The healthcare industry generates a vast amount of data daily. Collecting and analyzing it helps prevent future health issues, boost administrative tasks, and improve patient outcomes. As a result, healthcare vendors turn to software development companies with experience handling big data in healthcare to find ways to bring forward everything the technology offers.
Big data analytics is currently booming. For example, Statista indicates the Big data market to be valued at $70 billion and is expected to reach $103 billion by 2027. In such a case, we need to have an in-depth look at the big data in health care phenomenon and explore 20 distinct examples of big data analytics and the improvements they bring.
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Table of Contents
The Essentials of Big Data in Healthcare
Simply put, big data in health works with the volume of healthcare data emerging due to digital techs applied within the industry. It refers to the high-volume, high-velocity, and high-variety of information that exceeds the capacity of traditional databases and data warehouses. The critical focus is on finding new ways to collect data and process patient data via electronic health records (EHRs) to improve the health system’s functionality.
While we saw the prospects of the market in general, there is a similar growth tendency for healthcare big data analytics.
Big data is defined as a set of complex tools that try to redefine data sharing and usage. If this big data approach is applied correctly, it promises benefits for administrators and patients. When achieving an understanding of big data’s nature, one should know the sources of data that professionals use. With proper big data analytics in healthcare, data scientists can extract and process structured and unstructured data from sources such as:
- EHR systems
- Patient portals
- Clinical studies
- Internet of Things (IoT) devices
- Health databases
- Government agencies
- Billing records
- Staffing schedules
- Scholarly and scientific journals
For years, healthcare professionals have been diving into the sources above to look for insights that can translate into improving administration and care. However, with big data analytics, they receive new opportunities and can be prepared for data inflows the modern digital world offers.
Why Care about Big Data Analytics in Healthcare?
Nothing speaks better of a technology’s success than the tangible benefits of using big data. Data analytics can transform healthcare by making the use of data the cornerstone of every decision. Moreover, there is enough evidence to suggest analytics instruments will still be in demand in upcoming years. Thus, the following advantages make the most impact.
Cost-Reduction
The implications of big data for the economy are massive. Data Worldbank indicates that the current healthcare costs equal about 9.8% of the global GDP. With the global economy being approximately $104 trillion, simple arithmetic shows healthcare costs to be around $10 trillion. In turn, McKinsey states that in the U.S., healthcare expenses now take 17.6% of the nation’s GDP, which is about $600 billion. The key issue is that the current expenses are much higher than the expected benchmark, making cost reduction one of the vital healthcare industry needs.
Big data analytics in healthcare comes to the rescue. The only possible solution to the rising costs of healthcare is to offer sophisticated and intelligent data-driven approaches that redefine the industry. Many healthcare providers realized that focusing on patient outcomes through large data sets brings higher costs than traditional fee-for-service plans.
The reason for data-related cost-efficiency is that with big data tools, healthcare providers have a direct incentive to share patient data. Everything in this scenario has the means to improve patient outcomes and get financial incentives for that.
Eliminating Medical Errors
Medical errors are a huge issue. The scholarly evidence points out that about 100,000 patients die annually because of them in hospitals and clinics. Besides, medical errors cost the industry about $20 billion annually. Respectively, finding the tools to reduce their number is a direct way to save lives and cut costs.
There have been numerous attempts to deal with the problem. Some providers improve the risk assessment instruments by introducing automation. Others use big data analysis in healthcare. However, physicians and clinicians generally rely on evidence-based approaches when working with medical data. The problem is that processing a huge amount of data increases the chance of human error, leading to medical error. In such a case, big data analytics in healthcare brings forward technological tools helping to eliminate the impact of human error.
Big data tools can alert a care provider if some wrong medication was prescribed or some faulty clinical test occurred. The phenomenon serves as a safety check mechanism, double-checking all the output presented by human agents to ensure the minimization of medical error. The impact of big data here is a 66% reduction in errors when AI is involved in data validation.
Optimal Staff Management
There is a growing problem with healthcare staff management. This issue was particularly visible when the COVID-19 pandemic struck. The research states the rising rates of health workers’ burnout because of the workload and inefficient staffing procedures. Strikingly, people who provide health services are also experiencing an increase in health problems due to work-related pressures. In such a context, big data can help optimize personnel management, which is a lifesaver for the healthcare system.
