How Big Data Is Transforming the Healthcare Industry
Data collection is an important part for every organization. It is especially important in healthcare – one of the largest and most complex industries in the world, which deals with massive amounts of data. The more data healthcare providers gather, the better the tools they need to manage it. And where traditional means of data processing fail today, big-data solutions can lead to life-saving outcomes tomorrow.
Big data technology is an efficient means of systematic extraction, processing and analysis of vast quantities of data, which could help healthcare providers derive meaningful information from the data acquired, develop new strategies, and organize themselves and their patients to ensure the best outcomes.
In this article, we want to highlight the ways in which healthcare organizations could utilize big data solutions to transform the entire healthcare industry.
Human error reduction
The way data is collected and stored today makes it almost impossible for patients, hospitals, and other healthcare providers to communicate properly, which can lead to wrong diagnoses, prescriptions, and other forms of human error with heavy or even lethal consequences. In the United States alone, about seven thousand people die each year as a result of a medication error, while hundreds of thousands experience adverse reactions to the prescribed medicine.
Big data can be used to analyze the patient’s EHR (medical history, allergies, etc.) and compare the data to millions of EHRs of other patients, considering the diagnosis. Such analysis enables healthcare providers to produce personalized medication insights, red-flagging any potential ineffective or counterproductive medicine to ensure the best patient outcomes. This will not only reduce the overall amount of human error, but make healthcare a lot more personalized, enabling doctors to provide more efficient, accurate treatment even with the brief amount of time they have for each patient.
It can be very hard for a healthcare facility to provide effective service while avoiding financial waste. On the one hand, if you have too many workers on a shift with low admission, you risk unnecessary labor costs. On the other hand, being understaffed on a busy day can lead to fatal outcomes.
One of the most important long-term effects of big data for the healthcare industry is its potential to reduce the overall cost of healthcare. According to research conducted by McKinsey & Company, system-wide implementation of big data solutions in the healthcare sector could account for $300 billion to $450 billion in total annual cost savings. Big data can help healthcare providers improve their staff management, boost overall employee output, and optimize resources allocation by applying predictive analytics to patient admission rates. The analytical capabilities of big data enables hospitals to catch relevant patterns and predict the number of patients expected at a given time period, enabling them to provide adequate staffing, inpatient bed availability, and keep better track of inventory. Big data can not only improve the cost-effectiveness of operational management in the healthcare industry, but also reduce patient waiting times and substantially increase the quality of service.
Health insurance companies can also benefit from the application of big data solutions by promoting various wearables and health monitoring systems. Thus, insurance companies can make sure that their clients are directly involved in the monitoring of their own health condition, are better informed, and spend less time at the hospital.
In addition to the cost-effectiveness big data brings to healthcare stakeholders, it can also drive the switch from a pay-for-service to a value-based care model. Instead of being rewarded for the number of medical procedures performed on their patients, healthcare providers will be incentivised to provide patients with better treatment, experiences, and outcomes, which will also lead to lower healthcare costs for the population.
With the development of telecommunication technologies, from smartphones to different wearables and wireless devices, telemedicine is becoming a widely common practice. While today it is used primarily for consultations and initial diagnosis, big data and predictive analytics can make it much more productive. In addition to improving diagnosis and remote patient monitoring, big-data empowered telemedicine can significantly reduce hospital readmissions and prevent unnecessary ER visits, which helps both healthcare providers and patients to save time and money.
Machine learning for medical data processing
Lab tests include a lot of different data from electrocardiograms to ultrasound scans, tomography, x-rays, and other images or videos. Given the advancements in today’s medical care, like the increasing frequency and resolution of MRI scans, storing all this data costs healthcare providers a lot of money, in addition to manual processing of every image being very time-consuming. According to International Data Corporation, the compound annual growth rate (CAGR) of healthcare data will be 36 percent through 2025, while the latest developments in big data, AI, and machine learning can drastically change the way things are.
Technological advances enabled healthtech companies to begin working on solutions that can process millions of medical images and videos, learning from this data, analyzing it, identifying specific patterns, and digitizing these images into machine-readable text. Such solutions could not only require significantly less resources to maintain, but become a more efficient and accurate imaging tool that improves diagnosis, provides thorough insights, and leads to better patient outcomes.
Improved epidemics control
So much can change from one epidemic to another, which makes it nearly impossible for scientists to keep up with the epidemic trends and predict outbreaks early enough to prevent them, instead of having to fight the consequences. This is mostly due to disease control centers receiving the important variables too late and lacking the necessary data-processing tools to get a step ahead of an epidemic.
Gathering real-time data is essential for the predictability of outbreaks. That is why so many countries today are utilizing big data and machine learning to enhance disease surveillance and population health management. These technologies combined can help scientists use geographic, environmental, and demographic data to analyze and prevent the causes of outbreaks. Disease control centers can access cloud-based EHR systems to monitor the spread of infectious diseases, analyze which antibiotics these diseases are resistant to, and share this data to international databases. Such a level of data analysis enables healthcare organizations to predict future risk trajectories, identify the cause of an epidemic, and provide early warnings of disease conditions to minimize the spread.
Advancements in science and product development
Big data can become one of the largest drivers of scientific advancements in the healthcare sector, helping researchers uncover many new learning opportunities and find new, more effective approaches to development and testing of new treatments for various diseases and fatal conditions. Big data will remove the guesswork in trial and error experimenting. Being able to process exabytes of data in mere seconds will aid research and development companies in discovering high-quality drugs and other health-related products at a faster rate. It will also drive the development of new IoT solutions to improve preventive care and enable more accurate, personalized treatment for every patient.
From hospitals, pharmaceuticals, and insurance companies to patients, everyone benefits from system-wide adoption of big data solutions in healthcare and the efficiency they bring in solving the industry’s biggest problems that could not have been properly addressed before. Big data enables stakeholders to gain a deeper understanding of how to approach data processing in a way that makes it much easier and cheaper while improving patient care, increasing the productivity of health professionals, creating better communication, and ensuring better outcomes.
The healthcare industry has only just begun its long and complex transformation process, and there’s still a lot of changes that lie ahead. Nevertheless, a lot of IT experts today are at the frontier of development, working to help healthcare providers worldwide to become the pioneers of data-driven healthcare.
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