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Top 20 Examples of Big Data applications in Healthcare


Top 20 Examples of Big Data applications in Healthcare

Top 20 Big Data applications in Healthcare

Big data in healthcare is doing great, as modern people, we already know. 
Big data is vast and cannot be easily managed. Together with other technologies, big data plays an essential role in unlocking new possibilities. 
Medical data is sensitive and can cause serious problems if manipulated. 
Data science in healthcare can transform this data by protecting it and extracting many important features. 
Recent advances in Artificial Intelligence (AI), machine learning (ML) , image processing and data mining techniques can also be used in the medical field to find patterns and create expressive visuals using big data.
Recent AI and machine learning technologies are helping data scientists use a data-driven approach. 
Big data applications in healthcare can be easily adapted to databases containing many of the patient records currently available. 
Let's start with a comprehensive list and examples of big data and data science in healthcare.

1. Big data applications in healthcare: Estimate the expected number of patients

This application of machine learning and big data solves one of the critical problems in healthcare that thousands of shift managers face every day, many patients die  to the absence of doctors at the most critical time each year. 
This application allows shift managers to accurately predict the number of doctors needed to efficiently serve patients.

Insights from this app:

It helps to find a solution to the problem of estimating the number of doctors needed at a specific time.
We use 10-year records from hospitals and apply time-analysis techniques to measure rates of hospitalizations.
It focuses on reducing waiting times for patients and improving the quality of health care.
It provides an easy-to-use platform for all types of users: doctors, shift managers, nurses and more.
Estimate the expected number of patients is among the most important big data applications in healthcare 

2.Big data applications in healthcare: Electronic health records

is one of the best big data applications in healthcare. 
From the early days of healthcare, we faced a serious problem of data replication. Data replication is a useful process for storing data on multiple systems at once. 
This application has identified this problem, found a solution, and has become one of the most popular big data applications in the world.
is among the most important big data applications in healthcare 

Insights from this app:

It aims to make sensitive patient data, including medical records and general information, readily available to authorized users such as health care institutions, governments and doctors.
Emphasizes the importance of keeping data secure to prevent unauthorized access.
Generate electronic statistical reports that include all patient demographics, allergy history, medical examinations or medical examinations.
Notify the patient if routine examinations are needed or if the doctor's instructions are not followed.
Prevent unfortunate deaths by allowing people to track treatment or medical history.
Electronic health records is among the most important big data applications in healthcare 

3.Big data applications in healthcare:  Real-time notifications

This application is designed to reduce the loss of life unexpectedly by serving individuals and society. 
It aims to help people heal before they suffer. Many people have already died because they got to the hospital too late. 

Insights from this app:

Use impactful data generated by Clinical Decision Support software and help healthcare providers make decisions while creating prescriptions.
We collect patient health data for use in raising social awareness through wearable devices.
All data is stored on cloud-based storage and analyzed with sophisticated tools. 
When unreasonable activity is detected, the relevant person is automatically notified.
If the patient falls into a serious condition such as high blood pressure or asthma, notify the doctor.
The application also plans to use the power of data science to improve treatment processes for certain diseases.
Real-time notifications is among the most important big data applications in healthcare 

4.Big data application in healthcare:  Improving Patient Engagement

healthcare, underdeveloped data science technologies are using the power of wearable health tracking devices to predict future ailments a patient may experience. 
Eliminate potential patient risk by correlating results generated by medical devices with other traceable data. It also helps doctors identify the symptoms of certain diseases so that they can better serve you.

Insights from this app:

It focuses on enabling patients to use the necessary data they collect from wearable health tracking devices, such as heart rate and blood pressure.
They want to engage people to improve health care and use data analytics to identify symptoms.
By storing data collected from patients on a server, doctors can check if the patient is healthy and give advice accordingly.
Patients suffering from high blood pressure, asthma, migraines, or other serious health problems, a doctor can monitor their lifestyle and make changes if it matters.
The goal of this application is to reduce the frequency of visits to the doctor for minor problems by regulating your daily activities.
Improving Patient Engagement is among the most important big data applications in healthcare 

5.Big data applications in Healthcare: Prevention of opioids 

When America faced the serious problem of opioid abuse, the idea of ​​developing big data in healthcare came to mind. The need to address the problem of opioid drug use, including illicit drug heroin, synthetic opioids, and pain Pain relievers such as oxycodone have come to the fore in replacing traffic accidents, which are the leading cause of death in the United States. After many attempts, this problem was not resolved until the application introduced  big data to detect high-risk patients.

