By Jared Rhoads and Lynette Ferrara
By following six essential building blocks to developing data-driven initiatives, organizations will be in a better position for using their data to improve patient care, reduce costs, and boost performance.
Hospitals and health systems have more reasons-and more incentives-than ever to become data-driven. They are expected to take on more responsibility for improving the quality and safety of care, achieving better care outcomes, and tackling healthcare cost inflation.
In response, hospitals are finding new and productive ways to use data. For example, new health intelligence applications have been adopted by many hospitals to analyze data from disparate sources spanning the clinical, operational, and financial domains. Executives and managers can use these applications to create online performance dashboards and run reports on demand.
There are many data-intensive applications on the horizon that are even more exciting. But tomorrow's data-driven initiatives involve even larger amounts of data that are varied in nature and come from disparate sources.
To be prepared for industry changes and the influx of new data, healthcare leaders need to assess their approaches to managing and using data-and ensure they have the fundamentals covered in six key areas.
Data governance plan. This plan describes how the organization will collect, maintain, protect, and curate data assets. It also sets the expectations for the policies, standards, and business rules for using data.
A best practice is setting up special competency centers, or centers of excellence, that integrate analytics across the enterprise and participate in decisions about key data-related matters.
Data acquisition. Good data acquisition means ensuring that data are captured in a usable form. Best practices include consistent documentation of metadata and classification of data elements. Standard taxonomies for demographic fields and medical codes should be used. New tools can help with the technological challenges of capturing and processing unstructured data. Finally, for privacy and security, patient records should always be properly de-identified.
Data sharing. Organizations need to collaborate and cultivate relationships that encourage sharing data across the provider, plan, and life sciences communities. One new approach is cloud computing, which enables hospitals to migrate large amounts of data onto a temporary platform and run high-powered analytic tools or reports. Organizations only pay cloud service providers for the computing resources they use, thus avoiding large capital expenditures for servers.
Data standardization. Delivery organizations can learn from research efforts that have pioneered new solutions for standardizing data. At the National Institutes of Health, for example, the Biomedical Translational Research Information System translates data from different sources into a standard structure and language so that it can be managed and analyzed more easily. This allows researchers to query multiple data sources at once and get more comprehensive results.
Data integration. Data integration is the merger of data from internal and external data sources into a single, patient-centric data structure optimized for analysis. Examples include the merger of patient demographics, conditions, procedures, drugs, and observations from an electronic health record (EHR), along with lab values and diagnostic results from other clinical systems.
Analytics. Analytics can help monitor, predict, and optimize the financial and operational performance of a hospital by allowing areas like staffing, admissions, and reimbursements to be analyzed in depth. It also can improve clinical performance by assisting with clinical decision support, comparative effectiveness research, patient safety, and compliance with care protocols.
Despite the successful implementation of a new EHR system, UW Health, the academic medical center and health system for the University of Wisconsin, wanted to find a way to harvest more meaningful information out of the reporting database, despite some inherent complexities and quality issues with the data. Users spent more time extracting, transforming, and loading data than analyzing it and taking action based on their findings.
The answer: a new Health Information Management Center that is staffed with data warehousing, business intelligence, and subject matter experts from across UW Health.
The health system's data governance strategy establishes more accountability for information quality by putting data owners in charge of approving business measure definitions, formulas, and business rules. To improve data acquisition, automated tools have been put into place to handle tasks, like loading and error checking, with minimal human intervention.
Another key part of the solution is the data mall, which consists of a series of interconnected data marts. This enables faster access to more consistent information across the enterprise.
Not every provider organization and independent practitioner has the resources, scale, or expertise to launch their own data warehousing and analytics program. But large and medium-sized institutions should be able to assemble a compelling business case. The winners in the coming age will be the organizations that use larger, faster, and more disparate data sets to generate a competitive advantage.
Jared Rhoads is a senior research analyst, CSC Global Institute for Emerging Healthcare Practices, Waltham, Mass. (email@example.com).
Lynette Ferrara is a partner, CSC Global Health Care Sector, Falls Church, Va. (firstname.lastname@example.org).
This article is excerpted with permission from the following resource: Rhoads, J., Transforming Healthcare Through Better Use of Data, CSC Global Institute for Emerging Healthcare Practices, Falls Church, Va., March 2012.
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