Temporal variations in quality of acute stroke care and outcomes in London hyperacute stroke units: a mixed-methods study

The ‘weekend effect’ and 7-day working policies

In 2001, Bell and Redelmeier1 published a landmark study showing that mortality was higher among people admitted to hospital during weekends or ‘out of hours’ than among those admitted during normal hours on weekdays, based on the health records of 3.8 million Canadian patients. This effect has been repeatedly demonstrated across a range of acute conditions, patient populations and health systems,1–3 such as myocardial infarction4 and stroke,5–9 patients undergoing elective surgery10 and admissions to emergency departments (EDs)11 or intensive care units. 12 However, a number of studies report equivalent or better outcomes in some patients treated outside normal working hours. 10,12–14 Although contested, such studies indicate that high-quality out-of-hours care is possible and that changes in organisation of services might address the current performance gap.

The Secretary of State for Health made this issue a priority in 2015 by suggesting that ‘someone is 15% more likely to die if admitted on a Sunday than on a Wednesday because we do not have as many doctors in our hospitals at the weekends as we have mid-week’ (House of Commons, 2015. Contains Parliamentary information licensed under the Open Parliament Licence v3.0. See https://www.parliament.uk/site-information/copyright-parliament/open-parliament-licence/). 15 This led to controversial changes to doctors’ work contracts, provoking large-scale strikes16 and an NHS England policy for a 7-day NHS by 2020. 17 The NHS Long Term Plan18 has identified, in particular, that all stroke units will, over the next 5 years, meet the NHS 7-day standards for stroke care and the national clinical guidelines for stroke over the next 5 years.

Several possible causes for an ‘out-of-hours effect’ are proposed, but many relevant factors are likely to be associated with out-of-hours working, including differences in patient case mix affecting outcomes among patients admitted at different times of the week;19 reduced staffing levels;2,11,20 reduced access to supporting investigations;2,11 lower seniority and clinical experience of available staff;1,2 less familiarity of staff with patients;1 less familiarity with unit-specific processes and policies, and insufficient training to address this;2 and fewer supervisory staff and less supervisor familiarity with the staff they supervise. 1 Systems by which such issues may be identified and addressed, such as clinical governance, and cultures that support staff engagement in these systems, may play an important role in how the out-of-hours effect manifests. Ward and organisational leadership may also represent significant factors in supporting service development, learning and engagement. 21

The controversy raised a number of relevant issues for research: first, that exposure of differences in outcomes across the week was having an impact politically, and that it was necessary to answer the question of whether this was a true effect, or an artefact of the patient mix or the quality of the data; second, that it was important to measure other outcomes and care processes, as well as mortality; and, finally, to change the approach towards acknowledging patterns of temporal variation. 22–25 This movement is characterised by the idea that certain times of the day, or days of the week, are associated with poorer outcomes for the patient, but that this is not necessarily limited to the weekend or night-time. 22 As a result, the concept of ‘out-of-hours’ working or the so-called ‘weekend effect’, which once dominated the discourse of the literature on temporal variation,22,26–28 has been re-evaluated.

It is now recognised that temporal variation results, at least in part, from delivery models of care and the cultural values of the health-care sector, enshrined in everyday practice, funding models, staffing practices and wider policies. 29 Recent research, such as the High-Intensity Specialist-Led Acute Care study,30,31 have taken this approach to examine the intensity of specialist-led care in emergency medical admissions, focusing particularly on the organisation and delivery of care, and medical staffing. 30,31

In the case of urgent stroke care, recent studies have shown that temporal variation in mortality is now decreasing,32,33 including in London’s hyperacute stroke unit (HASU) model. 34 However, there has been no large-scale, organisational research to understand what creates temporal in consistency, particularly in services that aim to operate consistently over 7 days, such as the London stroke services. The current study adds a wealth of information to this gap.

Reconfiguration of stroke services and the aspiration of 7-day working

In 2010, acute stroke services in London (population 8.2 million) were centralised. Hospitals were selected to become sites for specialist stroke care (over the first hours following stroke) in a hub-and-spoke network. 35 In London, prior to the reorganisation, 30 hospitals provided acute stroke care. After the reorganisation, initial specialist stroke care was routinely provided to all patients with suspected acute stroke at eight designated HASUs, with all patients being assessed shortly after arrival at the designated hospital by specialised stroke assessment teams able to provide access to immediate brain imaging in all patients and delivering immediate intravenous thrombolysis, when appropriate (Figure 1). The HASU model involved the London Ambulance Service taking all patients with suspected stroke within 48 hours of onset to one of the eight HASUs. 36 Acute stroke patients seen at other medical facilities were similarly transferred as an emergency to a HASU. The aim of the HASUs was to provide specialised care for all acute stroke patients during the first 72 hours after onset of stroke. Twenty-four acute stroke units in London, linked to HASUs, were designated to provide specialist acute stroke care beyond 72 hours for those patients still requiring inpatient treatment. Eight of these were in the same hospital trust as a HASU. 37 On average, across all patients, the quality of acute stroke care in London was better than in the rest of England and increased as a result of the centralisation,38 with a decrease in mortality. 35

FIGURE 1.

Summary of London’s stroke service model before and after reconfiguration. A&E, accident and emergency; ASU, acute stroke unit; MAU, medical admission unit; SU, stroke unit.

