The pandemic and its associated challenges have sped up the adoption of innovation in even the most conservative industries. Healthcare is no exception, actively introducing new technologies. However, the ramifications of the digital divide – big raw data from disparate sources – could be an obstacle to a true digital transformation of medicine.
The healthcare sector needs to optimize resources to improve operational efficiency and enhance the quality of services. Therefore, the demand for clinical data and research performed by healthcare analytics is growing exponentially. The patterns revealed by healthcare data analytics help draw conclusions and identify trends and, in our experience, are extremely important for the development of healthcare.
But the main potential of analytics lies in its ability to transform healthcare into a data-driven culture using world-class analytics platforms.
Too Much Data, Too Little Information
Technological advances in health care have contributed to accumulating so much data that it has become unmanageable with the currently available technologies. It includes documentation related to patients and their treatment, statistics of diseases, demographic data, and payments, which is an ever-expanding pool of data.
In the past, such information was practically unavailable, as it was stored in paper form in disparate storage facilities – in different departments, buildings, or even geographic locations.
Fortunately, in today’s digital world, the healthcare sector has moved to more sophisticated storage systems. Data is now available more efficiently than ever before. The fundamental technology for the end-user is almost entirely automated and digitized – according to experts, the healthcare system generates 30% of the world’s data in a digital form.
Consequently, nowadays, the healthcare industry is overwhelmed by an enormous amount of data and numbers from various sources – from medical records to logs from search engines and wearables. However, our experts believe that the quantity of data is much less important than its quality and the availability of suitable tools for its interpretation.
Why is Big Data so Important?
It’s safe to say that it’s not the quantity of data that matters but the quality. Qualitative data analysis:
Saves you time. The invention and improvement of medicines and other health products require a considerable investment of time. In turn, by using big data methods, experts find valuable data much faster and more efficiently, which reduces the time to develop a product and bring it to market.
Saves you money. Big data helps drive down costs. For example, in 2019, companies using big data cut their total costs by 10% and increased their revenue by 8-10%.
Optimizes your offers. Big data eliminates guesswork, allowing you to get results faster and develop better products.
Pushes you to reasonable decisions. Improved data analysis techniques that enable big data make workflows more efficient and research advance at a faster pace.
What Does Analytical Mean in Healthcare?
Health analytics refers to the regular usage of medicine data accumulated amounts. New software and technologies that help explore large volumes of data for hidden information facilitated this process increasingly. Another plus of big data analytics in healthcare is the ability to link parameters that are usually analyzed and calculated separately.
As a tool or set of methodologies, health analytics transform raw data into meaningful information. Therefore, it helps healthcare organizations make more effective, strategic, and operational decisions to drive ideas and plan for the future.
Rising demands, increasing investment, and the availability of software and services will allow the market of healthcare big data analytics to reach a staggering $68.03 billion by 2024, with a CAGR of 19.34%. However, only a subset of healthcare-related organizations makes full utilization of data and analytics. For example, a Dimensional Insight study found that 56% of healthcare facilities and hospitals do not have a long-term analytic plan or good big data governance.
Types of Medical Analytics
There are four categories of analytics (based on maturity level) for analyzing a wide variety of health data in the healthcare field:
Descriptive analytics is designed to describe medical problems and comments on them.
Diagnostic analytics processes information to explain why certain events occur. For this, statistical methods of medical data analysis are used to detect correlation, drill down, classify, and cluster to identify the main factors influencing the results.
Predictive analytics focuses on the ability to predict future results by identifying trends and probabilities. These methods help prevent complications that patients may experience.
Prescriptive analytics performs predictive actions based on the analysis of accumulated information. There are many methods used here: modeling, mathematical statistics, machine learning, and other areas of Data Science, as well as Data Mining.
