Nurse's Achilles Heel: Using Big Data to Determine Workload Factors That Impact Near Misses.

PURPOSE: To explore how big data can be used to identify the contribution or influence of six specific workload variables: patient count, medication count, task count call lights, patient sepsis score, and hours worked on the occurrence of a near miss (NM) by individual nurses. DESIGN: A correlational and cross-section research design was used to collect over 82,000 useable data points of historical workload data from the three unique systems on a medical-surgical unit in a midsized hospital in the southeast United States over a 60-day period. Data were collected prior to the start of the Covid-19 pandemic in the United States. METHODS: Combined data were analyzed using JMP Pro version 12. Mean responses from two groups were compared using a t-test and those from more than two groups using analysis of variance. Logistic regression was used to determine the significance of impact each workload variable had on individual nurses' ability to administer medications successfully as measured by occurrence of NMs. FINDINGS: The mean outcome of each of the six workload factors measured differed significantly (p < .0001) among nurses. The mean outcome for all workload factors except the hours worked was found to be significantly higher (p < .0001) for those who committed an NM compared to those who did not. At least one workload variable was observed to be significantly associated (p < .05) with the occurrence or nonoccurrence of NMs in 82.6% of the nurses in the study. CONCLUSIONS: For the majority of the nurses in our study, the occurrence of an NM was significantly impacted by at least one workload variable. Because the specific variables that impact performance are different for each individual nurse, decreasing only one variable, such as patient load, will not adequately address the risk for NMs. Other variables not studied here, such as education and experience, might be associated with the occurrence of NMs. CLINICAL RELEVANCE: In the majority of nurses, different workload variables increase their risk for an NM, suggesting that interventions addressing medication errors should be implemented based on the individual's risk profile.

Using Real-Time Data to Warn Nurses of Medication Administration Errors Using a Nurse Situational Awareness Dashboard.

Electronic Health Records (EHR) are constantly gathering an exponential amount healthcare data. Historical data is often studied to identify trends and determine the effectiveness of interventions, but rarely is Real-time data utilized to positively influence the nurse at the Point of Care. A dashboard allowing nurses to visualize their individual Near-Miss (MN) medication error risk as the needs and subsequent workload of the patients they served changed was created and piloted for 30- days. Implementation of the dashboard resulted in a 15.6% reduction of NMs.

Stop. Think. Delirium! A quality improvement initiative to explore utilising a validated cognitive assessment tool in the acute inpatient medical setting to detect delirium and prompt early intervention.

AIMS AND OBJECTIVES: The paper examines the ability of nursing staff to detect delirium and apply early intervention to decrease adverse events associated with delirium. To characterise nursing practices associated with staff knowledge, delirium screening utilising the Modified Richmond Assessment Sedation Score (mRASS), and multicomponent interventions in an acute inpatient medical unit. BACKGROUND: Delirium incidence rates are up to 60% in frail elderly hospitalised patients. Under-recognition and inconsistent management of delirium is an international problem. Falls, restraints, and increased hospital length of stay are linked to delirium. DESIGN: A descriptive study. METHODS: Exploration of relationships between cause and effect among cognitive screening, knowledge assessment and interventions. RESULTS: Success in identifying sufficient cases of delirium was not evident; however, multicomponent interventions were applied to patients with obvious symptoms. An increase in nursing knowledge was demonstrated after additional training. Delirium screening occurred in 49-61% of the target population monthly, with challenges in compliance and documentation of screening and interventions. Technological capabilities for trending mRASS results do not exist within the current computerised patient record system. CONCLUSIONS: Delirium screening increases awareness of nursing staff, prompting more emphasis on early intervention in apparent symptoms. Technological support is needed to effectively document and visualise trends in screening results. The study imparts future research on the effects of cognitive screening on delirium prevention and reduction in adverse patient outcomes. RELEVANCE TO CLINICAL PRACTICE: Evidence-based literature reveals negative patient outcomes associated with delirium. However, delirium is highly under-recognised indicating future research is needed to address nursing awareness and recognition of delirium. Additional education and knowledge transformation from research to nursing practice are paramount in the application of innovative strategies. Focus is placed on nursing staff because nurses are at the bedside and are able to identify early signs of delirium.