How Big Data is Changing Healthcare

Thousands of hospitals, private practices, universities, and pharmaceutical labs are constantly working to improve patients’ lives and identify innovative healthcare opportunities...and it’s all resulting in petabytes of data. Data from patient studies, data from health records, even data from medical devices — all collected, analyzed, and used to make daily breakthroughs in diagnostics, medicine, and patient care.

If you think leveraging this volume of big data seems complicated, doing so in a way that maintains quality and manages the sheer volume must seem next to impossible. However, organizations who fail to find a way to harness the potential of big data are losing out — and potentially failing patients. By understanding the ways big data is changing the healthcare industry and how to effectively manage an accurate database, your organization can begin harnessing the benefits of the digital revolution.

What is big data in healthcare?

Big data in healthcare refers to the extremely large sets of healthcare data amassed from a wide variety of sources. The data can come from a myriad of sources including electronic health records, pharmaceutical research, genomic sequencing, medical devices, RPM wearables, insurance companies, physicians, hospitals, and more. 

These large data sets are so massive, complex, and varied that managing them is nearly impossible with traditional hardware or common data management methods. Cloud-based solutions help to establish a big data infrastructure within a scalable environment. When siloed data sets from various sources, different environments, and different formats are integrated and analyzed, this has a profound effect on the healthcare industry.

9 ways big data is revolutionizing healthcare

Advanced patient care, innovative new treatments, new medications, and other healthcare breakthroughs rely on making informed medical and financial decisions based on actionable data analysis and insight. The breadth and wealth of data-driven insights that could be made available across healthcare data providers, caregivers, and administrators enables an ever-increasing quality of care to patients. 

Using big data analytics to deliver evidence-based information will increase efficiency, define best practices, and decrease costs, among many other benefits. 

1. Better patient tracking 

Remote Patient Monitoring (RPM) solutions are already creating dramatic improvements in patient care by monitoring users outside typical clinical settings.
Ingesting, verifying, and organizing that data is crucial to the success of these systems. RPM data sets—properly managed—are reducing hospital readmissions, improving in-home care, and more. The opportunities to also use that data for future research and development is limited only by the systems that are created and used to manage it.

2. Improved diagnoses 

One of the greatest challenges to diagnostics is proper and timely disease identification. Early detection and differentiation, as well as improved care, is possible with big data technology that offers tools like predictive analytics. Data mining and analysis help to identify the cause of illnesses and reduce life-changing or life-shortening consequences. 

3. Improved treatment of opioid addiction

Big data is tackling opioid abuse head-on. With big data, machine learning and analytics are able to identify patients at high risk of opioid abuse and opioid-related death.

Big data helps to reverse medication events by detecting medication errors and flagging adverse reactions. Using petabytes of pharmaceutical and insurance data, data scientists are able to identify risk factors that predict opioid abuse tendencies. 

4. Faster development of treatments

Data-driven medical and pharmacological research is leading the discovery of new treatments and medicine for faster cure development. The need to observe variations in the human genome, as well as the varied nature of cures, demands that big data uncover key correlations within large sets of data patterns. Machine learning allows big data to study the genome and apply the correct treatment. This process works with non-genetically derived illnesses as well.  

5. Reduced fraud

The healthcare industry is one of the most vulnerable industries to data breaches due to the sheer amount of patient data it has access to. It is heavily reliant on proper security and advanced technology to mitigate risks associated with the extremely valuable and personal data within its possession. 

Healthcare organizations can use data management tools to quickly and efficiently identify threats and errors. Comprehensive data management solutions identify changes in network traffic and detect cyber-attacks and other suspicious behaviors like inaccurate claims.

6. More efficient medical imaging

Storage of medical imaging documents is costly, while their examination is tedious and relies on highly skilled professionals. Big data analytics for healthcare changes the nature in which these images are now assessed. 

Big data allows physicians to make a more accurate diagnosis by identifying specific patterns and offering specific numeric outputs. Machine learning algorithms can analyze a vast number of medical images at an exponential rate, saving both time and money.

7. Better healthcare staff scheduling models

Machine learning analyses uncover relevant patterns in visit and admission rates to solve key gridlocks and inefficiencies. Big data offers predictive solutions that are able to anticipate visits and admission rates. These solutions reduce labor costs and improve customer service, as well as reducing wait times and providing better quality care.

8. Reduced costs

Without proper data tracking and management, the healthcare industry is prone to costly and wasteful errors that affect both the organization and the patient. The accuracy and efficiency of big data enable a multitude of cost-saving opportunities for the healthcare industry.  

Healthcare data analytics can revolutionize business intelligence by uncovering usage patterns, offering supply chain analysis, enabling performance monitoring, and ensuring more strategic decisions. Organizations that were able to quantify their gains from analyzing big data reported an average 8% increase in revenues and a 10% reduction in costs.

9. Efficient electronic health records access

Due to the number of electronic health records—and, consequently, the multitude of demographic, historical, and medical information—digital record integration is a necessity. Big data has been a key player in not only minimizing paperwork and replication, but also reducing office visits and lab tests as a result of interdepartmental patient alignment. 

With big data, records are more easily available in both the private and the public sectors, within secure information systems. Additionally, warnings and reminders are now automated and immediately notify both patients and doctors of key information like prescription tracking.

Healthcare’s big data “ocean” ecosystem

Healthcare and pharmaceutical organizations are at the forefront of major transformation and now demand the use of advanced analytics available from big data technology. The healthcare industry produces massive amounts of data, all of which has the capability to improve the quality of healthcare solutions and invigorate the healthcare ecosystem. 

