All You Need to Know about Big Data Analytics in Healthcare

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BigDataScalability.com – Big data is more than just a massive amount of data. By utilizing big data, every organization can gain tremendous benefits. This is true for organizations in just about any sector. Yes, including healthcare. Healthcare is a very complex sector. This is why big data analytics in healthcare is very useful.

The question is, what exactly is big data analytics? Read on and you will know. Below, we explain what big data and big data analytics are, the types of big data analytics in healthcare sector, the benefits, as well as challenges of big data analytics in the sector.

What Is Big Data?

What-Is-Big-Data

The term big data refers to the massive sets of diverse data, whether structured, semi-structured, or unstructured, that are continuously generated in high volumes and at high speed.

More and more companies are using big data to gain meaningful insights and better their decision-making. What makes big data different from traditional data is the fact that it can’t be stored, processed, or analyzed using traditional data storage, processing units, and tools.

In the healthcare sector, big data refers to the enormous amounts of health-related data such as electronic health records or EHRs, diagnostic reports, medical imaging, medical research, wearables, X-rays, genomic sequencing, and others.

The data can be from various sources. It is diverse in its format and massive in its volume. This is why it is difficult to store in traditional databases. It is also too complex for traditional data processing technologies.

Big data has three “V”s, which describe its characteristics. These “V”s are

  • Volume, refers to the quantity of data. That is, the “big” in the big data.
  • Velocity, refers to the data processing speed
  • Variety, refers to the types of data

What Is Big Data Analytics?

What-Is-Big-Data-Analytics

While big data refers to the massive amount of data that is difficult to store and process in conventional ways, big data analytics refers to what is done with the data.

The term describes the process of discovering patterns, trends, as well as correlations in massive amounts of raw data. The purpose of big data analytics is to help make data-driven decisions. There are four key processes in big data analytics: collecting, processing, cleaning, and analyzing.

1. Collecting

Before processing, cleaning, and analyzing can be done, data must first be collected. How data is collected differs from one organization or another. With today’s technology, an organization can collect structured, semi-structured, and/or unstructured data from various sources.

For healthcare organizations, the data can come in the form of EHRs, diagnostic reports, and medical research, among many others.

2. Processing

After data is collected and stored, it needs to be organized properly. This is particularly true if the data size is large and it is unstructured. Without proper organization, it will be difficult to get accurate analytical query results.

3. Cleaning

Data, whether it is small or big, requires scrubbing to improve its quality and make stronger results possible. Also, all processed data must be formatted correctly. At this stage, any irrelevant or duplicative data must be accounted for or eliminated.

4. Analyzing

To be useful to us, big data must be analyzed. This process takes time. After data is ready, advanced analytics processes convert it from “just” big data into meaningful, actionable insights.

Some of the methods used to analyze big data include

Data mining, which sorts through massive datasets to discover patterns and relationships by creating data clusters and identifying anomalies.

Predictive analytics, a method that makes use of the historical data of the organization to identify upcoming opportunities and risks, making predictions.

Deep learning, which imitates human learning patterns. Deep learning utilizes machine learning and artificial intelligence to discover patterns and layer algorithms in the most abstract and complex data.

4 Types of Big Data Analytics in Healthcare

4-Types-of-Big-Data-Analytics-in-Healthcare

There are four types of big data analytics. Each type answers different questions. The four types are

1. Descriptive analytics

This type of analytics allows us to discover what happened and when. It uses data from the past to provide insights around benchmarks or trends, typically in a dashboard form.

Although analytics helps us understand what happened in the past, it cannot provide us with foresight into possible future events. Nor can it provide strong insights to influence health outcomes in the future.

2. Diagnostic analytics

The next one is diagnostic analytics. Diagnostic analytics explains how and why something happened. It does so by identifying relationships and patterns in available data.

Using diagnostic big data analytics in healthcare, we can understand what we need to explore further. Keep in mind that the raw data analyzed may be incomplete or inaccurate. This limits its usefulness.

3. Predictive analytics

Predictive analytics uses historical data to find patterns as well as make predictions on what is likely to happen next. While predictive analytics is useful as it provides us with predictive insights, it does have its limits.

The results are predicated on the assumption that the conditions remain the same. As such, it cannot provide insight into what might happen after the condition changes or if there is an intervention.

4. Prescriptive analytics

The last type is prescriptive analytics. This analytics deals with the question of what should be done better. The analytics utilizes machine learning to suggest a course of strategy or action. This is done by taking in a vast amount of input.

The analytics helps us understand what the effects of a certain action will be. While it can give much-needed insights on what should be done better, the analytics has limited maturity and inherent uncertainty, which could lead to implementing suboptimal actions.

7 Big Data Analytics in Healthcare Benefits

Big data analytics in healthcare certainly offer so many. Here are 7 main benefits of utilizing big data analytics in the sector.