Benefits of Big Data Analytics for Healthcare
- Individual patient care
- Preventing unnecessary emergency room visits
- Predicting the cost and risks of treatment
- Fewer medical errors and more accurate treatment
- Preventing mass diseases and preventive care
- Modeling the spread of disease
- New therapy and drug discovery
- Improved HR management
- Early detection of diseases
- Identifying and managing high-risk patients
- Real-time alert
- Preventing suicide and self-harm
- Optimized hospital operation
- Better customer service
- Reducing the cost of medical care
What is an Example of Data Analytics in Healthcare?
The utilization of big data analytics in the health industry is quite extensive and, as a rule, provides positive and life-saving results.
With big data and data analytics, diseases are diagnosed quickly and accurately. Previously, doctors examined patients and tried to recognize the disease by comparing symptoms with a list of conditions they knew, studying the literature, and consulting with colleagues.
But big data offers a more advanced way to diagnose patients. Patient data entered suggests an algorithm with the most likely diagnoses. The algorithm can also select relevant tests. Computer vision is also widely used in diagnostics. For example, with the help of retinal examination technology, abnormalities and diseases are detected at an early stage.
Treatment of rare and complex diseases
Data from patients with different treatment plans can be analyzed for trends and patterns, and those with the highest success rates can be found. This is especially important for the choice of tactics to treat such complex diseases as multiple sclerosis, cancer, etc.
Data analytics enables healthcare professionals to identify opportunities and solve problems in healthcare delivery. For example, the University of Florida used Google Maps technology and public health data to reach areas with health services. In such cases, they only needed to analyze the number of chronic diseases in specific localities.
Development of New Drugs
In addition, the analysis of big data in healthcare helps in the discovery of new treatments and drugs. Using a combination of historical and predictive analytics and real-time health data visualization techniques, experts can identify the strengths and weaknesses of clinical trials and invent new drugs with a more substantial effect.
Imaging data that CT, MRI, or PET offer is difficult to interpret. But big data analytics can simplify the way you read images. Algorithms discover certain patterns in pixels, convert them to numbers and help make diagnoses. In addition, they can memorize more images than thousands of physicians in their entire lives. Thus, physicians create complete catalogs of images that can be analyzed using computer vision and data science techniques.
Electronic Medical Records (EHR)
Such records are the largest source of big data, and HI TECH estimates that 94% of US hospitals have already implemented them in the systems. We observe similar dynamics in other countries. Electronic medical records store a complete picture of a patient’s medical history and are transmitted through secure information systems. They are open to suppliers from both the public and private sectors.
What is convenient is that doctors regularly change them without paperwork and data replication. Besides, EHR can remind you of necessary tests, track recipes and follow instructions.
For example, the EHR system, which has improved the control of patients with cardiovascular disease, has brought Kaiser Permanente an estimated $1 (!) billion in savings.
Big data is of paramount importance in telemedicine. For example, doctors can perform operations and manipulations in real-time while staying away from the patient using high-speed data and robots. Eventually:
- Doctors get rid of bureaucracy and unnecessary consultation;
- Patients are consulted and monitored remotely, without personal presence;
- The number of preventive hospitalizations or re-hospitalizations is minimized;
- Clinicians can forecast acute medical events and prevent a worsening of the patient’s condition;
- Costs are reduced, and service quality is improved.
Big data and data analytics can help detect and prevent fraud. With their help, you can identify changes in network traffic or any other behavior that reflects a cyberattack and take measures to block malicious activity. For example, the Centers for Medicare applied predictive analytics to prevent more than $210 million in healthcare fraud in a year.
Wearables and other IoT devices produced in large numbers by healthcare technology companies are key trends in healthcare technology. They automatically collect health metrics such as heart rate, oxygen concentration, blood pressure, pulse, temperature, blood sugar, and more.
As a result, these devices accumulate a wealth of valuable data that can assist clinicians in diagnosis and treatment. For example, Boston Massachusetts General Hospital is already using a monitoring program to treat patients with heart failure.