More organizations are using data lakes — repositories of data in its raw format — because they allow for easier storage of disparate data types than traditional data warehouses. As the costs of cloud data lakes drop and their reliability grows, organizations can now take advantage of are offered scalable, cost-effective, and regulation-compliant solutions.

Because of the massive amount of data that the healthcare industry generates, data lakes are becoming veritable data “oceans.” Many organizations are already investing ample resources in assembling some of the largest data repositories any industry has ever seen:

• As of 2016, Royal Philips has aggregated data from 390 million medical records to open access to a massive collection of information for healthcare personnel to obtain critical actionable data
• The National Institute of Health established Big Data to Knowledge (BD2K) to bring big data to researchers and clinicians and empower healthcare providers to improve patient care, reduce costs, and offer information for disease cures and prevention. 
• Open PHACTS, a platform for researchers and personnel, offers access to pharmacological data to allow users to extract actionable insights and make important decisions on complex pharmacologic matters.

Challenges of big data in healthcare

Data management, tracking, storage, accessibility, costs, and analysis are unanimous challenges across any data-driven industry. However, the healthcare sector can benefit most from a big data solution due to the amount of confidential data produced daily. For that reason, it’s important to understand how this sector is addressing and responding to big data challenges relative to the five Vs of big data: velocity, volume, value, variety, and veracity. 

Velocity challenge: Time is more than money in healthcare

Data velocity is concerned with how quickly big data is being created, moved, and accessed. The challenge is ensuring all datasets stream into the server efficiently, with a short delay time. While some data sets—like readmission reports or patient collection rates—are much slower, other data sets—like patient vital signs—must be up-to-date in real-time.

Cloud-based integration platforms have the ability to virtually consolidate heterogeneous databases. Performing data quality controls and corrections across these unified databases makes processing data faster and smoother. In addition, by helping synchronize data across clinical systems, these platforms have a positive impact on clinician decision-making, which promotes quality patient care.

Volume challenge: Bigger big data than any other industry

The amountof available data is experiencing unstoppable growth, especially in the healthcare industry. More sources of data and more complex, larger datasets contribute to the increasing volume. Organizations will need to find solutions that can handle the significant load of information without slowing down critical functions, like provider communication or electronic health records access.

Big data integration solutions help organizations move large data sets with minimal configuration requirements at a relatively low cost. Healthcare organizations, like AstraZeneca, could experience getting twice the value for half the cost while building a global data lake.

Value challenge: Data quality becomes vitally important in healthcare

The value of big data relates to whether the data can produce any actual and meaningful return on investment. Because of the size and complexity of healthcare data, deriving value from analytics relies on specific use cases like charting revenue loss, identifying a specific patient population, or reporting on performance. 

Value is available in the form of improved business efficiencies, strategic decisions, and better outcomes. However, organizations must adhere to governance principles, establish IT standards, and work with qualified data scientists to identify the correct insights and apply them to the organization. In this industry, the cost of bad data quality could be potentially life-threatening.

Big data integration solutions deliver a holistic view of patient data, allowing the business intelligence teams to look for actionable patterns. For example, well-integrated big data can predict which members are likely to un-enroll from their plans within a specific period of time. Predictive analytics also help with planning patient assessment timelines more efficiently. 

Healthcare organizations can use these data patterns to determine the appropriate time for patients to repeat assessments, and organizations can avoid wasting time and money by not scheduling tests too soon. 

Variety challenge: Data comes from a variety of sources, in a variety of formats

Variety is a challenge posed by the many different types and sources of big data that exist. New and diverse data formats, contexts, and types are continually produced and act as a major barrier to key insights about patients and operations. 

Moreover, data sets within separate locations make it difficult to merge big data into conventional databases. Data sets that cannot be handled through traditional processing techniques like manual preparation or ETL, require application programming interfaces (APIs) and new standards like Fast Healthcare Interoperability Resources (FHIR).

Open source big data technology integration solutions and data services have the ability to support proprietary data formats. They can also be configured to streamline migration processes that offer zero disruption to customers, meet or exceed SLAs for claims turnaround times and compliance standards, and provide the ability to scale and process more transactions.

Veracity challenge: Healthcare relies on trustworthy data

Veracity is a challenge that refers to whether big data and its insights can be trusted. Insights that have been derived from data that is biased or incomplete cannot be utilized. 

Increasing data integrity is a continual struggle for providers as data quality becomes compromised with unstructured data inputs. With data governance frameworks and data quality standards, healthcare organizations can ensure standardized, ready, clean, and complete data.

Cloud data preparation solutions make it possible for users to understand and interact with the data on their own. Healthcare organizations can accelerate time-to-insight by more than 50%, which enables them to ingest data faster and more efficiently in order to target communications with the right audiences.

Learn more about big data in healthcare

Data's influence on the healthcare industry is undeniable. With solutions like IT modernization and cloud environments at the forefront of the big data transformation, integration is becoming ever more critical to take advantages of the benefits that data can provide the industry.

While big data is already revolutionizing healthcare, many organizations are still not sure how to jump into adoption due to the many challenges such as privacy, security, siloed data, and costs. Nonetheless, the opportunities and potential of adopting solutions are both available and accessible.

Talend is helping healthcare organizations harness the power of big data by providing solutions that offer more access to better data. From data integration to governance, storage to analytics, Talend Data Fabric is helping healthcare organizations harness the power of big data.

Learn more about how data professionals in the healthcare industry are using Talend to benefit their organizations. Then, try Talend Data Fabric to experience the full suite of data integration and data integrity apps for yourself.