Performance standards for HASUs, linked to payments, were initially set by Healthcare for London39,40 and subsequently the London Strategic Clinical Networks to maintain high quality of care across the HASU stay. Standards include staffing quotas, required infrastructure and delivery of care. Some standards were set to provide rapid access to time-critical ‘front-door’ measures [e.g. dysphagia screen within 4 hours of admission, computerised tomography (CT) scan within 1 hour of admission and thrombolysis administration within 60 minutes]. Other standards were set with less stringent time constraints (e.g. stroke specialist consultant physician assessment within 24 hours and physiotherapist assessment within 72 hours). An evaluation of London’s stroke centralisation found that mortality and length of stay (LOS) were reduced, if all stroke patients receive hyperacute care. 35 It has also been shown that these changes are sustainable over time. 41

The London model has been shown to improve clinical outcomes and care provision,35,37,38 and is cost-effective. 42 Beyond London, many regions in the NHS are currently seeking to centralise stroke service organisation in different ways. 43,44 The development of mechanical thrombectomy (surgical removal of a blood clot from a blood vessel) services is an important new development for a subset of stroke patients. As of May 2020, further reorganisation of the London model is currently in progress with the designation of a subset of HASUs that can provide thrombectomy. At the time of our study (2014–15), no HASU was providing a formal thrombectomy service for stroke associated with large vessel occlusion. Therefore, 24/7 availability of scans and assessments by specialists will be vital to enable the correct patients to be transferred to a thrombectomy centre.

Research aims

This study aimed to identify the extent to which London stroke services were providing care 24 hours a day, 7 days a week (24/7), and, if they were, how this was being achieved. The research provides an opportunity to examine how and if a range of organisational factors influence 24/7 provision of stroke care, and to assess the extent to which temporal patterns of provision are reflected in patient outcomes, including mortality, LOS and functional independence. The research also aimed to explore service developments related to temporal consistency. These lessons are of potential value to people who commission, organise and run stroke services, both in London and across any area of the English NHS that seeks to develop or provide 24/7 hyperacute stroke care. They may also have relevance to research in, and provision of, 24/7 care in other health-care settings, such as emergency and intensive care, treatment of myocardial infarction and elective surgery.

Research objectives

Structure of the report

The report is structured as follows.

See Chapter 2 for:

• an overview of the study sites

• data and analysis methods.

(Detailed methods are provided within each findings chapter.)

See Chapter 3 for:

• costs of acute stroke care in London HASUs on different days and at different times of admission.

See Chapter 4 for factors that influence, temporally:

• consistent care

• inconsistent care.

See Chapter 5 for:

• nursing roles on the HASU

• temporal variation in nursing provision

• associated processes and outcomes.

See Chapter 6 for:

• temporal structures acting on the HASU patient pathway

• how this can have an impact on measurement of temporal variation.

See Chapter 7 for:

• discussion and summary of our findings in relation to our research questions

• implications for health services, research and 7-day working policies.

Changes to the protocol

We made some changes to the protocol because of difficulties in obtaining access to all our data sources, especially mortality data, as noted in Overview of the research project. Initially, we experienced difficulties gaining access to the HASUs, and later on it proved difficult to recruit patients and carers to our interviews. This is partly because of the long delay periods between recruitment on the wards and patients returning to their homes or care facilities when they would be ready to complete the interview. In addition, there was a significant dropout rate between patients expressing interest in taking part and completing interviews: 80 patients expressed initial interest, but either declined later on or would not respond to communications. Patient recruitment improved with registration of the study on the Clinical Research Network portfolio, which allowed access to the Clinical Research Network research practitioner teams in each HASU and a more systematic approach to patient identification. This released our team to discuss the study with patients who had already expressed interest in taking part.

Perspective

We approached this research with a commitment to the study of the HASU system from both a quantitative and a qualitative perspective. Although not an ethnographic study, we adopted some principles of multisited ethnography, such as following people and objects through systems. 50 For example, we based some of our observations around the journey being followed by a stroke patient all the way from the emergency room, through scans and tests, and up to the HASU ward itself. We followed members of staff, such as consultants on their ward rounds and nurses around the hospital. We also focused on relative cultural meanings, such as what 24/7 means from a variety of professional and patient perspectives. We committed to be reflexive and iterative in our approach to data collection, regularly meeting to debrief and make decisions about future plans for data collection, and quantitative and qualitative analysis. 51 Finally, we sought to understand both our qualitative and our quantitative data through our immersion in the services. This led to new additional quantitative analyses being undertaken, such as our analysis of HASU nursing data.

Setting

Our study was set in all eight of the London HASUs. These are dedicated wards, often sited in close proximity to relevant specialties, such as neurology, stroke wards, gerontology, etc. The wards range in size from 18 to 50 beds. We did HASU-specific profiles, noting bed numbers, shift hours, access to scans (e.g. magnetic resonance imaging) (see Tables 11 and 18). In addition to the ward, the HASU teams work in the ED to assess and treat incoming patients. This also includes moving them to scanning facilities and working with radiologists to interpret CT scans. Some patients are either assessed or temporarily situated in other wards when the HASU has inadequate capacity; these are known as ‘outliers’. Therefore, our observational work occurred in all of these locations, but not outside the hospital.

The London HASUs are in the following hospitals:

• Charing Cross Hospital

• The Royal London Hospital

• King’s College Hospital

• St George’s Hospital

• Northwick Park Hospital

• Princess Royal University Hospital

• Queen’s Hospital

• University College London Hospital.

Ethics approval

The study received approval from the National Research Ethics Service Committee London, Westminster Branch (reference number 14/LO/1355) and local approvals at each participating trust.

