Big data in healthcare

Big Data, Big Impact: How Analytics Are Transforming Healthcare

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You may have heard the term “big data” thrown around, but what does it actually mean for healthcare? As it turns out, big data analytics is transforming the industry in ways you may not expect. 

Analytics is revolutionizing patient care and business operations, from predicting outbreaks to personalizing treatment plans. This article will share some of CHI Software’s experience and unique insights from our industry research.

You’ll see and learn how putting data into action can impact everything from clinical trials to hospital workflows. We’ll also discuss the challenges health care faces when implementing analytics. Let’s not waste a minute!

The Growing Role of Big Data in Healthcare

Big data is transforming the healthcare industry in major ways. Hospitals, doctors, and insurance companies use advanced analytics from huge amounts of data for different purposes. But to get a better overview, let’s start from the beginning – what is big data in health care exactly? 

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Big data in healthcare refers to collecting and analyzing all sorts of information stored in clinics and hospitals that are too complex to grasp and understand using manual tools. Such data includes patient images, nursing notes, medical equipment reports, social media, etc. 

  • Based on our research, only 10% of respondents didn’t manage their data regularly as of 2023. 7% of them manage data randomly without a structured approach, and 3% ignore data administration in any form due to its complexity.

Data management methods in healthcare

  • In addition, almost 30% of our respondents believe that data analytics and machine learning are in high demand but have low supply now.

High-demand skills in the healthcare industry

  • At the same time, Roots Analysis states that the global market of big data in health care will likely grow with a 19.06% CAGR from USD 67 billion in 2023 to USD 540 billion in 2035.

Big data in healthcare market

The reason behind this leap may not be apparent, but Roots Analysis points out that with the expanding use of electronic medical records and wearable devices used in the healthcare sector. The more organizations adopt them, the more data is generated daily. On average, one hospital can generate 50 petabytes (50,000 terabytes) of data every day! 

Moreover, this amount is expected to grow with a CAGR of around 35% until 2025. Those who can effectively analyze this data will be able to manage operations and decision-making more successfully and, hence, outrun competition. But that’s only the tip of the iceberg. Let’s look at all the benefits more closely.

Benefits of Big Data in Healthcare: The Revolution Is Happening

Big data health systems offer significant benefits to healthcare organizations. We’ll divide them into two categories: operational, which helps improve hospital workflows and patient experience, and strictly medical, which helps doctors in making decisions.

Benefits of big data in healthcare

Operational Benefits 

Streamlining Hospital Operations

Hospitals generate vast amounts of clinical data from patient records, prescriptions, medical devices, and more. Analyzing this data helps identify inefficiencies and optimize hospital operations. 

For example, healthcare systems for data analysis can predict patient volume and enable better resource allocation. It can also minimize wait times by forecasting which departments will be the busiest.

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Enhancing Patient Experience

With the power of big data analytics, health care providers can elevate the overall patient experience. Big data is one of the superior care components, which leads to improved outcomes and greater satisfaction in interactions with medical professionals. Regular checkups and doctor appointments can become a pleasant routine for thousands of people.

Lowering Healthcare Costs

Data mining can detect patterns in patient data to reduce unnecessary tests and procedures. For instance, analytics may reveal that certain blood tests or scans rarely provide helpful information for a given condition. By eliminating unnecessary treatments, hospitals can potentially lower costs. 

Studying data helps prevent patients from returning to the hospital by identifying those most likely to have issues after discharge. Preventing readmissions can lead to significant cost savings.

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Strictly Medical Benefits

Improving Health Indicators

Discovering the potential of big data analytics has been a game-changer for public health outcomes, from tackling diabetes and mental health challenges to lowering suicide rates. A recent study in the Journal of Medical Internet Research highlighted the role of big data analytics in diagnosing illnesses and managing chronic conditions.

Personalizing Patient Care

Advanced data analytics in healthcare transform raw data sets into insights that help doctors make better treatment decisions. Algorithms can detect patterns linking symptoms, conditions, medications, and patient outcomes. Doctors can use these patterns to select the most effective treatment plan for a patient’s individual circumstances. 

Predictive modeling can anticipate a patient’s future health risks – and armed with these insights, doctors can develop personalized prevention and wellness plans.

Enabling Precision Medicine

Precision medicine uses data analytics in healthcare to understand how a person’s genes, environment, and lifestyle influence their health. Analytics help identify subgroups of patients who share biological or environmental characteristics that affect how they respond to certain treatments. Doctors can then tailor treatments based on a patient’s genetic profile and health data. Precision medicine powered by big data has the potential to revolutionize how we prevent and cure diseases.

Top Healthcare Data Analytics Technologies and Their Use Cases

Top healthcare data analytics technologies

Health care improves the way people live, and in turn, people improve how health care works. It’s a fair bargain, isn’t it? Let’s see how innovations impact our well-being.

Artificial Intelligence (AI)

It seems like AI is constantly in the spotlight. You see these two letters everywhere in the news, and guess what? The technology is also being successfully used in the healthcare industry.

Artificial intelligence and machine learning (an AI subset) allow computers to analyze huge amounts of medical data to uncover hidden insights and make predictions. 

In particular, healthcare AI consulting is employed in diagnosing diseases, personalized treatment plans, robot-assisted surgery, virtual nursing assistants, and administrative workflow automation. That’s a lot! These solutions hold the promise of significantly enhancing results and cutting down expenses, so expect to see continued growth in healthcare AI.

