There’s no question that users rely on the EMR In Basket for day-to-day workflow management. The “In Basket” or “Inbox,” depending on what EMR you’re using, provides a centralized location to receive notifications and important patient information, such as admission and discharge notifications, new lab results, refill requests, patient calls, appointment reminders, patient portal communication and much more.
The Inbox is an efficient communication hub except when it’s not. When there are too many messages to handle in a day without adequate prioritization, that’s not efficient. When an In Basket user has to break the context of the EMR view to find outside information, that’s not efficient. These types of exceptions often occur when managing non-interfaced lab results, which can contribute to EMR dissatisfaction. Or worse, these exceptions can result in a patient waiting for follow-up care.
Despite organizations’ ongoing efforts to improve turnaround, sometimes it takes weeks for the original test result document to show up in the EMR Inbox when faxed labs are sent to the HIM department to be scanned and attached to the patient record, or manually abstracted. Intelligent clinical data extraction tools such as LabDE can help you become more efficient when it comes to managing your Inbox.
With LabDE, manual data abstraction becomes data verification. Faxed labs quickly become structured results in the patient’s EMR. To make fast, accurate processing of non-interfaced labs even more Inbox-friendly, LabDE users can tell the software to speed critical results reporting for priority patients so they end up in the Inbox first. LabDE can also specify types of documents to be processed and sent to the In Basket first. Depending on business needs, LabDE users can include associated reference range from the lab and flags indicating whether the result is abnormal for results sent to the Inbox.
Most surveys show EMR satisfaction climbing with physicians. Why not explore complimentary technology that improves EMR satisfaction, such as intelligent clinical data extraction, to move the needle even farther?
Start your exploration here!
