Big Data Sweet Spots Identified

By Christine Kern, contributing writer

Recent case studies reveal the important role of algorithms in reducing healthcare costs.

Researchers have identified some of the clearest opportunities to reduce healthcare costs through the use of big data. The U.S. healthcare system is rapidly adopting EHRs which dramatically increase the quantity of clinical data that are available electronically.

Simultaneously, rapid progress has been made in clinical analytics – techniques for analyzing large quantities of data and gleaning new insights from that analysis – which is part of what is known as big date. Consequently, there are currently unprecedented opportunities to use big data to reduce the costs of healthcare.

In a study published in the July issue of Health Affairs, researchers discussed the role of algorithms in reducing cost in the following categories: high-cost patients, readmissions, triage, de-compensation, adverse events, and treatment optimization for diseases affecting multiple organ systems.

The study analyzes six use cases with strong opportunities for cost savings: high-cost patients; readmissions; triage; decompensation (when a patient’s condition worsens); adverse events; and treatment optimization when a disease affects multiple organ systems.

“The examples we present in this study provide key insights to the ‘low hanging fruit’ in healthcare big data and have implications for regulatory oversight, offer suggestions for addressing privacy concerns and underscore the need for support of research on analytics,” said David Bates, M.D., chief quality officer at Brigham and Women’s Hospital and lead author on the study.

The study also discusses the types of insights that are likely to emerge from clinical analytics, the types of data needed to obtain such insights, and the infrastructure – analytics, algorithms, registries, assessment scores, monitoring devices, and so forth – that organizations will need to perform the necessary analyses and to implement changes that will improve care while reducing costs.

The researchers emphasize that these six cases are not an exhaustive list of the ways in which big data can be useful in improving value in healthcare, and they note that these inpatient setting examples are transferable to the outpatient setting as well.

“Support for research that evaluates the use of analytics and big data to address these six use cases, as well as thoughtful consideration of regulation and payment is warranted,” Bates said. “Additionally, as multiple streams of data become available for analytic purposes, consideration of patients’ privacy and their desire to link disparate sources of data will be of the utmost importance.”

The authors suspect that cost savings will vary widely, though the current costs associated with all six scenarios will be significant. They suggest that using analytics for multiple conditions is likely to yield even stronger cost savings.

The work was supported in part by a grant from the Gordon and Betty Moore Foundation.