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Wearable and IoT Devices

Data generated with IoT devices offers a lot of insights into patients’ well-being outside the doctor’s office. By analyzing data from wearable heart rate monitors or smart pill bottles, doctors can get a more complete picture of a patient’s health and even detect potential issues early on. The health care wearable device market is booming and will continue to expand.

Personalized Medicine

With the rise of genetic testing and precision medicine, healthcare organizations can develop tailored treatment plans based on a patient’s unique genetic profile and health needs. 

By analyzing a patient’s DNA, family health history, lifestyle, and environment, doctors can determine which treatments are more likely to succeed and avoid those that may cause adverse reactions. Personalized medicine promises a lot of benefits – if not a revolution – in the health care industry.

But, as it often happens, every niche has challenges to overcome. We won’t hide a thing from you, so keep reading.

Healthcare Big Data Challenges: What Are They, and How to Eliminate Them?

Healthcare big data challenges

Although big data analytics holds great promise for enhancing healthcare, it also comes with obstacles that must be addressed. We’ll cover them now, one by one.

Data Silos

Healthcare facilities often have data spread across many disconnected health systems that don’t communicate with each other. Such systems include separate electronic health records, billing, and insurance systems. 

What to do?

To gain a full view of patient information, you’ll need to break down these data silos through system integration and interoperability. Advanced platforms can help collect and organize data from multiple sources for holistic analysis.

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Privacy and Security

Data security is non-negotiable when processing medical information. Strict laws like HIPAA regulate how healthcare information can be used and disclosed. Any big data health system must fully comply with these laws and have robust security measures in place to protect sensitive data. 

What to do?

  • De-identification techniques like masking or tokenization can help the required information to be used for analytics while preserving privacy. 
  • You’ll also need tight control over who can access data through identity management and access control policies.

Data Quality

Poor data quality is one of the biggest obstacles to gaining meaningful insights from big data. Inaccurate, incomplete, and inconsistent data will lead to unreliable conclusions. 

What to do?

  • Healthcare organizations need to invest in data governance and management to ensure high-quality information. This includes processes for data standardization, validation, and cleaning. 
  • Master data management can also help link related information and resolve inconsistencies.

Skills Gap

Working with a healthcare system based on big data requires specialized skills that many healthcare organizations lack. You’ll need people with expertise in data science, analytics, data engineering, and data visualization. This demand for big data talent currently outweighs the supply, making it difficult for some organizations to fill these roles. 

What to do?

Companies looking to overcome the skills gap partner with CHI Software to train existing staff, hire new talent, and cope with their data challenges. We provide big data software development, from consulting to integration. If you need help figuring out where to start, we’ve got you covered.

Conclusion

Now you know that data analytics in healthcare brings a lot of innovations to the industry. From improving patient outcomes to reducing costs and streamlining operations, the examples we covered show the tremendous potential of analytics in this field. 

Healthcare providers of all sizes stand to benefit immensely, so consider jumping on board. Start small if needed, but be bold and think big. With the right data strategy and solutions, you, too, can leverage big data to make a real difference in people’s lives. 

The future looks bright, but you’ve got to be proactive and get the ball rolling. It’s time to get excited about the impacts you can make. It’s time to message CHI engineers. Get those innovations going!

FAQs

  • How do organizations set up data collection in healthcare? arrow

    There are several sources providing big data for health care facilities. They include electronic health records (EHRs), medical imaging, genetic data, and wearable technology. But that’s not all. Any source of information can be a valuable tool if you know how to process every data set. Other sources are patient surveys, clinical trials, monitoring devices, etc.

  • What are the benefits of big data in healthcare? arrow

    We've divided the benefits into operational and strictly medical categories. By operational, we mean streamlined hospital operations, enhanced patient experience, and lowered healthcare costs. Strictly medical benefits are even more impressive. Think of improved health indicators, personalized care, and precision medicine.

  • How can I protect patient privacy when using big data? arrow

    To protect patient privacy, you must adhere to strict laws and regulations like HIPAA in the US and GDPR in Europe. This is the foundation of any future solution. To build a more resilient system, you also must employ robust encryption, secure data storage, and controlled access mechanisms for specific personnel. Finally, regular audits and compliance checks are crucial to maintaining privacy standards.

  • What are some examples of big data analytics in healthcare? arrow

    There are several scenarios for big data in health care. The first is predictive analytics, which helps find patients who might get chronic diseases like diabetes or heart problems. Hospitals can also use big data to improve patient flow and resource allocation to reduce wait times and operational costs. The most advanced example is personalized medicine, which uses big data technologies to facilitate genetic sequencing and analysis, enabling treatments for individual genetic profiles.

  • How can healthcare professionals prepare for the impact of big data? arrow

    Healthcare professionals can keep up with the times by pursuing education and training in data analytics and health informatics. Participating in workshops, online courses, and certification programs can provide new knowledge and advantages. Last but not least, collaborating with data scientists and IT professionals can help healthcare workers integrate data-driven insights into their clinical practices.

About the author
Alex Shatalov Data Scientist & ML Engineer

Alex is a Data Scientist & ML Engineer with an NLP specialization. He is passionate about AI-related technologies, fond of science, and participated in many international scientific conferences.

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