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Big data has changed the way we manage, analyze, and leverage data across industries. One of the most notable areas where data analytics is making big changes is healthcare.
In fact, healthcare analytics has the potential to reduce costs of treatment, predict outbreaks of epidemics, avoid preventable diseases, and improve the quality of life in general. The average human lifespan is increasing across the world population, which poses new challenges to today’s treatment delivery methods. Health professionals, just like business entrepreneurs, are capable of collecting massive amounts of data and look for the best strategies to use these numbers.
In this article, we’re going to address the need for big data in healthcare and hospital big data: why and how can it help? What are the obstacles to its adoption? We will then look at 18 big data examples in healthcare that already exist and that medical-based institutions can benefit from.
But first, let’s examine the core concept of big data healthcare analytics.
What Is Big Data In Healthcare?
Big data in healthcare is a term used to describe massive volumes of information created by the adoption of digital technologies that collect patients' records and help in managing hospital performance, otherwise too large and complex for traditional technologies.
The application of big data analytics in healthcare has a lot of positive and also life-saving outcomes. In essence, big-style data refers to the vast quantities of information created by the digitization of everything, that gets consolidated and analyzed by specific technologies. Applied to healthcare, it will use specific health data of a population (or of a particular individual) and potentially help to prevent epidemics, cure disease, cut down costs, etc.
Now that we live longer, treatment models have changed and many of these changes are namely driven by data. Doctors want to understand as much as they can about a patient and as early in their life as possible, to pick up warning signs of serious illness as they arise – treating any disease at an early stage is far more simple and less expensive. By utilizing key performance indicators in healthcare and healthcare data analytics, prevention is better than cure, and managing to draw a comprehensive picture of a patient will let insurance provide a tailored package. This is the industry’s attempt to tackle the siloes problems a patient’s data has: everywhere are collected bits and bites of it and archived in hospitals, clinics, surgeries, etc., with the impossibility to communicate properly.
Indeed, for years gathering huge amounts of data for medical use has been costly and time-consuming. With today’s always-improving technologies, it becomes easier not only to collect such data but also to create comprehensive healthcare reports and convert them into relevant critical insights, that can then be used to provide better care. This is the purpose of healthcare data analytics: using data-driven findings to predict and solve a problem before it is too late, but also assess methods and treatments faster, keep better track of inventory, involve patients more in their own health, and empower them with the tools to do so.
18 Big Data Applications In Healthcare
Now that you understand the importance of health big data, let’s explore 18 real-world applications that demonstrate how an analytical approach can improve processes, enhance patient care, and, ultimately, save lives.
1) Patients Predictions For Improved Staffing
For our first example of big data in healthcare, we will look at one classic problem that any shift manager faces: how many people do I put on staff at any given time period? If you put on too many workers, you run the risk of having unnecessary labor costs add up. Too few workers, you can have poor customer service outcomes – which can be fatal for patients in that industry.
Big data is helping to solve this problem, at least at a few hospitals in Paris. A white paper by Intel details how four hospitals that are part of the Assistance Publique-Hôpitaux de Paris have been using data from a variety of sources to come up with daily and hourly predictions of how many patients are expected to be at each hospital.
One of the key data sets is 10 years’ worth of hospital admissions records, which data scientists crunched using “time series analysis” techniques. These analyses allowed the researchers to see relevant patterns in admission rates. Then, they could use machine learning to find the most accurate algorithms that predicted future admissions trends.
Summing up the product of all this work, the data science team developed a web-based user interface that forecasts patient loads and helps in planning resource allocation by utilizing online data visualization that reaches the goal of improving the overall patients' care.
2) Electronic Health Records (EHRs)
It’s the most widespread application of big data in medicine. Every patient has his own digital record which includes demographics, medical history, allergies, laboratory test results, etc. Records are shared via secure information systems and are available for providers from both the public and private sectors. Every record is comprised of one modifiable file, which means that doctors can implement changes over time with no paperwork and no danger of data replication.
EHRs can also trigger warnings and reminders when a patient should get a new lab test or track prescriptions to see if a patient has been following doctors’ orders.
