data analytics corporate training for medical employees

Why Data Analytics is Important in Medical Industry

This article briefs the various implications about the significance of managing Data and the evolving need of integrating the medical information systems with Big-Data Analytics.

Table of Contents

  • Role of Big Data in the Medical and Health-Care Industry
  • Big Data Overview
  • Electronic Health Records (EHR)
  • What is the Big Data Analytics?
  • Types of Data Analysis
  • Characteristics of Big Data
  • Integration of Big Data Analytics with EHRs
  • Implementation of Big Data Analytics in Health-Care
  • Pitfalls to avoid
  • Conclusion

Role of Big Data in the Medical and Health-Care Industry

Health- Care industries are one such entities, where the data plays the most critical aspect. The Accuracy of the data predicts the accuracy of the treatment.

Hence, making the record of every single diagnostic data plays an evident role in both the prediction and analysis.

The accumulation of large amounts of data every single, over the years, is what is termed Big Data. Regardless of the volume and its complexity, this large storage of data is nevertheless important.

Considering this ground-level understanding, now let’s jump into understanding what roles and functionalities can this Big- Data make in the field of the Medical and Health-Care industry.

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Big Data Overview

  1. Electronic Health Records (EHR)

An EHR is an electronic version of recording the patient’s medical history. The provider may keep track of all of the administrative clinical data, including all the demographics, progress notes, medications, diagnostics, past medical history, and radiology reports etc.

Though the EHRs are the one-stop information systems for various diagnostic requirements, they alongside make it complex in terms of analysing and managing due to the integration with the traditional Software and Hardware.

  1. What is the Big Data Analytics?

Big Data Analytics is the process of interpreting data to find patterns, gain insights, and answer questions. You can analyse data manually or with the help of software and algorithms. 

Analysis of data can be done in the following four approaches

  1. Prescriptive Analysis
  2. Predictive Analysis
  3. Descriptive Analysis
  4. Diagnostic Analysis
  1. Types of Data Analysis

Prescriptive Analysis

Prescriptive analytics looks at what has happened, why it happened, and how to best take advantage of future outcomes.

In other words, prescriptive analytics shows you how you can avoid a future problem or capitalize on an emerging trend.

Predictive Analysis

Predictive analytics seeks to estimate what may happen in the future based on past events and trends. For example, data analysts can develop predictive models which estimate the likelihood of a future event or outcome.

This is especially useful as it enables businesses to prepare for certain events.

Descriptive Analysis

Descriptive analytics is a type of data analytics that looks at what has happened in the past. 

Unlike predictive or prescriptive analysis, descriptive analytics does not try to make predictions or suggest courses of action. It simply describes what has happened.

Diagnostic Analysis

Diagnostic analytics is used to identify and respond to anomalies within your data. The main purpose of diagnostic analytics is to find out why something happened. 

For example: If the descriptive analysis portrays that there was a 50% increase in the viral infection cases this year, you will want to know the reason behind it. This is when Diagnostic Analysis plays its part.

  1. Characteristics of Big Data

Usually, the Big-Data is characterized considering the following aspects

  1. Value – Analysing the importance and coherency of the information to the doctors and patients.
  2. Volume – Refers to the size and amount of the data.
  3. Velocity – The ease of retrieving, processing and analysing the data.
  4. Variety – Talks about the heterogeneity and complexity of structured, semi-structured and unstructured data sets.
  5. Veracity – It refers to the relevance and uniqueness of the data with other data.
  6. Variability – It cites the consistency and trends in the data.
  1. Integration of Big Data Analytics with EHRs

Big data analytics is the integration of heterogeneous data, quality control, analysis, modelling, interpretation and validation. It provides comprehensive knowledge discovery from the available huge amount of data.

Big data analytics is used in the healthcare industry to identify trends and patterns in large datasets.   

The data is often collected from thousands of patients, which can then be analysed for correlations between different factors. Data mining techniques are then used to create predictive models that can identify specific patient populations or risk factors.

It combines the analysis of several scientific areas, such as bioinformatics, medical imaging, sensor informatics, medical informatics and health informatics.

This is the ultimate reason why the data analytics corporate training is to be considered a must for all those who are into health care.

Implementation of Big Data Analytics in Health-Care

Data Analytics, when applied wisely, can bring many innovative changes in every aspect of the legacy health care systems

  1. Evaluating and Enhancing the Treatments

Data received from the patients regarding their experiences with the practitioners, treatments and the recovery rates, can be analysed efficiently to make amendments in the existing processes or to implement the new processes if any.

Such amendments and analyses are crucial in the healthcare industry to implement customized treatments.  

  1. Capturing the defects in the Scans

Another leverage of using machine learning algorithms in health care is to analyse the data quickly with much accuracy.

Due to the algorithm’s ability to analyse the data based on its previous data sets, it can detect the anomalies way too faster than a human being. But again, relying just too much only on the algorithm is also not a good idea. 

It can only show the expected results when the critical thinking of the practitioner is paired with the potential of the algorithms to save both lives and time.

  1. Predicting the outbreaks

This is the example of both predictive and prescriptive analysis as the algorithm can draw conclusions from the previous data sets and can visualize the future trends.

Taking this sort of leverage can never lead the intention of healthcare in an otherwise direction because predicting the future circumstances is always considered a boon for the medical industry.

Predicting the epidemic and making the appropriate decision making can not only serve as an advantage for the health sector but also for the entire market of the Country.

Pitfalls to avoid

While data analytics has the power to drive positive change, it also can create unprecedented issues.

With sensitive information such as patient data, you need to protect it at all costs while improving health and wellbeing. As a medical professional or administrator, this is your responsibility.

It is also important to note that, it is the people who write the algorithms and might train the datasets according to their biases. Hence, the machine-learning algorithms produce the results according to the data sets they are trained on.

Extreme vigilance should be incorporated while ensuring that the data is taken from the trusted representatives with accuracy, before using them as resources or concluding.


To positively impact both patients and healthcare providers, brush up on your analytical skills.

Enrolling on Data Analytics Training for medical industry employees from one of the best corporate training programs in India can help equip your medical principles with the derived powerful insights to predict trends, and make data-backed decisions.

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