Leveraging big data to guide better nurse staffing strategies

Impact of customised ICU handover protocol on the quality of ICU discharge reports

Background

The aim of this investigation was to evaluate the impact of implementing a handover protocol, based on a standardised mnemonic tool specific for a cardiovascular intensive care unit (ICU), on the quality of information transferred during ICU discharge.

Methods

In this prospective pre–post study, we evaluated the implementation of an ICU discharge handover protocol in 168 patients who underwent coronary artery bypass graft surgery. The primary outcome was the quality of the information. In the preintervention phase, 84 ICU standard discharge reports were evaluated. During the intervention period, a new handover protocol which included a written discharge report based on the I-PASS (illness severity, patient summary, action list, situation awareness and contingency plans, and synthesis by receiver) mnemonic tool was implemented. After the intervention, 84 new reports were assessed. The reports were evaluated by the ward physicians and by an external independent examiner using a standardised questionnaire. ICU discharge time and postoperative length of stay were also analysed.

Results

The overall quality of the reports was evaluated as ‘completely understood’ by the ward physicians in 17 patients (21%) in the preintervention phase compared with 45 patients (54.9%) in the postintervention phase (p<0.001). The independent examiner classified one report (1.2% of the total number) as ‘excellent’ in the preintervention phase and 30 (35.7%) in the postintervention phase (p<0.001). After protocol implementation, patients were released from the ICU 58 min later (p<0.001). There was no difference in the length of postoperative hospital stay.

Conclusion

Implementation of a customised handover protocol when discharging patients from the ICU was associated with improvement in the quality of the information transferred but also with ICU discharge occurring at a later time of day.

Long-term outcomes following severe COVID-19 infection: a propensity matched cohort study

BACKGROUND

There are limited data describing the long-term outcomes of severe COVID-19. We aimed to evaluate the long-term psychosocial and physical consequences of severe COVID-19 for patients.

METHODS

We conducted a multicentre observational cohort study; between 3 and 7 months posthospital discharge, patients who had been admitted to critical care due to severe COVID-19 were invited to an established recovery service. Standardised questionnaires concerning emotional, physical and social recovery, including information on employment, were completed by patients. Using propensity score matching, we explored outcomes between patients admitted to critical care with and without COVID-19, using data from the same recovery programme.

RESULTS

Between July 2020 and December 2020, 93 patients who had been admitted to critical with COVID-19 participated. Emotional dysfunction was common: 46.2% of patients had symptoms of anxiety and 34.4% symptoms of depression. At follow-up 53.7% of previously employed patients had returned to employment; there was a significant difference in return to employment across the socio-economic gradient, with lower numbers of patients from the most deprived areas returning to employment (p=0.03). 91 (97.8%) COVID-19 patients were matched with 91 non-COVID-19 patients. There were no significant differences in any measured outcomes between the two cohorts.

INTERPRETATION

Emotional and social problems are common in survivors of severe COVID-19 infection. Coordinated rehabilitation is required to ensure patients make an optimal recovery.

Quantifying paediatric intensive care unit staffing levels at a paediatric academic medical centre: A mixed-methods approach

AIM

To identify, simulate and evaluate the formal and informal patient-level and unit-level factors that nurse managers use to determine the number of nurses for each shift.

BACKGROUND

Nurse staffing schedules are commonly set based on metrics such as midnight census that do not account for seasonality or midday turnover, resulting in last-minute adjustments or inappropriate staffing levels.

METHODS

Staffing schedules at a paediatric intensive care unit (PICU) were simulated based on nurse-to-patient assignment rules from interviews with nursing management. Multivariate regression modelled the discrepancies between scheduled and historical staffing levels and constructed rules to reduce these discrepancies. The primary outcome was the median difference between simulated and historical staffing levels.

RESULTS

Nurse-to-patient ratios underestimated staffing by a median of 1.5 nurses per shift. Multivariate regression identified patient turnover as the primary factor accounting for this difference and subgroup analysis revealed that patient age and weight were also important. New rules reduced the difference to a median of 0.07 nurses per shift.

CONCLUSION

Measurable, predictable indicators of patient acuity and historical trends may allow for schedules that better match demand.

IMPLICATIONS FOR NURSING MANAGEMENT

Data-driven methods can quantify what drives unit demand and generate nurse schedules that require fewer last-minute adjustments.