The healthcare industry has come a long way to reach its current state—telemedicine, medical imaging, electronic health records, robots, and more. All these applications became possible with the help of technology. Big Data in the healthcare industry is one of the most significant contributors as it helps save lives, decrease costs, and improve the efficiency of operations.
However, innovation and the adoption of digital technology in the healthcare industry have also exposed its many weaknesses. Here, we outline the benefits of Big Data and data analytics in healthcare and give an overview of key applications and challenges of implementing Big Data in the healthcare sector.
Let’s take a look at how Big Data and data analytics can help solve many well-known and emerging problems in healthcare.
What is Big Data in healthcare?
Big Data in healthcare comes from various sources, from electronic health records to search engine server logs and wearable devices. This is an endless sea of data that offers an endless list of opportunities. The main thing is to know how to use this data effectively.
With proper storage and analytical tools in hand, all the players in the healthcare system, including healthcare organizations (HCOs), patients, medical staff, pharmaceutical manufacturers, etc., can reap a number of benefits. In broad terms, patients become more healthy, doctors can significantly enhance medical outcomes, and HCOs can save costs and improve the efficiency of operations. As for pharmaceutical manufacturers and other healthcare providers, they can make more informed decisions.
Benefits of Big Data and Big Data analytics in healthcare
- Reducing medical errors
- Preventing mass diseases
- Preventative care
- Modeling the spread of diseases
- Detecting diseases at their early stage
- More accurate treatment
- Real-time alerting
- Patients personalization care
- Predicting the cost of treatment
- Forecasting the risks of treatment
- Identifying and assisting high-risk patients
- Suicide & self-harm prevention
- New therapy and drug discovery
- Prevention of unneeded emergency room visits
- Improved staff management
- Streamlined hospital operations
- Better customer service
- Cost reduction
The benefits of Big Data in healthcare highlight its critical role in transforming the practices and outcomes in this industry. According to the IDC report, Big Data is expected to grow faster in healthcare than in other industries like manufacturing, financial services, or media. Roots Analysis states that the global Big Data market in the healthcare industry is expected to reach $540B by 2035 from $67B in 2023 at a CAGR of 19.06%.
Use cases of Big Data and data analytics in healthcare
Knowledge derived from Big Data analysis gives healthcare specialists insights that were not available before. Big Data in healthcare is applied at every step of the healthcare cycle, from medical research to patient experience and outcome.
1. Diagnostics
With the help of Big Data and data analytics, it is possible to diagnose a disease quickly and accurately. Usually, doctors examine patients, talk to them about their ailments, and compare their symptoms to disease pictures they know. In complex cases, they research the literature and consult with colleagues.
Meanwhile, Big Data presents a smarter way of diagnosing patients. Physicians can simply collect patient data and input it into an algorithm to suggest the most likely diagnoses. Algorithms will also propose high-value tests and reduce the overuse of unnecessary tests. Also, computer vision in healthcare is widely used in diagnostics. For example, this technology helps examine the retina to detect anomalies at an early stage and prevent diseases.
2. Modeling and forecasting outcomes
Big Data and predictive analytics assist healthcare specialists with clinical decision-making. Prognostic modeling is widely used in healthcare for different purposes. Some models aim to predict future outcomes of diseases and/or treatments. Others focus on identifying patients who may be at risk for the development of a particular condition. There are also models that forecast the spread of diseases among the population. For example, predictive modeling has been successfully applied in many countries to identify undiagnosed diabetes, predict survival after in-hospital cardiopulmonary resuscitation, and forecast the spread of the COVID-19 pandemic.
3. Real-time monitoring of patient vitals
Wearables and other IoT devices, which healthcare technology companies now produce quite enough of, are among key healthcare technology trends. They can automatically collect health metrics like heart rate, pulse, blood pressure, temperature, oxygen concentration, blood sugar level, and more. Thus, they eliminate the need for patients to travel to the providers or collect the data themselves. These devices generate tons of valuable data that can further help doctors with diagnostics and treatment.