Sentinel Stroke National Audit Programme

We obtained anonymised patient-level data from the SSNAP for all patients treated in London HASUs with a primary diagnosis of stroke (ischaemic stroke or primary intracerebral haemorrhage) between 1 January 2014 and 31 December 2014. The SSNAP collects data on clinical characteristics, care quality (from the time of admission up to 6 months after stroke) and outcomes for all stroke patients admitted to acute care hospitals in England. Data are entered into the SSNAP database by local clinical teams via a secure web interface. During our study period, the case ascertainment in the SSNAP, which is calculated as the proportion of all acute stroke patients admitted to hospitals, was estimated to be 90% for England. 52

Nurse staffing data

Comprehensive data on nurse staffing were collected from nursing rotas from each of the London HASUs for every time of the day across the 12-month study period (between 1 January and 31 December 2014). Nurse staffing measures comprised the band of the nurse in charge [bands 5–8 and registered bank (or agency) nurse] and whether the nurse in charge was also responsible for administering intravenous thrombolysis or whether there was an additional nurse to perform this activity. We also collected data on the total number of specialised stroke nurse staffing on duty in the HASU by band (as above), the number of beds in each HASU and the times of the nursing shift handover period. These data were combined with the SSNAP data set based on the time of arrival at the hospital, so that for every patient who was admitted to a London HASU during the study period we also knew the characteristics of the nurse staffing at the time that they were admitted to the hospital. Note that we were unable to collect medical staffing data and so did account for this in our analyses (see Chapter 5).

Sampling

Staff were sampled purposively to ensure coverage of a range of professional roles found in the HASUs (Table 2), including medical, nursing, therapy, administrative and managerial staff. Patients were also recruited purposively to reflect variations in sex, age, stroke type, discharge destination and carer perspectives (Table 3). The recruitment procedure was as follows: HASU staff provided researchers with a list of eligible patients in the HASU or stroke unit previously admitted to the HASU (see Recruitment and consent). Patients were introduced by clinical staff and given study information to consider. They were told that the interviewers were not members of current clinical staff and that their participation would not affect their care. Full and informed consent was obtained at the time of the interview. Interviews were conducted at participants’ homes, public places or clinical settings, according to their preference. The interviews were conducted between 6 and 99 days after admission to the HASU (average 38 days, median 33 days).

Recruitment and consent

To analyse the organisation of all eight London HASUs during the day and during the night, on weekdays and at weekends, authors GB, ABL, JE and PX conducted 76 interviews with HASU staff and 31 interviews with stroke patients and carers, and undertook 41 non-participant observations of HASU activity over approximately 85 hours. JE also gathered data through informal discussion on staff numbers and seniority on the ward, the composition of staff attending suspected stroke calls in the emergency room and the availability of scans across the week from each site. All interviewers were female and employed as research staff on the project. GB, ABL and PX are postdoctoral researchers with significant interviewing experience and no prior experience of working in stroke. JE was a stroke co-ordinator in a HASU prior to joining the project, with an occupational therapy background. She received research interview and analysis training and feedback from the rest of the team. Qualitative work was overseen by NJF and AIGR, who are experienced in both qualitative research and the London stroke system through previous work.

Patients were approached if they had a provisional or tentative stroke diagnosis and experience of receiving care in the HASU. We excluded patients and carers unable to sufficiently understand spoken or written English, and those who were unable to give informed consent by written or oral methods. Carers were interviewed alone or alongside patients, if patients requested it, and when the patient suffered from severe cognitive impairment or speech difficulty as well as when a patient had died during, or after, admission to a HASU.

The staff interviews were conducted according to a semistructured topic guide. The guide comprised questions about a typical day (see Report Supplementary Material 1), the perceived differences in and attitudes towards working in and out of hours, team working and the HASU environment. The patient interviews followed a broadly biographical topic guide, asking about their journey up to, during and after their HASU stay. We also prompted them to reflect on their experiences in and out of hours. All participants were interviewed only once and interviews were recorded digitally.

Observations

Global written consent for observations was gained initially from service leads and from a selection of staff at team meetings. Thereafter, verbal consent was given by staff members for researchers to make observations of staff meetings and of the stroke care pathway.

Non-participant observations were conducted at least four times at each HASU site, two visits on weekdays, one in the evening during the week and one at the weekend, with some extra visits to certain HASUs to confirm or add to our findings. We aimed to collect information about two dimensions of HASU service organisation and delivery, how (1) a patient progresses through the stroke pathway, assessment in the ED, care on the HASU and HASU repatriation; and (2) HASU staff organise themselves to meet the London specifications in day-to-day delivery operations [e.g. thrombolysis for at least 90% of eligible patients, timely brain scans, direct admission to HASU within 4 hours, daily multidisciplinary team (MDT) meetings]. This research design drew on ongoing research to identify likely service elements and factors influencing the design and implementation of HASU care (Table 4).

Quantitative analysis

We investigated variations in quality of acute stroke care and outcomes by day and time of admission in London HASUs and evaluated whether or not nurse staffing affected 24/7 access to high-quality acute stroke care in eight London HASUs. For the former, we ran patient-level regressions, regressing each measure against time period of admission. To inform our cost analysis we also tabulated the non-time-dependent measures described above against time period of admission. For the latter, we also included nurse staffing measures in models for London HASUs. In every model we controlled for baseline patient characteristics (sex, age and ethnic group) and case mix (type of stroke, comorbidities prior to admission, mRS before stroke, level of consciousness on arrival at the hospital, method of admission to the hospital, time from onset of stroke symptoms to admission and month of admission). We tested for statistically significant variations using Wald tests and likelihood ratio tests.

Qualitative analysis

The investigation was driven by understanding how London HASUs organise and deliver 24/7 stroke care in practice, how these service patterns have been influenced by workforce and organisational factors, and how stakeholders, including staff, patients and carers, experience these services.

The qualitative analysis drew together interview and observation data using a composite, thematic analysis across HASUs, as well as a comparative case study approach. The thematic approach55 allowed for developing an understanding of the central issues HASUs, as a particular type of service delivery model, face when delivering care in acute trust settings across 24/7 time scales. The comparative case study approach46–48 illuminated similarities and differences between HASUs and across professional groupings.

We used an iterative approach to analysis, following a five-step process to generate thematic and case study insights.

Synthesis of approaches

We used our monthly project meetings to bring together observations, queries and suggestions between the qualitative and the quantitative researchers. Interactions centred on key ideas such as:

• Can we explain why certain variables do and do not show temporal variation?

• What are the best ways to capture temporal variation?

