The healthcare industry generates vast volumes 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 in handling Big data in healthcare to find ways to leverage everything the technology offers.
Big data analytics is currently booming. For example, Statista indicates that the Big Data market is 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 healthcare phenomenon and explore 20 distinct instances of improvements it brings.
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The Essentials of Big Data in Healthcare
Big data in healthcare leverages the vast volumes of information generated by digital technologies applied within the industry. The critical focus of the phenomenon is on new methods for collecting and processing patient data through medical records to enhance the system’s functionality. Data analytics in healthcare is vital for healthcare vendors that scale and operate at a massive scale.
While we have seen the prospects of Big Data in the healthcare market in general, there is a similar growth trend for healthcare Big Data analytics (see Fig. 1).
Keeping that in mind, Big data and its analytics tools are redefining data usage in healthcare. When applied correctly, the technology promises numerous benefits for administrators and patients. On a broader scale, these benefits can be generalized to population health.
When understanding the nature of Big data in healthcare, one should be aware of the sources of information that professionals can use to gain better insights. Furthermore, with the ongoing digitalization, the number of sources is continually increasing. This provides the benefit of obtaining more valuable data, but also presents the challenge of handling a growing number of data sources.
In general, with proper Big data in healthcare analytics tools, experts can extract and process information from the following sources:
- Electronic Health Records (EHRs)
- Patient portals
- Clinical studies
- Internet of Things (IoT) devices
- Health databases
- Government agencies
- Billing records
- Staffing schedules
- Search engines
- Scholarly and scientific journals
For years, healthcare professionals have been diving into the sources above to look for insights that can improve administration and care. However, with Big data in healthcare analytics, the experts receive new opportunities to work with the given data sources. They can be prepared for any data inflow the digital world offers.
Why Care about Big Data in Healthcare Analytics?
Nothing speaks better of the success or failure of a particular technology than the tangible benefits it brings. While various healthcare software companies can get the best out of digitization, you still need to know why you care about the instrument in the first place.
Generally, investing in analytics tools is the right decision. Companies that want to work with data effectively cannot afford to avoid using data analytics. Moreover, there is sufficient evidence to suggest that analytics instruments will remain in demand in the years to come (see Fig. 2).
By examining the specific benefits of Big data in healthcare, you will gain insight into the role of technology in the industry. The following advantages make the most impact.
Cost-Reduction
Data from the World Bank indicate that current healthcare expenditures equal approximately 9.8% of the global GDP. With the global economy estimated to be roughly $104 trillion, simple arithmetic suggests that healthcare costs amount to around $10 trillion. In turn, McKinsey states that in the U.S., healthcare expenses now account for 17.6% of the nation’s GDP, which is approximately $600 billion. The critical issue is that the current fees are much higher than the expected benchmark. It means that cost reduction is one of the vital needs in the healthcare industry.
Big data in healthcare analytics comes to the rescue. The only possible solution to the rising costs of healthcare is to offer sophisticated and intelligent data-driven approaches that can redefine the industry. Many healthcare providers have realized that focusing on patient outcomes can result in higher costs than traditional fee-for-service plans.
The reason for data-related cost-efficiency is that with Big Data in healthcare tools, medical providers have a direct incentive to share patient data. Everything in this scenario has the potential to improve patient outcomes and receive financial incentives for doing so.
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. Additionally, medical errors cost the industry approximately $20 billion annually. Respectively, finding the tools to reduce their number is a direct way to save lives and cut costs.
Numerous attempts have been made to deal with the problem. Some providers improve the risk assessment instruments by introducing automation. Others use Big data in healthcare analytics. However, physicians and clinicians generally rely on evidence-based approaches when working with medical data. The problem is that processing vast amounts of data increases the likelihood of human error, which can lead to medical errors. In such cases, Big data in healthcare brings forward technological tools that help eliminate the impact of human error.
Big data in healthcare tools can alert professionals if the wrong medication is prescribed or a faulty clinical test occurs. Essentially, the phenomenon serves as a safety check mechanism, double-checking all output presented by human agents to minimize medical errors.
