Data analytics is the major driving force behind modern innovation, revolutionizing the way people track statistics and vitals. Leveraging technology to analyze and understand the petabytes of data has been fundamental for businesses in every industry to become more productive and efficient. One of the key areas where big data is making major transformations is the healthcare sector. With an ever-expanding population, increasing average human life span, rapidly changing models of treatment delivery, necessity to manage patient care, and innovation in health care technologies, the demand for big data analytics in healthcare has advanced over the years.

Big data in healthcare industry refers to the complex and massive volumes of information from a myriad of sources, including pharmaceutical research, genomic sequencing, electronic health records, physicians, RPM wearables, etc., collected through the adoption of digital technologies. For years, collecting, analyzing, and managing the huge amount of data gathered for medical use has been costly and time-consuming. However, utilizing cloud-based solutions can help establish a big data infrastructure within a scalable environment, which can be further integrated and analyzed to create comprehensive healthcare reports and relevant critical insights. In simple words, the purpose of big data in healthcare is to assess methods and treatments faster, enhance the patient experience, and increase administrative efficiency. Besides raising profits, and cutting overhead costs, big data and analytics assist healthcare providers to useful insights for predicting pandemics, avoiding preventable deaths, curing diseases, picking up warning signs at early stages, and improving quality of life. 

According to TechSci Research report, “Global Big Data in Healthcare Market By Component, By Deployment, By Analytics Type, By Application, By End User, By Region, Competition, Forecast & Opportunities, 2024”, global big data in healthcare market was valued at around USD 14.6 billion in 2018 and is projected to grow at a CAGR of 20% to reach USD 42.7 billion by 2024 owing to increasing adoption of Electronic Health Record (EHR), control healthcare spending, advance patient outcomes, etc. Health related data is growing at a rapid pace driven by the government initiatives to promote the adoption of healthcare information system and introduction of cloud storage. Moreover, increasing adoption of mobile health apps and wearable devices, are further stressing on the need for managing large amount of data to obtain critical information, thereby driving the demand for big data in healthcare sector. Additionally, elevating popularity of electronic prescriptions eliminates the need for paper-based prescriptions, which is further positively influencing the growth of the market.

Here are the six benefits of big data analytics healthcare:

·         Tracking of Patient Health and Identifying Risks

Big data analytics along with the Internet of Things (IoT) enables researchers and healthcare providers to visualize a patient’s overall health profile throughout their life. Identifying potential health problems and risks at an earlier stage by continuously monitoring the body vitals with sensor data collection can help to improve patient’s health and allow hospitals to predict future admissions trends, plan resource allocation utilizing online data visualization and improve overall patient’s care. 

·         Reduced Healthcare Costs 

Big data analytics helps increase the pace of the treatment in order to save both lives and healthcare costs. The precise diagnosis enables healthcare providers to make informed decisions and formulate the best treatment regimens in real-time. Data analytics tools can serve as a preventive approach for patients to reduce health care costs as they provide real-time health monitoring and send automatic alerts and updates when they are due for immunizations or lab work. Utilizing data analytics, clinicians and clinical pharmacists can co-manage drug therapies in real-time and assess the possible side-effects, additive toxicities, and drug interactions for better Medication Therapy Management (MTM), which would ultimately lead to reduced healthcare costs. Predictive analysis also allows hospitals and providers to ensure adequate medical supplies and accurate staffing. 

·         Improved Patient Engagement

Patient disengagement can lead to serious repercussions, therefore healthcare providers are utilizing big data analytics to gain valuable insights for better engagement strategies. Health literacy is an important part of patient engagement, which can help supplement patient knowledge of their conditions to make them aware of their responsibilities and risks. Thus, mobile applications, real-time health monitoring wearable devices, and other gadgets can help improve clinical trial participation, patient engagement, and responses. 

·         Reduced Human Errors

Often, the medical professionals tend to give wrong prescriptions or dispatch a different medicine, which be fatal for the patient. However, these errors can be reduced by analyzing the user data and their medical history. Sometimes the human error can occur in administrative roles, which can also be damaging in clinical settings. Machine learning powered by big data in healthcare can also help to tackle the problem of frauds by identifying abnormal patterns and outliners from individual providers based on the historical data, which can help insurers recover more losses.

·         Telemedicine

Smart devices are the future of telemedicine and they rely on big data. The “The Internet of Medical Things” and cloud health information systems signify potential health problems and real-time data on vital patient measurements such as blood pressure and heart rate, thus keep the high-risk patients out of the hospitals, cut down costs and allow patients to live a healthy life. Using electronic health records of patients, doctors can provide more accurate diagnosis and reduce health risks. In a way, combining the power of telemedicine with big data can reduce the number of unnecessary hospital visits, and alert providers, care givers and patients about their status if they require in-person care.