Although EHR is a great idea, many countries still struggle to fully implement them. U.S. has made a major leap with 94% of hospitals adopting EHRs according to this HITECH research, but the EU still lags behind. However, an ambitious directive drafted by the European Commission is supposed to change it.
Kaiser Permanente is leading the way in the U.S. and could provide a model for the EU to follow. They’ve fully implemented a system called HealthConnect that shares data across all of their facilities and makes it easier to use EHRs. A McKinsey report on big data healthcare states that “The integrated system has improved outcomes in cardiovascular disease and achieved an estimated $1 billion in savings from reduced office visits and lab tests.”
3) Real-Time Alerting
Other examples of data analytics in healthcare share one crucial functionality – real-time alerting. In hospitals, Clinical Decision Support (CDS) software analyzes medical data on the spot, providing health practitioners with advice as they make prescriptive decisions.
However, doctors want patients to stay away from hospitals to avoid costly in-house treatments. Analytics, already trending as one of the business intelligence buzzwords in 2019, has the potential to become part of a new strategy. Wearables will collect patients’ health data continuously and send this data to the cloud.
Additionally, this information will be accessed to the database on the state of health of the general public, which will allow doctors to compare this data in a socio-economic context and modify the delivery strategies accordingly. Institutions and care managers will use sophisticated tools to monitor this massive data stream and react every time the results will be disturbing.
For example, if a patient’s blood pressure increases alarmingly, the system will send an alert in real-time to the doctor who will then take action to reach the patient and administer measures to lower the pressure.
Another example is that of Asthmapolis, which has started to use inhalers with GPS-enabled trackers in order to identify asthma trends both on an individual level and looking at larger populations. This data is being used in conjunction with data from the CDC in order to develop better treatment plans for asthmatics.
4) Enhancing Patient Engagement
Many consumers – and hence, potential patients – already have an interest in smart devices that record every step they take, their heart rates, sleeping habits, etc., on a permanent basis. All this vital information can be coupled with other trackable data to identify potential health risks lurking. Chronic insomnia and an elevated heart rate can signal a risk for future heart disease for instance. Patients are directly involved in the monitoring of their own health, and incentives from health insurance can push them to lead a healthy lifestyle (e.g.: giving money back to people using smartwatches).
Another way to do so comes with new wearables under development, tracking specific health trends, and relaying them to the cloud where physicians can monitor them. Patients suffering from asthma or blood pressure could benefit from it, and become a bit more independent and reduce unnecessary visits to the doctor.
5) Prevent Opioid Abuse In The US
Our fourth example of big data healthcare is tackling a serious problem in the US. Here’s a sobering fact: as of this year, overdoses from misused opioids have caused more accidental deaths in the U.S. than road accidents, which were previously the most common cause of accidental death.
Analytics expert Bernard Marr writes about the problem in a Forbes article. The situation has gotten so dire that Canada has declared opioid abuse to be a “national health crisis,” and President Obama earmarked $1.1 billion dollars for developing solutions to the issue while he was in office.
Once again, an application of big data analytics in healthcare might be the answer everyone is looking for: data scientists at Blue Cross Blue Shield have started working with analytics experts at Fuzzy Logix to tackle the problem. Using years of insurance and pharmacy data, Fuzzy Logix analysts have been able to identify 742 risk factors that predict with a high degree of accuracy whether someone is at risk for abusing opioids.
To be fair, reaching out to people identified as “high risk” and preventing them from developing a drug issue is a delicate undertaking. However, this project still offers a lot of hope towards mitigating an issue which is destroying the lives of many people and costing the system a lot of money.
6) Using Health Data For Informed Strategic Planning
The use of big data in healthcare allows for strategic planning thanks to better insights into people’s motivations. Care managers can analyze check-up results among people in different demographic groups and identify what factors discourage people from taking up treatment.
The University of Florida made use of Google Maps and free public health data to prepare heat maps targeted at multiple issues, such as population growth and chronic diseases. Subsequently, academics compared this data with the availability of medical services in most heated areas. The insights gleaned from this allowed them to review their delivery strategy and add more care units to the most problematic areas.