Optimal Staff Management
There is a growing problem with healthcare staff management. This issue was obvious when the COVID-19 pandemic struck. The research highlights the rising rates of burnout among health workers due to excessive 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, optimization of staff management can be a lifesaver.
For health workers to improve patient outcomes, they need to work in a facility with optimal schedules and proper general organization. Big data in healthcare can help achieve that. Professionals can use predictive analytics in healthcare to collect and process real-time data, measuring an organization’s staffing and personnel management performance.
With the insights from the assessment, providers can adjust their staffing schedules to ensure patients receive the best care while health workers have an optimal workload. Big data in healthcare optimizes scheduling and helps reduce burnout among health professionals.
Challenges with the Usage of Big Data in Healthcare
With the benefits of Big Data in healthcare, providers receive tools that help reduce costs, minimize medical errors, and optimize scheduling and personnel management. However, even when using the services of well-recognized software companies with expertise in Big Data, there are still particular challenges one needs to keep in mind.
Data Storage and Management
To utilize the full potential of Big data in healthcare, you need to address data storage first. Vast volumes of data are spread across various sources, stored and governed within hospitals and administrative departments. To manage these sources safely and integrate them, you need an intact complex infrastructure. Additionally, it ensures the proper degree of collaboration between providers and reduces data corruption resulting from 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. Big data in healthcare brings forward tools such as machine learning (ML) and predictive analytics. These future-oriented instruments help create collaborative environments, bringing reporting to another level.
Security, Privacy, and Compliance
Security is always a top priority when working with patient data. Hospitals deal with sensitive patient information daily. Considering the industry’s numerous data sources, there is a higher chance of data breaches for several reasons. To keep data protected, you need to have top-grade encryption tools and comply with all the related rules, like HIPAA. Making software compliant is a step forward for a tool to succeed in healthcare.
User Literacy
Working with data requires a certain degree of knowledge. If a healthcare professional does not have the proper training to handle Big data, they can corrupt some valuable information or increase the chance of data exposure. Remember that Big data in healthcare is a technological instrument that users need to know how to handle. It means that providers need to plan and implement data literacy training to ensure that people with access to technology know how to work effectively with it.
Healthcare Data Usage Examples
With all the benefits and challenges of Big Data in healthcare analytics now in the open, it is time to take an in-depth look at particular examples. Big data can improve different aspects of the healthcare industry. The 20 examples mentioned further correspond to specific segments of the healthcare industry aided by this technology.
- Instances 1 to 5 cover administrative improvements brought by Big data.
- Examples 6 to 10 demonstrate the power and benefits of Big Data in healthcare analytics.
- Instances 11 to 13 illustrate how Big Data can support critical processes within the industry.
- Examples 14 to 16 illustrate better patient outcomes achieved through big data in healthcare analytics.
- Instances 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 in healthcare can help solve the question of patient predictions used to improve staffing approaches. Equipped with proper medical software that utilizes Big Data methods, healthcare professionals can accurately predict the number of patients visiting hospitals. Establishing correlating staffing and scheduling procedures is crucial to ensure the provision of optimal care and a balanced workload for healthcare workers.
Intel offers a report showing how French healthcare providers effectively used Big Data to predict patient inflow daily and even hourly. Experts analyzed ten years’ worth of hospital admissions to develop a prediction model anticipating future admissions based on past experiences. Researchers utilized big data in healthcare analytics to identify 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 have shown before, health workers are currently facing massive rates of burnout and struggle to find a proper work-life balance due to inadequate staffing and scheduling. In such a case, Big data in healthcare can help streamline various administrative activities.
The technology ensures the right degree of personnel fluidity. It entails having staff distributed in areas that require the most attention. It is about avoiding overcrowded or understaffed departments. That can be achieved through Big data in healthcare analytics tools. Professionals collect and analyze data on peak admission periods and evaluate 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 technology, hospitals can determine when to allocate additional personnel 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 healthcare workers play a vital role in ensuring the adoption of innovation. 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 discussing this point, it becomes clear that learning and development are essential aspects that should be integrated into a broader organizational capability. For a hospital to run at optimal capacity, it is crucial to monitor performance and identify areas that underperform.