Insights from this app:

Using fuzzy logic techniques, we identify 742 risk factors that patients can assess to predict whether they will abuse opioids.
Insurance companies and pharmacies collect data and combine it with data science to create accurate forecasts.
Not only identify patients who abuse opioids, but report them to their doctor.
Finding effective ways to prevent people from subconsciously overdosing on opioids using Forest Algorithm.
Combining big data and healthcare to ensure patients don't waste too much money and live longer.
Prevention of opioids  is among the most important big data applications in healthcare 

6.Big data applications in healthcare:  Strategic planning 

This application uses health-related data to induce people to visit medical institutions for treatment. We collect a variety of data, such as demographics, population numbers, and test results. After analyzing vast amounts of data, the results are used in strategic planning to carry out specific activities.

Insights from this app:

Implement data science to identify problems that are not visible at first glance.
You want to evaluate a patient's behavior by analyzing a heatmap of the patient's location.
Identify the causes of some problems, such as rapid population growth or the spread of infectious diseases.
After analyzing the results of the data-driven approach, the relevant person in charge is notified of any updates to the treatment process.
Highlight the number of hospitals or medical services you need. The results can lead to important decisions such as building a new medical institution.
Strategic planning  is among the most important big data applications in healthcare 

7.Big data applications in Healthcare: Cancer treatment 

Cancer is a disease that has no specific treatment and is caused by abnormal cell growth. This is one of the best initiatives ever taken using big data to find solutions to serious problems. We use patient data and analyze it to devise better treatments to treat cancer. 
The project is still under development and may bring new light to address other dangerous disease problems.

Insights from this app:

You want to fit complex data gathered from many sources. 
All previous biopsy reports are collected and your doctor can get information before making a decision.
It has helped find Desipramine, which acts as an antidepressant for some lung cancers.
This allows doctors to compare the provided health care systems to identify the best system for better outcomes.
Provides tumor samples, recovery rates, and treatment records. Thus, medical researchers can find the best treatment trends in the real world.
Cancer treatment  is among the most important big data applications in healthcare 

8.Big data applications in healthcare: Predictive Analytics in Healthcare

is an automotive big data tool in the medical field that helps doctors prescribe drugs to patients in less than a second. 
It has over 30 million electronic health records collected by many insurance companies, hospitals, diagnostic centers and community health centers. 
It is easy to detect if someone is at high risk of getting the disease in the future. In addition, databases containing sensitive data can be further used to improve health care processes.

Insights from this app:

Physicians want to be guided by a data-driven approach to treating patients without marginal errors.
Use the power of relational databases for predictive analytics tools that will improve care delivery.
Some patients have a very important and unusual medical history. 
This application allows doctors to treat these patients well.
People suffering from multiple health ailments and serious health problems can be treated through this system.
The best part of this app is that it can predict if there are patients at high risk of diabetes and other chronic diseases.
Predictive Analytics in Healthcare is among the most important big data applications in healthcare 

9.Big data applications in Healthcare:  Telemedicine

may have heard of this name, which has been in operation for over 40 years. Although many years have already passed in delivering healthcare through digital platforms, we see hope only after big data, smartphones and wearables combine. 
Big data analytics in healthcare encourages digging deep into data sets and extracting meaningful learning. This application can use technology to provide medical care remotely.

Insights from this app:

Designed to provide primary care and remotely monitor critical patients. We also provide medical training for professionals.
Bringing the power of data science to healthcare. 
This allows the surgeon to complete the operation remotely with real-time data delivery.
It helps to adjust the patient's treatment plan to track the patient's condition and prevent deterioration of the patient's condition.
Digitize the treatment process by allowing patients to receive advice from their doctors anytime, anywhere.
It can monitor the patient's health status, which saves a lot of patient time and ensures an efficient medical flow.
Telemedicine is among the most important big data applications in healthcare 

10.Big data applications in healthcare:  Combination of big data and medical imaging

Data science in healthcare has brought about many changes that were unthinkable just a few years ago. This app has solved one of the important problems of health care. 
Medical images with precise values. 
Medical imaging is essential for radiologists to identify a disease or symptom. This application refers to going deeper into the data by turning the images into numbers for better results and performing algorithms.

Insights from this app:

It means incorporating algorithms to replace radiologists. It focuses not only on image evaluation, but also on each byte and bit contained in the data.
Generate metric results and fully expose specified patterns associated with pathology.
It can also count the number of bones and predict whether a patient is at risk of fracture. It helps doctors make decisions.
Increase the effectiveness of current radiologists. 
This process allows radiologists to examine more images than they currently do.
It is intended to promote preventive care and make the best decisions about medical testing.
Combination of big data and medical imaging is among the most important big data applications in healthcare 

11.Big data applications in healthcare:  Prevention of frequent emergency room visits due to big data

This application focuses on saving patients money and time by using big data analytics in healthcare. 
What would you think if you had 900 or more emergency room visits within 3 years? 
This application is intended to reduce the amount of taxpayers and medical institutions. We also strive to provide the best possible care to our patients.

