Healthcare is all about information: Patient information, treatment plans, scans, diagnosis papers, medical trials, and prescriptions are produced daily, turning into vast volumes of data that must be stored, analyzed, and appropriately secured.
Big data in healthcare market is predicted to reach $70 billion in 2025 and is expected to grow by 568%. What are the drivers of this growth, and is big data analytics indeed as beneficial in healthcare as it seems?
At IntelliSoft, we have over 13 years of experience in big data analytics and healthcare, so we know all the intricacies of their connection and are ready to share them with you. Buckle up, and let’s transform your healthcare organization with big data.
Table of Contents
What is Big Data Analytics in Healthcare Industry?
What is big data in health care? Every day, medical professionals receive tons of information that needs to be analyzed. This information comes from medical equipment, treatment plans, patient images and test results, clinical trials, etc. This information helps medical professionals make decisions based on evidence and proof and improves patient care. But if all the information came on paper, would it be possible to offer high-quality, timely patient care? That is where big data analytics plays a crucial role.
Data analytics allows for quickly processing huge volumes of information instead of spending hours and days reviewing all paper documentation. By analyzing text, images, graphs, and videos with advanced big data solutions, healthcare professionals can improve their decision-making process, decrease patient care costs, improve patient outcomes, and improve overall productivity.
The Use of Big Data Analytics in Healthcare Industry: Insights
In the past couple of years, big data in healthcare has witnessed impressive growth, considering the impressive amounts of healthcare information generated, new technological advances, and the need for information-driven decision-making in the healthcare industry.
In 2022, the global Big Data Analytics in the healthcare market was valued at $32.2 billion, and it is projected to reach $90.45 billion by 2030. From 2023 to 2032, the industry is expected to grow at a CAGR of 16.7%.
Benefits of Big Data Analytics in Healthcare Industry
The main purpose and benefit of big data in healthcare is to back up any professional judgment with evidence and proof from the analyzed information. It helps medical practitioners uncover patterns and trends in information and improve patient care.
The main advantages of big data in healthcare are as follows:
- Improved Clinical Care. First and foremost, data analytics help practitioners improve patient care by making more informed decisions. By analyzing patient information, their medical histories, and treatment outcomes, it is possible to make patient care more personalized and tailored to specific people, develop more effective treatment plans, and predict how various treatments will work.
- Improved Diagnosis. ML and AI algorithms can identify anomalies and compare them with past information, resulting in more precise diagnoses than humans would. This benefit is all about correctly identifying rare or complex diseases and developing treatments for them at earlier stages, increasing the chances of effectively treating them.
- Better Preventive Care. Instead of treating diseases at more severe stages, algorithms help prevent the likelihood of developing certain conditions by analyzing patients’ genetics, family history, and predisposition to specific diseases. With the help of data analytics, it is possible to prevent hospitalizations and costly treatments.
- Accelerated Clinical Research. Data analytics helps speed up clinical research and innovation in the industry. The algorithms analyze information from EHRs, clinical trials, and other sources to improve treatment efficiency and patient outcomes.
- Personalized Treatment. Since all patient information is stored in one place and constantly analyzed, big data analytics help create more personalized treatment plans. Thus, personalized treatment is done by analyzing patients’ biomarkers, genetic profiles, and other patient-specific information.
Use Cases of Big Data and Analytics in Healthcare
Healthcare practitioners use data analytics to gain actionable insights from analyzed information and turn these insights into personalized treatment plans and more accurate diagnoses. Here are the big data analytics in healthcare examples:
Hospital Admissions and Readmissions Prediction
Hospitals worldwide are often overcrowded, and there is not enough staff to keep up with the number of patients, leading to issues with patient care.
One of the main solutions to hospital overcrowding is predictive analytics in healthcare using big data. These algorithms, which analyze historical information, help predict future demand trends and prepare for them.
Often, patients can be readmitted shortly after their treatment at the hospital, so predictive analytics in healthcare using big data helps determine whether a specific patient will be readmitted. The algorithms analyze patient demographics, initial admission details, medical history, hospitalization history, and lab results. This information makes it possible to predict the number of readmissions.
