Prompting with electronic checklist improves clinician performance in medical emergencies: a high-fidelity simulation study

Abstract Background Inefficient processes of care delivery during acute resuscitation can compromise the “Golden Hour,” the time when quick interventions can rapidly determine the course of the patient’s outcome. Checklists have been shown to be an effective tool for standardizing care models. We developed a novel electronic tool, the Checklist for Early Recognition and Treatment of Acute Illness (CERTAIN) to facilitate standardized evaluation and treatment approach for acutely decompensating patients. The checklist was enforced by the use of a “prompter,” a team member separate from the leader who records and reviews pertinent CERTAIN algorithms and verbalizes these to the team. Our hypothesis was that the CERTAIN model, with the use of the tool and a prompter, can improve clinician performance and satisfaction in the evaluation of acute decompensating patients in a simulated environment. Methods Volunteer clinicians with valid adult cardiac life support (ACLS) certification were invited to test the CERTAIN model in a high-fidelity simulation center. The first session was used to establish a baseline evaluation in a standard clinical resuscitation scenario. Each subject then underwent online training before returning to a simulation center for a live didactic lecture, software knowledge assessment, and practice scenarios. Each subject was then evaluated on a scenario with a similar content to the baseline. All subjects took a post-experience satisfaction survey. Video recordings of the pre-and post-test sessions were evaluated using a validated method by two blinded reviewers. Results Eighteen clinicians completed baseline and post-education sessions. CERTAIN prompting was associated with reduced omissions of critical tasks (46 to 32%, p < 0.01) and 12 out of 14 general assessment tasks were completed in a more timely manner. The post-test survey indicated that 72% subjects felt better prepared during an emergency scenario using the CERTAIN model and 85% would want to be treated with the CERTAIN if they were critically ill. Conclusion Prompting with electronic checklist improves clinicians’ performance and satisfaction when dealing with medical emergencies in high-fidelity simulation environment

Customized Reference Ranges for Laboratory Values Decrease False Positive Alerts in Intensive Care Unit Patients

<div><p>Background</p><p>Traditional electronic medical record (EMR) interfaces mark laboratory tests as abnormal based on standard reference ranges derived from healthy, middle-aged adults. This yields many false positive alerts with subsequent alert-fatigue when applied to complex populations like hospitalized, critically ill patients. Novel EMR interfaces using adjusted reference ranges customized for specific patient populations may ameliorate this problem.</p><p>Objective</p><p>To compare accuracy of abnormal laboratory value indicators in a novel vs traditional EMR interface.</p><p>Methods</p><p>Laboratory data from intensive care unit (ICU) patients consecutively admitted during a two-day period were recorded. For each patient, available laboratory results and the problem list were sent to two mutually blinded critical care experts, who marked the values about which they would like to be alerted. All disagreements were resolved by an independent super-reviewer. Based on this gold standard, we calculated and compared the sensitivity, specificity, positive and negative predictive values (PPV, NPV) of customized vs traditional abnormal value indicators.</p><p>Results</p><p>Thirty seven patients with a total of 1341 laboratory results were included. Experts’ agreement was fair (kappa = 0.39). Compared to the traditional EMR, custom abnormal laboratory value indicators had similar sensitivity (77% vs 85%, P = 0.22) and NPV (97.1% vs 98.6%, P = 0.06) but higher specificity (79% vs 61%, P<0.001) and PPV (28% vs 11%, P<0.001).</p><p>Conclusions</p><p>Reference ranges for laboratory values customized for an ICU population decrease false positive alerts. Disagreement among clinicians about which laboratory values should be indicated as abnormal limits the development of customized reference ranges.</p></div

Normal and abnormal Laboratory Values displayed by both Interfaces subclassified according to Gold Standard Judgment.

<p>Percentage of true positive (TP), false positive (FP), true negative (TN) and false negative (FN) values shown relative to the total number of laboratory values displayed by each interface as percent (number). Truly abnormal laboratory test results (TP) commonly signal health-care providers the need to take action with regards to their patients’ health status. Laboratory values falsely indicated as abnormal (FP) represent in this sense a distraction or “noise” clouding this important “signal”. While an abnormal value in the traditional interface reflects a true abnormality in roughly 1 out of 9 times this “signal-to-noise ratio” is 1 in 4 (i.e. more than twice as high) in the novel interface.</p

Studyflow and Results.

<p>Sensitivity, Specificity, Positive and Negative Predictive Values (PPV, NPV) are given as estimate (95%-Confidence Interval). Only specificity and negative predictive values differed significantly (for details see text).</p