For health workers to improve patient outcomes, they need to work in a facility with optimal schedules and proper general organization. Big data can help achieve that. Professionals can use predictive analytics in healthcare to collect and process real-time data measuring a healthcare organization’s staffing and personnel management performance. With the insights from the assessment, healthcare providers can alter their staffing schedules and ensure patients get the best care while health workers have an optimal workload. Therefore, big data in healthcare optimizes scheduling and helps reduce burnout among health professionals.
Challenges with Healthcare Big Data Usage
Despite the benefits of big data in healthcare, even when using the services of well-recognized vendors with expertise in big data, there are opportunities and challenges to keep in mind.
Data Storage and Management
To use the full potential of big data, you need to handle the data storage first. In healthcare, vast volumes of data are spread across various sources stored and governed within hospitals and administrative departments. Managing the quantity of data spread across hospitals requires a complex data storage infrastructure. In addition, it ensures the proper degree of collaboration between providers and reduces data corruption due to inadequate data storage.
Data-Sharing
After you have the infrastructure up and running, it is time to proceed with the data-sharing challenge. Providers often implement online reporting software based on distinct business intelligence methods to create a connection between relevant users. In the past, healthcare entirely relied on regression-based approaches. Moving toward big data analysis in healthcare requires transitioning from old regression models to machine learning for better reporting. These future-oriented instruments help create collaborative environments.
Security, Privacy, and Compliance
When working with patient data, security is always a top priority. Daily, hospitals deal with sensitive patient information. Considering the industry’s numerous data sources, there is a higher chance of data breaches for several reasons. To protect health information, providers need top-grade data security and must comply with HIPAA rules.
User Literacy
Working with data requires a certain degree of knowledge. A new generation of healthcare professionals must be trained to analyze big data to avoid corrupting valuable health data. Remember that big data is a technological instrument that users need to know how to handle. It means providers need to plan and implement data literacy training to ensure people with access to the relevant technology know how to deal with it appropriately.
Healthcare Data Usage Examples
Big data is a massive asset when put into practice. Here are 20 most critical applications of big data instances that are beginning to revolutionize healthcare.
- Examples 1 to 5 cover administrative improvements brought by big data.
- Examples 6 to 10 show big data analytics’s power and its benefits.
- Examples 11 to 13 indicate how big data can aid vital processes within the industry.
- Examples 14 to 16 illustrate better patient outcomes presented by big data analytics.
- Examples 17 to 20 indicate how it can benefit general population health trends.
1. Predictions and Scheduling
Starting with the administrative and staffing segment, Big data can help solve the question of patient predictions used to improve staffing approaches. Big data enables healthcare professionals to predict the number of patients coming to hospitals. Establishing correlating staffing and scheduling procedures is vital to ensure the best care provision and a balanced workload for health workers.
The report offered by Intel showed how French healthcare organizations effectively used big data to have daily and even hourly predictions on the part of patient inflow. Experts managed to analyze ten years’ worth of hospital admissions to develop a predictive model anticipating future admissions based on past experiences. Researchers used big data analytics in healthcare to explore relevant patterns. These were exposed through ML tools, the technology presenting the most accurate prediction algorithms.
2. Personnel Management
Without an engaged workforce, it is impossible to provide high-quality care. However, as we showed before, health workers are currently facing massive rates of burnout and cannot find the proper work-life balance due to inadequate staffing and scheduling. In such a case, big data in healthcare can help streamline various administrative activities.
Big data also ensures the right degree of personnel fluidity. It entails having staff distributed in the areas requiring the most attention. It is about avoiding overcrowded or understaffed departments. That can be achieved through big data analytics tools. Professionals collect and analyze data on peak admission periods and evaluate the recent cases of staff overcrowding.
Big data researchers can present predictive models guiding staffing and scheduling decisions within the scope of personnel management. With the tech, hospitals know when to attribute additional workforce to peak sectors and when to withdraw personnel from segments on the brink of overcrowding.