Early Disease Detection and Forecasting
Detecting a disease early and treating it right away is much easier than dealing with it after it has progressed. However, how do healthcare professionals detect diseases at the earliest stages, especially if the patient information is inconclusive?
Some serious diseases have almost no symptoms at early stages, or the symptoms overlap with other conditions, making it challenging to make a proper diagnosis. This is why big information analytics is needed to resolve this issue. With a predictive algorithm, it is much easier to predict the onset of specific diseases and identify them at earlier stages using historical information.
Researchers from Nottingham University proved how predictive analytics in healthcare using big data can help prevent heart disease. The team used algorithms to analyze a dataset of 378,256 patients and estimate the changes in people having cardiovascular issues. The algorithm correctly predicted that 7.6% would develop a heart condition compared to standard risk predictions.
Expedited Drug Discovery and Development
With the help of these systems, it is possible to identify potential drug candidates that are more efficient than other drugs while reducing the time and cost of their development.
Al and ML will keep transforming how drugs are discovered and developed, bringing safer, more efficient, and more high-quality treatments to patients worldwide.
In 2021, more than 100 drug applications submitted to the FDA used machine learning and advanced analytics. Moreover, big data and predictive analytics in healthcare can help eliminate ineffective or even dangerous drug candidates without testing them on humans and putting them at risk. This saves time, eliminates side effects, and helps save costs on drug development.
Enhanced Operational Decision-Making
One of the biggest issues in the healthcare sector is the workforce shortage —there are not enough people in the hospitals, leading to inefficiency. However, hospitals need to do more with fewer people. That’s why data analytics is needed—it can help improve decision-making without involving more people in the process.
Another issue is that patients often get admitted to the wrong departments due to limited capacity, which results in longer stays and high admission rates. Big data analytics and AI in mental healthcare help predict the number of patients, discharges, and transfers, making the process faster and smoother.
Swifter and More Accurate Diagnostics
Medical imaging is the key to accurate diagnosis, yet analyzing medical images by hand is both time-consuming and ineffective as it requires too much human effort. ML can help optimize the process and receive more accurate diagnosis results, identify anomalies, and compare cases already in the database.
For example, Google Health, which combines computer vision and image search capabilities, analyzes skin, hair, and nail conditions. It is one of the most effective data analytics tools healthcare practitioners use, as it shows over 90% accuracy.
Optimization of Procurement and Supply Chains
Medical organizations rely heavily on resources, including energy, water, equipment, and supplies, making an efficient supply chain vital.
However, supply chain processes often lack effectiveness, with over 80% of healthcare professionals managing inventory manually. Manual processes in the supply chain pose significant challenges, impacting patient care and leading to resource waste and increased operating costs.
Big data analytics offers a solution by optimizing supply chains, as demonstrated by Johnson & Johnson’s unified data analytics platform. This platform enhances visibility and enables various optimizations, from maintaining stock levels to managing spending efficiently.
Related Readings:
- The Internet of Things (IoT) in Health Care
- What Is Interoperability in Healthcare and Why Is It Crucial?
- How Artificial Intelligence Innovates the Healthcare Industry
- A Step-by-Step Guide to Developing HIPAA-compliant Medical Apps
What Are the Challenges of Implementing Big Data Analytics in Healthcare?
The greater the benefits of big data analytics in healthcare – the greater the challenges. Even though big data analytics has the potential to revolutionize healthcare, it also causes hurdles that healthcare organizations have to deal with when implementing big data analytics. Let’s dive into the key challenges of implementing big data in health care settings:
Information Quality and Standardization
The main challenge is to ensure the quality of information collected from various sources, such as electronic health records, medical imaging, patient-generated information, and wearable devices. Oftentimes, this information is inconsistent, incomplete, or inaccurate, leading to issues with decision-making, analysis, and treatment. This is why healthcare practitioners need to implement information quality control processes and information governance frameworks and drive a more data-driven culture through education and training.