• What factors would we expect to influence temporal variation?

• What further data can we gather or analyse to expand our findings?

Data and measures

As outlined in Chapter 2, Quantitative data collection, we used anonymised patient-level data from the SSNAP for all patients in London HASUs with a primary diagnosis of stroke between 1 January and 31 December 2014.

Statistical analysis

We ran patient-level logistic regressions by regressing each measure against the time period of admission. For LOS, we used parametric survival models (modelled as time to event of discharge), assuming a log-normal survival distribution. In every model we controlled for baseline patient and case mix characteristics, month of admission (12 categories) and hospital trust. When analysing mRS scores 0–2 compared with 3–5 at the end of the inpatient spell, we additionally controlled for the number of days after admission the mRS score was measured. We were unable to do this for the analysis of mRS score 0–2 compared with 3–6, as date of death was not available. We tested for statistically significant variations across the week using Wald tests and reported the results as joint p-values under the null hypothesis that the regression coefficients for every time period relative to the omitted time period were zero. We calculated the average predicted probability of each outcome (predicted median LOS in the case of the LOS variables) in each time period controlling for the covariates. Patients admitted with a diagnosis of acute stroke in London who were not treated in a HASU were excluded (6% of all London patients in our data set were not treated in a HASU). p-values < 0.05 were considered to be statistically significant. Data on National Institutes of Health Stroke Scale (NIHSS) score, a validated measure of stroke severity on a scale from 0 (no stroke symptoms) to 42 (severe stroke), were available for 93% of patients in London HASUs. Owing to the extent of missing NIHSS data, in our main analysis we controlled for stroke severity using level of consciousness on arrival at the hospital (one component of NIHSS, which was available for all patients in London HASUs). We then re-ran all analyses controlling for NIHSS on arrival at the hospital on the smaller sample, instead of level of consciousness on arrival. The findings using NIHSS score on arrival were qualitatively the same and are presented in the Report Supplementary Material 2, Figures S1–6.

Cost analysis

To investigate the costs of acute stroke care in London HASUs on different days and at different times of admission, we explored whether or not mean costs per patient in the HASU varied by time period of admission. Our cost perspective was the HASU. We examined costs associated with variations in LOS in the HASU. In a secondary analysis, we also included total LOS in the hospital. We re-ran the LOS models, described above, using ordinary least squares and computed adjusted mean HASU and total LOS by time period of admission (although mean costs are affected by skewness and median costs are not, it is the arithmetic mean costs that are most informative for health-care policy decision). 66 We also examined costs associated with variations in the non-time-dependent quality-of-care measures described in Chapter 2 (see Box 1) for patients in London HASUs (whether or not the patient had brain imaging; was admitted to a stroke unit; had dysphagia screening; was assessed by a nurse trained in stroke management, or a stroke specialist consultant physician, physiotherapist, occupational therapist or a SLT). Note that the reason for focusing on the non-time-limited measures was that costs are driven by whether or not the patient received these processes, rather than whether or not they received them within a given time period. We also included receipt of thrombolysis (administration of intravenous thrombolysis to eligible patients). As it was simpler to identify trends, we focused on variations between the four broad time periods across the week (Monday to Friday, 08.00 to 19.59; Monday to Friday, 20.00 to 07.59; Saturday and Sunday, 08.00 to 19.59; and Saturday and Sunday, 20.00 to 07.59). Measures for which there were variations by time period of admission were included in the cost analysis. In this case, we multiplied the incremental volume of resource use associated with each measure for the three other time periods compared with Monday to Friday 08.00 to 19.59 by its unit cost, taken from two published economic evaluations of acute stroke care in London HASUs67,68 and inflated to 2017/18 prices. 69 We assumed that unit costs of the non-time-dependent measures did not vary by the time period of admission, because these are typically provided during normal working hours (see Chapter 6, What this analysis adds).

Cost analysis

Results were similar when controlling for NIHSS score on arrival at hospital, instead of level of consciousness, on the smaller sample of patients with non-missing NIHSS data: results with p-values < 0.05 and trends across the week were unchanged (see Report Supplementary Material 2, Figures S1–S6 and Table S3).

Variations in the non-time-dependent quality-of-care measures by the four time periods are shown in Figure 10).

FIGURE 10.

Non-time-dependent quality-of-care measures across four periods in the week in London HASUs. Figures are average predicted probabilities of each measure in each time period controlling for the covariates. We tested for statistically significant variations across the four time periods using Wald tests and reported the results as joint p-values under the null hypothesis that the regression coefficients for every time period relative to the omitted time period were zero (p-values in parentheses). All patients received a brain scan in every time period. n/a, not applicable.

In contrast to the findings for the time-dependent versions of these measures in Table 6, Figure 10 shows that > 90% of patients receive every measure and that there is little variation across the four time periods. We also note from Table 6 that there is no variation in administration of intravenous thrombolysis across the four time periods. We conclude from these data that the costs of acute care in London HASUs, with respect to these measures, is unlikely to vary on different days and at different times of admission. Table 6 shows significant variation in median LOS for London HASUs, with longer HASU and total LOS among patients admitted at the weekend. Table 7 shows a similar trend for mean LOS (note that the mean values in Table 7 are higher than the corresponding median values in Table 6, indicating skewed data).

In Table 8 we compute the cost implications of the difference in LOS. Focusing on differences in mean LOS, patients who were admitted between 08.00 and 19.59 at the weekend were, on average, £166 more costly to treat in the HASU (£668 when including total LOS) than those admitted between 08.00 and 19.59 on a weekday. Those who were admitted between 20.00 and 07.59 at the weekend were on average no more costly than those admitted between 08.00 and 19.59 on Monday to Friday (£445 more costly when including total LOS). For those admitted between 20.00 and 07.59 on a weekday, the cost difference per patient was small compared with those admitted between 08.00 and 19.59 on a weekday.

