools and colleges?

Yes, Big Data is used by schools, colleges and universities. It helps educational institutes with the information they need to track their students' academic performances and understand their students better. Big Data can help teachers understand what interests the students to connect with them better. And very significantly, it can also help improve the gradation systems that different schools adopt. Teachers can track and analyze a student’s performance in real-time and base their assessments on information derived through Big Data. This data-driven approach enables mentors and teachers to provide accurate feedback that helps students learn efficiently and improve their performance.

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