Towards prompt electrocardiogram acquisition in triage: Preliminary testing of a symptom-based clinical prediction rule for the Android tablet

Abstract

Background: For patients with suspected acute coronary syndrome, international guidelines indicate that an Electrocardiogram (ECG) should be performed within 10 min of first medical contact, however success at achieving these guidelines is limited. Aims: The purpose of this study was to develop and perform initial testing of a clinical prediction rule embedded in a tablet application, and to expedite the identification of patients who require an electrocardiogram within 10 min. Methods: This derivation of the Acute Coronary Syndrome Application (AcSAP) comprised of three local studies, an unpublished audit and literature critique. The AcSAP was prospectively tested over four months in patients presenting to the Emergency Department (ED) of a Dublin teaching hospital. An audit form retrieved data pertaining to times of: registration to the emergency department, triage, first electrocardiogram and diagnosis. The AcSAP was subsequently evaluated by experienced triage nurses (n=18) who had utilised it. Results: The AcSAP was activated 379 times. Patients with ST Elevation Myocardial Infarction (STEMI) and non-ST Elevation Myocardial Infarction (NSTEMI) were significantly more likely to return a categorisation of 'immediate ECG' or 'ECG within 10 min' (p<0.001). There was a significant difference in 'triage to ECG' times across categories, the 'immediate ECG' categorisation resulting in the shortest time (p=0.002). Evaluations suggest that staff found the tool quick and easy to use and results seemed accurate. Conclusion: Testing of the AcSAP suggests that it accurately identifies patients who require an ECG within 10 min. As such, it has the potential to support the meeting of clinical guidelines for ECG acquisition.

Handheld Computer Devices to Support Clinical Decision-making in Acute Nursing Practice: Systematic Scoping Review

“…In total, 28 studies were included in the final analysis. These comprised randomized controlled trials (3/28, 11%) [ 28 - 30 ]; quasi-experimental studies (9/28, 32%) using nonequivalent [ 31 ], crossover [ 32 , 33 ] and before-after designs with [ 34 , 35 ] and without [ 36 - 39 ] controls; observational studies (10/28, 36%) involving prospective [ 40 , 41 ], retrospective [ 42 ] and cross-sectional [ 43 - 49 ] designs; qualitative descriptive studies (2/28, 7%) involving in-depth [ 50 ] and focus group [ 6 ] interviews; and studies of diagnostic accuracy (2/28, 7%) [ 51 , 52 ]. In addition, 7% (2/28) of mixed method studies were included in the review [ 53 , 54 ].…”

“…The characteristics and outcomes of the 12 studies that investigated HCDs as clinical decision-making supports are summarized in Table 3 . The studies in this group investigated the extent to which HCDs could improve the quality of clinical assessment and management decisions or processes (10/12, 83%) [ 28 , 31 - 33 , 35 , 36 , 40 , 42 , 51 , 52 ] and enhance nurses’ capacity for clinical decision-making (2/12, 17%) [ 37 , 38 ]. HCD interventions targeted processes of nurse clinical decision-making using a range of modalities, which varied according to the predominance of clinician versus algorithmic judgment.…”

“…HCD interventions targeted processes of nurse clinical decision-making using a range of modalities, which varied according to the predominance of clinician versus algorithmic judgment. These were as follows: (1) clinical reference guides to support unstructured clinical judgments (2/12, 17%) [ 37 , 38 ]; (2) aide-mémoire to structure clinical judgment (3/12, 25%) [ 28 , 31 , 35 ]; and (3) fully computerized algorithmic assessments [ 32 , 52 ], drug dosing [ 40 ], and clinical pathways [ 33 , 36 , 42 , 51 ], with varying levels of clinician override (7/12, 58%). In total, 8% (1/12) of studies investigating the impact of HCD use on clinical decision-making used PDAs [ 35 ], whereas the remaining 92% (11/12) of studies used modern HCD technology.…”

“…In total, 8% (1/12) of studies investigating the impact of HCD use on clinical decision-making used PDAs [ 35 ], whereas the remaining 92% (11/12) of studies used modern HCD technology. Studies within this theme were undertaken in emergency departments (4/12, 33%) [ 31 , 36 , 42 , 51 ], in various health and hospital settings (3/12, 25%) [ 28 , 37 , 38 ], in inpatient wards (3/12, 25%) [ 35 , 40 , 53 ], in an infectious pediatric ward (1/12, 8%) [ 52 ], and in the context of a laboratory-based simulation (1/12, 8%) [ 32 ].…”

Development and Validation of an Artificial Intelligence Electrocardiogram Recommendation System in the Emergency Department

“…Other than that, the decision for an ECG examination is generally symptoms-based and driven by physicians. Thus, the time to complete an ECG acquisition in the ED depends on when a patient’s assessment by a physician started, except for patients with cardiac chest pain, who are prioritized for immediate ECG examination once certain pre-established rules of ACS criteria are met at triage [ 3 , 4 , 5 , 6 ].…”