Because of big data, the way we manage, analyze, and use information has changed across industries. This is especially noticeable in the healthcare industry, where each point of contact between patients, medical staff, researchers, and other interest groups generates massive amounts of clinical data.
Healthcare data must be stored and processed safely, quickly, and cautiously, as noticing patterns within its records can save or destroy lives. Modern data processing allows for continuous big data analytics, lowering treatment costs, predicting epidemics, improving public health surveillance, steering people away from the severe illness path, and keeping an alert on the symptoms of family-bond sicknesses.
This translates to a significant improvement in life’s quality – which is especially important in times of evolutionary progress as the average human lifespan has drastically increased across the world population. And since some countries are having trouble with their aging populations – and their health outcomes – developing data-driven, AI-powered health services will be necessary for the (not-so-distant) future to help healthcare organizations.
But before we get to that, it's important to understand what big data analytics for the healthcare industry is all about.
Table of contents:
- Big data in healthcare: the definition
- Healthcare data analytics with big data tools: seven applications
- Seven benefits of modern data processing – summary
Big data in healthcare: the definition
Big data, in general, refers to vast amounts of unstructured data that can be processed by data scientists using machine learning algorithms. They are usually the result of digitalization when a business uses various digital technologies to improve efficiency, cut costs, and automate processes. Healthcare providers and medical researchers are going down a similar path for the very same reasons.
In the healthcare sector, big data refers to using health-related data of a population (wholly or partially) generated by collecting patient records or test results. It can be processed to help prevent epidemics, enhance patient care, observe and cure chronic disease(s), or keep an individual's health under check. It can also aid in managing hospital performance or research progress and enhance patient engagement.
Such data would otherwise be tough to organize with traditional technologies due to the big data complexity and need for constant, manual updates.
This is a huge step forward for healthcare providers, as adequately trained intelligent components can be as valuable for their work as Google's browser was for the average Internet user. Or, as the AI-powered chats are now for us, because smart components will soon be able to not only compare healthcare data but also suggest the most probable diagnosis and discuss their logic with the doctor.
Prevention is better than cure: gathering the learning materials
As our societies age, we become more interested in delaying disease progression, particularly for aging-related illnesses. Data collection can help with that. As healthcare expenses are easier to manage and treatment plans are more effective when implemented early on, doctors are now making a significant effort to learn as much as possible about the origins of illnesses, particularly chronic diseases. This way, its further progression can be blocked even before the symptoms become noticeable.
So, if the hypothetical patient's organism cannot combat the initial symptoms, the doctor could simply reinforce its defensive mechanism by offering a customizable treatment method. Which will also be constantly adjusted based on the ongoing treatment results.
To make this version of the future work, all those little bits and pieces of health data are already being collected and archived in manual and digital databases (EHRs) by hospitals, dentists, patient portals, clinics, surgeries, pharmacies, and other types of healthcare providers. Hence, full patient data integration is required to create a real-time digital representation of a person's health. And, because the records are collected in various formats, each with its upgrade history, it is best to extract information from all of those sources using the appropriate technology.
It's no longer important how big the dataset is, as the number of sources from which health professionals can gain insights about their patients will continue to grow. We have now moved on to the more critical question of how "smart" big data management can become.
This is what modern data processing can be used for.
Healthcare data analytics with big data tools: seven applications
Improve diagnosis with medical imaging
Medical imaging gives a detailed picture of how an organ is doing without having to cut a person open for examination. To get a scan, the patient has to do several things: stop eating, go to a specific place, pay for the picture, and then bring it back to the doctor for further discussion. It is inconvenient, generates much anxiety, and takes up a solid portion of the patient's day.
On the other hand, the procedure is essential to identifying most "inside" problems and stating their progression phase, or lack thereof. That’s why, according to the WHO’s estimation, globally, we perform approximately 3.6 billion diagnostic examinations each year. Each of them requires safe data storage for at least a few years.
A substantial part of those pictures takes time to analyze. However, the factors that may cause medical errors may impact the outcomes. The person may have laid on the wrong side. They may have eaten something too close to the examination time. The equipment may have glitched. Their unique body composition may have looked worrisome — even if the patient is completely healthy. The doctor could also have been tired or preoccupied with finding proof for their diagnosis confirmation (which also sometimes happens).