3. Learning and Development
Forbes indicates that learning and development among health workers play a vital role in ensuring innovation adoption. In addition, innovation delivered through software products is key to success in any given industry. In a healthcare environment, having particular skills and knowledge can be the difference between a patient’s life and death. Respectively, learning is a direct way to boost patient outcomes.
When digressing the point, you can see that learning and development are aspects that need to be a part of a broader organizational capability. For a hospital to run at optimum capacity, it is crucial to keep track of performance and understand what underperforms. Big data analytics comes with tools that help track staff performance. It identifies who needs additional training, ensuring that medical data is handled by skilled hands to improve patient care.
4. Electronic Health Records (EHRs)
Handling EHRs is one of the parts of having smooth administrative tasks. The tool has been there for years and is still proven to have a certain degree of demand.
Often, if a healthcare provider has a well-developed EHR platform, one can be confident of partial success. In turn, electronic health systems are the most widespread use of big data in healthcare. That is because various patient data aspects, such as their medical history and demographics, are stored across the health system infrastructure. In addition, with data being stored collectively, healthcare professionals can directly access it from different places.
While EHR has proved to serve well to healthcare, there are massive concerns regarding ways of its adoption. The evidence offered by HITECH research states that while about 94% of hospitals in the U.S. managed to adopt the system, many nations worldwide struggle to use the technology universally. At this point, one can expect new EHR alternatives to emerge. These use the power of big data analytics in healthcare and learn from the past mistakes linked to implementation-based challenges.
5. Patient Engagement
With a well-designed telehealth solution, you can boost patient engagement significantly. That makes care delivery more accessible and more seamless. Also, coupling healthcare data with IoT promotes people to use wearable devices and track health indicators for disease prevention.
All the vital information can be used to develop better healthcare solutions. However, to access trackable data, patients must provide it to physicians. Patients willing to monitor their health are a gold mine for practitioners. Thus, big data analytics in healthcare coupled with IoT and various types of healthcare software is an excellent opportunity to improve disease prevention.
6. Telemedicine
The COVID-19 pandemic forced many industries to adopt models with a significant focus on remote operations and processes. In such a case, telemedicine proved to be the next big thing. It emerged at the right time and the right place. For providers, knowing how to develop a good telemedicine platform now represents a direct way toward better care provision. In addition, there is evidence portraying the positive trend with telemedicine.
Telehealth solutions use big data analytics to provide tailored treatments. With predictive analytics, professionals can anticipate acute events through health records shared via secure platforms. Such health information is collected with IoT devices, delivered to physicians through HIPAA-compliant telemedicine platforms, and processed by big data analytics tools.
7. Cloud Computing and Alerting
Real-time patient alerting is the instrument that can save lives. Many healthcare organizations use Clinical Decision Support (CDS) tools to analyze patient data simultaneously. That helps health practitioners make more accurate patient-related decisions. The method described above works best when patients are in a hospital, which enables staff to collect all the necessary data.
However, due to the emergence of big data in healthcare, the case of patient alerting is changing. Physicians can collect health data in real-time with people now having wearable and smart devices. After that, the data is sent to the cloud, where it is safely stored and managed.
With data being available to all the relevant parties and stored in one place, various health indicators can be examined by multiple professionals. Such real-time alerting leads to better decision-making and can help with individual and general population health. For instance, platforms like Glooko and Omada Health use cloud computing coupled with big data to help people with diabetes track their health indicators to anticipate any worsening of their condition.
8. Predictive Analytics
Based on AI and ML, predictive analytics is the next-generation method of anticipating specific outcomes based on vast amounts of data analyzed with neural networks mimicking human-type reasoning. Such outputs are the most accurate and cover many factors boosting their precision.
To illustrate, initiatives like Optum Labs managed to collect data points on more than 150 million people while being HIPAA-compliant. As a result, predictive analytics is accurate, and it helps healthcare professionals access the range of data physicians of the past could only dream of.
Big data analytics in healthcare helps deal with various pressing health concerns on the global level. For instance, it can help diabetes patients to receive treatment tailored to each person’s specific case. Using AI to analyze vast amounts of data helps healthcare organizations identify data sets for 415 million diabetes patients worldwide, allowing for individualized treatment.
