Abstract Background Microhospitals are small hospitals of approximately 16-20 beds, including emergency and inpatient departments. The laboratory testing at these sites is typically point-of-care (POC) performed by healthcare professionals, rather than traditional laboratory staff and results from these tests are input directly into the electronic health record (EHR) without review. A questionable result requires collection of a second sample for retesting to confirm the original results. We identified a knowledge gap where staff performing complete blood count with or without differential (CBC) did not take appropriate action when results were flagged by the hematology instrument. For example, flags for suspected blasts/abnormal lymphocytes should be evaluated by a laboratory professional before reporting by performing a blood smear review or manual differential. In the POC model, the testing personnel should review these flags and take appropriate action (e.g., collect a new sample for reanalysis and/or forward testing to the hematology laboratory). Alternatively, the provider may decide based on clinical presentation that the results are potentially erroneous, although the instrument flag messages to support this decision-making process are not transmitted to the EHR. These approaches can be unsuccessful due to a busy patient workload or lack of information for a fully informed decision. To address the gap, we implemented a process improvement project to more efficiently enable follow-up testing at the main lab for CBC specimens with specific, clinically significant flags or results, such as those suggestive of an acute leukemia. Methods Using the middleware (Telcor QML) and LIS (Epic Beaker), a new CBC order is placed automatically when specific instrument flags or results were produced. The middleware was revised to capture the desired flags into specific fields, in alpha and numeric forms. Rules were written in Beaker to automatically 1) populate a comment in a CBC follow-up component field indicating a sample was collected for analysis at the main lab based, which was based on alpha flags and numeric values exceeding a defined threshold, and 2) place an order for testing at the main lab, which adds a task for new sample collection to the patient’s chart. Once placed, the provider receives a notification to sign the order. The CBC follow-up comment is visible in the chart and acts as an indicator to other lab professionals and providers that those results may have an inaccuracy. Results Data was analyzed for 2 months prior to implementation and 2 months post-implementation. Overall, approximately 5% of total samples had flags or results that were chosen for follow-up. Confirmation testing was considered completed if performed within 12 hours of the flagged results. Pre-implementation, 5% of results were confirmed, typically after the patient was transferred to a larger hospital. Post-implementation, 64% of results were confirmed. Of those confirmed, 49% were unchanged from the original, 51% had a manual review with different results, and 16% also went for pathologist review. Conclusion Follow-up of suspect CBC results can be significantly improved by implementing an automated process using middleware and the LIS, thus, closing a knowledge gap and improving patient care.
Laura Smy (Wed,) studied this question.