7) Big Data Might Just Cure Cancer
Another interesting example of the use of big data in healthcare is the Cancer Moonshot program. Before the end of his second term, President Obama came up with this program that had the goal of accomplishing 10 years’ worth of progress towards curing cancer in half that time.
Medical researchers can use large amounts of data on treatment plans and recovery rates of cancer patients in order to find trends and treatments that have the highest rates of success in the real world. For example, researchers can examine tumor samples in biobanks that are linked up with patient treatment records. Using this data, researchers can see things like how certain mutations and cancer proteins interact with different treatments and find trends that will lead to better patient outcomes.
This data can also lead to unexpected benefits, such as finding that Desipramine, which is an antidepressant, has the ability to help cure certain types of lung cancer.
However, in order to make these kinds of insights more available, patient databases from different institutions such as hospitals, universities, and nonprofits need to be linked up. Then, for example, researchers could access patient biopsy reports from other institutions. One of the potential big data use cases in healthcare would be genetically sequencing cancer tissue samples from clinical trial patients and making these data available to the wider cancer database.
But, there are a lot of obstacles in the way, including:
- Incompatible data systems. This is perhaps the biggest technical challenge, as making these data sets able to interface with each other is quite a feat.
- Patient confidentiality issues. There are differing laws state by state which govern what patient information can be released with or without consent, and all of these would have to be navigated.
- Simply put, institutions that have put a lot of time and money into developing their own cancer dataset may not be eager to share with others, even though it could lead to a cure much more quickly.
However, as an article by Fast Company states, there are precedents to navigating these types of problems and roadblocks while accelerating progress towards curing cancer using the strength of data analytics.
8) Predictive Analytics In Healthcare
We have already recognized predictive analytics as one of the biggest business intelligence trends two years in a row, but the potential applications reach far beyond business and much further in the future. Optum Labs, a US research collaborative, has collected EHRs of over 30 million patients to create a database for predictive analytics tools that will improve the delivery of care.
The goal of healthcare online business intelligence is to help doctors make data-driven decisions within seconds and improve patients’ treatment. This is particularly useful in the case of patients with complex medical histories, suffering from multiple conditions. New BI solutions and tools would also be able to predict, for example, who is at risk of diabetes and thereby be advised to make use of additional screenings or weight management.
9) Reduce Fraud And Enhance Security
Some studies have shown that 93% of healthcare organizations have experienced a data breach. The reason is simple: personal data is extremely valuable and profitable on the black markets. And any breach would have dramatic consequences. With that in mind, many organizations started to use analytics to help prevent security threats by identifying changes in network traffic, or any other behavior that reflects a cyber-attack. Of course, big data has inherent security issues and many think that using it will make organizations more vulnerable than they already are. But advances in security such as encryption technology, firewalls, anti-virus software, etc, answer that need for more security, and the benefits brought largely overtake the risks.
Likewise, it can help prevent fraud and inaccurate claims in a systemic, repeatable way. Analytics help to streamline the processing of insurance claims, enabling patients to get better returns on their claims and caregivers are paid faster. For instance, the Centers for Medicare and Medicaid Services said they saved over $210.7 million in fraud in just a year.
Telemedicine has been present on the market for over 40 years, but only today, with the arrival of online video conferences, smartphones, wireless devices, and wearables, has it been able to come into full bloom. The term refers to the delivery of remote clinical services using technology.
It is used for primary consultations and initial diagnosis, remote patient monitoring, and medical education for health professionals. Some more specific uses include telesurgery – doctors can perform operations with the use of robots and high-speed real-time data delivery without physically being in the same location with a patient.
Clinicians use telemedicine to provide personalized treatment plans and prevent hospitalization or re-admission. Such use of healthcare data analytics can be linked to the use of predictive analytics as seen previously. It allows clinicians to predict acute medical events in advance and prevent deterioration of patient’s conditions.
By keeping patients away from hospitals, telemedicine helps to reduce costs and improve the quality of service. Patients can avoid waiting in lines and doctors don’t waste time on unnecessary consultations and paperwork. Telemedicine also improves the availability of care as patients’ state can be monitored and consulted anywhere and anytime.