Supply Chain Management
If an institution’s supply chain is fragmented, everything from patient care and treatment to long-term finances will suffer. Big data analytics is employed to ensure a smooth and efficient supply chain from start to finish.
Using analytics tools to track supply chain performance and make perfect, data-driven decisions about operations and costs can save hospitals up to $10 million a year.
Hospital Management with Practical Ideas
Big data can enhance the performance of any healthcare facility and significantly reduce the budget. For example, you can use data-driven analytics to predict when you might need employees in specific departments during peak times while simultaneously assigning skilled personnel to other areas during periods of quiet. Plus, by tracking employee performance across the board, you can use health data analytics to understand who needs support and training and when.
Prescriptive, predictive, and descriptive-analytic techniques in big data offer opportunities to improve the quality of different aspects of healthcare.
- Medical diagnosis. It is based on data that can detect diseases at an early stage and reduce complications during treatment.
- Patient care in real-time. BDA-facilitated personalized patient care can provide quick relief and reduce hospital readmission rates.
- Hospital monitoring. Real-time hospital monitoring can help government agencies ensure optimal quality of service.
- Public health. The authorities can take preventive measures against the projected risks of chronic disease and outbreaks of infectious diseases in the population.
The power of advanced predictive and benchmarking analytics to shape data-driven strategies will go a long way in health plans.
Machine Learning and AI
The next level of analytics in healthcare involves a deeper analysis of data to recognize patterns and trends. Our experts are convinced that machine learning and AI are indispensable for obtaining information from a less structured data type, as they surpass human capabilities in performing some medical tasks.
AI-powered tools can extract meaningful information from large amounts of data and generate helpful insights that can be applied in many applications. Patient-centric analytics and AI will enable collaborative decision-making between patients and healthcare providers. Mass General Hospital and Brigham and Women’s Center for Clinical Data Science plan to introduce AI for daily use by doctors.
Turning Data into Useful Information
To transform health data into actionable insights, medical organizations will demand targeted information about the actual costs of their organization, the quality of the services provided, and the relevance of the service.
This information can also help organizations achieve positive trends, such as:
- Service models are developing from quantitative payment-for-service models to patient-centered cost-based service systems.
- Clinical devices are becoming increasingly associated with automatic data integration.
- Real-time location devices and collaboration tools will connect clinical and corporate IT networks.
- Clinical IT develops more centralized enterprise IT infrastructures.
Adding Value Without Excess Work
Value paths strengthen as new data emerges, creating a feedback loop. For example, the concept of good care may change if recent evidence suggests that a standard protocol for a specific disease is not producing optimal results.
But the mere introduction of new technologies and capabilities that guarantee enhanced clinical results and patient care is not enough. Systems must provide value to service providers and patients while maintaining simplicity and efficiency for the user.
How Can Healthcare Analytics Improve Data?
Effective management, analysis, and interpretation of big data open up new opportunities for modern healthcare. Here are the trends emerging from efforts to bring big data to healthcare:
- Driving from models of emergency and ad hoc care to value-based care with hospital analytics.
- Empower you to capitalize on extensive collections of medical data with clinical analytics.
- Reliable patient analytics, using data from a combination of sources to find it and help solve complex health problems.
Let's take a look at the most common types of clinical trial management software and examine the offers from the best-known clinical trial management system vendors.
How to Make the Data Work
Implementing analytical big data systems requires an integrated step-by-step approach and is often performed as part of business digitalization. To create optimal management decisions based on data, it is necessary to have an appropriate amount of information sufficient for the proper training of ML algorithms.
Some analytical tasks can be solved using modern BI tools and open-source solutions. While development costs can be lower in the latter case, since these tools are open source and free, the disadvantages are lack of technical support and minimal security.
Analytics Implementation Stages
Healthcare organizations must process these numbers and extract information from the big data stored in the healthcare system. In many cases, organizations must revise existing systems and apply new strategies, approaches, and tools.
