Recently, as the world has watched the coronavirus crisis unfold, the power of big data to track, illustrate, and inform has taken center stage. A compelling example is the near real-time map created by Johns Hopkins Center for Systems Science and Engineering (CSSE) that shows all the cases as the disease spreads across the globe.
With far-reaching implications for healthcare, big data enables deep analysis of data from a myriad of sources – electronic medical records (EMRs), claims, wearables, Internet of Things (IoT), even social media and patient-reported data. What’s more, it creates the foundation needed to take advantage of Artificial Intelligence (AI), Machine Learning (ML) and Natural Language Processing (NLP) to analyze and draw conclusions from large amounts of data at speeds created a few years ago. The level of investment spotlights its potential: The global healthcare big data analytics market was worth $19.6 billion in 2018 and is projected to reach $47.7 billion by 2024, with a CAGR of roughly 16 percent from 2019-2024.
To succeed with big data initiatives, healthcare organizations must manage the enormous – and exponentially growing – amount of data flowing through their enterprise. In order to use data effectively, healthcare organizations must adopt the technology and tools needed to securely store data, while also making it accessible to provide better patient care to improve outcomes.
Understanding Key Capabilities
To map out the best strategy, healthcare organizations need to assess the key capabilities needed to meet their goals for big data initiatives.
Storage: By its very nature, big data requires extensive, reliable, cost-effective storage. For many organizations, a cloud-based or hybrid cloud and on-premise approach offer the most flexibility. In the cloud, resources for data management and analytics can be scaled up or down as required.
Volume: As data sharing among payers, providers, health systems, and government agencies increases, the sheer volume and variety of data, such as claims, EMRs, lab systems, and IoT available is incredible. Healthcare organizations need to employ effective tools for working with huge amounts of data at high speeds – real-time and near-real-time – using features, such as replication, horizontal scalability and high fault tolerance.
Data Quality: Closely following data volume is attention to quality using specialized tools to aggregate, normalize and draw insights from large and diverse data sets. The impact of data quality is illustrated by something as fundamental as identifying the correct patient. According to Black Book Research, roughly 33 percent of denied claims can be attributed to inaccurate patient identification, costing the average hospital $1.5 million in 2017.
Silos: For many healthcare organizations, data exists in silos and there is little, if any, collaboration across groups. Organizations benefit tremendously from an enterprise-wide view and collaboration around the purchase, management, and sharing of data, as well as the insights, gained.
Speed: As an organization offers more analytics tools to its customers, the expectations for ease of use and speed increase. It’s inevitable that today’s user expectations are shaped by their daily digital experience. For example, in 2006, the average online shopper expected pages to load in four seconds or less. Today, one in five users expect pages to load instantly. It’s no different for healthcare professionals tasked with using analytics tools to measure, monitor, and act upon clinical, contractual, and operational metrics – they expect instant and accurate results.
Platform Approach to Infrastructure
With organizational goals and key capabilities in mind, healthcare organizations achieve success for their big data initiatives using an enterprise-wide approach that takes technology, process, and data into account. This approach leverages centralized data management and analytics, optimized clinical, financial and administrative processes, and effective engagement of all stakeholders. A key benefit is the option to build and expand incrementally to enable predictive, prescriptive, and cognitive analytics. For example, organizations can start with the foundation for data management, and then add processing and quality assurance along with the presentation, visualization, and analytics capabilities.
Over time, new data and new tools can be incorporated into the existing framework to address new use cases – without duplicating ramp-up time and costs. With this approach to centralizing data management, storage, access and analytics—new benefits emerge, which can include:
- The organization’s own data can be continuously augmented with new data sets, which can then be used and reused enterprise-wide across many projects and use cases. Central management of data as well as any associated fees and subscriptions reduces overall costs.
- The framework and processes for data consumption, processing and quality assurance can be standardized and optimized. Readily available technology tools provide the scalable, secure environment needed to ensure data coming from external sources is automatically checked for accuracy, completeness, redundancy, duplicity and logical correctness.
- Tools for advanced analysis, such as NLP, can be put in place for use by multiple groups.
- Tools for communication and collaboration can make each project’s results, guidance and insights available to other groups to be applied in new ways on future projects.
Putting Big Data to Use
In today’s rapidly changing environment, it is a wise choice for healthcare organizations to support its big data initiatives with a technology platform that enables quick integration of large, disparate data sets into a common data model, analytics to support a wide range of use cases and the ability to scale flexibility to meet business demands. By putting processes and tools in place to manage the accuracy, completeness, consistency, uniqueness and timeliness of data, organizations can maximize the impact on its use cases for population health, clinical gap closure, clinical alerts and point-of-care decision support. Further, this approach provides the right foundation for leveraging AI, ML and NLP technologies. Ultimately, realizing the promise of big data, organizations will be able to deliver more actionable data at the point of patient interaction, offer more preventive health intervention, and provider more evidence to improve care – resulting in better care outcomes across communities.
Abhay Singhal is Sr. Vice President Provider and Healthcare Services at CitiusTech.