1. Improves patient healthcare

Improves-patient-healthcare

Big data analytics provides knowledge. And from this knowledge, better clinical insights for healthcare providers. For example, using big data analytics in healthcare, doctors can not only prescribe effective treatment but also make better and more accurate clinical decisions.

The latter is possible as big data remove any ambiguity involved in the treatment. In other words, the data in big data is used to find out which clinical practices are the most effective for patients.

2. Predicts patients who are at higher risk quickly and efficiently

dicts-patients-who-are-at-higher-risk-quickly-and-efficiently

The next benefit is it predicts patients who are at higher risk quickly and efficiently. Big data allow healthcare providers to do predictive analytics to pinpoint which segment of patients are at higher risk for diseases.

In addition, predictive analytics also hints at early intervention, thus enabling healthcare providers to protect patients from diseases, particularly chronic ones.

These predictive big data analytics in healthcare are made possible by aggregating big data related to various factors such as

  • the medical history of the patient
  • socio-economic profile data
  • demographic area data
  • patient’s comorbidities that exist within the area
  • etc.

3. Eases patient diagnostics with electronic health records

Eases-patient-diagnostics-with-electronic-health-records

Big data analytics in healthcare makes effective patient diagnostics possible. This is achieved by using patients’ electronic health records or EHRs. The EHRs include details such as patients’ allergies, medical history, diagnostic results of previous as well as current illnesses, and demographics.

Moreover, these EHRs are shared through secured information systems. Doctors, as well as other healthcare professionals, can access these records easily.

Note that while healthcare professionals are granted access to these files, they cannot modify patients’ data. Doctors, however, are allowed to modify patients’ diagnostics and treatments.

The EHRs can also be utilized to trigger notifications that alert patients about their next doctor/diagnostic visits. They can track patients’ prescriptions, too.

4. Reduces overall costs of healthcare

Reduces-overall-costs-of-healthcare

The costs of healthcare are high. The good news is big data analytics in healthcare can reduce it.

Leveraging the EHRs, healthcare providers can discover patterns that lead to a better understanding of patients’ health patterns. This can cut the costs of healthcare by reducing unnecessary care or hospitalization.

In general, better insights from big data analytics translates to improved patient care. In addition, it also pinpoints patients for shorter hospital stays. These reduce healthcare costs due to reduced hospitalizations.

Predictive big data analytics in healthcare enables healthcare providers to estimate individual patient costs and further improve healthcare efficiency by planning the treatment carefully.

5. Provides better insights into patient cohorts

Provides-better-insights-into-patient-cohorts

As healthcare providers analyze big data in healthcare, they can draw a better insight into patient cohorts who are at greater risk for illnesses. This, in a way, helps healthcare providers to take necessary proactive prevention measures.

Moreover, this kind of analytics data can also be used to inform, educate, and even motivate patients so they take responsibility for their own well-being. Such data also bring more effectiveness to treatment plans for the patient, which ultimately leads to better patient outcomes.

6. Allows improved healthcare with fitness devices

Allows-improved-healthcare-with-fitness-devices

Consumer fitness products are widely available nowadays. These fitness devices keep track of their users’ physical activity levels. The data collected by such devices are sent to cloud servers.

Using this data, physicians can know the overall health of an individual and plan for their wellness programs better. Physicians can also use it to know about certain health-related trends.

7. Enables real-time alerting

Enables-real-time-alerting

Last but not least, big data analytics in healthcare enables real-time alerting. Specific medical healthcare decision support software makes it possible. The software analyzes the data on the spot. It then delivers real-time alerting, which in turn helps healthcare providers to make better prescriptive decisions.

Doctors are insisting on patients to wear wearables that collect the health data of the patient continuously and send it to the cloud. This is hardly surprising. After all, using the collected data, doctors can better prescribe medicines accordingly.

The Challenges of Big Data Analytics

The-Challenges-of-Big-Data-Analytics

While big data analytics in healthcare offers tremendous benefits, it also comes with challenges to watch out for. Some of the biggest challenges include

  • Keeping data organized and accessible. The amount of data is vast, so organizing and making it accessible is difficult.
  • Keeping data secure. There are also security issues. Keeping data secure is very important as data breaching is on the rise.
  • Quality control. Maintaining the quality and accuracy of big data can be hard and time-consuming.
  • Choosing the right tools. With so many options out there, it can be difficult to choose the right tools for big data analytics in healthcare.

Evidence-based healthcare is the direction the world is heading in today. This requires not just vast amounts of data in the form of big data, but also big data analytics. In other words, the data needs to be not just collected but also processed to enable a data-driven approach to decision-making.

Big data analytics in healthcare offer tremendous benefits for healthcare organizations. For example, it helps improve patient healthcare, predicts patients who are at higher risk quickly and efficiently, eases patient diagnostics, reduces overall costs of healthcare, and generates real-time alerting, to name a few.

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