DOI: 10.5061/dryad.v9s4mw6s7
title: Data from: An exploratory study of staff perceptions of shift safety in the critical care unit and routinely available data on workforce, patient and organisational factors

description: Objectives To explore: bedside professional reported (BPR) perceptions of
 safety in intensive care staff; and the relationships between BPR safety,
 staffing, patient and work environment characteristics. Design An
 exploratory study of self-recorded staff perceptions of shift safety and
 routinely collected data. Setting A large teaching hospital comprising 70
 critical care beds. Participants All clinical staff working in adult
 critical care. Interventions Staff recorded whether their shift felt
 “safe, unsafe or very unsafe” for 29 consecutive days. We explored these
 perceptions and relationships between these and routine data on staffing,
 patient and environmental characteristics. Outcome measures Relationships
 between BPR safety and staffing, patient and work environment
 characteristics. Results 2836 BPR scores were recorded over 29 consecutive
 days (response rate 57.7%). Perceptions of safety varied between staff,
 including within the same shift. There was no correlation between
 perceptions of safety and two measures of staffing; care hours per patient
 day (r= 0.13 p=0.108) and Safecare Allocate (r= -0.19 p=0.013). We found a
 significant, positive relationship between perceptions of safety and the
 percentage of Level 3 (most severely ill) patients (r=0.32, p=0.0001).
 There was a significant inverse relationship between perceptions of safety
 and the percentage of Level 1 patients on a shift (r= -0.42,
 p=&lt;0.0001). Perceptions of safety correlated negatively with
 increased numbers of patients (r= -0.44, p=0.0006) and higher percentage
 of patients located side rooms (r=0.63, p&lt;0.0001). We found a
 significant relationship between perceptions of safety and the percentage
 of staff with a specialist critical care course (r=0.42. p=0.0001).
 Conclusion Existing staffing models, which are primarily influenced by
 staff:patient ratios may not be sensitive to patient need. Other factors
 may be important drivers of staff perceptions of safety and should be
 explored further. Trial registration- UK Health Research Authority
 approval was obtained (ID249248).
description: Data were collected over 29 consecutive days during October and
 November 2018. During the study period each site was visited or contacted
 daily by one of the investigators (CLV), to collect and verify
 data.  <b>Data collection and measures
 </b> <b>  Bedside Professional Rating of Safety
 (BPR).</b> We asked all ICU staff to
 rate each shift as “safe, unsafe, or very unsafe” using a coloured sticker
 (green, amber or red). This question was generated from previous work into
 staff experiences of safety.  During staff briefings and daily visits to
 sites, participants were encouraged to interpret the term “safe” as their
 own perception of safe in its normal conversational sense in
 ICU. Staff were asked to choose one sticker to rate
 each shift and put this onto a card labelled with the relevant date and
 shift (e.g. October 4<sup>th</sup> Night shift). Participants
 posted the cards in data collection boxes, allowing them to complete the
 process anonymously, thus we were not able to identify participants by
 profession. To allow us to look for relationships
 between perceptions of safety and other variables, including staffing,
 patient and work environment factors, a BPR shift score was created for
 each shift using the following method; we counted the responses and
 allocated a score of 1 for each red response, 2 for each amber response
 and 3 for each green response. We summed the total score and divided this
 by the number of responses for that shift. This created a mean shift
 safety score <i>between</i> 1 (very unsafe) and 3 (safe),
 which reflected the diversity and weighting of responses and is simple to
 reproduce. This method was discussed with senior nursing staff to confirm
 that they felt it reasonably summarized perceptions of safety on a given
 shift. <b>Routinely available data on staffing,
 patient and work environment </b>
 <b>Staffing and workload data. </b>
 We recorded the number of staff (including nurses, doctors and
 allied health professionals) working clinically in the ICU during the
 study period. Staffing data were extracted from an electronic rostering
 system and verified with the nurse in charge.  Where professional groups
 did not use electronic rostering e.g. physiotherapists, the data were
 collected daily and confirmed weekly with the relevant manager. Staff who
 worked clinically for part of a 12-hour shift were represented as a
 proportion of a whole-time equivalent (e.g. a physiotherapist working for
 6 hours on ICU was counted as 0.5). We calculated
 workload intensity scores by summing the total number of organs in failure
 for each day and dividing this by the number of staff working that day. We
 looked at this by profession and by all professions combined. We used data
 from the critical care minimum data set (CCMDS)  to calculate the number
 of organs each patient had in failure, this was only available by 24-hour
 period, and therefore, data were collapsed into a 24-hour
 period. We recorded the care hours per patient day
 (CHPPD) from our electronic roster. CHPPD is a measure that can be used to
 assess productivity and facilitates benchmarking. Werecorded staffing utilization data from
 SafeCare in the commercial rostering system provided by Allocate
 (AllocateSoftware Richmond 2017). This uses the composite
 acuity/dependency scoring system from the Safer Nursing Care Tool to
 provide nursing utilisation reports to support real-time deployment
 decisions. This is done by calculating required nursing hours (based on
 acuity and dependency data entered by senior nursing staff) and dividing
 this figure by the number of rostered nursing hours to produce a
 percentage of nursing hours utilization (e.g. required hours 126.4,
 rostered hours 115.00 = 109.9 % utilisation).  
 This figure is reported in the following (partially) colour coded
 categories:  &lt;90% Green, 90.1% -105.1% %, 105% Amber, 105.1%- 110%,
 110.1% Red. There is meaning attached to these five categories, so that
 green is reported as underutilisation whilst all figures falling into
 amber and above are reported as overutilisation. In our analysis we
 reflected these categories by allocating a number to each so that green =
 5 through to red =1.  During the study if we found
 within-shift changes in staff numbers, patient numbers or patient acuity
 we recorded the status at approximately 5:00 and 17:00 (day shifts). This
 was a pragmatic decision that reflected the timings of local processes to
 manage staffing. <b>Patient characteristics
 data.</b> Data regarding the number of Level 1,2
 and 3 patients were recorded by visiting or contacting each site daily. In
 ICU, patients are broadly categorised by the number of organs in failure,
 with Level 3 patients being the most severely ill.
 <b>Work environment data. </b> We
 recorded the total number of patients and the number of patients in side
 rooms per shift and confirmed this daily with a senior nurse.
 <b>Data regarding nurse training.</b>
 We recorded the percentage of nursing staff on each shift who had
 completed a post registration critical care course (CCC), the accuracy of
 the data were confirmed with local clinical nurse educators. We did not
 collect staff characteristic data for other professional groups as this is
 not routinely available, and the smaller numbers of the group sizes would
 impact on our ability to perform statistical analysis
      