Data Security and Privacy
Healthcare organizations deal with highly sensitive patient information that should be protected from unauthorized access, breaches, and cyber threats. Data security and privacy should be a top priority in any healthcare organization, yet it’s challenging to achieve that. This is why all organizations need security solutions for big data analytics in healthcare.
Healthcare organizations should adhere to regulations, such as the Health Insurance Portability and Accountability Act (HIPAA), to protect sensitive patient information. It is also important to implement encryption techniques and access controls to minimize the risk of breaches.
Data Integration and Interoperability
Healthcare information is collected from different sources, so ensuring their interoperability is essential, which is another challenge. Information in healthcare organizations is stored in various forms, according to different standards and data models, so ensuring its seamless integration is complex. How do healthcare organizations address this challenge? It is important to develop standardized information exchange protocols, interoperability frameworks, and data integration platforms to promote seamless exchange across settings.
Resource Constraints
Issues with resources are common in healthcare settings; not enough personnel, limited funding, lack of technological infrastructure – all of this creates serious boundaries for implementing big data analytics. Some hospitals might not have enough funding to optimize their processes through the use of big data analytics; others might have healthcare workers not acquainted with such systems and unable to use them. Still, there’s a solution. Organizations can look for investors, create skill development programs, and collaborate with industry partners to gather extra funding.
Real-Time Data Analysis
Data should be analyzed in real-time to improve patient care, yet this is challenging due to the complexity and volume of healthcare information. Real-time analytics require powerful infrastructure, lots of computing resources, and advanced analytical tools that healthcare organizations might lack. Thus, overcoming this challenge requires significant investments into scalable infrastructure and analytics platforms to generate real-time insights and streamline the process.
Ethical and Regulatory Issues
Healthcare organizations must adhere to the latest ethical principles and regulations, such as patient consent, fairness, and information transparency in all their processes. The regulations include HIPAA, GDPR, and HITECH; they protect patient rights. However, ensuring that the information collected does not interfere with these regulations is challenging, as organizations must make sure that the ML algorithms are free of bias and do not interfere with patient’s right to privacy.
Resistance to Change
Data analytics is an advanced technology that requires healthcare staff to learn how to use it properly and change their old working methods. Even though healthcare organizations realize it is necessary to adopt new technologies for better patient care and process optimization, workers might resist change. It is essential to educate the staff on the importance of big data analytics, explaining why it benefits the organization, and fostering a data-driven culture to overcome resistance.
Interpretability of Results
Gaining analytical insights is only one part of the process; it is also essential to interpret them properly, which is a challenge in itself. Healthcare analytics platforms generate huge amounts of information and complex statistical models, and it is challenging for the staff to interpret and use this information. Thus, healthcare organizations should develop intuitive information visualization techniques and user-friendly interfaces to help analyze and interpret information more easily. One of the ways to achieve this is through collaboration between scientists, domain experts, and end-users.
Scalability
Healthcare information is always growing, and so is the need to scale up easily. Scalable infrastructure is not easy to build, and many healthcare organizations lack this ability. Organizations need to invest in cloud computing platforms, scalable storage solutions, and computing frameworks to scale up as the amount of data increases. Implementing scalable analytics solutions and data processing pipelines enables healthcare organizations to handle large-scale data analytics tasks, support real-time data processing, and accommodate future growth without compromising performance or reliability.
Conclusion
Big data analytics is already revolutionizing healthcare by improving patient care, helping analyze data in real time, testing new drugs, and diagnosing diseases more accurately. In the coming years, the impact will be even more significant, especially if more organizations start implementing data analytics, ML, and natural language processing into their practices.
Organizations must collaborate closely with domain experts and professionals proficient in data analytics to speed up this process and overcome potential challenges. By combining expertise from both domains, healthcare organizations can experience the benefits of big data analytics in healthcare and improve patient care delivery.
Have you taken a look at challenges and opportunities of big data analytics in healthcare and are ready to use the power of data analytics to revolutionize your practices? Contact IntelliSoft today to explore how our advanced analytics solutions can transform your organization and enhance patient outcomes. Let’s embark on this journey together.
