Healthcare is one industry with some of the most collated data available along with the most regulation surrounding data. So what is the state of big data in healthcare?
What is Big Data?
How do you identify and quantify big data, let alone know how to categorize and leverage it? It might help to know there are five “V”s associated with Big Data:
The measure for quantifying the amount of healthcare data you have is volume. There is more data than ever, thanks to advancing technologies and COVID-19 driving data collection. You have the overwhelming volume of clinical data associated with lab tests and physician visits, plus the administrative data related to scheduling, insurance, payments and payers.
Healthcare data keeps expanding in scope. As genomic and environmental data become more widely used, and data continues to become more personalized, thanks to wearables and the Internet of things, we can expect to see the volume of health data continue to grow exponentially.
Velocity, regarding big data, refers to both the blistering pace at which new data is being created thanks to technological advances, and the need for that data to be sorted, cleaned and analyzed — in real-time, when possible. Healthcare data is more than an EHR, it is multiple apps, portals, IoT devices and other sources generating and pouring data in at a tremendous rate.
As more and more avenues for data flow become commonplace, including the proliferation of medical devices designed to monitor patients and collect data across a variety of touch points, there will be greater and greater demand for the ability to analyze that data before transmitting it to clinicians and other stakeholders in the patient’s healthcare team.
Fresh sources of data are being added to health records all the time, and there is a pressing need to categorize, organize, and use this data effectively. There is now a vast diversity of data types in use by healthcare organizations every day.
Electronic health records, lab reports, medical devices — each data funnel collects different data, which can flow into a centralized system, go directly to a specialist for interpretation, or forward to a primary care provider for review. Standardizing and distributing the broad variety of data, collected daily within healthcare information systems, is a massive and critical challenge for continuity of care.
As population health and big data analytics are increasingly gaining ground in this data landscape, it leads to the combining of traditional clinical and administrative data with socioeconomic data, unstructured notes from physicians, and even social media data fed in by patients themselves.
Finally, healthcare organizations need to know they can depend on the trustworthiness and quality of their data for best outcomes and security. Stakeholders must subject incoming data to rigorous protocols to ensure it is accurate, complete, and correctly organized to fit into a standardized system. Digitization and automation play a large role in this task of ensuring data veracity.
The end-result of all the data is what it can deliver for an industry or organization. Healthcare data is incredibly rich, and also incredibly vulnerable because of its value. Organizations intending to invest in infrastructure designed to collect and interpret data need solutions that enable them to generate insights that can lead to measurable improvements.
Challenges of Big Data for Healthcare
There are four key challenges facing healthcare organizations regarding Big Data:
- Storage: How do you store that much data? On-site can be prohibitively expensive, meaning a shift to the cloud may be in order.
- Security: How do you keep the data safe? Maintaining security can be difficult when your patients’ data is being accessed by so many people.
- Accessibility: How do you make the data accessible? Locking down data opens the door for violations of data accessibility regulations, but failure to secure data brings with it the risk of data security and patient privacy violations.
- Usability: How do you make the data useful? Unless you have a plan for data analysis and reporting, your data will not help you.
Healthcare Goals for Big Data
Healthcare organizations have specific goals that require leveraging big data:
- Know their patients — medical history, familial history, social history.
- Build better pictures of patients’ conditions and comorbidities.
- Coordinate care between patients, caregivers, facilities (hospitals/labs) and providers (doctors/specialists).
- Identify at-risk patient populations across racial, socioeconomic, and gender divides.
- Implement value-based care to drive higher levels of incentive based compensation and better patient outcomes.
- Reduce costs of care for an improved bottom line and adherence to expectations from payors.
The Future of Big Data in Healthcare
Data is how healthcare organizations achieve the goals listed above. Particular points to focus on include:
The Internet of things and connected portable or wearable devices or medical equipment are making it easier to gather real-time data about patients and build a clearer picture of their day-to-day and hour-to-hour health conditions. This data can track compliance, plan next steps in treatment, and alert providers to potential emergency situations.
The ability to provide patient care services remotely is opening up ways to better service patients living in provider deserts, from quick and easy diagnostics of common conditions to prescribing medications and completing follow ups after a procedure or illness.
Comprehensive reporting, based on large data sets, can increase revenues through submission of verified data to incentive-based reimbursement programs. Targeting individual metrics and tracking them over time makes it possible to show improvements and qualify for a variety of revenue streams.
Improved patient outcomes are achievable by leveraging data for enhanced Quality of Care (QoC) and better care team coordination. Using big data and analytics can drive better decision-making for providers serving vulnerable patient populations or those in remote areas.
How Tangible Can Help
Integration Platform as a Service (iPaaS) from Tangible Solutions helps you find and leverage the right information in the world that is Big Data. Our iPaaS offering is a complete solution for data integration, and supports clinically integrated networks (CINs.) For more information, contact us today.