The projected value of big data-based financial analytics services in healthcare stands at more than 13 bn USD by 2025. However, with data and technology being the two major drivers of transformation and growth in the healthcare industry, we expect the figure to be actually higher than this.
However, what makes big data analytics of such a big consequence in healthcare? How can it spur what the global technology and business experts are calling the Fourth Industrial Revolution in the healthcare sector?
And what are the major applications of big data analytics in the healthcare industry?
Let’s explore the answers to all these questions and examine the various big data analytics use cases in the global healthcare industry.
Table of Contents
In easy terms, big data analytics is the process of parsing, processing, and making sense of huge chunks of organized or unorganized hybrid data blocks. Big data analytics is a powerful tool to get actionable insights from the huge amount of data that is otherwise dormant and of no use because of its unstructured composition.
Presently, the healthcare industry generates around 30% of the world’s data volume and by 2025, this figure will sit at 36%.
While you might argue that is not a significant increase, it is 6% faster than manufacturing, 11% faster than media & entertainment, and 10% faster than financial services.
There are various sources of data in the healthcare industry, such as patient data, medical insurance data, research data, innovation and medicine data, medical institution data, student data, business development data, etc.
Further, as healthcare has unbreakable ties with many other industries, such as medical tourism, wellness and counseling services, different schools of medicine, equipment manufacturing, etc, the data can become a huge complex puzzle.
And, big data analytics is a powerful and reliable key to not only solving this puzzle but to using the insights generated for improving all the business processes and discovering hidden trends in healthcare.
Below, we are sharing a basic overview of the various business use cases of big data analytics in healthcare.
The business side use cases focus on the business aspects of the healthcare industry and comprise many sectors, as shown in the following image:
These use cases focus on the operational side of the healthcare industry and some of the common use cases are shown in the following visual:
Next, we present some big data analytics use cases on the application side:
Before we discuss these use cases in detail, let us understand why big data analytics is important in healthcare.
World’s health and wellness data are invaluable, in fact, it is as indispensable for modern healthcare services, as water is for life!
And, all the major brands and stakeholders operating in the sector are in a battle to access and utilize the digital representation of this data.
As the number of people that are using their mobiles and personal devices for healthcare services is increasing massively, the market is estimated to reach a market value of 189 bn USD in 2025. Apart from the widespread concern about health and well-being because of the pandemic, the penetration of the internet and the availability of mobiles and tablets are the key drivers of this market boom.
Apart from creating opportunities for new healthcare services and offerings for digital consultation, information sharing, and personalizing the medical healthcare services, these trends are also generating heaps of data.
However, in order to make the most of this data, the industry stakeholders need proper means to process and analyze this data to understand the key pain points and discover actionable insights.
Hence, big data analytics!
Big data analytics empowers the medical caregivers and service providers to learn granular details about their patients and facilitate the care, attention, and services accordingly. When healthcare professionals can see the key service points and unfulfilled patient requirements they can optimize their service portfolios and business processes.
They can also add new services, products and create new treatment routines depending on the results of the previous ones.
With big data analytics and other advanced technologies, such as blockchain technology medical claim frauds and health insurance frauds can be detected even before they have happened and can be stopped before they become a business risk.
Using big data analytics will also spawn innovation in medicine and established treatment procedures or medicines, to make way for smarter, better, and more targeted healthcare facilitation.
Healthcare professionals can also find ways to deliver more cost-efficient and clinically relevant services to patients.
Next, we discuss some of the most impactful and compelling applications of big data analytics in healthcare.
Staffing is one of the major concerns and challenges that need robust and reliable resolution in a healthcare institution. Sometimes, there are too many nurses in a department while the patient inflow and tasks at hand are low, and on others, a few nurses are grappling with the hospital duties.
Using big data analytics for predictive analysis of staffing requirements and scenarios based on the historical data and upcoming forecast can resolve these and many other issues with ease.
You can see which type of staff is the most suitable for your business and operational model.
Medical institutions can not only save money via proper staffing but also increase their turnover rates and increase job satisfaction among their staff.
Electronic health records consume very small space (they typically reside on your server) and come with a bucketload of benefits. You can access the data for any patient, any treatment record, and any transaction that was done at any point in time, without having to go through the innumerable files in the data room of your hospital.
While the majority of hospitals are using electronic health records, they are not able to utilize their full potential, because of the lack of vision into that massive data and proper processing.