Principal findings

In our study, we found no evidence for an admission effect across the week on early outcomes in acute stroke patients admitted to a London HASU: 3-day mortality and mRS score at hospital discharge did not vary by day and time of admission in London HASUs. This is consistent with a recent study based on administrative data in the UK. 34 The study34 found a steady reduction in in-hospital mortality difference between weekday and weekend stroke admissions in 2008–14 across England, and that this difference is no longer statistically significant in 2014.

There was also no variation by day and time of admission across the week in terms of rapid access to brain scanning, stroke nursing care and thrombolysis in London HASUs. Other quality-of-care measures did significantly vary across the week in London HASUs and three patterns of variation were detected: (1) by time of day, but not day of the week; (2) by day of the week, but not time of day; and (3) by time of day and day of the week. LOS was longer among patients admitted to London HASUs at the weekend. We hypothesised that there would be non-significant variation across the week in care quality measures in London HASUs and that this would translate into non-significant variations in outcomes. The lower variation in care quality measures across the week in London HASUs was confirmed, but only with respect to ‘front-door’ measures of acute stroke care. With respect to the health outcomes, there was no variation in mortality at 3 days and disability at hospital discharge by day and time of admission across the week in London HASUs. This is consistent with previous studies showing that timely access to thrombolysis is associated with good stroke outcomes. 70

We found that patients who were admitted at the weekend were on average more costly than those who were admitted during the week because of their longer LOS. When incorporating total LOS, the additional mean cost per patient was £445–668, depending on when the patient was admitted at the weekend. Those who were admitted between 20.00 and 07.59 on a weekday had similar costs to those who were admitted between 08.00 and 19.59 on a weekday.

Strengths and weaknesses

The main strength of our study is the rich data set we have used, containing detailed information on quality of care, outcomes and patient characteristics. We have examined whether or not time of admission was related to quality of care using a comprehensive set of indicators from across the acute stroke care pathway. Most of the measures were from a pre-existing set of national acute stroke care indicators, and those that were added had more stringent time constraints to reflect the time-critical nature of acute stroke care. Our outcomes were stroke mortality and disability, whereas previous studies have focused on mortality. 6,25,34,71–73 The rich set of patient characteristics in the data set meant that we could control for patient factors likely to affect quality of care and outcomes that vary by day and time of admission across the week. There are several weaknesses. First, although case ascertainment in the SSNAP was 90% during the time period of our study, these data might not be representative of all stroke patients. Second, although analyses of hospital administrative data to investigate weekend effects in stroke may have been undermined by evidence of variations in inaccurate coding across the week,58 in the SSNAP data are inputted voluntarily by hospitals and we cannot exclude the possibility of inaccurate or selective reporting. Particularly problematic for our study would be if this bias was more likely to vary by time of admission. Third, we were unable to measure long-term outcomes, as these were not available in the SSNAP. Mortality data in the SSNAP are currently available only for patients who are in hospital and, therefore, to reduce the risk of bias, we measured mortality at 3 days after admission, when most patients will still be in hospital. Three-day mortality has been used in previous studies to evaluate the centralisation of acute stroke services in London,35 but the focus in our study on in-hospital mortality only is a further limitation. Similarly, long-term disability data are not reliably collected in the SSNAP, and so disability was measured by the mRS at the end of the inpatient spell. Fourth, although the richness of our data set means that we have been able to control for confounding factors, we cannot exclude the possibility of confounding due to unobserved patient characteristics or staffing levels. Fifth, although the sample size of our study is relatively large (n = 7094), when evaluating quality of care and outcomes across the week the number of observations in each of the 42 time periods was considerably smaller (mean of 169). We cannot exclude the possibility that this number of patients resulted in wide confidence intervals (CIs) around the adjusted predicted probabilities in each time period, making it less likely to show significant variation in the measures evaluated. It is difficult to provide sample size calculations for statistical analyses between 42 groups (time periods). However, when we re-ran our analyses of quality of care and outcomes across the four pooled time periods in the week in London HASUs, the results were broadly similar, and this provides some reassurance about the statistical power of our analyses. Sixth, we acknowledge that previous studies have identified that improvements in outcomes following acute stroke in England have been shown to be statistically unrelated to the centralisation of stroke services in London, but are consistent with an overall national focus on improving stroke services. 34 Further research examining whether or not the findings seen in the present study for London HASUs were also achieved in the rest of England would be beneficial.

Comparison with other studies

There is a large literature examining weekend effects in health care across a range of clinical areas. 74 In acute stroke, there is conflicting evidence on whether patients admitted at weekends have higher or lower quality of care and better or worse outcomes. 6,9,40,71,72,75 However, recent analyses have shown that care quality and outcomes in acute stroke vary across the week, and that comparing weekend with weekday or in-hours effects with out-of-hours effects is flawed, as it does not take into account variations by day of the week and time of day. 22 This study,22 using the same data set as ours, but from an earlier time period and analysing the whole of England and Wales, found that quality of care varied across the entire week, not only between weekends and weekdays, with a number quality-of-care measures showing different patterns of variation over the week. What our study adds is analyses of variation in quality of care and outcomes in London HASUs by separately following the centralisation of acute stroke services in London in 2010, which has been shown to increase the quality of care and outcomes overall. 35,38,41

