The ever-increasing number of publicly reported measures of some aspect of quality or patient safety is one of the most concrete signs of the rising expectations of the public, employers, and payers. These provide a large set of targets for improving care, and much of the same patient information is required to measure performance as to deliver safe, high-quality care consistently.
Luckily, the pace of progress on the electronic health record—long envisioned by the Institute of Medicine and others as key infrastructure for provider organizations in the journey toward safer, higher quality care—has picked up considerably thanks to the HITECH incentive program. More and more patient data is being captured electronically and providers are reaching the stage that electronic support can become part of the toolset for clinicians at the point of care.
But putting the EHR to work to improve clinical performance at the bedside requires breaking down some of the traditional silos in our approaches to using HIT in the hospital.
The key to harnessing the value of clinical data and HIT in the interest of improving clinical performance is bringing quality reporting from the background (and after the fact) to the bedside, delivering actionable information about risks of care deficiencies or potentially poor outcomes to the clinicians caring for the patients in time for them to intervene. It also involves a focus on capturing key information needed for care management and quality reporting that still resides for the most part in paper documentation, free-text electronic notes, or in separate databases that capture documentation in the emergency room, surgical suite, or intensive care unit.
When the often similar logic of measurement and clinical decision support can be applied to this integrated patient information in real-time and clinicians at the bedside notified when action is likely warranted, it becomes possible for deficiencies in care to be identified in time to address them. In some cases, medical evidence and clinical experience are also sufficient to develop more complex logic to identify patients at risk of future, avoidable adverse events.