Abstract: Introduction: Critical values (CV) are lab results suggesting a patient is in imminent danger unless appropriate therapy is initiated promptly. Patient care and regulatory requirements dictate that clinical providers be immediately alerted of these by the lab. Commonly, platelets ≤20 K/mcL, WBC ≤0.5 K/mcL, or Hemoglobin ≤6.0 g/dL are considered CVs. Many of our bone marrow transplant or chemotherapy patients have such results but do not require immediate attention as they are expected. These are false positive critical values, and if called to the floor would contribute to clinician fatigue and burnout. Currently, technologists manually review CVs for criteria indicating the patient is not in imminent danger averting an alert. This alleviates the clinical team, but the burden is now on the laboratory technologists. An automated process of correctly identifying critical values would be more ideal.
Objective: Create a set of rules within the laboratory information system (Cerner) critical callback module to filter out false positive critical values. This would decrease the amount of time technologists spend reviewing them.
Methods: Rules are built preventing cases meeting the following criteria from going to the critical callback queue: platelets ≤20 K/mcL from inpatients where the previous platelet count was <30 k/mcL if within the last 7 days, WBC ≤0.5 K/mcL if the patient is not new, and Hgb ≤6.0 g/dL if patient has had another critically low Hgb within the last 24 hours. For 28 consecutive days reports are run tallying CVs triggered by the original method and by the new rules. Reports are reviewed for false positives or negatives. At 5 minutes per case for technologist review, the total amount of time saved is calculated.
Results: CVs decreased from 2853 to 202, a 93% reduction. An average of 2.64 hours per 8-hour-shift, or 0.33 FTEs, were saved. No critical values were missed by the new rules.
Conclusion: It is possible to effectively use a rules-based approach in Cerner to decrease unnecessary critical value alerts in cancer patients alleviating burden on technologists and clinical teams. To our knowledge this is the first report of rules coded into Cerner using a patient’s encounter history, inpatient status, and previous values to direct a sample to the critical value queue.
Describe the new knowledge and additional skills the participant will gain after attending your presentation.: Participants will learn that rules can be coded into Cerner PathNet to reduce unnecessary CV alerts which alleviates a significant burden from healthcare workers.
Samuel McCash (Presenter)
Memorial Sloan Kettering Cancer Center