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No One Tells You What Cooks Behind the Doors of Healthcare Data Analytics Agencies. But We Do!

Data is a fancy word. And when we connect it to analytics, it becomes fancier.

Because data itself is useless unless we put it to good use by analyzing it.

So, today, we will go on a very knowledgeable ride and explain how we as a healthcare IT company perform data analytics and deliver outstanding results to our healthcare clients.

Our whole motive is to share what other companies never want to share – insights into healthcare data analysis practice.

So, let’s get started.

What is Data Analytics in Healthcare?

Data is everywhere and anyone can analyze that data with basic math. But when data gets complex because of its large size and unorganized nature, you have to utilize computerized and algorithms-led methods to analyze it.

This practice of analyzing data with digital mediums is called data analytics. And when we analyze healthcare or clinical data, it is called healthcare data analytics.

The purpose of this data analysis is to find meaningful patterns or insights out of large historical data.

For example, by studying the last 20 years of patient admission data, you can easily predict on which days, weeks, months, and for which clinical requirements, the highest number of patients is estimated to get admitted into your hospital.

So that you can prepare your hospital for the increase in the number of patient admissions.

This is just one example.

By doing the practice of healthcare data analysis, you can analyze multiple types of data and improve the overall quality of care and internal workflows.

4 Types Of the Healthcare Data Analytics

How is Data Analytics Used in Healthcare?

Data analytics comes into the picture when there is data.

Using data, healthcare entities and startups can predict several things and be ready to grab those opportunities.

The following are the top ways data analytics is used in healthcare.

  • In clinical assessment
  • In healthcare chatbot
  • In healthcare marketing
  • In the care plan assessment
  • In radiology images assessment
  • In-patient admission prediction
  • For real-time alerting
  • For enhancing patient engagement
  • To identify cancer in the early stage
  • In healthcare cyber security
  • To assess care requirements
  • To manage staff smartly based on demands
  • In supply chain management
  • In identifying the most workable medicine
  • In clinical trials
  • In clinical trial recruitment
  • To predict care costs

What are the Roles of Healthcare Data Analysts?

Data analysis requires a dedicated skill set and knowledge of AI, machine learning, and important programming languages such as Python and R Language.

The following are the top roles and responsibilities of healthcare data analysts.

  • Extract clinical and other data from primary and secondary sources using automated methods
  • Build datasets
  • Remove duplicate or malicious data from datasets
  • Filter out the data using automated methods
  • Apply AI and machine learning algorithms to find patterns from data
  • Build programs or models using R Language or Python
  • Train those programs or models on machine learning algorithms
  • Capture or store rules or patterns in an understandable or graphical format

What are the Benefits of Medical Data Analysis?

Healthcare entities including hospitals and healthcare startups struggle with labor shortages and delivering quality, rapid & affordable care to patients.

With medical data analysis, they can get rid of these challenges.

Here are the top benefits of the ‘data intelligence you get as the result of data analysis practice.

  • Predicting patient inflows
  • More accurate clinical assessment
  • A very high cost saving
  • Productive staff
  • High patient engagement
  • Easy risks mitigation
  • High cyber security standards
  • Personalized care delivery
  • Effective marketing campaigns
  • Enhanced patient experience
  • Better asset management
  • More revenue
  • Early illness detection
  • Higher patient safety standards
  • Fast clinical decision

How are AI, Machine Learning, and Deep Learning Technologies Used in Data Analysis?

All of these 3 technologies belong to the same family but have different capabilities and limitations.

AI: AI is neither a programming language nor a technology. It is just the ability of a computer or any digital system to first understand, then plan and react to certain situations by itself. In other words, it enables manmade digital systems to make decisions without human intelligence.

Machine learning: It is a subset of AI. AI uses machine learning algorithms to make decisions by itself. Using machine learning algorithms and datasets, we can train any program to understand and react to any situation.

Deep learning: Deep learning is an advanced version of machine learning. In the case of machine learning, we have to manually define values such as skin colour, skin spots, redness, and spot diameters if we are a training model to identify skin cancer through images. But in the case of deep learning, it defines these values.

How to Carry Out Healthcare Data Analytics? A Live Example of Disease Detection Using a Genetic Algorithm

This is what we were talking about in the blog’s title.

Let us give you insights into how data analytics plays its role in building a program that detects diseases with early symptoms.

Problem statement: Build a program that detects the diseases with early-stage symptoms that doctors feed in EHR.

Solution: We will use a genetic algorithm (machine learning algorithm), a rule engine (computer program), and a historical dataset of diseases and their associated symptoms.

We run the genetic algorithm on the dataset.

Inspired by Charles Darwin’s theory of natural evolution, the algorithm finds and selects the fittest values (accurate values) and produces offspring of the next generation.

It continues executing the loop several times (generations) and what it produces last is the fittest or most accurate values.

With these fittest values, the rule engine makes rules in the form of If-Then.

For example, if fever > 102, runny nose = yes, weakness = yes, loss of appetite = yes, then it is COVID-19 and not flu.

Rule engine makes multiple such rules and it keeps making it with new data it gets.

Now another program compares the real-time symptom data of a patient (added by doctors in EHR) with these rules and based on matching percentages, it predicts the disease.

So, this is how, by using historical data, we make rules and by comparing rules with real-time data, we can find diseases with early symptoms of patients.

Yes, we cannot use this as a conclusion but it surely helps doctors in clinical decision-making, especially when the symptoms indicate the probability of complex neurological or cancer diseases.

We Turn Your Data into Intelligence Which You Can Use as Your Most Intelligent Resource

We are an Ontario-based healthcare-focused IT company.

We deal with healthcare data without messing it up. Want to know how?

Read our case study of leveraging data for efficient resource management at a UK-based hospital.

And wait… we forgot to tell you why we chose to be a healthcare-specific IT company.

Because we want to make healthcare free of chaos, permanently!