Big data is a term used to describe the enormous datasets made possible for analysis by cloud computing and the more recent increases in processing power and storage. ‘Big data' is contrasted with regular data by three Vs: volume, velocity and variety—referring to the sheer size of datasets, the speed at which it is added to, and the integration of data types often considered incompatible with traditional data analysis techniques.
Big data analytics is the processing of big data into comprehensible formats for analysis. It often incorporates AI and/or machine learning pre-processing to detect trends and patterns that may offer actionable insights.
As data sources increase and big data analytics evolve and become more cost effective, more organizations are moving toward using it to optimize their processes. This is leading to both its more widespread use across different industries as well as organizations finding new innovative ways to use it.
Companies with a strong big data strategy are at the forefront of identifying goals and use cases that big data could potentially assist with.
One industry that big data analytics have been making an impact in and will continue to develop with is healthcare. In this article, we will explore the vast amounts of data produced by the healthcare sector and the role that big data analytics plays in ultimately improving patient outcomes.
The healthcare industry generates a huge amount of medical and patient related health data and is constantly experiencing exponential growth.
Every second, the amount of healthcare data present in the world increases drastically. It is estimated that approximately 30% of the world’s data volume is being generated by the healthcare sector.
The types of information that fall under the umbrella of “healthcare data” is expansive, as are the sources of that information. As far as types of data, these sources can contain data that is structured, semi-structured, or unstructured. Sources include biomedical data, medical research data, and public record data, as well as information housed in electronic health records (EHRs), and gathered from devices connected to the Internet of Things (IoT), and that’s only naming a few.
Electronic health records (EHRs) refer to real-time patient-centered medical records and other clinical data. EHR data can include personal patient data, clinical notes, medical history, lab tests, medical images, magnetic resonance imaging (MRI), ultrasound, computer tomography (CT) data, and more.
IoT devices could include wearable devices that monitor vital health information, as well as medical equipment found in hospitals.
While traditional human health data has historically been used for decision-making, looking at the healthcare information generated as a whole, its magnitude, complexity in data type, and ever-growing nature is consistent with the 3 Vs of a big data set.
This makes it nearly impossible to analyze and manage with traditional software. However, by applying big data analytical techniques to these data sets, healthcare organizations can look holistically at factors that may influence health, gain insights on trends in the data, and create predictive models.
Descriptive Analytics: Monitors data in a medical situation and provides commentary on what is happening.
Diagnostic Analytics: Delves into the “why” behind things observed in medical data. It explains the reasons behind why certain events occur.
Prescriptive Analytics: Helps in optimal decision making. It analyzes data and proposes an action based on the desired outcome.
Predictive Analytics: Predictive models using machine learning are fed historical data to determine trends and probabilities. Using these, it can predict future outcomes.
One of the primary goals for healthcare providers is improving patient outcomes. There are many benefits to big data analytics in the healthcare industry, but its ability to improve patient outcomes is certainly an important one.
Predictive risk modeling and proactive intervention made possible by big data analytics serve as the foundation for improving patient care.
Both processes benefit from and are made more accurate as more data is fed into the machine learning models. By incorporating the data on individual patients found in clinical records and wearables with the massive amounts of information available through the larger network of healthcare, big data analytics can identify patterns and more accurately predict future outcomes. So, as healthcare big data continues to grow, and analytics continue to evolve, their predictive ability will only increase.
By collecting, maintaining, and analyzing health data across the population, doctors and researchers can get vital context for individual diagnoses which can be used as the basis for risk modeling. In the same way that genetic markers are now used to assess the chance of someone developing, for example, breast cancer, big data analysis opens the opportunities for all sorts of health and lifestyle factors to be considered. Wearables, like smart watches and fitness bracelets that monitor vital health information, like heart rate, step counts, and even sleep patterns, are set to bolster this type of modeling by supplying ever-greater amounts of data for analysis, on top of existing lab results, questionnaires, and patient outcome data.
The overall effect is a shift in the delivery of medicine from a reactive, episodic model, to a proactive, holistic, and continuous approach. Rather than treating a symptom after the fact, patients are given contextual, risk-based recommendations to prevent it from occurring in the first place. Instead of diseases unknowingly developing, they can be detected at earlier stages when they can be treated more effectively and less intrusively. Healthcare professionals are equipped with insights into patients that may be more at risk while in their care, aiding in clinical decision-making.