9. Medical Imaging
Medical imaging is one of the critical sources of patient data. With proper healthcare software tools, providers can get their hands on an invaluable source of information speaking volumes on patient health. With the medical imaging market booming, one can expect more and more data to be produced by different medical imaging instruments like CT, MRI, and X-Ray.
Big data refers to the ability to process 600 million annual imaging procedures in the US alone. Thus, a vast amount of data can be processed to get valuable insights. At this point, big data analytics in healthcare offers all the needed tools. With big-style data techs like optical character recognition (OCR), healthcare professionals can seamlessly turn images into readable data. So, predictive algorithms can later analyze the information and turn it into valuable insights.
10. Prevent Human Error
The human error factor is responsible for about $29 billion in losses and often can lead to preventable patient deaths. Human error is visible when analytics is performed. For example, a person collects data, analyzes it, and forgets about certain variables. Thus, the results of such analysis are corrupted and later result in wrong drug prescriptions or treatment plans.
AI takes on the data processing of huge amounts of data without human fatigue. This big data approach removes the chance of human error corrupting the data analysis. Here, the report indicates a 66% reduction in data errors when AI is used.
11. Strategic Planning
Big data analytics provides insights healthcare executives use to change their decision-making process on a broader scale. Leaders utilize data analytics to make informed decisions and analyze admissions across demographics and locations. That may help determine which factors need to be improved to boost patient engagement. Thus, with good appointment management software at hand, providers can get data that help redefine their approach to care provision.
Healthcare providers are using the data from Google instruments to change their approaches to care delivery. For example, a researcher from Emory University used Google Cloud to develop algorithms analyzing more than 70 factors to predict sepsis in patients. Staggeringly, their predictions offered an 85% accuracy and played a crucial role in how practitioners changed their planning and preparation for the onset of sepsis across the chosen population. As a result, big data analytics in healthcare make strategic planning more effective.
12. Supply Chain Management
If a healthcare organization has a fragmented supply chain, it will have an adverse effect on all aspects of care provision. Luckily, with big data analytics, there is a great chance to improve supply chain effectiveness. There is evidence indicating that data-driven decisions brought by predictive analytics can help hospitals save up to $9.9 million in supply chain costs annually.
Data analytics helps in price negotiations and ensuring clinical data informs product ordering. The data-driven approach grants insights that medical institutions can effectively utilize to deal with supply chain management. One should also remember that smooth supply management has a direct effect on patient outcomes. And it can avoid delays affecting treatment plans.
13. Security and Fraud-Prevention
Expertise in big data services is a direct way to make processes more secure and protected from any fraud. It matters because the healthcare fraud analytics market is booming.
Thus, companies understand the value of the technology and know the damages fraud and lack of security bring. With an average data breach costing about $9.42 million and more than 93% of healthcare organizations experiencing a data breach, the problem is pressing. In most cases, the reason behind the issue is that patient data is a valuable commodity on the black market.
In turn, for a healthcare organization, a data breach is incredibly costly because it can lead to litigation and audits. Big data allows organizations to detect vulnerable areas and anticipate a data security breach by tracking traffic inconsistencies. Essentially, these algorithms keep track of all the traffic and can detect inconsistencies and potentially fraudulent behaviors.
Moreover, big data brings forward new encryption standards and innovative firewalls that use AI-powered tools to apply bank-grade security into healthcare. This scholarly piece indicates that effective fraud-prevention measures can save up to $2.6 billion annually. Cybersecurity is something no company can afford to avoid. In this context, preventing fraud by boosting security helps save money and avoid issues linked to exposure to sensitive patient data.
14. Emergency Room (ER) Visits
The Department of Health and Human Services indicates about 46% of preventable ER visits in the State of Rhode Island. In New York, the percentage can go up to 74%. It means many people might have avoided going to the ER. In turn, millions in costs could have been avoided. For instance, this report suggests avoidable ER visits are responsible for $32 billion annually.