4. Treating difficult diseases
Data collected from patients on different treatment plans can be analyzed for trends and patterns to find those with the highest success rates. This is one of the significant Big Data use cases in healthcare because it helps to identify which treatments are most effective to further optimize patient care. In cases like fighting severe illnesses such as cancer, AIDS, multiple sclerosis, and other critical conditions, data collection is especially important. Analyses of large datasets help healthcare professionals tailor treatment plans to individual patients, improving outcomes and enhancing the quality of life for those suffering from serious diseases.
5. Population health
Big Data helps improve the quality of people’s lives. It has the potential to predict and prevent the outbreaks and spread of infectious diseases. Big Data tools were not available during previous pandemics. In the coronavirus case, Big Data helped improve epidemic surveillance and response. Nowadays, countries worldwide are using Big Data and data analytics to provide real-time stats, track the spread of the virus, and predict the outbreak's impact.
6. Preventive care
Preventing diseases is better than curing them for patients, hospitals, and insurance providers. Doctors want patients to be healthy and stay away from hospitals. This is where Big Data comes into play. Thanks to Big Data, it is possible to predict the chances of someone getting ill based on their behaviors and identify warning signs of serious illness at an early stage.
7. Telemedicine
Big Data has huge relevance in telemedicine. With the use of robots and high-speed real-time data, for example, doctors can perform operations while physically being miles away from the patient. Big Data plays a crucial role not only in robot-assisted surgery but also in initial diagnosis, remote patient monitoring, and virtual nursing assistance. Thanks to telemedicine and Big Data, which makes it all possible, the lives of doctors and patients become easier:
- Patients can avoid waiting in lines
- Doctors don’t waste time on unnecessary consultations and paperwork
- Patients can be monitored and consulted anywhere and anytime.
- Prevention of hospitalization or re-admission;
- Clinicians can predict acute medical events in advance and prevent deterioration of patient’s conditions;
- Telemedicine helps reduce costs and improve the quality of service.
8. Imaging
Analyzing imaging data such as CT, MRI, or PET is challenging. But Big Data analytics can streamline the way radiologists read images. Algorithms can identify specific patterns in the pixels and convert them into numbers to help healthcare specialists with the diagnosis. So doctors have the possibility to build history catalogues of images and use computer vision and data science techniques for their quick analysis.
9. Electronic Health Records
Electronic Health Records (EHRs) are one of the biggest sources of Big Data in healthcare. Many HCOs have already implemented them. According to the HITECH research, 94% of US hospitals have adopted EHRs. EHRs give patients and doctors a complete picture of a patient's medical history. Records are shared via secure information systems and are available for providers from both the public and private sectors. Doctors can implement changes over time with no paperwork and no danger of data replication. EHRs can also trigger warnings and reminders when a patient should get a new lab test or track prescriptions to see if a patient has been following doctors’ orders.
10. Security
Big Data and data analytics can help with fraud prevention and detection. Among the many Big Data use cases in healthcare, one critical application is identifying changes in network traffic or any other behavior that reflects a cyber-attack. With the help of advanced analytical tools, healthcare organizations can detect unusual access patterns, abnormal data transfers, and irregular usage times. Once these potential threats are identified, immediate measures can be taken to block harmful activities.
11. Hospital management
Big Data is a key to hospital management. It can improve hospital operations and significantly reduce costs. For example, through data-driven analytics, it’s possible to predict when you might need staff in particular departments at peak times while distributing skilled personnel to other areas during quiet periods. Also, by keeping track of employee performance across the board, you can use healthcare data analysis to gain insight into who needs support or training and when.
However, implementing a Big Data solution in healthcare requires a thorough strategy. You can develop your own solution or purchase a ready-made product. The main thing is to have a clear idea of what your requirements and goals are.
12. Strategic planning and smart decision-making
Bid data and data analytics allow healthcare specialists to better understand and spot problems and opportunities which derive from them. For instance, it is possible to identify which areas suffer the most and more care units in those places. Also, Big Data analysis in healthcare assists in new therapy and drug discoveries. By using a mix of historical, real-time, predictive analytics, and data visualization techniques, healthcare experts can identify potential strengths and weaknesses in trials or processes and discover new drugs.