Big data in healthcare analytics comes with tools that help track performance. It grants an objective view of who needs additional training and what departments can benefit from learning and development first. Healthcare professionals should also evolve if a healthcare organization aims to achieve better patient outcomes.
4. Electronic Health Records (EHRs)
Handling EHRs is one part of having smooth administrative tasks. The tool has been around for years and is still proven to have a certain degree of demand (see Fig. 3).
Often, if a healthcare provider has a well-developed EHR platform, one can be confident of partial success. In turn, EHRs can be considered the most widespread adoption of Big Data in healthcare. That is because various patient data aspects, such as their medical history and demographics, are stored across the organization’s infrastructure. Additionally, with data stored collectively, healthcare professionals can access it directly from various locations.
While EHR has proven to serve well in healthcare, there are significant concerns regarding its adoption. The evidence provided by HITECH research indicates that while approximately 94% of hospitals in the U.S. have adopted the system, many nations worldwide struggle to implement 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 past mistakes linked to implementation-based challenges.
5. Patient Engagement
With a well-designed telehealth solution, you can significantly boost patient engagement, making care delivery more accessible and seamless. Besides, with the advent of IoT technologies, many people are willing to use smart wearable devices to track different health indicators for disease prevention.
All this vital data can be used to develop better healthcare solutions. However, to access trackable data, patients must be engaged and provide it to physicians. Patients willing to monitor their health are a gold mine for practitioners. In such cases, Big data in healthcare analytics, coupled with IoT and various types of healthcare software, presents an excellent opportunity to improve disease prevention by making patients more engaged in their health monitoring.
6. Telemedicine
The COVID-19 pandemic forced many industries to adopt models with a significant focus on remote operations and processes. And healthcare was not an exception. In such a case, telemedicine proved to be the next big thing. It emerged at the right time and the right place. For providers, understanding how to develop an effective telemedicine platform now represents a direct path toward improved care provision. Additionally, there is evidence indicating a positive trend with telemedicine (see Fig. 4).
Thanks to the tool, physicians can provide treatment tailored to each patient’s specific needs. In this context, telemedicine is often applied in conjunction with Big data analytics in healthcare. With predictive analytics, professionals can anticipate acute medical events and offer timely treatment. All these are possible due to health information 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 an instrument that can save lives. Many healthcare organizations use Clinical Decision Support (CDS) tools to analyze medical data simultaneously, which helps health practitioners make more accurate patient-related decisions. The method described above works best when patients are in the hospital, enabling professionals to collect all the necessary data.
However, due to the emergence of Big data in healthcare, patient alerting is changing. Physicians can collect health data in real time, and people now have wearable and smart devices. The data is then sent to the cloud, where it is safely stored and managed.
Data is available to all relevant parties and stored in one place, allowing multiple professionals to examine various health indicators. Such real-time alerting enables better decision-making and can contribute to both individual and population health. For instance, platforms like Glooko and Omada Health use cloud computing coupled with Big data in healthcare to help people with diabetes track their health indicators to anticipate any worsening of their condition.
8. Predictive Analytics
To illustrate, initiatives like Optum Labs collected 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 that physicians of the past could only dream of.
Big data in healthcare analytics can help deal with various pressing health concerns on a global level. For instance, it can help diabetes patients receive treatment tailored to each person’s specific case. With 415 million diabetes patients worldwide, predictive analytics is the instrument that can make a difference.
9. Medical Imaging
Medical imaging is a critical source of patient data. With proper healthcare software tools, providers can get their hands on an invaluable source of information that speaks volumes about 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 (see Fig. 5).
An estimated 600 million medical imaging procedures are performed annually in the United States alone. Therefore, 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 data technologies like optical character recognition (OCR), healthcare professionals can seamlessly convert images into readable data, and predictive analytics algorithms can later analyze the information to derive insights that improve patient outcomes.