For example, before we make big data work, we take five key steps:
Stage 1. Developing a big data strategy.
It will help you control and improve how data is received, managed, stored, shared, and used internally and externally. In doing so, it is important to consider existing and future aims and business and technology initiatives. Big data is an important business asset, not just a byproduct of applications.
Stage 2. Identify big data sources.
These sources can be streamed or publicly available data that is analyzed and sorted as it comes in. Initially, this data is often unstructured or semi-structured and represents a unique data pool for use and analysis. To prepare for ever-changing big data, you need to make sure that the data is accessed, profiled, cleaned, and transformed.
Stage 3. Developing methods for accessing, managing, and storing your big data.
Along with this, we define methods for integration, ensure data quality, and prepare data for analytics.
Stage 4. Analyzing big data.
An essential requirement is that big data analytics in healthcare should be done in real-time, with no delay between data collection and processing.
Stage 5. Make well-informed decisions.
Well-managed and reliable data enables data-driven action based on evidence rather than gut.
As with all new technologies, it will take time for companies to uncover the potential of big data and achieve their goals. But whoever starts now, having defined the development strategy correctly, will receive an immense advantage for years to come.
Medical Analytics Integration Problems
Using BDA in healthcare can face various obstacles. Common problems in this area include:
Deploying the prerequisites to take profit from big data requires substantial upfront costs for healthcare providers.
Variety of Data Formats
Typically, different healthcare facilities such as hospitals, pharmacies, and private clinics use different medical software and data formats, making it difficult to compare, analyze, transfer, or share data.
Data Storage Cost
It is estimated that healthcare facilities generated 2,314 exabytes of new medical data in 2020, up from 153 exabytes in 2013. The sheer size of medical data and various types of structured and unstructured data append to storage costs. Although storage, which offers slow data access, is relatively cheap, performing high compute workloads on large amounts of data can be costly.
Information Technology and Personnel
Medical organizations need high-quality analytical products and solutions. In addition, a specialized technical team with a healthcare data analyst must be hired to process medical data, which can be costly.
Quality of Data and Insights
Resistance to changes and a lack of trained staff, including a data scientist, can affect the quality of the big data that an organization accumulates. Accordingly, heterogeneous biomedical data of inferior quality have a severe disadvantage — they do not provide adequate information.
Privacy and security
It is known that 93% of healthcare organizations have already experienced a data breach. So patients’ concerns about the privacy and security associated with accessing data during intersystem communication are not accidental. Data analytics must be synchronized with data security processes and protocols.
About the Future of Health Analytics
The global pandemic sharpened the focus on big data and sped up the application of analytics for health decision-making. As a result, more healthcare providers will probably become data-driven over the next few years. However, more vigorous steps are inevitable to transform the power of big data analytics into better services and financial benefits.
Vendors can support terrain change in several ways. The first direction is to create own infrastructure for data analysis and train staff to use analytics and interpret the findings. This will require suitable premises and expensive equipment for collecting data and their subsequent analysis.
The second area is cooperation with companies engaged in the development of corporate software. You can order the development of your solution, as Zeiss did, or purchase a finished product. The critical thing is to be clear about your requirements and goals.
Organizations that understand the potential of data analytics in healthcare and can successfully overcome all difficulties will uncover the unique possibilities of digital assets. These assets will never run out and can be used countless times at virtually zero cost. In addition, such organizations will use their data pools as a platform for creating additional value, which makes it possible not only to improve the efficiency of working with data but also to monetize it.
However, implementing this strategy will require a trusted partner who provides solutions specifically tailored to healthcare organizations’ needs and rigorously tested for safety and quality.
The Jelvix team has deep industry experience in data and analytics and has already developed a range of innovative software for the health industry. We can help you invest in the future and put data analytics strategy into practice to keep you competitive in a rapidly growing healthcare industry.
Need a qualified team?
Access the talent pool to scale your team capacity.