How Big Data Can Transform Healthcare
With numerous incentives aimed at improving healthcare and constraining costs, health delivery organizations have more reasons than ever to become data-driven. However, while there’s tremendous buzz these days about harnessing Big Data to solve business intelligence challenges, healthcare organizations need to lay some basic cornerstones before tackling their data issues.
Most organizations have more data to work with than they realize, and the data landscape is rapidly changing. The size, scope and types of data continue to evolve rapidly, as do the tools. For health delivery organizations, the key to responding to this multiplicity of changes is to become skilled at leveraging data to improve patient care, drive innovation and enhance organizational performance.
Getting value from data
The winners in the coming age will be organizations that use bigger, faster and more disparate data sets to generate competitive advantage. Before moving forward, however, organizations should be aware of six key building blocks that help them identify competitive advantages and achieve better command and control over their data.
1. Data governance. Before embarking on a new large-scale data initiative, organizations should develop a clear data governance plan that describes how they will collect, maintain, protect and curate data assets. In healthcare, governance includes policies that ensure against unauthorized access of protected health information.
2. Data acquisition. Traditional data warehouses tend to contain highly structured data acquired from traditional in-house styles. There are many new opportunities, however, emerging from the acquisition of unstructured and semistructured data. For instance, healthcare providers can improve patient satisfaction by monitoring and analyzing data from social media.
3. Data sharing. Organizations with terabyte- and petabyte size data warehouses may think they have more data than they need. But to maximize the value of the data, organizations need to collaborate and cultivate relationships to share data across the provider, plan and life sciences communities. Health information exchanges with other providers can improve the continuity of care.
4. Data standardization. Despite numerous standards, relatively little interoperability exists. Organizations need to carefully select and adhere to common-data models so that data can be combined and compared. Doing this can deliver value, especially in medical research efforts.
5. Data integration. Data integration is the merger of data from internal and external sources into a single patient-centric data structure optimized for analysis. For example, data from physician electronic medical records may be combined with data from inpatient and administrative claims records.
6. Analytics. Once an organization aligns the other blocks — from governance to standardization — it can apply analytics tools to glean actionable insights. New tools go beyond standard reporting and business intelligence. They include 3D graphing and visualization, interactive interfaces, and animation, and can deliver insights valuable to both clinical and financial performance.
A Big Data revolution
The current generation of health information systems is based largely on data collected during medical encounters: visits, procedures and medication, lab and diagnostic tests results. In his keynote to the 2011 America Medical Informatics Association, Dr. Gregory Abowd warned that data generated outside the clinical setting will soon be as — if not more — important to care delivery.
Data warehouses, standard reporting tools and current analytics applications will continue to provide value for some time to come, but the next wave of data will be a highly disruptive force. The ability to cope with large amounts of data — which starts with these basic building blocks — will soon be a competitive necessity.
Jared Rhoads is a senior research specialist with CSC’s Global Institute for Emerging Healthcare Practices.