The scenarios described above are ideal use cases for big data analytics tools capable of detecting even the earliest stages of the disease by recognizing self-taught patterns. Machine learning algorithms are trained on hundreds of thousands of real, fully anonymized medical imaging images, and the results are confirmed or denied by a medically trained supervisor. The algorithm's accuracy gradually improves with each iteration, allowing it to identify even minor changes, such as discoloration, as potential risks.
Similar big data-driven software is expected to become radiologists' second pair of eyes and, in the future, serve as the primary diagnostic tool for medical image analysis, reducing the need for even looking at the images. Imagine that after you upload the photo to the database, it will one day be compared to all the other images (we're talking billions here!) that show the same disease from the moment the first cancerous cell appears in the body.
This goes beyond any learning course that any school can provide. In addition, the same big data analytics tool would be able to provide accurate results to concerned patients almost immediately.
Reduce costs with real-time alerting
One of the most visible advantages of business digitalization is real-time readiness, or the ability to constantly monitor configured metrics, allowing for adaptation to changing conditions and rapid response to crises. With big data analytics on board, this could generate much-needed savings in two ways.
A lack of flexibility makes it difficult for healthcare organizations to avoid putting medical supplies in the wrong places at the wrong times. They can use Clinical Decision Support (CDS) software to avoid excessive spending, which instantly analyzes hospital medical data and provides recommendations as they make critical decisions.
To avoid personnel overcrowding, a data-driven system can predict when specific healthcare departments require staff during busy periods and when qualified staff can be sent to other parts of the institution during slower periods. Or when to reduce hospital admissions by delving deeper into factors like medication type, symptoms, and visit frequency, among many others.
This level of risk assessment will not only result in less money being spent on internal patient care and guarantee space and support for those who need it the most. It can also respond to crisis alerts.
Another way to reduce spending is for patients to avoid going to hospitals (if unnecessary). This is savings both ways, as the home visit is usually more expensive – and the patient pays the full price. A much cheaper, data-driven solution can be used instead: smart devices. They can continuously collect patient vitals (and other healthcare data collected via, i.e., daily notes) and store it safely in the cloud, where physicians can access it via dedicated software. Real-time monitoring lets you make health statistics and send a live alert to the doctor if a patient's blood pressure rises alarmingly. The doctor would then locate the person’s record and start treating them to lower their blood pressure.
The built-in big data processing component could also advise on the optimal frequency of caretakers and nurses on home visits, adjusting the schedule to the patient's condition.
Organize big data with Electronic Health Records
Electronic Health Records (EHRs) is a technology allowing the use of an individual's digital profile by combining data from various health systems. It usually includes a person’s background, medical history, treatment plans, list of allergies, lab test results, and basic socioeconomic information (address, employer, ethnicity, social security number, and place of accommodation).
Both public and private providers can access such data, which can be shared through secure information systems. The fact that every record is composed of a single editable file allows doctors to make changes over time without submitting additional paperwork or worrying about data replication. As the file stays ‘connected’ to the person, every crucial detail can be downloaded from the database on the first visit – and the system itself can notify the person about the upcoming event as a new prescription, a scheduled test, or a suggested visit, based on the family health history.
For one of Codete's projects with a medical university, we have developed a secure iOS application which stored sensitive clinical data. The application was built to support research on hearth diseases.
Read more about our cardiac healthcare application project >>
As convenient as they are, EHRs are widely adopted in the United States (in over 96% of hospitals in 2017), but other nations still need help fully implementing them. There is severe concern about storing sensitive files of millions of patients in the same area, especially in times of rising cybercrime – but also the EU-approved GDPR, allowing anyone to be erased from the web.
Yet work on implementing electronic health records in the EU is progressing. The game is worth the candle, as another study on big data healthcare, reported by McKinsey, claims that “The integrated system [HealthConnect in the US] has improved outcomes in cardiovascular disease and achieved an estimated $1 billion in savings from reduced office visits and lab tests.”
