How big data is used in healthcare

here are a few instances of how big data is used in health care.

Product Development

Finding and growing new medications and other wellbeing-related items takes a stunning measure of time and cash.

Large information can help lessen the time engaged in various ways. This, obviously, serves to diminish the expenses in question.

For instance, during the normal innovative work period of item advancement, it’s anything but in every case clear how to utilize all the information found. With large information methods, R&D groups can discover valuable information a lot quicker and all the more proficiently, consequently decreasing the time expected to foster the item and get it to advertise.

Item improvement includes extensive experimentation testing, which requires a lot of time. Huge information eliminates the mystery permitting innovative work organizations to get results all the more rapidly and along these lines foster more exact items quicker.

Patient Outcomes

Large information improves patient results since it helps specialists and other clinical experts be more proficient and precise with their conclusions and medicines. With the improved information examining strategies enormous information gives, specialists can expect to discover answers for treat uncommon and genuine conditions that would somehow or another appear to be serious on the grounds that exploration can advance at a quicker speed.

Operational Efficiency

The assortment of labor force information implies medical care associations, for example, clinics and pharma organizations can improve the representative yield. For instance, chiefs can overhaul work processes to be more effective and divert assets to where they are generally required.

Driving innovation

This is maybe probably the biggest utilization of large information in medical services. Without development, there would be no progressions in medication by any means. Huge information will speed the rate at which new medications can be found and the nature of care is improved.

These are only a couple of the spaces where enormous information can be utilized in medical services. Be that as it may, perhaps the main use for enormous information is to decrease the general expense of medical care. Exploration directed by McKinsey and Company showed that large information could save Americans between $300 billion to $450 long term.

Truth be told, by utilizing enormous information and information examination, Parkland Hospital in Dallas, Texas has diminished 30-day readmissions to Parkland and all-region emergency clinics for Medicare patients with cardiovascular breakdown by 31%, for investment funds of $500,000 per year. By staying away from readmission, patients additionally set aside significant cash.

The objective of proficiently utilizing enormous information is to comprehend what is happening, recognizing issues, and discovering creative answers for them that will help lessen costs. This advantages different members in clinical cycles like medical care suppliers, makers, guarantors, and, above all. beneficiaries/patients.

Why Every Healthcare Company Should Use Big Data In 2019

Dimensional Insight study tracked down that 56% of emergency clinics and clinical practices don’t have fitting huge information administration or long haul investigation plans.

Without appropriate information administration, associations risk making copy clinical records, missing entitled repayments, discovering challenges for monetary benchmarking, and other operational shortcomings. Huge information can assist with this load of things!

Without investigation, patient consideration is additionally more troublesome. Medical care associations report seeing disparities among clinical and bookkeeping offices because of information bungles and blunders.

The presence of these issues is upheld by the examination: 71% of individuals overviewed said they have discovered irregularities in information from various sources inside their association. The most prominent areas are the monetary, clinical and authoritative datasets.

High-Risk Patient Care

As per the Society of Actuaries (SOA), medical services payers utilize the prescient enormous information examination to pinpoint significant expense patients. They take a gander at different patient subtleties like age, sex, and spending history. These variables and more assistance to decide if a patient ought to be viewed as a significant expense.

The Healthcare Cost Institute Database detailed that 17% of patients are liable for almost 75% of all medical services uses. Hence, it is imperative to know which patients spend more on medical care so professionals can give preventive measures.

Some work on enormous information examination has effectively started, however, there is as yet afar approach to acquire the most effective and the best expense decreases.

Who Benefits From Big Data Analytics?

Most, if not all, healthcare sectors stand to benefit from the implementation of big data analytics. Here are a few of the big winners:

Providers (Clinics, Hospitals)

The insights generated from big data analytics enables healthcare providers, such as clinics and hospitals, to improve patient care. For example, the workflow process will improve dramatically, giving doctors more time with their patients.

Payers (Insurance)

Insurance providers will benefit greatly from big data in healthcare. For example, they can use it to reduce fraudulent activity, rectify false claims, provide better service to their customers and reconcile records faster. Health systems cost an average of $96 per record to manage. Anything that can help to reduce that cost will consequently improve profits.


Patients will benefit from big data in healthcare more than anyone else. They can enjoy better overall care, live healthier lives, save money on insurance and so much more.


Big data analytics will enable device manufacturers to create better, innovative products to solve health issues. They will benefit from devices relevant to their needs.


A 2013 study, published by Nature Review Drug Discovery, found that only 10% of medicines in development ever reach patients. With this in mind, big data in pharma will benefit from better research and development, resulting in more effective drugs and shorter production times. Pharma companies will also save on the costs related to drug development because the process for determining which drugs are worthwhile to enter clinical trials will be more accurate.

Examples of Big Data in Healthcare

analytics to cure cancer, there are many ways that big data could improve the industry for payers, patients, and more.

Here are some of the highlights.

Improved Staff Management

This is particularly useful for healthcare managers in charge of shift work. Having too many employees working on any given day runs the risk of spending too much on labor. On the other hand, not enough staff can result in poor customer service. For the healthcare industry, not enough professionals on hand can lead to fatal circumstances.