Using big data analytics in electronic health records management will open multiple new avenues, such as:
A visual representation of some ways big data analytics can help you manage electronic health records and optimize them is shown below:
Big data analytics will also help you offer more personalized and specifically targeted services, to improve the overall patient engagement. You can gather different types of data, such as clinical, demographical, etc., and identify the targeted census.
You can use patient data to tailor the treatment plans and chronic disease management or enhance hospital service standards and acquired conditions during the patient’s stay in the hospital.
Big data analytics can also be a key driver of clinical trial engagement by helping you identify the target audiences for them. Clinical trials are crucial for developing novel treatments, more precise diagnoses, and discovering new cures for critical diseases.
Adopting big data analytics in your business and operations will facilitate the rendition of more personalized and better services, as shown in the image below:
Real-time alerts are of immense importance in the healthcare industry, and they can be of many types, such as:
While most of the institute’s management systems and software tools either crumble down in the light of expansion or don’t scale well with an increase in the number of expectations, big data analytics is free from such constraints.
You can use big data analytics to get as many types of real-time insights as many times as you want during the day. Be it security alert, patient status alert, staffing alert, or management of business operations – big data analytics is infallible support!
By incorporating big data analytics in healthcare, the stakeholders are able to take more well-informed decisions stemming from data-driven insights.
You can build better strategies for:
The following image shows various ways big data analytics facilitates decision-making and strategizing in healthcare institutions:
When it comes to improving medical treatments, information is the key change driver and with heaps of medical R&D data generated every year, big data analytics is nothing short of a blessing for medical researchers. Right from gathering new insights about a particular treatment innovation to finding successful cases all over the globe – big data analytics is of extreme importance.
What makes the application of big data analytics so crucial is the fact that medical R&D and its results take years for proper determination. This is because even if a drug is developed for some disease or health condition, it needs to be tested on a diverse pool of patients with varying body constitutions. Further, even if the majority of patients show positive results, the medical researchers have to keep an eye on the emergence of any new issues or symptoms in the long run.
Hence, right from development to large-scale adoption, there are many years of efforts, observations, and massive data recording in medical R&D.
Using big data analytics in medical R&D will not only facilitate the availability of such diverse data sets but will also help in making sense of them.
Predictive analytics can be of immense help in many areas, such as:
Incorporating predictive analytics in all types of decision-making processes empowers the management with smarter decision-making capabilities as all of them stem from smart data insights.
The image shows some other ways predictive analytics can find applications in healthcare:
Insurance fraud and medical claim frauds are fairly common in healthcare, almost 15% of the total claims. From false claims to insurance frauds where a person with a serious medical issue gets insurance just for duping the insurance providers – the number and nature of frauds are alarming!
Insurance analytics leverages the power of data to discover and contain the risk of fraud and failed payments at all the stages, as shown in the picture:
Big data analytics can use the data sets to discover hidden patterns that, in turn, help in uncovering fraud and false claims. As medical insurance and medical claims, data sets are huge, they form the best quality raw material for big data analytics and help in better identification of people who are most likely to commit fraud.
Using other advanced technologies, such as AI and ML augment the working of big data analytics software and also reduces the amount of time required for data processing. The experts can also train big data processors to identify and steer clear of false positives to ensure accurate fraud prediction.
Market forecasts suggest that the global value of the telemedicine market will sit around 460 bn USD by 2030 and the ongoing COVID-19 pandemic is one of the most powerful drivers behind the trend. As more and more patients want to avoid going out in the light of the pandemic and are looking for ways to keep the doctor consultation ongoing remotely, telemedicine is all set to see a major boom.
Further, the number of people who are anxious about their health and fitness and are not able to go to a hospital for getting checked because of the pandemic is also on the rise. Also, there are many people who are having flu or cold symptoms and are looking for medical advice for getting themselves tested for COVID.
All these patients are the ideal use cases for telemedicine.
Now, you might ask how big data analytics can be of assistance in this regard.
Big data analytics finds many crucial use cases in the telemedicine industry as well, such as:
Big data analytics also finds applications in handling diverse medical imaging data from various techniques, such as CT-Scan, X-rays, MRIs, etc. for facilitating better insights for improved diagnosis.
The medical professionals can use the technology to:
Big data analytics, along with the other advanced technologies, such as data computation and AI / ML can facilitate better and faster diagnosis as well as advanced prediction of treatment plans.