11) Integrating Big-Style Data With Medical Imaging
Medical imaging is vital and each year in the US about 600 million imaging procedures are performed. Analyzing and storing manually these images is expensive both in terms of time and money, as radiologists need to examine each image individually, while hospitals need to store them for several years.
Medical imaging provider Carestream explains how big data analytics for healthcare could change the way images are read: algorithms developed analyzing hundreds of thousands of images could identify specific patterns in the pixels and convert it into a number to help the physician with the diagnosis. They even go further, saying that it could be possible that radiologists will no longer need to look at the images, but instead analyze the outcomes of the algorithms that will inevitably study and remember more images than they could in a lifetime. This would undoubtedly impact the role of radiologists, their education, and the required skillset.
12) A Way To Prevent Unnecessary ER Visits
Saving time, money, and energy using big data analytics for healthcare is necessary. What if we told you that over the course of 3 years, one woman visited the ER more than 900 times? That situation is a reality in Oakland, California, where a woman who suffers from mental illness and substance abuse went to a variety of local hospitals on an almost daily basis.
This woman’s issues were exacerbated by the lack of shared medical records between local emergency rooms, increasing the cost to taxpayers and hospitals, and making it harder for this woman to get good care. As Tracy Schrider, who coordinates the care management program at Alta Bates Summit Medical Center in Oakland stated in a Kaiser Health News article:
“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.”
In order to prevent future situations like this from happening, Alameda county hospitals came together to create a program called PreManage ED, which shares patient records between emergency departments.
This system lets the ER staff know things like:
- If the patient they are treating has already had certain tests done at other hospitals, and what the results of those tests are.
- If the patient in question already has a case manager at another hospital, preventing unnecessary assignments.
- What advice has already been given to the patient, so that a coherent message to the patient can be maintained by providers.
This is another great example where the application of healthcare analytics is useful and needed. In the past, hospitals without PreManage ED would repeat tests over and over, and even if they could see that a test had been done at another hospital, they would have to go old school and request or send long fax just to get the information they needed.
13) Smart Staffing & Personnel Management
Without a cohesive, engaged workforce, patient care will dwindle, service rates will drop, and mistakes will happen. But with big data tools in healthcare, it’s possible to streamline your staff management activities in a wealth of key areas. By working with the right HR analytics, it’s possible for time-stretched medical institutions to optimize staffing while forecasting operating room demands, streamlining patient care as a result.
Too often, there is a significant lack of fluidity in healthcare institutions, with staff distributed in the wrong areas at the wrong time. This imbalance of personnel management could mean a particular department is either too overcrowded with staff or lacking staff when it matters most, which can develop risks of lower motivation for work and increases the absenteeism rate. An HR dashboard, in this case, may help:
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Though data-driven analytics, it’s possible to predict when you might need staff in particular departments at peak times while distributing skilled personnel to other areas within the institution during quieter periods.
Moreover, medical data analysis will empower senior staff or operatives to offer the right level of support when needed, improve strategic planning, and make vital staff and personnel management processes as efficient as possible.
14) Learning & Development
Expanding on our previous point, in a hospital or medical institution, the skills, confidence, and abilities of your staff can mean the difference between life and death. Naturally, doctors and surgeons are highly skilled in their areas of expertise. But most medical institutions have a range of people working under one roof, from porters and admin clerks to cardiac specialists and brain surgeons.
In healthcare, soft skills are almost important as certifications. To keep the institution running at optimum capacity, you have to encourage continual learning and development. By keeping track of employee performance across the board while keeping a note of training data, you can use healthcare data analysis to gain insight on who needs support or training and when. If everyone is able to evolve with the changes around them, you will save more lives — and medical data analytics will help you do just that.
15) Advanced Risk & Disease Management
Big data and healthcare are essential for tackling the hospitalization risk for specific patients with chronic diseases. It can also help prevent deterioration.
By drilling down into insights such as medication type, symptoms, and the frequency of medical visits, among many others, it’s possible for healthcare institutions to provide accurate preventative care and, ultimately, reduce hospital admissions. Not only will this level of risk calculation result in reduced spending on in-house patient care, but it will also ensure that space and resources are available for those who need it most. This is a clearcut example of how analytics in healthcare can improve and save people’s lives.
