Dhaval Shah

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The shift to value-based payment is closer than you might think. Its sharpened focus-set against a backdrop of regulatory reform, industry-wide consolidation, and cost pressures-is driving organizations to optimize operations and find opportunities to be more efficient, save money, and gain deeper understanding of available data and outcomes.

Yet, it’s often data-or rather the lack of reliable, easily accessible and sharable data-that continues to be an obstacle. In fact, one fifth of healthcare CIOs said in the last year 

 an adverse event due to mismatched records. Simply missing a key data element not only affects patient care outcomes but also increases costs. For example, when discharged, high-risk patients require intervention, but if their phone number was not verified, they cannot be contacted, which often results in an unnecessary emergency department (ED) visit or inpatient admission and the 

: $930 per ED visit and $12,824 per inpatient stay.

There are also gaps for different stakeholders-health plans, physicians and care teams-who don’t have the data they need to deliver the most effective care. For instance, while 75% of health plan executives said electronic health records (EHRs) have everything physicians need, only 54% of doctors agreed. Moreover, 

of physicians say access to quality and performance measures specific to their patients is key to achieving value-based care.

In order to be successful under the expanding influence of value-based care models, organizations must be committed to long-term quality improvements, including quality measures and clean, reliable data. By taking a long-term, platform approach to data and quality management, organizations can create the right foundation for monitoring and improving quality metrics, managing risk and improving outcomes under value-based contracts.

Data Management is the Foundation for Long-Term Quality Improvements

There is no shortage of valuable data available from a wide range of sources. The challenge is to facilitate analytics and decision support using this myriad data at scale.

Data Analytics Improving Care Coordination Programs

% of surveyed insurance executives believe a co-investment in health IT by health plans and providers would accelerate value-based care adoption. In addition, implementing a platform approach-with three key characteristics below-sets up the organization to implement the tools needed to provide an easy-to-use, do-it-yourself interface for rules management. This allows business users and care teams to easily configure, audit and operationalize clinical quality measures, cohorts, clinical alerts and other rules without dependency on IT expertise.

A single data strategy with strong governance built around a common organizational language, including diligence around managing complex data security and privacy needs, is critical. This enables healthcare organizations to employ effective tools for working with large amounts of data at high speeds-real-time and near-real-time-using features, such as replication, horizontal scalability and high fault tolerance. In addition, strong data governance facilitates the business intelligence applications that rely on rules engines to query and analyze vast amounts of data efficiently.

: In addition to data governance, healthcare organizations can offer more data from more sources. With a platform approach, enterprise data from both traditional and newly available sources can reside in a single well-governed source that assures consistently defined terms and concepts. Over time, this allows the organization to go beyond the standard healthcare sources, such as claims, HL7, and CCDA, to also include historically cost-prohibitive data, such as benefit information and unstructured data. These data sets can be leveraged to structure programs with the most effective engagement models, risk patterns and communications.

 Building upon the data foundation, organizations can define key quality metrics using flexible technology tools that allow for ongoing changes and updates. Allowing care teams to define population-specific measures enables accurate and timely identification of gaps within targeted populations, which is critical to improving outcomes. Care teams can then be more intentional about coordinating efforts to engage with patients and providers. For example, when care teams have accurate data, proactive follow-up with high-risk patients can prevent ED visits and inpatient admissions. In addition, when multiple programs and initiatives are well coordinated, care teams are better able to consolidate metrics into fewer interactions. This approach also sets the stage for proactively delivering patient-specific information about quality and performance measures directly to physicians at the right time to ensure gaps are understood, closed and documented.

Data-Driven Decisions Lead to Long-Term Success


	Considering that most healthcare provider organizations operate on very slim margins, a one-to-two percent improvement can have an enormous impact. With a strategic platform approach, including clear quality measures and clean, reliable data, healthcare organizations can enable their teams with the best tools to support the long-term quality and cost improvements that are integral to value-based care. Giving care teams tools to support effective, data-driven decisions for specific regulatory, clinical, financial and operational metrics will result in improved patient care and decreased healthcare costs. Ultimately, the organizations that successfully optimize their operations, while identifying and acting upon opportunities to be more efficient, save money and gain deeper understanding from available data and outcomes, will thrive in the value-based care environment.

Swanand Prabhutendolkar is Sr. Vice President of Data Management at 

 and Dhaval Shah is Sr. Vice President of Medical Technology at 

Articles

Achieving success under the expanding influence of value-based care models.

Dhaval Shah

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Data is the Foundation for the Shift to Value-Based Care