Big Data in Healthcare – Unlocking the Potential of Medical Records

What is big data and how does it impact healthcare? Big data refers to a large set of data, typically complex in nature, which can be analyzed and leveraged to improve fields like healthcare. Big data in healthcare comes in the form of healthcare analytics, which reduce cost of treatment, predict concerns to avoid disease and illness, and overall improve lives. 

 

Big data in healthcare is one of the key trends of digital transformation in this field. Instead of collecting data that is not used for any purpose — or leaving data uncollected and unused — big data gathers information and puts it to work. It is only possible with technology, as we are talking about massive amounts of information.

 

Not only is big data in healthcare a key part of digital transformation, but it also represents an opportunity to invest in data analytics and create platforms that leverage this information. 

 

What are the sources of Big Data in healthcare?

Many aspects of healthcare are now digitized, where they used to exist in print formats. Whether it is the aggregate health data of an entire population, or individual health data, this information is now digital. All of those pieces of information can come together to inform providers and make decisions.

 

You now know that big data in healthcare refers to the volumes of health data that are generated, collected or analyzed. But what does that mean in practical terms? Big data is made up of information including patient and health facilities records that come from various digital technologies used in healthcare like EHRs, wearables and other medical devices, as well from other sources like medical imaging, payment records or research studies.

 

One of the most common sources of big data in healthcare is electronic health records, now a commonplace feature for most patients. Electronic health records are secure and detailed, but not every healthcare setting uses them. When they are in use, however, they have a lot of potential to do things like send alerts and warnings, or track prescription refills to determine compliance.

Big data in healthcare is generally used in diagnostics, preventative medicine, personalizing care, medical research, spotting medication errors or adverse reactions, reducing costs, and monitoring and improving population health.

 

Big Data market statistics

Let’s look at the potential for big data in healthcare, within the current market and predictions for the future. One set of statistics says that global big data in the healthcare market is expected to reach over $34 billion by 2022. The same information estimates the big data analytics segment, globally, will reach over $68 billion by 2024 thanks to investment in electronic health records, and practice and workforce management tools.

 

Further market research suggests that the global big data analytics in healthcare market could reach $101.07 billion in 2031, showing that the market only has potential to expand.

 

Use case of Big Data in healthcare

Boosting operational efficiency

Without knowing how many patients will likely show up to a hospital or walk in clinic in a day, it can be hard to staff adequately. There is either too much staff, wasting time and money when there are not enough patients, or not enough staff for the influx of patients. But being able to look at previous clinic or hospital use, via big data, allows for smart predictions based on historic patterns, to get as close to adequate staffing as possible.

 

When we discussed using big data in healthcare to predict staffing levels, we were talking about an application that exists in real life. The largest university hospital in Europe uses big data to predict expected patient visits and hospital admissions. With data from four emergency departments within the system, data scientists and medical experts created an approach to analyze that information and create a predictive platform. 

 

 

The resulting solution predicts patient visits and admissions over a moving 15-day window. Partners in the project expect to be able to use similar predictive analysis and retrospective big data to categorize emergency department visits. This would allow hospital staff to plan for different demographics, like children, and different medical specialities like infections versus surgeries.

 

Other ways of using big data in aggregate include strategic planning, based on the information gathered from certain populations. Is the population growing in a certain area? If so, there will likely be a greater need for healthcare in that location soon.

 

Preventing fraud and human errors

Medical errors put an additional financial burden on the healthcare institution, not to mention that they put patient health at risk. According to the National Healthcare Anti-Fraud Association, loss to healthcare due to abuse, fraud and waste amounts to $80 billion annually, while some sources say even about $200 billion.

 

By using technology that is capable of analyzing a large number of various records, big data can help reduce the error rates dramatically by detecting and preventing them before they occur or have any harmful effects on health.

 

In another case, big data is being used to combat opioid addiction. Pharmacy data and insurance claims data are combined to determine risk factors as to who is in danger of developing an opioid abuse problem. Doctors and specialists armed with this information can intervene before addiction or abuse takes hold, most certainly improving quality of life. 

 

Certain traceable behaviors like using different prescribers and dispensaries, in combination with other behaviors, are predictive of opioid issues. There are 742 predictor variables in total, which is why this is something big data can handle but individual practitioners can’t. The sheer volume of information is what allows the preventative work to happen. Besides, decision support tools using big data can reduce prescription errors by spotting them before they occur.

 

Preventive care and identifying high-risk patients

Some big data endeavours work to generate the data instead of tapping into existing information. Asthmapolis is a company that wants to find solutions to asthma, leveraging sensor technology and mobile data monitoring. With a snap on Bluetooth sensor, information on how often someone uses an inhaler, when they use it, and where, is collected. This shows triggers and trends for users, who can then try to avoid those situations.

 

Doctors can also use the information to find out which of their asthma patients are at high risk, or need more help managing the condition. The ideal is to prevent asthma attacks before they can happen, which again improves patient outcomes and saves money. Instead of having to wait for an attack to happen in order to respond, big data offers predictive analytics.

By permitting a proactive approach to prevention, big data can lower the number of hospital visits by identifying high-risk patients and offering customized patient care, and by the same token reduce treatment costs.

 

Enhancing patient outcomes and engagement

Wearable devices monitoring our daily activity and health condition are a great source of valuable data for healthcare providers allowing them to monitor patients’ condition remotely. They also enable patients to take better care of their health by improving their engagement and save time by reducing unnecessary visits to hospitals.

