Meaningfully managing the mountains of patient data
As the definition of “meaningful use” begins to solidify, it becomes apparent that there is also a need to define more innovative methods of managing the mountains of clinical data collected every day. Resources are pinched, reimbursements are a challenge and the added responsibility of outcomes measurement shines an especially bright light on the cardiovascular service line as it is often the primary revenue generator for the hospital.

How do we balance the needs of sophisticated management metrics with comparative outcomes analysis and analytics necessary for superior patient care? Is there really anything new beyond quantifying severity-adjusted length of stay (LOS) that will bring us closer to effective data management?

For the most part, conventional cardiovascular data-mining tools are stratified into four primary categories, which often include tools from device vendors or from EHRs that perform some subset of the following:

  • Resource Management – Often provided by device vendors as their field of vision is closely aligned with their product offerings. These metrics mostly include lab utilization, case duration and inventory. These basic data objects provide insight into utilization, but are not aware of external factors that might skew the results.
  • Service line management – Treating the service line as a true P&L requires more than just resource management. Service line management adds functionality to aid cost mitigation, marketing and more sophisticated financial performance analysis.
  • Clinical data analysis – Data collected for clinical trending is also useful for research and decision support. It requires near real-time data transfers and can be pivoted against financial data for prognostication and regressive analysis.
  • Quality metrics – Most notably the National Cardiovascular Data Registry (NCDR) and The Joint Commission quality initiatives, but there many more focusing on individual episodes of care.

It is not enough today to have silos of information, not matter how well each silo performs. Today’s service line manager needs to be able to interact intelligently with all sources of patient data, including time of arrival, time to procedure, resources used, complications, patient satisfaction, rehab, readmissions and more. And these data also should complement staff quality metrics. The interchange of any data from any reference point along the way should seamlessly flow into any and every segment of patient care. Managers should be able to mine these data in real time to determine trends and problems, and to help enact solutions.

In the end, one should have quality analysis data, coupled with financial and clinical information, which should broaden the perspective on several fronts, leading to new data opportunities that benefit the patient as well as the care provider.

Mr. Bardwell is director of product management for Lumedx.