Implications

There are several implications of our study. The first is that London HASUs appear to operate a uniform service across the week with respect to some, but not all, aspects of acute stroke care. Performance standards originally set by Healthcare for London stipulated that London HASUs should operate a 24/7 service with respect to first assessment by a stroke nurse, rapid access to brain scans and administration of thrombolysis to eligible patients; our findings show that London HASUs do operate a 24/7 service with respect to these measures. However, for other less time-critical measures, such as senior stroke physician assessment within 24 hours and therapist assessments within 72 hours, we found significant variation by day and time of admission in London HASUs. This suggests that some performance standards, such as ‘front-door’ interventions, may be emphasised more than others. The second implication is that our study focuses only on patients admitted to London HASUs and not other hospitals in London; our data suggest that 6% of acute stroke patients in London are not treated in a HASU. However, some of these patients will not have been eligible for HASU care because of greatly delayed presentation or identification of stroke, and others will have had a stroke after surgical procedures or in another context which precluded their admission to a HASU. Our focus on London HASUs was deliberate, as the aim of our study was to evaluate the HASU model. However, it means that our findings for London HASUs should not be generalised to all patients in London. Indeed, there is evidence that quality of care is lower for acute stroke patients in London not treated in a HASU than for those who are. 38 Second, the London model may not apply to services operating in other areas, in particular rural settings, where the greater travel times make centralisation of this kind challenging. 76 This means that potential benefits of the London model, in terms of 24/7 care, are unlikely to be achieved nationwide. Third, the centralisation of acute stroke services in London was estimated to occur at an additional cost of £20M, allocated to cover the increased cost per bed-day in a HASU. 77 With this additional level of funding it might be expected that the quality of care in London should improve, although whether or not it should produce less variation in quality of care and outcomes across the week in London is unclear. There is some evidence that the reorganisation in London was cost-effective,67,68 but further analyses accounting for the size of the upfront investment, the relatively high costs per day of hyperacute stroke care, the impact on mortality and disability, and the lifetime costs incurred by the NHS, social services and families caring for stroke survivors at different levels of disability, would be helpful.

We evaluated the costs of acute stroke care in London HASUs on different days and at different times of admission. We found that patients who were admitted at the weekend were on average more costly than those who were admitted during the week, due to their longer LOS. Those who were admitted between 20.00 and 07.59 on a weekday had similar costs to those who were admitted between 08.00 and 19.59 on a weekday.

Non-participant observations

We conducted non-participant observations at least four times at each HASU site, two visits on weekdays, one in the evening during the week and one at the weekend, with additional visits to confirm or add to our findings. We collected data on various aspects of HASU activity likely to influence care provision, guided by clinical interventions in our quantitative analysis and our initial observations (Table 9).

Purposive sampling of observations is described in Methods.

We initially structured observations so that availability of staff and key processes (e.g. handover and ward rounds) were covered. Subsequently, we targeted our observations towards our emerging analysis, such as following patients’ journeys from the ED to the HASU ward, and shadowing the nurse in charge and consultants to understand their roles.

We obtained global written consent for observations from service leads and from a selection of staff at team meetings. Thereafter, verbal consent was given by staff members for researchers to make observations of staff meetings and of care provision.

Interviews

Staff were sampled purposively to ensure coverage of a range of professional roles within all eight HASUs, with perspectives on the clinical interventions65 (e.g. interpreting brain scans, thrombolysis, therapy assessments), including medical, nursing, therapy, and administrative or managerial staff (see Chapter 2, Sampling).

Researchers approached staff during the observation work and through existing contacts with study information, stating that participation would be confidential and anonymously presented in publications. Interviews were conducted with fully informed, written consent and in private settings, according to a semistructured topic guide (see Report Supplementary Material 1), and with the aim of understanding temporal variations from the staff perspective, including typical daily activities and attitudes towards working in and out of hours. Interviews lasted between 20 minutes and 1 hour. No field notes were taken, but each interview was discussed with the other team members. Interviews were audio-recorded and transcribed verbatim.

Factors influencing temporally consistent care

We found that there was no variation between the HASUs in terms of adherence to the London stroke service standards. The London stroke service standards required that HASUs have 24/7 availability of staff who are trained to assess eligibility for and deliver thrombolysis; this was normally completed by a team of medical and nursing staff who were on standby to immediately attend patients at the ED when alerted to new potential thrombolysis patients by the ED team (Figure 11).

FIGURE 11.

Thrombolysis pathway. This figure is adapted from Black and Ramsay et al. 85 This is an Open Access article distributed in accordance with the terms of the Creative Commons Attribution (CC BY 4.0) license, which permits others to distribute, remix, adapt and build upon this work, for commercial use, provided the original work is properly cited. See: http://creativecommons.org/licenses/by/4.0/. The figure includes minor additions and formatting changes to the original figure.

This figure is adapted from Black and Ramsay et al. 85 This is an Open Access article distributed in accordance with the terms of the Creative Commons Attribution (CC BY 4.0) license, which permits others to distribute, remix, adapt and build upon this work, for commercial use, provided the original work is properly cited. See: http://creativecommons.org/licenses/by/4.0/. The figure includes minor additions and formatting changes to the original figure.

Thrombolysis was administered by the stroke team when appropriate, after a battery of clinical and imaging tests had been conducted. The time taken to complete this process is one of the core performance metrics by which each treating unit is measured. Each HASU attempted to make this pathway as efficient as possible, by having a designated ‘thrombolysis team’ irrespective of clinical activity elsewhere in the stroke service and through local arrangements with radiology, to guarantee that 100% of stroke patients potentially eligible for thrombolysis were scanned within the next CT scan slot. However, as detailed in Table 11, the staff supporting ‘front-door’ activity reduced both in number and in seniority in the evenings and at the weekends. As a result, the HASU teams made significant adaptations out of hours, including extending roles and responsibilities, and introducing processes to ensure continuity between different times of day.

Factors influencing temporally inconsistent care

Variations in outcomes and delivery of clinical interventions in London HASUs are summarised in Table 12. We explain these findings in terms of reductions in both the number and the seniority of medical, managerial and allied health professionals out of hours, and reductions in repatriation options out of hours.

Data and measures

Interview and observational data collection are explained in Chapter 2, Qualitative data collection, as are quantitative nurse staffing data collection and linkage. Each day was divided into 48 30-minute shifts. Every patient admitted within each of these shifts was assigned a corresponding nurse staffing measure during the same time period.