As a result, big data for healthcare can improve the quality of patient care while making the organization more economically streamlined in every key area.
16) Suicide & Self-Harm Prevention
Globally, almost 800,000 people die from suicide every year. Plus, 17% of the world’s population will self-harm during their lifetime. These numbers are alarming. But while this is a very difficult area to tackle, big data uses in healthcare are helping to make a positive change concerning suicide and self-harm. As entities that see a wealth of patients every single day, healthcare institutions can use data analysis to identify individuals that might be likely to harm themselves.
In a 2018 study from KP and the Mental Health Research Network, a mix of EHR data and a standard depression questionnaire identified individuals who had an enhanced risk of a suicide attempt with great accuracy. Utilizing a predictive algorithm, the team found that suicide attempts and successes were 200 times more likely among the top 1% of patients flagged according to specific datasets. Speaking on the subject, Gregory E. Simon, MD, MPH, a senior investigator at Kaiser Permanente Washington Health Research Institute, explained:
“We demonstrated that we can use electronic health record data in combination with other tools to accurately identify people at high risk for suicide attempt or suicide death.”
This essential use case for big data in the healthcare industry really is a testament to the fact that medical analytics can save lives.
“If somebody tortures the data enough (open or not), it will confess anything.” – Paolo Magrassi, former vice president, research director, Gartner.
17) Improved Supply Chain Management
If a medical institution’s supply chain is weakened or fragmented, everything else is likely to suffer, from patient care and treatment to long-term finances and beyond. That said, the next in our big data in healthcare examples focus on the value of analytics to keep the supply chain fluent and efficient from end to end.
Leveraging analytics tools to track the supply chain performance metrics, and make accurate, data-driven decisions concerning operations as well as spending can save hospitals up to $10 million per year.
Both descriptive and predictive analytics models can enhance decisions for negotiating pricing, reducing the variation in supplies, and optimizing the ordering process as a whole. By doing so, medical institutions can thrive in the long term while delivering vital treatment to patients without potentially disastrous delays, snags, or bottlenecks.
18) Developing New Therapies & Innovations
The last of our healthcare analytics examples centers on working for a brighter, bolder future in the medical industry. Big data analysis in healthcare has the power to assist in new therapy and innovative drug discoveries. By utilizing a mix of historical, real-time, and predictive metrics as well as a cohesive mix of data visualization techniques, healthcare experts can identify potential strengths and weaknesses in trials or processes.
Moreover, through data-driven genetic information analysis as well as reactionary predictions in patients, big data analytics in healthcare can play a pivotal role in the development of groundbreaking new drugs and forward-thinking therapies. Data analytics in healthcare can streamline, innovate, provide security, and save lives. It gives confidence and clarity, and it is the way forward.
How To Use Big Data In Healthcare
All in all, we’ve noticed three key trends through these 18 examples of healthcare analytics: the patient experience will improve dramatically, including quality of treatment and satisfaction levels; the overall health of the population can also be enhanced on a sustainable basis, and operational costs can be reduced significantly.
Let’s have a look now at a concrete example of how to use data analytics in healthcare:
a) Big Data In Healthcare Applied On A Hospital Dashboard
This healthcare dashboard below provides you with the overview needed as a hospital director or as a facility manager. Gathering in one central point all the data on every division of the hospital, the attendance, its nature, the costs incurred, etc., you have the big picture of your facility, which will be of great help to run it smoothly.
**click to enlarge**
You can see here the most important metrics concerning various aspects: the number of patients that were welcomed in your facility, how long they stayed and where, how much it cost to treat them, and the average waiting time in emergency rooms. Such a holistic view helps top-management identify potential bottlenecks, spot trends, and patterns over time, and in general assess the situation. This is key in order to make better-informed decisions that will improve the overall operations performance, with the goal of treating patients better and having the right staffing resources.
b) Big Data Healthcare Application On Patients' Care
Another real-world application of healthcare big data analytics, our dynamic patient dashboard is a visually-balanced tool designed to enhance service levels as well as treatment accuracy across departments.