Insights from this app:

Understand the need to prevent readmissions and apply data science techniques to identify causes.
We help health insurers provide the best service and make it easy to detect fraudulent activity.
When a patient has to pay for the same treatment multiple times, it costs money. 
This application tries to avoid this situation.
Keep records of treatment received by one patient and allow counselors to review the history before making a decision.
Make data available to local health care providers stored in databases to investigate emergency room use, hospital admissions and preventable readmission rates.
Prevention of frequent emergency room visits due to big data is among the most important big data applications in healthcare 

12.Big data applications in healthcare: Big Data in Fraud Reduction and Security Enhancement

Since the concept of health insurance was established, service providers face a serious problem of false claims and need to better serve their true consumers. 
Moreover, the threat of data copying and manipulation of sensitive data has reached its peak. 
This application seeks to implement data science in healthcare. It protects the valuable data of many patients from criminals who can sell them on the black market.

Insights from this app:

Cybersecurity & network traffic is a huge threat to data collection companies. This application helps businesses working with sensitive and sensitive data by protecting data from security threats.
Successfully detect fraudulent claims and enable remedial insurance companies to provide better returns on the needs of real victims.
It protects valuable data from malicious users that criminals can use to create unpleasant situations.
Besides, it can generate reliable detection of inaccurate claims and saves a lot of money every year for insurance companies.
Big Data in Fraud Reduction and Security Enhancement is among the most important big data applications in healthcare 

13.Big data applications in healthcare:  Using Big Data to Transform Diabetes Management

Every year, many people become diabetic, and diabetes has already reached epidemic levels. 
It is one of the leading causes of health problems that take seven lives. 
This application collects patient behavioral, physiological and situational data for evaluation using big data to provide better care for diabetics.

Insights from this app:

We collect data using wearable digital devices such as blood glucose meters, blood pressure monitors, and scales. Storing data in an accessible database is also part of this application.
Evaluate data to extract potential lifestyle information and provide feedback when patients need lifestyle changes.
Automate the insulin delivery process. It uses a closed-loop system to know how users respond to food, exercise, and insulin.
It combines the power of AI with data gathered from a variety of wearables. These technologies raise blood sugar, insulin, blood pressure, diet and weight data from users.
Understand the patient's health status and trigger alerts before catastrophic events occur.
Transform Diabetes Management is among the most important big data applications in healthcare 

14.Big data applications in healthcare:  Big data analysis of heart attack prediction

Heart attack is one of the most deadly health problems, taking many lives each year. Facing an unpredictable heart attack is not easy and requires large data sets. 
It also requires the application of data mining to compare, establish relationships, and extract hidden patterns between data sets to be able to predict the likelihood of an acute heart attack. 
This application monitors trends and tells you if you need to take any necessary action.

Insights from this app:

It is intended to evaluate complex data sets to predict, prevent, manage and treat heart-related diseases such as heart attack.
Extensive national and international databases are researched to achieve the goal of producing better results.
You can predict your risk of cardiovascular disease by analyzing your eating habits, lifestyle, and prescription history.
Tracks records collected from wearable devices that can predict the likelihood of a future heart attack by counting blood cell flow, heart rate, and blood pressure. '
It also uses data mining for visualization and deep analysis of data sets.
Big data analysis of heart attack prediction is among the most important big data applications in healthcare 

15.Big data applications in healthcare: Nutrition management using big data

We live in the information age. Data science in healthcare is the most valuable asset. This application uses big data to outline nutritional plans for people who may suffer from many diseases in the future. 
Our data is available on our social media, browser history and even some of the most advanced technologies can track and store our data in bulk. 
This application attempts to develop health care with an appropriate nutrition plan using this important data readily available around us.

Insights from this app:

It is to use big data to open up thousands of possibilities to make nutrition better.
Assess nutritional insights by collecting data from wearable devices such as pedometers, heart rate monitors, smart watches, and cell phones.
Excessive weight can be life threatening. This application helps people lose weight by observing their daily life, eating habits and behavior.
It also utilizes the sensor of the smartphone to accumulate data for predicting and evaluating the symptoms of nutrition-related diseases.
Collect data from supermarkets and evaluate invoices to trigger notifications to users for obesity prevention when evaluating food shopping.
Nutrition management is among the most important big data applications in healthcare 

16.Big data applications in healthcare:  Big Data in Ophthalmology

The ophthalmic imaging center produces a vast amount of data that can be called big data. Big data is changing the world by providing more reliable services in every aspect of our daily lives with the powerful power of AI, images, natural language processing, and machine learning. 
This application attempts to systematically review structures to diagnose eye diseases using artificial intelligence models.