And this was in 2013. Think of the possible numbers guaranteed by the introduction of electronic health records now, with our current state of technology and ever-used smart gadgets filled with sensors!
Use predictive analytics in healthcare
Predictive analysis has been named one of the top big data trends for the past three years. Still, the potential of its applications goes well beyond businesses and far into the future. Healthcare analytics aims to help doctors make quick decisions based on data and improve patient care.
Combining big data and healthcare is critical for lowering the risk of hospitalization for patients with chronic diseases, such as cancer patients. Think about how scientists can now examine tumor samples in biobanks linked to patient treatment histories. Using this data, they can investigate interactions between various cancer proteins, mutations, and therapeutic approaches and identify patterns that will improve patient outcomes.
This is especially useful for patients with complicated medical histories and multiple conditions. Such improved healthcare services will also soon predict who might be at risk for diabetes and should have more tests or watch their weight, or who, based on their location, genetic type, and family history, should not take a high-risk job due to some pattern spotted. Care managers can also look at checkup results from different demographic groups to see what prevents patients from seeking treatment.
Outbreak control and crisis management
Predictive analysis was also extremely helpful in tracking the progression of COVID-19. The data will almost certainly help us predict (and control) the next outbreak.
Health professionals could use cutting-edge data management tools to track COVID's spread, how quickly it changed under different conditions and its impact on various global economies. Massive data sets from multiple sources, including medical records and specific human behaviors, were (and continue to be) analyzed to accomplish this. For example, tracking how many of us stayed at home, took public transportation, or went to school aided in simulations of how quickly the virus spread.
At Codete, we have developed a Natural Language Process (NLP)-based solution for automated COVID-19 papers analysis. >>
Furthermore, artificial intelligence (AI) advances have enabled medical imaging modalities such as X-rays, tomography, ultrasounds, and others to diagnose patients earlier and prevent disease spread. To make this process easier, the EU funded InferRead in 2020. This AI-driven program "analyses CT scan images of the lungs to detect coronavirus signs and evaluate lesions. A skilled doctor typically needs to examine the procedure carefully, but the machine only needs a few seconds."
With this level of technology, hospitals across Europe were able to halt the virus's spread, causing the curve to flatten.
Prevent human error
Decisions made by doctors are increasingly evidence-based, which means they consider a wide range of clinical and research data rather than just their training and experience. Nevertheless, there is always a potential for human error. Despite their extensive training, doctors are still only human, and choosing the wrong medication or treatment for someone can have serious consequences, if not fatal.
Companies use big data and forecasting to quickly spot and alert when the wrong medication, test, or treatment has been given so they can correct it right away.
Experts, for example, can detect prescription errors before they occur by analyzing massive amounts of prescription pattern data. The same can be said about test results, dosage ranges, and other procedures. Over time, this allows medical professionals to rely on technology for decision-making, allowing hospitals to save money while providing the best care possible.
Codete's software engineers have been involved in designing a module of an in-hospital medication administration system, Infofinder. The system's goal is to carry out and optimize the crucial logistics involved in giving the correct patient the appropriate dose of the right medication.
Learn more about the Infofinder project in our case study >>
Create new therapies and innovations
Big data analysis in healthcare has the potential to help develop groundbreaking new drugs and cutting-edge treatments by analyzing genetic information and predicting how a patient will react. Healthcare professionals can identify potential strengths and weaknesses in trials and processes by combining a variety of historical, real-time, and predictive metrics with a well-balanced array of data visualization techniques.
Seven benefits of modern data processing – summary
With today's ever-evolving technologies, it is now easier to collect data, create comprehensive healthcare reports and transform them into relevant critical insights that can be used to provide better care. This is critical for assisting decision-makers in making more informed choices that will improve operational efficiency, patient care, and adequate staffing.
Institutions are already being streamlined to provide faster, safer, and more precise patient care, while data-driven analytics enable early illness detection and intervention. These invaluable features will become more critical as technology advances; data is the key to future healthcare.
Using big data in healthcare requires numerous processes and tools to collect, clean, process, manage and analyze the enormous amounts of data available. For typical users unfamiliar with these processes, this necessitates knowledge and abilities that may be a barrier.