As the healthcare transitioned to the modern era of technology, it has historically generated incredible amounts of varied data. Most of these data come from patient/client record-keeping, compliance and regulatory requirements, and patient care. To this day, most data in the industry is stored in hard copies but digitization is getting greater momentum as the need to store an ever-larger amount of data becomes more evident.
Data in the US healthcare industry has grown from an approximate of 150 exabytes (10¹⁸bytes) in 2011 to a zettabyte (10²¹bytes) levels today and it’s still growing. This ushered in Big data in healthcare and allied industries, with data sets so large and complex no single system can meaningfully manage all that information. The diversity in the data sources and formats add to the inherent complexity. These data sets are made up of data collected from clinical data, ERPs, machine-generated diagnostic data, and even those coming from social media and other platforms of live customer feedback.
9 out of 10 healthcare professionals have switched to adopting electronic health records
With the advent of Big Data Analytics, the promise of improved quality of healthcare services, smoother transactions, better client/patient management, and clinical decision support has now been more attainable than ever. With the digital collection, storage, and analysis of healthcare big data, advancements within healthcare and allied organizations at multiple levels are remarkably significant. Improvements in early detection and diagnosis, treatment methods, fraudulent claims detection, patient scheduling, health risk management, all contribute to an overall enhancement of patient care and experience. No other industry contributes to directly saving human lives with the use of Data Analytics than that of Healthcare.
When healthcare industry leaders opted to digitize their data, the first apparent benefit that these companies received were the savings. It has been estimated that in the US, companies who made use of Data Analytics are poised to garner $300 billion in annual savings, a significant 8% of the total national healthcare expenditure. These savings are primarily divided between three sectors: clinical operations, medico-pharmacological research, and general public health.
Clinical Operations - Healthcare consumers require exceptional and personalized care and treatments. Data Analytics addresses this area by improving treatment relevance, efficacy and augmenting general patient care and satisfaction. Together with driving down significant costs in terms of resources, Data Analytics has become an integral component of how healthcare providers operate.
Medical R&D - Predictive data analysis and algorithms are the most utilized techniques in this sector. The development of new medicine and medical equipment are streamlined with a faster and leaner data pipelines.
Public Health - Advanced pattern analysis helps identify disease outbreaks and epidemics to quickly combat spread. Faster vaccine development also benefits from analytics, allowing pharmacologists to catch-up to rapidly evolving viruses such as the flu. Data analytics also provides useful information to improve access to public healthcare services, identify obscure issues that need addressing, and prevent unforeseen public health circumstances.
Besides these three main sectors, Big Data Analytics also advances the following areas: preemptive fraud detection, patient medical profile analytics, and remote medical machinery monitoring.
1/3 of all healthcare consumers make use of apps and other similar technology to track their healthcare charges and costs
Data Analytics’s biggest impact across all these healthcare areas is the identification of individuals who consume health resources and services at a markedly greater rate than everyone else, at high risk of harmful outcomes. By focusing on the reason why and how they consume at elevated rates, companies and organizations can meaningfully give counsel on how these people could become less reliant on healthcare resources, while at the same time improving their health and well-being. This eventually reduces readmission and save resources at multiple levels.
According to IBM, Big data is defined by three attributes: It’s big data if it has large volume, rapid velocity, and significant variety. As stated above, healthcare data is still increasing, with existing physical data converted into digital ones, and the generation of new data contributing to this increase. The varied formats and data types also contribute to this dilemma. Established and emerging technologies such as cloud infrastructure and AI are helping alleviate this data crunch. Scalable service options, as well as massive parallel processing, enables companies to accommodate the increasing data requirements.
Only 16% of office-based healthcare professionals have the service option of viewing, downloading and transmitting electronic health records
With volume comes issues with regard to velocity. Processing large amounts of data from generation to feature implementation can be sluggish. The continuous influx of new data can lead to bottlenecks in data pipelines further decreasing data velocity. Thankfully, AI and efficient algorithms combined with powerful cloud computing solutions and Agile methodologies have paved the way for rapid data processing and analysis cycles. Despite these innovations, these methods are yet to be universally integrated by all healthcare providers.
Another challenge in applying Data Analytics initiatives are compliance issues with HIPAA/HITECH regulations. These are regulations that protect the security and privacy of the information of patients/clients. Pooling the resources and brain-trust to develop Data Analytics within an organization while allocating resources to avail services compliance specialist and other related costs and eats up a lot of time, time that translates to a loss in market share.
Healthcare companies and organizations have opted for outsourced data services from external consultants that have expertise in utilizing Data Analytics tools and techniques to improve their clients’ business process. Advanced data management and best-in-class methodologies yield noted success stories for companies who opted for Big Data Analytics but do not have the IT infrastructure to pull-off its full integration.
96% of privately-owned healthcare institutions have certified IT support for their data management
There are a lot more challenges facing Data Analytics in the Healthcare industry. Thankfully, people behind data analytics innovations are working to determine new solutions, with the help of industry experts, data scientists, and healthcare advocates.
Data Analytics can usher in a new age for a more accessible, affordable and ultimately effective healthcare industry. Insights taken from data collected from the patients themselves are now transformed into better ways to take care of them. With more and more healthcare organizations opting to maximize their use of data, either through internal initiatives or through the help of strategic partnerships, these will put increased momentum into a more data-driven transformation.
To that end, the challenges highlighted here, despite being just small parts of a bigger picture, need to be addressed first. By that time, the benefits will outweigh the challenges and costs; all that it takes is bold, decisive action towards enhancing Big Data Analytics integration within the healthcare industry.