Using general population data comprised of lab results, questionnaires, and biometrics, conditions can be identified. By collecting data on the individual, the correspondence to those patterns can be used to assess the risk of developing that condition. This kind of risk modeling is crucial in an era typified by lifestyle diseases like cancer and diabetes, that are best (and most cheaply) treated by preventing them in the first place.
At an individual patient level, by utilizing more information about an individual's health, doctors can diagnose more proactively. In this case, the use of devices, especially wearables, that give more insight about the individual patient can be helpful in collecting the information needed for medical professionals to identify individuals who would benefit from preventative care, including lifestyle changes.
Similarly, continual monitoring of an individual's health can lead to early-stage interventions. As their vitals and exercise patterns begin to match the pattern for pre-diabetes, for example, they can be called in to see their family doctor for personalized recommendations on how to avert the transition into full diabetes.
This ability to catch diseases when they are in the early stages, and more treatable, can increase the amount of people who recover from diseases, or at least avoid complications associated with later stage diseases.
Once a patient has been admitted to the hospital, predictive risk modeling and proactive intervention can be used to identify patients at risk for medical complications—including those that are more common during medical procedures like sepsis and MRSA, as well as identifying other risk factors like co-morbid conditions. This will improve the quality of care being delivered by healthcare professionals and can decrease the number of patients that might need more extensive critical care while in the hospital.
In addition to assisting with patients, predictive modeling can also be applied to the plethora of vital medical devices found within the healthcare industry. Predictive analytics can be used to ensure medical device equipment is in working order and shorten the amount of time it is out of service. Using real-time machine health monitoring, remote technicians can detect signs of possible deterioration, enabling them to schedule preventative servicing. In this way, X-ray machines, MRIs, and other lab equipment can be available for crucial medical tests without delay, impacting patient outcomes as well.
As we’ve experienced first-hand over the past years, the ability to quickly discover, redesign, and develop new drugs can make a huge impact on patient outcomes once they become available.
By using healthcare big data analytics, research and developers can make sense of the large amounts of relevant data they have access to with more ease, which in turn can reduce the amount of time spent on product development.
Additionally, predictive analytics can cut down time spent on the trial-and-error process typically involved in product development.
There are three challenges facing companies that are exploring how to implement big data analytics in healthcare.
For big data analytics to yield any substantial benefits, the datasets need to be large enough. This requires professional medical devices to be connected, a process that is underway, largely fueled by preventative maintenance capabilities, but will take time to fully permeate. And it requires the further proliferation of wearables in the general population. Fitness bracelets and smart watches are present, but they need to become far more popular for big data analytics to have the desired impact.
A key part of big data analytics in healthcare is electronic health records (EHR). These need to contain a persistent digital thread of every piece of data connected to an individual. They also need to be transmittable to centralized repositories for use in larger datasets. Without the population data to reference against, risk modeling is all but ruled out, and predictive analytics are of limited use. The challenge here is a lack of interoperability. EHRs are often not transferrable even between two hospitals in a local area, let alone on a national or international level. And each silo has its own standard. However, there is a race to solve this problem, spearheaded by Apple and several competing blockchain initiatives.
In most countries, health information is treated as ‘sensitive data.' In the US, we have strict governance owing to HIPAA regulations. The EU faces similar restrictions under the European General Data Protection Regulation (EU GDPR). People are rightly concerned about their intimate details being pored over unscrupulously or stolen by hackers. Both scenarios are made more likely by inclusion in large pools of data.
Great strides are being made in this area focused on the anonymization and pseudonymization of data before inclusion in large datasets, but it's an issue that will always loom over big data analytics in healthcare. To get an insider's view on the now and future of big data analytics in healthcare, read the Axendia IoMT Impact Survey.
All these challenges are surmountable and have potential solutions on the horizon. Once these obstacles are overcome, the IoMT and the big data analytics it serves have the potential to revolutionize healthcare—shifting it from a model of episodic, reactive treatments, to proactive, contextual interventions. That kind of medicine is exactly what our era needs.
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