For this analysis, the following quality of care indicators were used, as nurses’ input was essential to meet the standards of care: brain scan within 1 hour, dysphagia screen within 4 hours, admission to a stroke unit within 4 hours, administration of thrombolysis to eligible patients and door-to-needle time. Outcomes were measured as whether or not the patient died within 3 days and disability using the mRS score (0–2 vs. 3–6; moderate, moderately severe, severe disability or death) at the end of the inpatient stay. We also analysed the effect of nurse staffing on LOS in HASU and total LOS in the hospital.

We matched the nurse staffing data set with the SSNAP data set based on the time of admission to the hospital, not the HASU, in line with the way in which clinicians describe the ‘clock start’ of other process measures in the SSNAP audit. As reflected in Chapter 5, many of the processes of care analysed are likely to be delivered in the ED on arrival at the hospital. Note that data were not collected on medical staffing, so we were unable to control for this in our analyses.

To examine trends across the week we used four time periods of time of admission to the hospital, similar to broad trends used in Chapter 3, Data and measures: Monday to Friday, 08.00 to 19.59; Monday to Friday, 20.00 to 07.59; Saturday and Sunday, 08.00 to 19.59; and Saturday and Sunday, 20.00 to 07.59. This is different from previous research in which the only comparison was between weekdays and weekends. 60

Sequential mixed-methods analysis

Nursing roles on the hyperacute stroke unit

Senior nursing roles interacting with the hyperacute stroke unit

There were other nursing roles, however, that are not tied to bed numbers, and these have specific functions (Figure 12).

FIGURE 12.

Senior nursing roles and working sites.

Senior nurses had very different roles (Table 14) from junior nurses (see Table 13), although we did find that some HASUs had trained their junior nurses to fulfil specific tasks, such as thrombolysis and dysphagia screening. Senior nurses were always present in the daytime during the week, but were not always present during the nights or at weekends. Someone in the ward will always fulfil the role of nurse in charge and thrombolysis nurse, but at night and at weekends this may be a lower band nurse or the same person may complete both roles. However, the actual tasks performed by these roles are different in weekday hours (Tables 14 and 15). It is worth noting that when a senior nurse is actively involved in patients’ care, their ward management and oversight activities are reduced.

TABLE 14 - Senior nursing activities that are 24/7

Role	Task
Communication roles	Update/liaise with families Act as a link between doctors, nurses and therapists Update patient boards or lists
Patient care roles	Replace missing nurses to provide direct patient care Take over the roles that other nurses are unable to do (e.g. if staff have not completed training) Dysphagia screening Attend thrombolysis calls and assess patients in the ED with doctor Assess patients to determine their needs
Support roles	Oversee and mentor junior nurses Training junior nurses Supporting doctors Supporting new staff
Co-ordination and oversight	Escalate urgent patient issues Bed management Complete rotas and ensure that staffing levels are adequate Co-ordinate the transient ischaemic attack clinic Co-ordinate ward staff to ensure that tasks are completed (e.g. paperwork, repatriation) Co-ordinate the discharge or transfer of patients, including transport, medications, etc. Oversee the stroke pathway

TABLE 15 - Senior nursing activities in weekday only in addition to 24/7 duties

Role	Task
Education	Provide education to patients, such as risk factors, lifestyle, driving, medication Teaching at university Provide advice to patients and families after discharge
Patient assessment	Assess, admit and manage non-urgent referrals
Service management	Attend MDT meetings Managerial role Nurse-led ward rounds to review care plans Ward rounds with doctors Make sure doctors’ orders are carried out Attend strategy meetings Manage investigation of complaints, clinical incidents and near misses Introduce and embed trust policy and care initiatives Manage ward budget Responsible for trust-based ward quality metrics Responsible for upkeep of ward environment

How senior is the nurse in charge of the ward at different times of day and days of the week?

Across all HASUs, for the majority of patients admitted to a hospital, the nurse in charge was a band 6 nurse (for 68–90% of patients in each time period across the week) (Figure 13). The nurse in charge was band 5 for 7–29% of patients across the week, with higher proportions of band 5 nurses in charge during the night. The nurse in charge was band 7 for 0–13% of patients across the week, mainly during the day on weekdays. The nurse in charge was rarely a band 8 nurse or a bank or agency nurse.

FIGURE 13.

Band of nurse in charge, by day and time of admission. Variation across the week (p < 0.001).

For 61% of patients there was a dedicated thrombolysis nurse who was separate from the nurse in charge on arrival (Figure 14), varying from 36% to 80% across the week. For patients admitted at night, there was a greater likelihood that the thrombolysis nurse and nurse in charge were roles held by one person.

FIGURE 14.

Whether or not there were separate nurses managing thrombolysis and the ward, by day and time of admission. Variation across the week (p < 0.001).

The mean number of HASU nurses varied between 8 and 11 across the week, with higher numbers working during the day (Figure 15). The most common band of nurse in each time period was band 5, with, on average, three to five band 5 nurses working across the week, followed by health-care assistants (two or three nurses working across the week). The next most common were band 6 nurses (one or two nurses) and bank or agency nurses (one or two nurses).

FIGURE 15.

Number of nurses on duty involved in patient care by band, by day and time of admission. Variation in all but band 8 across the week (p < 0.001). HCA, health-care assistant.

Although we have granular data describing nurse staffing for every half-hour of the day over the study period, nurse staffing varies largely by shift. On average, the number of band 7 and 8 nurses was small (< 1), seen mainly during the day on weekdays. Band 7 and 8 ward managers in most cases are not included in rotas, as they do not provide patient care. There was greatest variation across the week in band 5 nurses, health-care assistants and bank and agency nurses, with higher numbers of band 5 nurses and health-care assistants during the day, and higher numbers of bank and agency nurses at night.