Big Data and Big Data Analytics (BDA) techniques have been used during the COVID-19 pandemic for predicting, tracking, mapping, monitoring, and sharing information about the pandemic among huge masses.
Big data analytics can be used along with statistical techniques, such as correlation and regression, to identify and predict major hotspots that need immaculate care and management.
Patient management, allotment or resources, inventory management, and reducing the spread of infection by taking proper caution from the beginning – there are various ways BDA can be of immense help in disease risk management and pandemic management.
As discussed earlier, there are many people with medical conditions that are inclined towards inflicting self-harm or self-slaughter. Using predictive analysis, the medical consultants can identify these patients and give them proper counseling and attention to contain the risk.
However, there are many people that are unaware or devoid of any major mental or emotional disorder and are likely to commit suicide when provoked.
Also, there are many youngsters that get deluded by social media groups to commit crimes or suicide as a challenge.
Big data analytics can not only identify these population groups but also rank them on the basis of their suicidal tendencies and inclinations towards self-harm.
Having organized them on the basis of their suicidal tendencies, the medical counselors can arrange proper therapy sessions and discussions for them and help them realize the value of life.
The healthcare supply chain management suffers from many serious drawbacks, such as:
BDA empowers supply chains by delivering actionable data insights stemming from reliable and expert data processing. Using BDA in medical supply chains will not only improve logistics and transportation but also tackle the shady ordering and counterfeit drug issues by implementing smart contracts (blockchain technology).
3D imaging, AR, VR, Internet of Medical Things, Smart Wearables for remote patient monitoring, and disease tracking – big data analytics can not only improve the pace of development but also spearhead medical innovation in various ways.
One of the major benefits offered by big data analytics in medical innovation is information facilitation and efficient data processing to procure advanced medical services.
Likewise, big data analytics is also of immense help in developing and discovering new drugs, as shown in the following image:
Another image, shared below, shows a detailed overview of big data analytics applications in new drug discovery:
Biomedical signals are collected from human bodies, at the organ level, molecular level, or cell level to detect or diagnose specific bodily conditions and diseases. These signals are also used for the analysis of biological systems in the healthcare industry.
As the medical signal data is highly unstructured and massive, using BDA for processing and understanding this data, is one of the best ways to use it for medical treatment facilitation.
Medical or biomedical signal analytics is a very recent domain in the healthcare industry and is one of the most promising trends as well.
Replicating the human genome used to be an extremely complex and costly task back in the days when computational power was limited and sequencing technology required for the task was primitive.
Computational biology faces many challenges in this regard, such as care delivery to a large pool of patients and implications of current healthcare policies.
Now, you might think – “Why does it matter so much?”, and the answer is – “Because, in the USA alone, 96 out of 100,000 people die every year from conditions that are considered treatable”, and having the right information at the right time can save their lives.
BDA can enhance the exploration and discovery of patient data in a comprehensive manner and draw valuable insights from this data for:
Manual data processing is vulnerable to redundancies and average quality results. Further, in a complex organizational setting, such as healthcare, data, and data processing algorithms can fall prey to bias that might creep in intentionally or unintentionally. This can further degrade the analysis results.
Big data analytics has an immensely reliable and efficient data processing power that always yields the truest data insights, without any bias. Using BDA for improving algorithms and data processing in medical and healthcare will not only optimize the overall services but will also improve the R&D.
There are many use cases of patient data privacy and protection in the healthcare industry, such as:
The hackers are becoming increasingly focused on disabling the public infrastructure services such as healthcare by data breaches and hijacks, that disrupt the entire system.
Using big data analytics for enhanced system security and improved data protection for keeping patient data private comes with various benefits, such as:
By now you must have realized that BDA finds massive and extremely crucial applications and business use cases in the healthcare system.
It is being adopted at such a rapid rate that the experts are calling the trend the “gold rush of the fourth industrial revolution” in healthcare.
From offering rich insights stemming from reliable institutional data to facilitating data from all over the world – big data analytics is going to be a game-changer in the healthcare industry and is all set to revolutionize it.
From digitization to automation, and innovation to testing – there is hardly a medical sphere where the technology doesn’t seem to spur positive changes and generate promising results.
However, the “three Rs” are extremely crucial for getting the best value out of big data analytics in healthcare:
To get started with your big data analytics journey and to know more, schedule a consultation today!