Key challenges of Big Data and Big Data analytics in healthcare
Big Data has great potential to change the healthcare landscape. It can save people’s lives by preventing diseases, forecasting medical outcomes, and reducing medical errors. Also, it can improve the quality and cost of care. However, not every healthcare organization has incorporated Big Data into everyday operations. According to a recent PwC survey, 95% of healthcare CEOs are exploring better ways of using and managing Big Data, but only 36% have made any headway in getting to grips with Big Data. So, what are the main obstacles to the mass adoption of Big Data in healthcare? Let’s take a look at some of the most pressing issues:
Data integration and storage
The Big Data ecosystem was created to solve the problems of ingesting and storing not only a large amount of data but also extremely diverse data. A concept like a data lake provides the possibility of solving the problem of storing a variety of healthcare data, such as images, document files, exports from old RDBMS systems, and so on.
Data standardization
Healthcare is known for a series of standards applied to data; however, it is possible to standardize a variety of data from data lakes into a structured form as data warehouses.
Data quality
AI and ML algorithms need reliable input data without duplications and inaccuracies to drive trustworthy insights. If the data quality is poor, doctors may misidentify a patient or prescribe the wrong treatment. Thus, HCOs should work on data governance and master data management solutions to improve data quality. They should implement automated checks for all incremental pipelines and pay special attention to data preparation and cleaning.
Data mining
Data exploration tools from the Big Data ecosystem that are regularly used in healthcare Business Intelligence are extremely useful for data mining problems. Data engineers and data scientists could also help with data mining for healthcare.
Data sharing
Sharing healthcare data between different organizations is one of the main pain points due to the lack of standardization. Moreover, such sensitive information requires robust privacy protections. In public health emergencies, it is crucial that data is shared in a timely and accurate manner.
Data visualization
Big Data healthcare projects call for high visibility. Thus, real-time monitoring, operational dashboards, and periodic business/report dashboards are essential. However, visualizing health data in the healthcare industry is problematic as it requires specific tools and expertise.
Scalability
Some enterprise data warehouse systems commonly used in the healthcare sphere lack horizontal scalability and support only vertical ones. Migration to a massively parallel processing (MPP) data warehouse or the Big Data ecosystem can resolve such scalability issues.
Security and privacy of data
Security is a top concern in healthcare. Healthcare is a highly regulated industry with strict laws regarding storing and sharing sensitive data. Nonetheless, there are many examples of breaches and leaks of confidential data. Thus, setting up necessary configurations, conducting regular audits, performing risk assessments, and training employees on security best practices are fundamental.
Integrating legacy systems with the Big Data ecosystem
It is important to identify if legacy systems can be integrated into the new pipeline and continue to do some job or should be rewritten to suit the Big Data ecosystem totally to gain cost and performance benefits in the future. If rewriting is not an option, which is quite common in healthcare due to regulations and software certifications, common practices like middleware buses are used.
Lack of Big Data skills
Leveraging the benefits of Big Data in healthcare requires a robust infrastructure, advanced analytical tools, and skilled personnel to harness its full potential. Collecting, cleaning, processing, managing, and analyzing such huge volumes of data is challenging. However, with a reliable partner by your side, you can easily overcome these and many other issues that may arise during the implementation and use of Big Data and Big Data analytics in healthcare.
How N-iX can help with implementing Big Data in healthcare
- Since 2002, N-iX has developed many innovative software products helping healthcare companies to create transformation in the healthcare industry. Our clients include Weinmann Emergency, Think Research, Brighter AB, Cure Forward, and others.
- N-iX boasts an internal pool of more than 2,200 experts and a team of over 200 data specialists.
- Our specialists have solid expertise in the most relevant tech stack for implementing Big Data in healthcare, including BI, Data Science, AI/Machine Learning, Computer Vision, etc.
- N-iX partners with Fortune 500 companies to help them launch Big Data projects and migrate their Data to the cloud.
- Our Big Data experts have experience working with open-source Big Data technologies (both on-premises and cloud-based) such as Apache Spark, Hadoop, Kafka, Flink, Snowflake, Airflow, etc.
- N-iX complies with international regulations and security norms, including ISO 27001:2013, PCI DSS, ISO 9001:2015, and GDPR, so your sensitive data will always be safe.
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