Big data analytics can overcome these problems. In fact, it is being used right now in hospitals in Paris. This article describes how Assistance Publique-Hôpitaux de Paris hospitals are using data from a variety of sources to predict how many patients are expected to be at each hospital.

The result:

“A web browser-based interface designed to be used by doctors, nurses and hospital administration staff – untrained in data science – to forecast visit and admission rates for the next 15 days. Extra staff can be drafted in when high numbers of visitors are expected, leading to reduced waiting times for patients and better quality of care.”

Better Patient Engagement

Smart devices and mobile apps enable patients to track their own medical information. For example, they can track the number of steps they take over the course of the day or keep an eye on their heart rate during a workout.

This health data is then stored in the cloud where doctors can analyze it and keep an eye on their patients. This means that patients don’t need to visit the medical practice for unnecessary checkups.

This information can be used to identify potential health risks that may not be easily detectable. Furthermore, when patients take more control over their health, they can be encouraged by payers and other organizations to live a healthier lifestyle. For example, companies can provide cash incentives to patients for wearing a smartwatch or fitness tracker.

Strategic Planning

Insights from big data analytics can provide key strategic planning in terms of analyzing check-up results among people in different demographic groups, identifying why they may not want a particular treatment.

For example, the University of Florida used Google Maps and free public health data to tackle issues such as chronic diseases. After comparing this data with the availability of medical services in the most heated areas, they could add more care units to the areas suffering the most.

Electronic Health Records (EHRs)

Every patient in the US has an electronic health record (EHR) that includes medical history, allergies, demographics etc. The EHR is the most used application of big data in healthcare.

Each record can be modified by doctors across the country, meaning no paperwork is required to record a change in medical history. This improves efficiency and avoids the creation of duplicate records.

94% of hospitals in the US use EHRs. Unfortunately, other nations are not up to this standard. In the EU for example, an electronic European health record system is planned for 2020.

According to this report on big data healthcare:

“EHR that has improved the management of disease among cardiovascular disease patients, as well as yielding Kaiser Permanente an approximate savings of $1 billion…”

Improved Data Security

Considering how much data is contained in an EHR, without appropriate protection, records are prone to attacks.

There is evidence to suggest that the healthcare industry is far more likely to experience a data breach when compared to any other. In fact, research suggests that it’s 200% more likely. This is because health records contain a large quantity of personal information. In the hands of a hacker, it is incredibly valuable.

With this in mind, big data can be used to enhance cybersecurity and prevent data breaches.

Measures such as encryption technology, blockchain, firewalls, and anti-virus software provide layers of protection, bringing a host of benefits.

There is another advantage here too; big data analytics can be used to prevent fraud. By systematically analyzing claims, patients can get more reliable results from their claims and get paid faster.

Predictive Analytics In Healthcare

The potential of predictive analytics in healthcare extends beyond standard business applications. A US research collaborative (namely Optum Labs) has collected 30 million EHRs in order to improve the delivery of care.

The overall objective of healthcare business intelligence is to give doctors the ability to make quick data-driven decisions. In the case of patients who suffer from complex, rare illnesses, this ability becomes very useful.


Telemedicine is not a new concept. However, with advancements in technology, patients can connect with their doctors in ways that are much more productive. For example, they can attend virtual calls and video conferences through their smartphones and both parties can track progress with wearable tech.

Telemedicine is mostly used for initial diagnosis and remote patient monitoring. It can also be used for more advanced medical procedures, such as telesurgery – a technique where doctors perform surgeries using robots and real-time data without having to actually be in the room.

Doctors recommend the use of telemedicine to patients for personalized treatment solutions and to prevent readmissions. Healthcare big data analytics can then be linked to a predictive analytics program to predict medical events and improve the overall quality of patient care.

By reducing admissions to hospitals, telemedicine consequently reduces the cost of care – both for the patient and the medical practice itself. Patients avoid long waiting times and doctors don’t have to waste their own time on unnecessary appointments. Since telemedicine is highly accessible, doctors can monitor patient activity from anywhere and at any time.

Prevent Unnecessary ER Visits

This follows from the previous point. It is important for healthcare professionals and practices to manage their time and resources as efficiently as possible.

Kaiser Health News: reported that in Oakland, California, a woman who suffers from mental illness and substance abuse visited multiple local hospitals almost every day. It was hard for this woman to get the care she needed because her medical records were not shared among the practices, increasing costs to taxpayers and the hospitals themselves.

“Everybody meant well. But she was being referred to three different substance abuse clinics and two different mental health clinics, and she had two case management workers both working on housing. It was not only bad for the patient, it was also a waste of precious resources for both hospitals.”

So, to avoid these scenarios in the future, Alameda county hospitals created the PreManage ED program – an initiative that shares patient records between emergency departments.

This system provides workers with a host of useful data, such as whether a patient has had certain tests at other hospitals, what the results of those tests are, and the advice was given to the patient.

From this, it is clear that the application of big data analytics is needed. Before PreManage ED, hospitals would be required to repeat tests on a patient. Even if it was possible to see the results of the tests, it would be necessary to fax them to the other institution in order to get the required information.

