According to marketresearchfuture, the global big data in the healthcare market is expected to reach US$ 17,278.13 million by 2022, and the market is projected to grow at a CAGR of 20.69 % during the forecast period 2015-2022.
Big data in healthcare is overwhelming not only because of its volume but also because of its diversity (clinical and financial). By understanding the trends and the patterns within the data, healthcare systems can improve the quality of care of patients and curb healthcare costs. The pharmaceutical and biotechnological companies are harnessing the power of big data for product cross-selling, financial risk management, regulatory compliance management, and others. Big data analytics in pharmaceutical manufacturing will also help to better forecast production demand, understand the plant’s performance, and provide faster support services to the customers.
The increasing supply of health-related data from various sources has the potential to transform the healthcare delivery system, reduce costs, improve patient outcomes and provide value-based care. The volume of healthcare data accounted for over 700 Exabytes in 2017 from 153 million in 2013 and is projected to grow to 2,314 Exabytes by 2020.
Organizations are employing analytical tools and artificial intelligence and machine learning techniques on this growing pool of data to derive data-driven insights to reduce healthcare costs, enhance revenue streams, develop personalized medicine, and manage proactive patient care.
PRNewswire states, Global big data in healthcare market was valued at over $ 14.7 billion in 2018 and is projected to grow at a CAGR of around 20% to reach $ 42.8 billion by 2024 owing to increasing adoption of Electronic Health Record (EHR), control healthcare spending, advance patient outcomes, etc.
Big data in the healthcare market can be segmented based on component, deployment, analytics type, application, end-user, and region. Based on components, the market can be segmented into software and service. On-premise is the dominant segment, however, the cloud segment is expected to grow at the highest rate during the forecast period owing to the myriad of benefits, such as efficient resource utilization, low maintenance and no capital cost, offered by cloud deployment.
Health data has been growing at unprecedented rates, driven by a fall in storage costs, the emergence of cloud storage, growing regulatory mandates, and the increasing government initiatives to promote the adoption of healthcare information systems. The increasing adoption of wearable devices, at-home testing services, and mhealth applications that are empowering patients to proactively manage their health are further contributing to the pool of personal data. The availability of large volumes of health information has paved way for massive advances in clinical research, the development of precision medicine and clinical decision support tools, quicker drug discovery, and more detailed view of population health, which has opened new arrays for managing chronic diseases.
Analysis of big data is already proving critical in building accurate models of disease progression and providing personalized medicine in clinical practice. It has also facilitated the evaluation of the impact of health policies and improved the efficiency of clinical trials.
In this article, we will discuss the top 5 ways Big Data can help and change the Healthcare sector.
- Track Health Activities
- Better Patient Care
- Minimize Human Errors
- Improved Healthcare
Track Health Activities
A healthy patient is the desired goal of doctors. Healthcare organizations are focusing more on the continuous monitoring of patient’s vitals with the help of sensor data collection. This helps hospitals to minimize the patient’s visits since hospitals can identify potential health issues and provide care before the situation goes worse. The data can be obtained by basic wearables that can detect the patient’s sleep, heart rate, exercise, distance walked, etc. New medical innovations/devices that can monitor the patient’s blood pressure, pulse Oximeters, glucose monitors, etc. can provide patients data to analyze and provide treatments proactively, monitor, and track their health activities.
Hospitals identify Big Data technology as one of their cost-saving options. Through the collection and analysis of large amounts of data, healthcare organizations will find quantifiable ways to improve performance and efficiency without spending more than being allocated. One of the advantages of using big data’s predictive analysis technology is that hospitals and clinics will be able to estimate and allocate the proper staff to deal with patients. This saves money and reduces emergency room wait times when a facility is understaffed.
Better Patient care
Health systems are turning to Healthcare big data are providing the insights necessary to drive a higher level of personalization. The current health system is adopting the new norm of patient convenience and, personalized care. By digitizing hospital records, the perfect data can be accessed to understand the pattern of many patients. By doing so, better patient care can be delivered by the hospitals and provide an insight into corrective measures to reduce their frequent visits.
Minimize Human Error
Big Data can be leveraged to analyze user data and prescribed medication. This leads to minimize errors such as prescribing the wrong medicine or dispatch a different medication by mistake. Big Data analysis can identify and alert on potential out-of-place prescriptions to reduce mistakes and save lives. Medical Professionals with huge walk-in patients daily can use this technology to minimize errors.
Big Data is the key to advancement in Healthcare. Artificial Intelligence can be used to Analyse volumes of data within seconds to find solutions for various diseases. Such advancement will not only be able to provide accurate solutions, but also offer customized solutions for unique problems. All of this adds up to an incredible amount of information, spurring health systems to adopt big data systems and technologies to effectively collect, analyze, and take advantage of this information.