·         Advanced Disease Management

Expanding knowledge and understanding about various diseases utilizing the data-drive medical research can lead the discovery of new treatments and medicines for faster disease management. Machine learning allows big data to uncover key correlations and study genome to study the nature of some of the world’s dangerous diseases and then develop and test corresponding treatments. Moreover, data-driven genetic information and predictive analysis can play a pivotal role in development of forward-thinking therapies, prevent pandemics, and save lives.

Big data Analytics in Healthcare Market—Major Trends & Developments

·         One of the major providers of healthcare technology, CitiusTech is positioned as a “Leader” owing to its ability to drive transformational changes across the global healthcare value chain. Delivering next-gen digital solutions, deep healthcare domain expertise, and strong partnerships with Microsoft, IBM, AWS, and GCP has enabled it to the leadership position in the Everest Group’s Healthcare IT Services Specialists PEAK Matrix® Assessment 2021."

·         In 2019, the health IT giant Cerner launched its Learning Health Network to access a network of de-identified and standardized data and resources to support research efforts. In December 2020, Cerner announced its plans to acquire the health division of Kantar Group to create a leading data insights and clinical research platform and harness that data to improve efficiency of research across pharmaceuticals, life sciences, and healthcare at large.

·         Ehave Dashboard, which has been at the forefront of using data analytics to provide relevant insights to clinicians and patients are layering on new tools such as artificial intelligence and machine learning for big data management in mental healthcare for more efficient patient management.

Big data applications in Healthcare during COVID-19 Pandemic:

The big data analytics have helped organization highlight and reduce disparities among the patient population during the novel coronavirus pandemic.

·         Identification of infected cases

By assessing the collected data and travel history of individuals, the big data analytics helps in identification of the infected cases and undertake further analysis of the level of risks.

·         Identification of virus at early stages

The big data healthcare analytics enables to analyze and identify individuals who can be infected by virus in the future, which reduces the mortality rate and prevents further prevention of virus.

·         Identification and analysis of fast-moving diseases

Potentially handling appropriate information regarding the disease, big data analytics help to effectively analyze the fast-moving disease as efficiently as possible.

·         Faster development of medical treatments

The big data in healthcare assists in fast-tracking the development of future medicinal needs and helps in gaining insights of new pandemic with previously analysed data.

Big Data Challenges in Healthcare:

·         Data security

From phishing attacks to malware, healthcare data is subject to an infinite number of vulnerabilities. Thus, data security is one of the top concerns for most health care providers with constant hacking and security violations, that needs to be handled continuously. The leakage of highly sensitive patient data can prove costly to healthcare companies. One of the major healthcare cyber-attacks include Excellus BlueCross BlueShield, which exposed medical information of more than 21 million members for which it was penalised for $2.67 Billion dollars.

·         Data Classification

There is a need to classify massive, unstructured and heterogenous data so that it can be used effectively. Although the big data is ideal for modelling and simulation, it requires to be contextualized so that it can become more relevant to specific individuals or groups. Without proper structuring, analyzing and visualizing data can be challenging.

·         Cloud Storage

Just as the big data provides organizations of terabytes of data, it also presents an issue of managing the data under a traditional network system. In the era of high-speed connectivity, storing and moving large chunks of data can be a problem. Also, some cloud models are still in the nascent stages and basic Data Base Management System is not tailored for cloud computing. 

According to TechSci Research report, “Global Big Data Analytics Market By Deployment Mode (On-Premise, Cloud and Hybrid), By Application (Risk & Fraud Analytics, Enterprise Data Warehouse Optimization, Internet of Things, Customer Analytics, Operational Analytics, Security Intelligence and Others), By Component (Solutions and Services), By Organization Size (Large Enterprises and SMEs), By End Use Industry (BFSI, Healthcare, Government, IT & Telecom, Manufacturing, Retail and Others), By Region, Competition, Forecast & Opportunities, 2025”, global big data analytics market is forecast to grow at a compound annual growth rate of over 12% during the forecast period and surpass $ 87 billion by 2025. Big Data Analytics is a combination of various tools such as Hadoop and Apache. The main function of these tools is to collect, manage, organize, access and deliver structured as well unstructured data. Increasing expansion in IoT devices market and implementing AI solutions are some of the factors which are driving the growth of big data analytics. The major challenge with the big data analytics is that as data sets are becoming more diverse, there is a big challenge to incorporate them into an analytical platform. Another challenge is, there is acute shortage of professionals in the field of big data analytics.

Conclusion