 <b>Analysis </b> Data were
 collated into an Excel spreadsheet (Microsoft, Redmond, Washington State,
 USA).  We used descriptive statistics to analyse frequency of responses.
 Data were tested for normality using the D’Agostino-Pearson Omnibus test
 and non-parametric tests were used where appropriate. We used Spearman’s
 rank correlation to evaluate strength and direction of relationships;
 regression lines and 95% confidence intervals were plotted for
 illustrative purposes.
 Prism (v8.01 for Windows, GraphPad Software, La Jolla California USA) was
 used throughout. Although the shift BPR
 was primarily analysed as a mean for simplicity – we further explored this
 using both median and a weighted approach (whereby a weighting of 5 was
 given to very unsafe, 3 to unsafe and 1 to safe). The purpose of this was
 to allow us to explore the sensitivity of the BPR tool.
  
description:   The raw data is collated on an Excel
 spreadsheet. Sheets-1-3 called Site 1,2,and 3 display
 for each site; the number of responses of each colour sticker by shift
 .The response rate for the number of nurses on shift and for the total
 number of healthcare staff on shift.  Shift rating by
 different scoring methods, these are all described in the methods section
 above. BPR score - This is described in the methdos
 section : a score of 1 per red sticker, 2 per amber sticker and 3
 per green sticker was calculated and the sum divided the number  of
 responses. Median - this method  calculated the
 score as above but used a median SJB ,this method
 calculated a weigthed score so that 5 was given to very unsafe, 3 to
 unsafe and 1 to safe . Workload scores for each shift,
 these are the care hours per patient day (CHPPD)  and the safe care
 scores- these are described in the methods section .
 The number of patients and the percentage of side room
 occupancy. Subset of responses from staff
 identifying as being in a side room There are 3 further
 sheets intensity site 1,2 and 3  These show the data
 for each shift of the number of staff in total and broken down by
 professional group . The total number of organs in failure  and an
 intensity score calculated as described in the methods section (number of
 organs dvivded by number of staff ) The final 3 sheets
 CCC site 1,2,3 show the percenatge of nursing staff on each shift who have
 a critical care course  There are some shifts where no
 responses were returned , these values are missing and the box left
 empty.