The healthcare industry has come a long way to reach the point where it is right now - telemedicine, medical imaging, electronic health records, robots, and more. All this has become possible with the help of technology. And big data is one of those disruptors that have revolutionized the healthcare industry. Big data in the healthcare industry help save lives, decrease costs, and improve the efficiency of operations.
The outbreak of the global pandemic has accelerated innovation and the adoption of digital technology, especially big data and big data analytics. But also it has exposed many weaknesses of the healthcare industry. Here we outline the benefits of big data and data analytics in healthcare as well as give an overview of key applications of big data in the healthcare sector. So let’s take a look at how big data and data analytics can help solve many well-known as well as emerging problems in healthcare.
Overview of big data in healthcare
According to the IDC report, big data is expected to grow faster in healthcare than in other industries like manufacturing, financial services, or media. It is projected that the healthcare data will see a compound annual growth rate (CAGR) of 36% through 2025.
The global big data market in the healthcare industry is expected to reach $34.27B by 2022 at a CAGR of 22.07%. Globally, it is forecasted that the big data analytics segment will be worth more than $68.03B by 2024, driven largely by continued North American investments in electronic health records, practice management tools, and workforce management solutions. The research by McKinsey & Company highlights that big data in healthcare can save Americans between $300B to $450B each year.
Big data in healthcare comes from a variety of sources - starting from electronic health records to server logs of search engines and wearable devices. This is the endless sea of data that offers an endless list of opportunities. The main thing is to know how to put this data to good use. 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, HCOs can save costs and improve the efficiency of operations, pharmaceutical manufacturers and other healthcare providers 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
“Data builds on data. Patient-generated data, from a clinical perspective, improves outcomes by creating a more complete picture of the patient outside of the exam room. In addition, as HCOs collect more data, machine learning will get better and enable more proactive outreach. As we gain deeper insights on individuals through data and AI, HCOs will create richer, individualized experiences that yield higher customer loyalty.” - Healthcare 2020: The State Of The Doctor-Patient Relationship In The US, Forrester Research Inc., March 10, 2020
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.
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. But big data presents a smarter way to diagnosing patients. Physicians can simply collect the patient data and feed it into an algorithm that will suggest the most likely diagnoses. Algorithms will also propose high-value tests and reduce the overuse of unnecessary tests. Also, computer vision (CV) is widely used in diagnostics. For example, this technology helps examine the retina with the aim to detect anomalies at an early stage and prevent diseases.
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 are aimed at predicting 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 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.
Real-time monitoring of patient vitals
The use of wearables and other IoT devices, which healthcare technology companies now produce quite enough, is 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 for patients to collect it themselves. These devices generate tons of valuable data that can further help doctors with diagnostics and treatment.
Treating difficult diseases
Data collected from patients on different treatment plans can be analyzed for trends and patterns to find those with the highest rates of success. This is specifically important for fighting such severe illnesses as cancer, AIDS, multiple sclerosis, etc.
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 case of the coronavirus, big data helps improve epidemic surveillance and response. Countries around the world are using big data and data analytics to provide real-time stats, track the spread of the virus, and predict the impact of this outbreak.
Preventing diseases is better than curing them for patients, hospitals as well as insurance providers. Doctors want patients to be healthy and stay away from hospitals. And this 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.
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 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.
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 it into a number 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.
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.
Big data and data analytics can help with fraud prevention and detection. It is possible to identify changes in network traffic or any other behavior that reflects a cyber-attack and take measures to block harmful activities.
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 on 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 requirement and goals are.
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 are suffering the most and more care units to 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 exactly to solve problems of ingesting and storing not only big amount of data, but also extremely diverse data. Such concept as a data lake provide the possibility to solve the problem of storing a variety of healthcare data like images, document files, exports from old RDBMS systems, and so on.
Healthcare is known for a series of standards applied to the data, however it is possible to standardize a variety of data from data lakes into some structured form as data warehouses.
To drive trustworthy insights, AI and ML algorithms need reliable input data without duplications and inaccuracies. If the 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 the quality of the data. They should implement automated checks for all incremental pipelines and pay special attention to data preparation and cleaning.
Data exploration tools from the big data ecosystem that are regularly used in Business Intelligence are extremely useful when it comes to data mining problems. And data engineers and data scientists could help with data mining for healthcare too.
Due to the lack of standardization, sharing healthcare data between different organizations is one of the main pain points. Moreover, such sensitive information requires robust privacy protections. Under public health emergencies, and particularly the COVID-19 pandemic, it is crucial that data is shared in a timely and an accurate manner.
Big data healthcare projects call for high visibility. Thus, real-time monitoring, operational dashboards and periodic business/report dashboards are essential. In the healthcare industry, however, there is a problem with visualizing health data as it requires specific tools and expertise.
Some enterprise data warehouse systems that are commonly used in the healthcare sphere lack horizontal scalability and support only vertical one. Migration to 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 that has strict laws regarding storing and sharing sensitive data. Nonetheless, there are a lot of examples of breaches and leaks of confidential data. Thus, it is fundamental to set up necessary configurations, conduct regular audits, perform risk assessment, and train employees on security best practices.
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 for gaining cost and performance benefits in the future. In case rewrite is not an option which is quite common in healthcare due to regulations and certifications of a software, common practices like middleware buses are used.
Lack of big data skills
Big data in healthcare is really ‘big’ and diverse. 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 with 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 1,100+ experts and a team of 70+ data analytics 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-premise 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, GDPR, and HIPAA, so your sensitive data will always be safe.