 

Thanks to insights derived from big data, there will be no room for guesswork any longer. Doctors can make well-founded clinical decisions and prescribe treatments with greater accuracy. Leveraging big data in healthcare will be supportive in the decision making process resulting in lower costs and enhanced patient care.

 

There are so many ways big data in healthcare can be used, and so many places to find the data. It’s little wonder that the market outlook is so positive! 

 

Benefits of Big Data in healthcare 

The benefits of big data in healthcare extend to patients and providers alike, and ultimately spill over into societal benefits.

 

As you can see from the examples above, big data in healthcare offers cost reduction in many areas. Staffing, medication, admission rates, and other factors all add up, and the more targeted these resources can be, the better. Healthcare systems obviously stand to save money, and patients are able to save money as well, reducing the length of hospital stays, and tackling problems as they arise instead of waiting until they cost more to manage.

 

Overall, big data in healthcare improves patient experience and outcomes, in real time. Providers have a better understanding of what is happening with their patient populations, and deeper knowledge. Their decisions are thus more accurate, and treatments are more effective. This is especially true when big data is used to personalize healthcare and treatment. 

 

 

Big data in healthcare also offers benefits in terms of long term health, which also impacts the cost of healthcare. When providers can predict patient outcomes, medical compliance, readmissions and other factors to follow-up and long term care, everyone benefits. Further illness or injury can be better avoided, and long-term health is improved. 

 

Other benefits of big data and big data analytics in healthcare include reduced medical errors, predicting and preventing mass diseases and pandemics, discovering new drugs and therapies, and forecasting the risks of treatment.

 

Obstacles of Big Data Implementation

As with any topic, of course, there are challenges that come with big data in healthcare as well. 

 

The biggest concern for many is security. People hear about data and healthcare and are concerned that their private information will be shared and used. Data security is always a concern when working with medical information or other sensitive data, but people need assurances. 

 

HIPAA covers technical safeguards for companies that are storing and using protected health information. This governs how PHI can be transmitted, authenticated, accessed, and audited. For many larger big data initiatives, identifying details are scrubbed from the data so it can be used without targeting a certain individual. 

 

That said, any organization using big data for healthcare needs to understand the gravity of data security and have protocols and processes in place to ensure compliance. Staff training and awareness of all things big data is another challenge. Many people are used to working with conventional, non-digital methods. Adopting healthcare technology can be slow going when staff are reluctant, which is why it is important to have buy-in from everyone involved.

 

Many organizations struggle with fragmented data. Information is gathered and stored, but not in a manner in which the data is connected. Instead, it is stuck in silos, which often leaves critical information out of the picture. There is also the challenge of inconsistent or low quality data.

 

If your organization is considering working with big data in healthcare, it is important to address all of these issues, and any others that arise. By overcoming big data challenges, you can realize the benefits outlined above. 

 

Commercial Platforms Offering Healthcare Data Analytics

In order to leverage big data in healthcare, organizations need to first find commercial platforms that can analyze the data. With the big data market on the rise, it is fairly easy to find healthcare analytics software. 

 

There are very large companies that offer healthcare analytics services. IBM is one of them, as you may have noticed in the study on hospital admissions. On top of cloud and healthcare analytics, IBM also offers artificial intelligence solutions ad machine learning platforms. These advanced technologies analyze information and ideas from a variety of sources. They then offer predictions, and confidence levels for their insights. It’s up to human decision-makers to take action on these predictive analytics, but it’s the data system doing the analysis. 

IBM’s offerings include complex population health data analysis and reporting, insights for vulnerable populations, electronic health records, and performance and leadership at hospitals.

 

SAS is another big name analytics provider. SAS works within the entire spectrum of health industries, including life sciences, insurance, healthcare providers, and public health. Their system uses machine learning and AI, turning data into insights. 

 

 

Then, there are companies that are focused solely on healthcare analytics, like Linguamatics. Linguamatics is a natural language processing based AI platform, which means that the system reads documents and understands how to find the information you need. Instead of working like a keyword search, natural language processing allows users to ask questions and discover knowledge in return. 

 

Natural language processing works on data sources like scientific literature, electronic health records, patents, news feeds, clinical trials data, and any proprietary content an organization might have. Users can essentially ask a question and get accurate results, saving countless hours of effort.

 

There are many commercial platforms offering healthcare data analytics, large and small, and general to specific. Whether you want to build something based on these platforms, or have your own ideas, we can help you.

 

Work with Us

Do you have an idea for using big data within healthcare? Our team is ready to help you turn that idea into reality! We are experienced in creating Internet of Things Solutions and Services, which means we can work within systems that transfer and analyze data. 

 

Along with our IoT solutions, we offer decades of experience in healthcare technology. We understand the specific requirements and risks of healthcare, and take our role very seriously. Digital transformation is key to healthcare, and we want to use our experience to help you attain your data-based goals.

 

Send us an email or fill in our contact form to describe your idea. We will work with you to understand your vision and combine it with our technical knowledge. With a detailed estimate of costs and timeline, we can get started with your approval.

 

Our contact form is available for you to fill out or you can email us at success@concisesoftware.com to get started today. We’re looking forward to working with you to transform healthcare for the better!

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