Articles, PSQH September 5, 2011

Web 3.0 Data-Mining for Comparative Effectiveness and CDS

by Barry P Chaiken, MD

“Turbulent times” accurately describes the state of the American healthcare system. The list of critical challenges is well known—upward spiraling healthcare costs now approaching 17% of GDP, healthcare payment reform, shortage of clinical professionals, aging population, and the economic downturn. While current investments in health information technology (HIT) begin to deliver increased reimbursements to providers, these same at-risk organizations, along with payors, seek better ways to leverage HIT to enhance quality care and reduce costs.

Although much effort focuses on improvement of clinical workflows, an opportunity exists to transform healthcare delivery by implementing evidence-based clinical decision support at the point of care. Such clinical content delivered effectively within new, efficient clinical workflows directs patients toward evidence-based therapeutic plans that produce desired clinical and financial outcomes. While informaticists work on developing these clinical workflows, the lack of clinical knowledge limits the ability of organizations to leverage HIT in order to personalize therapeutic care plans.

Identifying Affordable Therapies

Comparative effectiveness research, supported by data mining, allows organizations to identify affordable therapies that enhance patient care. With the implementation of HIT, data warehouses contain petabytes of searchable clinical, outcomes, genomic, and financial data across multiple patient populations. Bringing together this data using sophisticated knowledge analytic tools and domain-specific interfaces allows researchers to discover relationships among multiple variables gleaned from previously unconnected databases.

In turn, this new clinical knowledge enables clinicians to personalize treatment for patients based upon their genetic background by linking it to descriptive patient data and outcomes. Personalized medicine transcends analysis of a population-based cohort by placing the patient within a sub-population that better reflects the expected outcome from a prescribed treatment. Embedding this personalized medicine knowledge within an EMR’s clinical decision support module facilitates the delivery of these evidence-based best practices at the point of care.

Semantic Web

Sophisticated software indexes the databases on metadata that “describe” each data point. Although the indexing allows for rapid retrieval of the data, it more importantly builds links among each data point based upon the descriptive information contained in the metadata. Discovery of these relationships is impossible without semantic web technology and the ability of computers to utilize it to read and understand metadata. Experts can utilize semantic web technology to query multiple large data sets to explore comparative effectiveness hypotheses.

Excerpts from: Web 3.0 Data-Mining for Comparative Effectiveness and CDS. PSQH, September/October 2011

 

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