In addition, medical imaging processing, coupled with emerging mHealth solutions, can be considered a key to preventive healthcare.
10. Prevent Human Error
The human error factor is responsible for about $29 billion in losses and can often lead to preventable patient deaths. Human error is most visible when analytics is performed. For instance, a person collects information, analyzes it, and forgets about certain variables. Thus, the results of such analysis are corrupted and later translate into wrong drug prescriptions or inadequate treatment plans.
When AI and ML emerge, there is no room for human error. Companies often use Big data in healthcare analytics to identify areas potentially affected by such errors. Therefore, AI and ML automate processes such as analyzing, parsing, dissecting, and drilling down into vast data volumes without requiring human input. That drastically reduces the chance of human error corrupting the analytics process. This report indicates a 66% reduction in data errors when AI is used.
11. Strategic Planning
When it comes to informed strategic planning, Big data in healthcare analytics provides insights that healthcare administrators and leaders can use to change their decision-making process on a broader scale. For instance, check-up results, appointments, and admissions across different demographics and locations can be analyzed to identify which factors need improvement to enhance patient engagement. With good appointment management software at hand, providers can access data that helps redefine their approach to care provision.
Healthcare providers are utilizing data from Google instruments to modify their approaches to care provision. For example, a researcher from Emory University utilized Google Cloud to develop algorithms that analyzed more than 70 factors to predict sepsis in patients. Notably, their predictions achieved an 85% accuracy and played a crucial role in influencing how practitioners modified their planning and preparation for the onset of sepsis within the chosen population. As a result, Big data in healthcare analytics makes strategic planning more effective.
12. Supply Chain Management
If a healthcare organization has a fragmented supply chain, it will adversely affect all aspects of care provision. Luckily, big data in healthcare analytics offers a great chance to improve supply chain effectiveness. Evidence indicates that data-driven decisions brought by predictive analytics can help hospitals save up to $9.9 million in supply chain costs annually.
With analytics tools brought by Big data in healthcare, providers can make more informed decisions about price negotiations and product ordering. Thus, the data-driven approach grants insights that medical institutions can effectively utilize to manage supply chains. One should also remember that smooth supply management has a direct effect on patient outcomes and can avoid delays that affect treatment plans.
13. Security and Fraud-Prevention
We will show how expertise in Big Data services directly improves your processes and protects them from fraud. This matters because the healthcare fraud analytics market is booming (see Fig.6).
It means that companies understand the value of the technology and recognize the damage that fraud and a lack of security can bring. With an average data breach costing about $9.42 million and more than 93% of healthcare organizations experiencing a data breach, the problem appears to be pressing. In most cases, the reason behind a data breach is that patient data is considered a valuable commodity on the black market.
In turn, a data breach is incredibly costly for a healthcare organization because it can lead to litigation and audits. Many healthcare providers now utilize predictive analytics to identify the most vulnerable areas and anticipate potential data breaches. Essentially, these algorithms track all traffic and can detect inconsistencies and potentially fraudulent behaviors.
Moreover, Big data in healthcare brings forward new encryption standards and innovative firewalls that use AI-powered tools to apply bank-grade security. 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 enhancing security helps save money and avoid issues associated with exposure to sensitive patient data.
14. Emergency Room (ER) Visits
The Department of Health indicates that about 46% of preventable ER visits occur in the State of Rhode Island. In New York, the percentage can reach as high as 74%. This means many people might have avoided going to the ER, which could have saved millions in costs. Some reports suggest avoidable ER visits are responsible for $32 billion annually.
How can ER visits be prevented with Big data in healthcare? Hospitals can be integrated with Big Data platforms that share patient data across departments. In this case, the ER professionals know whether a patient has already undergone tests in other medical organizations, been assigned to other hospitals, and received treatment plans from different healthcare vendors.
With the above knowledge, ER professionals can avoid unnecessary tasks or reduce the required input. Such platforms are already up and running, and tools like PreManage ED are 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 can access instruments that can help millions of individuals.