How can ER visits be prevented with big data? Hospitals can be integrated with big-style data platforms sharing patient data across departments. In this case, ER professionals know:
- whether a patient has already undergone tests in other medical organizations
- whether a patient has been assigned to other hospitals
- whether a patient has already received treatment plans from healthcare vendors
With such knowledge, ER professionals can avoid unnecessary tasks or reduce the required input. There are already such platforms up and running. There are tools like PreManage ED already helping healthcare organizations avoid preventable ER visits.
15. Therapies and Innovations
Bringing innovation to care provision is a direct way to boost patient outcomes. If providers develop innovative therapies that prove their worth in practice, healthcare organizations get their hands on instruments that can help millions of individuals. Big data has the potential to speed up drug discovery. By analyzing a large amount of data in real-time, researchers can bring innovative medication to patients much sooner.
In addition, with existing data visualization techniques, it is much easier to make sense of the output offered by big data predictive analytics in healthcare. Thus, big data helps bring forward innovative drugs and develop new therapies to help practitioners save lives more effectively.
16. Drug Prescription
The evidence shows how big data can help improve the drug prescription process. Numerous prescription errors can adversely impact patient outcomes.
Each error mentioned above can cost lives. However, with big data analytics in healthcare, practitioners can avoid them. To do that, it is vital to process vast volumes of data. For instance, this research indicated how drug prescriptions could be improved through processing patient data. The study explored millions of patient records and developed an algorithm illustrating how big data analysis identifies which populations are vulnerable to addiction. Healthcare big data helps practitioners prescribe safer alternatives based on millions of health records.
17. Early Disease Detection
The ability to detect diseases early on stages leads to higher chances of tackling the condition. Dealing with early disease prevention is among the greatest areas linked to big data and propagated by healthcare software tools. With the emergence of IoT and big data in the health sector, practitioners received new sources of information for onset disease prevention. Whether a professional looks for cancer or multiple sclerosis – detecting the condition early on is vital for staying ahead of the disease.
For instance, Prognos Health is an AI-powered platform utilizing big data approaches to grant access to multi-source diagnostic data. It helps physicians detect diseases early by accessing up-to-date medical data illustrating corresponding symptoms directed toward certain conditions to develop. There is also PeraHealth, the company standing behind the so-called Rothman index. This scoring system for measuring the patient’s health includes data from EHRs, IoT, and lab results. The overall score helps detect diseases, even when the slightest symptoms appear.
18. Cancer
The World Cancer Research Fund indicates that there are an estimated 18.1 million cancer cases around the world. Helping those people is often considered the top priority because of the mortality rates the condition brings. At this point, big data in healthcare may help boost cancer treatments’ effectiveness. Starting from telehealth solutions improving access to care and to predictive analytics, helping to detect early stages of cancer.
To illustrate, the Cancer Moonshot program was created as a tool for gathering and processing large data linked to cancer. Thus, medical researchers tapped into the program to develop new treatment plans. The key was to determine which therapies had the highest recovery rates.
With all the data collected and analyzed with big data tools, researchers saw which hospitals worldwide had the most effective cancer treatments. Big data in oncology has promoted the adoption of similar approaches on a large scale and improved the total cancer recovery rates, reaching up to 50% in some regions.
19. Opioid
The opioid crisis is one of the most pressing issues in healthcare. In the U.S. alone, thousands of people perish due to the problem. The main concern coming with such a crisis is all about preventable overdose and drug misuse deaths. In addition, PEW estimates the economic tool for the phenomenon to be about $35 billion in healthcare spending annually. Finding new ways to tackle the issue can save lives and money.
The instrument can aid by identifying certain risk factors for opioid abuse or drug misuse. The Institute of Health and organizations like Blue Cross Blue Shield use big data to identify 742 risk factors for opioid abuse, saving lives and healthcare costs. Keeping that in mind, practitioners can use findings offered by Blue Cross Blue Shield and engage in new analytical studies to be a step ahead of drug abuse. Equipped with valuable data, they can make more appropriate drug prescriptions and consider factors fueling the issue.
20. Suicide
Big data analytics can help with suicide prevention measures. The evidence shows the number of suicide deaths is almost comparable to the number of deaths from alcohol abuse.