**click to enlarge**
By offering a perfect storm or patience-centric information in one central location, medical institutions can create harmony between departments while streamlining care processes in a wealth of vital areas. For instance, bed occupancy rate metrics offer a window of insight into where resources might be required, while tracking canceled or missed appointments will give senior executives the data they need to reduce costly patient no-shows.
Here, you will find everything you need to enhance your level of patient care both in real-time and in the long-term. This is a visual innovation that has the power to improve every type of medical institution, big or small.
Why We Need Big Data Analytics In Healthcare
There’s a huge need for big data in healthcare as well, due to rising costs in nations like the United States. As a McKinsey report states: “After more than 20 years of steady increases, healthcare expenses now represent 17.6 percent of GDP — nearly $600 billion more than the expected benchmark for a nation of the United States’s size and wealth.”
In other words, costs are much higher than they should be, and they have been rising for the past 20 years. Clearly, we are in need of some smart, data-driven thinking in this area. And current incentives are changing as well: many insurance companies are switching from fee-for-service plans (which reward using expensive and sometimes unnecessary treatments and treating large amounts of patients quickly) to plans that prioritize patient outcomes
As the authors of the popular Freakonomics books have argued, financial incentives matter – and incentives that prioritize patients' health over treating large amounts of patients are a good thing. Why does this matter?
Well, in the previous scheme, healthcare providers had no direct incentive to share patient information with one another, which had made it harder to utilize the power of analytics. Now that more of them are getting paid based on patient outcomes, they have a financial incentive to share data that can be used to improve the lives of patients while cutting costs for insurance companies.
Finally, physician decisions are becoming more and more evidence-based, meaning that they rely on large swathes of research and clinical data as opposed to solely their schooling and professional opinion. As in many other industries, data gathering and management are getting bigger, and professionals need help in the matter. This new treatment attitude means there is a greater demand for big data analytics in healthcare facilities than ever before, and the rise of SaaS BI tools is also answering that need.
Obstacles To A Widespread Big Data Healthcare
One of the biggest hurdles standing in the way to use big data in medicine is how medical data is spread across many sources governed by different states, hospitals, and administrative departments. The integration of these data sources would require developing a new infrastructure where all data providers collaborate with each other.
Equally important is implementing new online reporting software and business intelligence strategy. Healthcare needs to catch up with other industries that have already moved from standard regression-based methods to more future-oriented like predictive analytics, machine learning, and graph analytics.
However, there are some glorious instances where it doesn’t lag behind, such as EHRs (especially in the US.) So, even if these services are not your cup of tea, you are a potential patient, and so you should care about new healthcare analytics applications. Besides, it’s good to take a look around sometimes and see how other industries cope with it. They can inspire you to adapt and adopt some good ideas.
18 Big Data Examples In Healthcare - A Summary
The industry is changing, and like any other, big-style data is starting to transform it – but there is still a lot of work to be done. The sector slowly adopts the new technologies that will push it into the future, helping it to make better-informed decisions, improving operations, etc. In a nutshell, here’s a shortlist of the examples we have gone over in this article. With healthcare data analytics, you can:
- Predict the daily patients' income to tailor staffing accordingly
- Use Electronic Health Records (EHRs)
- Use real-time alerting for instant care
- Help in preventing opioid abuse in the US
- Enhance patient engagement in their own health
- Use health data for a better-informed strategic planning
- Research more extensively to cure cancer
- Use predictive analytics
- Reduce fraud and enhance data security
- Practice telemedicine
- Integrate medical imaging for a broader diagnosis
- Prevent unnecessary ER visits
- Smart staffing & personnel management
- Learning & development
- Advanced risk & disease management
- Suicide & self-harm prevention
- Improved supply chain management
- Developing new therapies & innovations
“Most of the world will make decisions by either guessing or using their gut. They will be either lucky or wrong.” – Suhail Doshi, chief executive officer, Mixpanel.
These 18 real-world examples of data analytics in healthcare prove that medical applications can save lives and should be a top priority of experts across the field. Even now, data-driven analytics facilitates early identification as well as intervention in illnesses while streamlining institutions for swifter, safer, and more accurate patient care. As technology evolves, these invaluable functions can only get stronger – the future of healthcare is here, and it lies in data.
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