Medical Imaging

Imaging procedures so so important to a large number of people. However, storing these images is costly, not to mention the time and costs involved to analyze them.

Carestream, a medical imaging provider, shows how big data analytics can change the way images are read. They explain how algorithms can analyze vast numbers of images to identify patterns in the pixels. These patterns are then converted into a number to help physicians with the diagnosis.

With this knowledge, it could be possible that, in the future, radiologists will no longer need to look at the images. Instead, all they will need to do is analyze the outcomes of the algorithms. This could change the role of radiologists and their skillsets in the medical community.

Curing Cancer

the outcome has the potential to help millions of people.

Data collected from patients on different treatment plans can be analyzed for trends and patterns to find those with the highest rates of success. For example, tumor samples can be analyzed to see how their mutations and proteins react to different treatments, leading to better outcomes.

A few hurdles must be overcome to make this happen, such as making data systems interfacing abilities compatible with each other, overcoming confidentiality issues (particularly in the US where states have their own laws over the sharing of personal information), and putting time and money into a dataset that institutions may not want to share with each other, even if it would more quickly lead to finding a cure.

As described by this Fast Company article, there are precedents that deal with these problems:

“The U.S. National Institutes of Health (NIH) has hooked up with a half-dozen hospitals and universities to form the Undiagnosed Disease Network, which pools data on super-rare conditions (like those with just a half-dozen sufferers), for which every patient record is a treasure to researchers.”

Hopefully, in the future, these roadblocks can be overcome and accelerate the progress being made towards curing cancer through the use of data analytics.

The Challenges Of Implementing Big Data In Healthcare

challenges these practices face by not having big data analytics.

First, inadequate governance leads to duplicate records, missing entitled reimbursements, difficulties in financial benchmarking, and other operational inefficiencies.

Patient care is also more complex these days. Without proper analytics, it becomes increasingly difficult to provide quality and safe patient care that has much better outcomes.

Finally, many healthcare organizations have seen discrepancies between clinical and accounting departments due to data mismatches.

Even with big data in healthcare, there are still challenges to face:

  • Capturing the data – There are several sources of data. The challenge is capturing the relevant data from these sources to form a cohesive picture
  • Cleaning the data – The data should be clean and precise for it to be of actual value. The challenge is in knowing that the data being used is actually fit for analysis
  • Storage and security – Patient data is sensitive, so deciding where and how to store it is critical from a data security point of view. Cloud solutions are gaining popularity in this area
  • Stewardship – Data stewardship is important to the overall management of patient data and to ensure that there is proper knowledge about from where the data was obtained, when it was obtained, by whom, the associated conditions etc.
  • Analysis, reporting and visualization – Knowing what to analyze is as important as knowing how to analyze. For proper outcomes and predictions, the onus lies on accurately analyzing the data and producing reports that have measurable outcomes and value. Creating reports that allow proper visualization with charts and images is a great help to analysis.
  • Updating information – Patient care is constant. It always helps to be certain that the latest data is available. For example, if a patient visits a clinic every month, information about each of their visits should be available.
  • Sharing data – Sharing data between different healthcare service providers is very important to form a complete picture of patient care. This will help to create a proper analytical model that has all the data it needs to predict proper outcomes.

So how can these challenges be overcome?

  • Data-driven mindset – Training all institution staff and patient care personnel to accurately record data, store and share it.
  • Proper collection and storage mechanism – Using proven processes and mechanisms to collect, store and access data.
  • Smart algorithms – Building smart algorithms that will consume the large volume of data, properly analyze it and produce relevant results, which will then be used to predict the right outcomes for patient care.


Insights gained from big data can allow healthcare businesses to solve problems that could not previously be tackled with traditional software or analytics. These new insights can help gain a deeper understanding of data to improve the results of clinical trials, boost the productivity of healthcare professionals, and improve the revenues of the practices themselves.

Big data can help in development by reducing the time needed to develop a product and get it to market. It can also keep the costs of security down, improve patient outcomes and drive innovation.

In the modern world, every healthcare organization should be using big data. Otherwise, they run the risk of creating duplicate medical records, missing entitled reimbursements, difficulties for financial benchmarking, and other operational inefficiencies. Big data can help with all these things!

Without analytics, patient care becomes more difficult too. Healthcare organizations report seeing discrepancies between clinical and accounting departments due to data mismatches and inaccuracies.

The insights generated from big data analytics enable healthcare providers, such as clinics and hospitals, to improve patient care. Insurance providers will also benefit because they can reduce fraud and more easily rectify false claims. Patients also stand to benefit because they will get better care from more efficient processes and the production of innovative products.

Many challenges come with big data in healthcare. For instance, It is important to capture relevant data from the right sources and using cloud solutions to keep the data safe and secure.

It is also important to know how to analyze this data. After all, if no one can understand what to do with it, one might just as well not have the data at all.

There are ways to overcome these challenges, such as having a data-driven mindset and using smart algorithms to produce the intended results.

Overall, though, it seems clear that everyone in healthcare benefits from big data analytics.


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