Using big data analytics in healthcare, researchers and practitioners flag behaviors indicating a higher risk of suicide among particular study groups. Essentially, the experts managed to find out the risk factors indicating whether a person is likely to commit suicide or not. This evidence can effectively prioritize treatment and therapy for at-risk groups based on large data sets.
Final Thoughts
The 20 examples above prove big data in healthcare has the potential to revolutionize healthcare for the better. Investments in big data are rising, and the value of big data in saving lives while making profits is undeniable. As the amount of data available grows, the healthcare industry will continue to rely on data scientists and big data technologies to lead the way.
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FAQ
What are the main benefits of using big data in healthcare?
The benefits of using big data in healthcare are extensive, primarily focusing on cost-reduction, eliminating medical errors, and improving patient care. Big data analytics allows healthcare organizations to identify patterns in healthcare data that can predict disease outbreaks or identify at-risk patients before they reach a critical state. Besides, the use of big data helps in optimal staff management, reducing burnout among healthcare professionals by creating more efficient schedules. Ultimately, big data has the potential to revolutionize healthcare by making the healthcare system more proactive, efficient, and data-driven.
How does big data in health help reduce medical errors?
Big data in health helps reduce errors by acting as a sophisticated safety check for healthcare providers. When practitioners use big data, systems automatically cross-reference a patient’s health records with current prescriptions to flag potential drug interactions or allergies. Because big data refers to the ability to analyze a huge amount of data in seconds, it eliminates the risk of human oversight. According to reports, there is a big impact on safety, with a 66% reduction in data entry errors when big data technologies and AI are used to collect data and validate it.
What are the biggest challenges of big data in the healthcare industry?
The opportunities and challenges of healthcare big data revolve around data storage, security, and user literacy. Managing the volume of healthcare data requires massive data warehouses and sophisticated data processing capabilities. Additionally, data security is a primary concern, as health data is highly sensitive and must be protected according to HIPAA standards. Finally, there is a challenge of data sharing between different healthcare organizations, as siloed systems often prevent a unified view of the patient data, making big data analysis less effective.
What types of healthcare data are used in big data analytics?
Big data analytics uses various types of healthcare data, involving structured and unstructured data. Structured data consists of organized information like patient demographics and lab results found in electronic health records. Unstructured data, which is a large amount of data, includes physician notes, medical images, and sensor data from IoT devices. Big data analytics in healthcare must be able to process all these sources of data to provide a complete picture of the health system, allowing data scientists to analyze big data and find value from big data.
How can big data enable better strategic planning for hospitals?
Big data enables better strategic planning by providing healthcare providers with a vast amount of data on patient admissions, treatment outcomes, and resource usage. By using big data in health, administrators can analyze trends across locations and demographics to decide where to build new clinics or which services to expand. Big data analysis also allows for more accurate budgeting, as the amount of data available helps in predicting future healthcare costs and identifying areas where to make operations more efficient can save millions of dollars.
What is the role of data scientists in big data analysis in healthcare?
Data scientists are critical to big data analysis in healthcare as they are responsible for building the algorithms that turn raw data into information from big data. They handle the data collection process, manage data warehouses, and ensure the data quality of large data sets. Their expertise allows them to analyze big data to identify population health trends and help develop predictive analytics tools. In the modern healthcare era, these professionals are essential for translating the quantity of data into value in healthcare organizations.
How does the Institute of Health use big data for disease prevention?
The Institute of Health uses big data to track global health trends and identify risk factors for widespread conditions. By analyzing huge amounts of data from clinical studies and public health data, they can develop predictive model instruments for early disease detection. For instance, big data in oncology has been used by such institutes to share research findings globally, leading to the application of big data in finding more effective cancer treatments. The National Institute of Health often funds projects that aim to revolutionize healthcare.
Why is data security so important when you use big data in health?
Data security is vital because the medical data used in big data projects is highly personal and valuable. When healthcare organizations use big data analytics, they are handling sensitive health information that, if breached, could lead to identity theft or insurance fraud. To maintain patient care standards, all big data in health care must be encrypted and stored in secure data warehouses. Complying with regulations like HIPAA is a necessary step to build trust between patients and healthcare providers in the digital age.