Insights from this app:

It uses big data to enable AI to generate intelligent and complete diagnostic reports to provide better healthcare.
Through deep integration with ophthalmology, data is drawn from image processing used to diagnose and generate noteworthy clinical impressions.
In machine learning, we want to use new algebra to get patterns and combine them with big data to predict future trends.
Because there is no loss of medical data, the proportions that predict high risk or describe the current state of the eye are nearly accurate.
Data available in datasets from advanced AI algorithms EyePAC, Messidor, and Kaggle can bring unprecedented changes to eye problems.
Big Data in Ophthalmology is among the most important big data applications in healthcare 

17.Big data applications in healthcare:  Arthritis treatment using big data

application tries to recognize the relationship between periodontal disease and rheumatoid arthritis. It is already understood that the causes of periodontal disease can also lead to arthritis. Now that a comprehensive data set is available, the application attempts to display and find evidence behind this association.

Insights from this app:

It focuses on finding the relevant mechanisms of periodontal disease and rheumatoid arthritis.
Evaluate whether effective treatments that can help with periodontal disease can help relieve pain from arthritis.
Different types of data are analyzed, including demographics, diagnostic codes, outpatient visits, hospital admissions, patient orders, vital signs, and laboratory tests.
Identify better treatment by looking at the history of treatment a patient has received over their lifetime.
People's demographics, age, behavior, medical reports, and hospitalizations are also taken into account to generate improved outcomes.
Arthritis treatment is among the most important big data applications in healthcare 

18.Big data applications in healthcare:  Big Data Prevents Dengue Fever Outbreaks

As with other infectious diseases such as malaria, influenza, chikungunya and Zika virus; Dengue fever has become one of the most well-known viruses in the world, taking many lives each year. 
The mosquito Aedes spreads dengue. There is currently no proposed treatment for this disease. Mosquito control is the only solution that can save us from the deadly situation of a dengue outbreak. 
This application of big data to healthcare seeks to present digital tools that process data with KDT and ML to produce results. The government is working hard to deal with this situation and put it under control.

Insights from this app:

There is not yet a vaccine that can fight the dengue virus. This application introduces a data science approach to solving the problems of this epidemic.
It pulls data from social networks such as Twitter and combines it with big data to predict the likelihood of a fatal event from dengue.
You are trying to find the cause and evaluate how dengue fever spreads. 
It also identifies how the environment and humidity affect and create suitable conditions for Aedes mosquitoes.
The database is created directly from user interactions with friends and family.
Implement classification algorithms and text mining to extract meaningful information.
Big Data Prevents Dengue Fever Outbreaks is among the most important big data applications in healthcare 

19.Big data applications in healthcare:  AIDS detection using big data

This application combines big data and healthcare. Many applications have already attempted to embed big data in healthcare. 
It is an incurable disease and destroys the body's immune system . This application focuses on detecting HIV at an early stage. 
Huge amounts of data are available in many databases and are available to real employees in today's world. 
It implements big data analysis in the healthcare field and applies data mining to extract hidden characteristics of data.

Insights from this app:

It focuses on storing significant amounts of data and ensures proper management for applying big data analytics in healthcare.
Using clustering, data mining methods are used to extract the necessary information from the medical records of AIDS patients.
When the data set goes through a classification process, it can identify whether a person is normal or abnormal.
The data set is moved to the detection phase and then HIV is detected.
It proposes and aims to reach communities that traditional health care providers cannot reach.
AIDS detection is among the most important big data applications in healthcare 

20.Big data applications in healthcare:  Improving health in low- and middle-income countries

Providing health care to many people is a huge challenge and a combined effort at the individual and community levels. 
This vast amount of data is an asset, but seldom pays close attention. Again, data is usually wasted in low-income countries and no attempt is made to evaluate the information needed. This creates a gap between health care providers and patients. 
This application tries to establish a bridge between two ends. Data is carefully considered to take appropriate action to overcome health-related challenges.

Insights from this app:

We provide solutions for generating, analyzing and applying clinical data. Besides, it focuses more on low- and middle-income countries.
Motivate relevant governments to apply technology to provide the best service.
We share logistical, technical, ethical and governance issues that can be addressed.
It makes our activities more efficient and perfect to deal with the dire situations caused by human immunodeficiency virus, tuberculosis, malaria and other infections.
It ensures a “healing insurance policy” for low-income families by allowing the government to track each individual.
Remove barriers and ensure that all citizens receive the best possible treatment.
Big data in healthcare can track and predict system losses, epidemics, and critical situations. Accordingly, the government can take necessary measures.
Improving health in low- and middle-income countries is among the most important big data applications in healthcare 

last thoughts:

big data applications in healthcare can help doctors fight dreadful diseases like cancer and AIDS. 
Data science has a huge impact on healthcare. Data science in healthcare can provide ample time to solve health problems, save lives, and take preventative action. It can save you a lot of money and even your most precious time.