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EHR Implementation Projects Impact EHR Optimization Efforts

The push for EHR implementations under HITECH has many organizations seeking out EHR optimization opportunities.

By Kyle Murphy, PhD

A hasty approach to EHR implementation presents healthcare organizations and providers now with opportunities for EHR optimization through a thorough, data-driven assessment of EHR technology already in place, according to a recent PricewaterhouseCoopers report on EHR data.

As the authors of the report observe, current EHR optimization opportunities are likely showing themselves now that the EHR Incentive Programs have ceased flooding the EHR marketplace with billions in EHR incentives.

"But now that the dust has settled and many providers have successfully hurdled their initial implementations, those providers are objectively assessing their EHR systems and identifying areas that may not have delivered the value they had hoped for," they write. "It is a prudent approach, because there are typically opportunities to enhance EHR systems at any stage of an implementation — with the ultimate goal of improving outcomes."

The report comprises four use cases where advanced analytics can support EHR optimization projects.

The first centers on the importance of pre-EHR implementation testing to mitigate financial losses.

"It is vital to obtain an independent assessment of the implementation build and testing processes through a risk, compliance, and controls lens," the authors write. "A data-driven, holistic approach to the testing of key revenue cycle risks and controls supports thorough and independent testing of relevant charge interfaces as well as back-end record population gap analysis for added comfort with build prior to go-live."

The second focuses on identifying and addressing inefficiencies following EHR go-live. The academic medical center mentioned in the use case leveraged data analytics to address the performance of both providers and coders.

"Specifically, the drill-down features enabled the organization to view granular account-level details to identify trends within work queues and correlations between providers and coders," the report states. "That step aided in the recognition of operational inefficiencies in the forms of (1) coders that were possessing large numbers of unworked accounts, (2) high-volume work queues that required additional resources, and (3) high-risk providers who more frequently had missing or incomplete documentation."

According to the authors, the high-level view of coding activities allowed leadership to set benchmarks and adequately measure performance based on those metrics.

"By taking an iterative approach to the design and execution of data analytics programs, organizations can deploy resources cost-effectively and thereby continuously improve efficiency," they add. "One of the critical advantages of such an agile approach is that it facilitates the rapid design of sophisticated analytics programs that use existing technology architectures to achieve more-efficient operational work flows."

Revenue also features in the third use case — identifying the root causes of revenue leakage through data visualization. An academic health system performed a post-EHR implementation audit to uncover more than 13,000 charges missing key information tied to $2.5 million in revenue leakage.

"The data-driven solution established the scope of the audit by providing a risk-based, high-level overview of the revenue cycle," the authors note. "Dynamic drill-down data visualizations allowed for further segmentation and root cause analysis with a more intuitive interface for the identification of missed charges and their effective follow-up."

The fourth and final use case deals with integrating data from multiple sources. Again the area for cost savings was a part of the revenue cycle — 340B claims. The use of analytics-power monitoring tools enabled an organization to increase savings nearly ten-fold, from $1.6 million to $16 million.

"Dashboards facilitate integration of disparate data sets, including data from multiple EHRs and other external sources such as laboratory, radiology, procurement, and general-ledger systems," the authors maintain. "A risk-based approach to sampling and drill-down testing leads to the extrapolation of findings from manual testing across data populations."

Not only is an EHR implementation an opportunity for providers to improve care coordination and delivery, but it is also the first step toward realizing great return on investment when appropriately monitored and analyzed.




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