Abstract
BackgroundEmergency medicine is characterized by a high patient flow where timely decisions are essential. Clinical decision support systems have the potential to assist in such decisions but will be dependent on the data quality in electronic health records which often is inadequate. This study explores the effect of automated documentation of vital signs on data quality and workload.MethodsAn observational study of 200 vital sign measurements was performed to evaluate the effects of manual vs automatic documentation on data quality. Data collection using questionnaires was performed to compare the workload on wards using manual or automatic documentation.ResultsIn the automated documentation time to documentation was reduced by 6.1 min (0.6 min vs 7.7 min, p < 0.05) and completeness increased (98% vs 95%, p < 0.05). Regarding workflow temporal demands were lower in the automatic documentation workflow compared to the manual group (50 vs 23, p < 0.05). The same was true for frustration level (64 vs 33, p < 0.05). The experienced reduction in temporal demands was in line with the anticipated, whereas the experienced reduction in frustration was lower than the anticipated (27 vs 54, p < 0.05).DiscussionThe study shows that automatic documentation will improve the currency and the completeness of vital sign data in the Electronic Health Record while reducing workload regarding temporal demands and experienced frustration. The study also shows that these findings are in line with staff anticipations but indicates that the anticipations on the reduction of frustration may be exaggerated among the staff. The open-ended answers indicate that frustration focus will change from double documentation of vital signs to technical aspects of the automatic documentation system.
Highlights
Emergency medicine is characterized by a high patient flow where timely decisions are essential
Completeness Completeness was calculated in percent of vital sign measurements present for each documentation method
Further concerns in the manual workflow included what values were transferred to the Electronic Health Record (EHR) “There may be errors in the measurements, saturation may be low in a cold patient, so there has to be some manual check before the measurements are automatically documented.”
Summary
Emergency medicine is characterized by a high patient flow where timely decisions are essential. Clinical decision support systems have the potential to assist in such decisions but will be dependent on the data quality in electronic health records which often is inadequate. Many practitioners specialized in emergency medicine work in emergency departments or other settings where patient flow usually is high and the outcome of decisions is time-dependent. In this setting, the use of clinical decision support systems (CDSS) could have a potential to transform workflow and improve clinical outcome thereby reducing workload and improving quality. The response rate was similar in both groups the degree of completed questionnaires was somewhat lower in the automatic group (Table 3). When comparing the means in the manual and automated documentation group (Table 4) it shows that the Temporal demand, 50 (47–53 CI95%) vs 23 (14–31 CI95%), and the Frustration level 63 (59–66 CI95%) vs 33 (22–45 CI95%) are rated significantly lower in the Manual workflow
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