Big data analytics in healthcare has the most potential in realms like drug discovery. By analyzing historical data and tracking information in real-time, predictive algorithms help accelerate the speed of the drug development cycle. Thus, patients can receive the innovative medication they need sooner.
In addition, existing data visualization techniques make it 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 in healthcare can help improve the drug prescription process. Numerous prescription errors can have a detrimental impact on patient outcomes (see Fig. 7).
Each of the errors mentioned above can cost lives. However, with Big data analytics in healthcare, practitioners can avoid them. To achieve this, it is essential 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 which populations are most vulnerable to becoming addicted to a particular medication. With such insights at hand, practitioners can prescribe drugs that people will be less likely to be addicted to.
17. Early Disease Detection
It is no secret that the ability to detect diseases at the early stages of their development increases the chances of tackling the condition. Dealing with early disease prevention is one of the significant areas linked to Big data in healthcare and propagated by healthcare software tools.
With the emergence of IoT and instruments like video calling used in telemedicine, practitioners have received new sources of information for early disease prevention. Whether a professional looks for cancer or multiple sclerosis, detecting the condition early on is vital for staying ahead of the disease.
Companies are utilizing Big data analytics in healthcare to help practitioners with detection. For instance, Prognos Health is an AI-powered platform that utilizes big data approaches to provide access to multi-source diagnostic data. Using this instrument helps physicians detect diseases early by accessing up-to-date data that illustrates corresponding symptoms directed toward specific conditions that are developing.
PeraHealth is also the company behind the Rothman index, a scoring system for measuring a patient’s health. It includes data from EHRs, IoT, and lab results. The overall score helps detect diseases, even when the slightest of symptoms are showing.
18. Cancer
The World Cancer Research Fund estimates that there are 18.1 million cancer cases worldwide. Helping those people is often considered the top priority because of the mortality rates the condition brings. At this point, Big data in healthcare can help boost the effectiveness of cancer treatments. This can include telehealth solutions that improve access to care and predictive analytics that help detect early stages of cancer.
To illustrate, one should mention the Cancer Moonshot program. It was developed as a tool for gathering and processing vast volumes of data linked to cancer. Moreover, medical researchers utilized 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 in healthcare tools, researchers saw which hospitals worldwide had the most effective cancer treatments. It allowed the adoption of similar approaches on a large scale, which improved the total cancer recovery rates, reaching up to 50%. As a result, simple data analysis revealed the best-performing cancer treatments, enabling practitioners to utilize them on a broader scale.
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 (see Fig. 8).
The primary concern coming with such a crisis is all about preventable overdose and drug misuse deaths. Additionally, PEW estimates the economic toll of the phenomenon to be approximately $35 billion in annual healthcare spending. Finding new ways to tackle the issue can save lives and money. How can Big data analytics in healthcare help?
The instrument can aid in identifying certain risk factors for opioid abuse or drug misuse. To illustrate, there is a case of Blue Cross Blue Shield organizations that began using predictive analytics to identify specific risk factors and predict drug abuse. They managed to identify a staggering 742 factors offering accurate predictions on whether a particular population is at high risk of being exposed to opioid abuse.
Keeping that in mind, practitioners can use the findings offered by Blue Cross Blue Shield and engage in new analytical studies to stay ahead of drug abuse. Equipped with valuable data, they can make more informed drug prescriptions and consider factors that fuel the issue.
20. Suicide
Similarly to the opioid crisis, Big data in healthcare 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 (see Fig. 9).
Using Big data analytics in healthcare, researchers and practitioners develop new methods of predicting suicide risk. For instance, Kaiser Permanente offered a study showing how predictive algorithms can help 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 and help reduce suicide deaths.
Final Thoughts
All in all, the 20 specific examples mentioned above prove That Big data analytics in healthcare has the potential to change the industry for the greater good of everyone involved. Companies working with Big data in healthcare will always have clients. With the demand for Big data services rising, the ones that manage to apply the technology early on will reap benefits and gain a competitive advantage. With all being said, one can add that Big data analytics in healthcare appears to be an excellent opportunity to save lives while making profits.