How does nursing seniority impact on clinical processes and patient outcomes?

Results we report are for patients admitted during all time periods combined and in each of four time periods separately. In Report Supplementary Material 2, Table S4, we report analogous results but only for patients eligible for thrombolysis on admission to the hospital (according to the Royal College of Physicians’ definition of eligibility). In Report Supplementary Material 2, Table S5, we report analogous results for patients not eligible for thrombolysis.

Nurse staffing had a statistically significant impact on having a brain scan within 1 hour, dysphagia screening within 4 hours and the probability of being discharged with at least moderate disability (Table 16). It did not have a significant impact on administration of thrombolysis, admission to the HASU within 4 hours, inpatient mortality within 3 days or LOS on HASU and in the hospital.

Limitations

There are several weaknesses in our analysis. First, nurse staffing shifts were not the same in all eight HASUs. We chose 12-hour time periods, starting at 08.00, to be consistent with the in-hours and out-of-hours definition adopted in Chapter 3. However, in several HASUs, the first shift started at 07.00 or 07.30, and in one HASU there were three daily shifts instead of two. This was taken into account in the handover control variable. Second, mRS score at discharge from a HASU is a complex measure that accounts for many aspects of patient care and, as uncovered in our qualitative observations, is also affected by the organisation of repatriation processes, availability of local services and HASU demands, which might not always reflect an outcome of quality of care. Third, our data set did not include medical staffing and, therefore, we were unable to control for this in our analyses, which could confound the results. Future research exploring the interaction between medical and nursing staffing in this context would be beneficial. Fourth, similar to what is discussed in Chapter 3, Discussion, there was limited follow-up of patients, including mortality and disability indicators not available in the SSNAP.

Summary

• There was variation in nurse staffing by time of the week, particularly in terms of the seniority of key roles, rather than the number of nurses overall.

• Senior nursing roles were more differentiated than junior roles, which can be substituted by bank nurses and other nursing staff.

• When senior nurses took on the nurse-in-charge role during the day in the week, it had an impact on the delivery of clinical interventions.

Analysis

An inductive coding process initiated the analysis, for which authors GB, AIGR, ABL and JE derived a group of codes relating to definitions of ‘in hours’ and ‘out of hours’. We developed this with a set of working ideas about how these different definitions had arisen. This was incorporated into iterative processes of discussion with the whole authorship team, in which we developed different ideas about how best to use our quantitative data to truly measure temporal variation. Many of these discussions involved ‘dialogues’ between the qualitative (particularly observational) and the quantitative data. For example, when we felt that we had witnessed temporal variation, we then tried to find a measure that might reflect the dynamic we had identified. This exposed difficulties with the SSNAP data set and provoked new measures (such as looking at patients who had not been eligible for thrombolysis) to expose procedural elements that might be prone to greater temporal variation. The resultant analysis was the result of these processes, presented partly in a thematic form around the concept of temporal structuring, with graphically illustrated examples.

Understanding the patient pathway

The first part of this analysis was a schematic or generalised account of the patient’s pathway through the HASU and how patients’ clinical outcomes are influenced by the temporal structures of the HASU. The second part describes in more detail what the temporal structures operating in the HASUs are and how they came to exist. The final section describes how the patient pathway interacts with temporal structures to create measured temporal variation.

Consistent with patient-focused studies,92 our observations and interviews characterised the HASU patient pathway in three main temporal intervals: (1) acute care, (2) assessment and stabilisation, and (3) repatriation (Figure 17).

FIGURE 17.

Patient-centred model of stroke patient HASU pathway and temporal structures. a, Quality assurance metrics refer to SSNAP,54 Royal College of Physicians guidelines93 and the London stroke service standards. 36

‘Front-door’ activities

Front-door activities begin when a patient arrives via ambulance or other transport to the ED of a hospital. Ambulances are instructed to take patients suspected of stroke to a hospital with a HASU. Patients arrive at hospitals with a suspected stroke at all hours of the day, although the numbers of admissions are higher during the day than at night, and during the week than at the weekend. From there, patients receive a number of neurological and medical tests and a brain scan, before immediate treatment decisions are made, such as tPA. Those who are still suspected to have had a stroke are admitted to the ward when a bed becomes available.

Clinical assessment, investigation and provision of time-critical treatments

During their stay on the ward, patients are given a range of clinical input, including medical, nursing and allied health professionals (e.g. occupational therapy, speech and language therapy, physiotherapy). Therapists assess the patient for independence, nutrition, continence, mood, etc., and repatriation is contingent on these findings. Patient-centred temporal structures are created by patients’ needs and biorhythms, such as needing to eat, move around and connect with their families. Clinical input was therefore broadly temporally structured around these needs, with therapeutic input, such as assessments and curative therapy, and non-emergency nursing care, such as washing, feeding and liaising with family members, given during the day. Similarly, medical assessments, scans and reviews take place during the day, although emergency medical attention can be provided at any time.

Repatriation

Hyperacute stroke units are designed to provide hyperacute intervention, with an expected LOS for the patient of around 3 days. The price was calculated on a bed-day basis. There was a different price for day 1 compared with days 2–4, which have the same price, and post day 4, which will be priced at the standard excess bed-day rate. 36 This means that repatriation from the HASU to a stroke unit (ASU), a patient’s home or a care home is an important part of HASU staff work. Preparing a patient for discharge involves co-ordinated assessments and paperwork from a variety of staff, including nursing, allied health professionals and the repatriation co-ordinator:

So we, we certainly don’t rush patients home, um, but there is a continuous pressure to rush patients out to create beds, [. . .], so, some patients, we can get into the hospital much quicker than other patients, just by knowing what access they have to necessary services in the community. In terms of repatriation, there are ongoing huge delays, there are patients unnecessarily in the HASU who have been ready for 7 days, up to 2 weeks sometimes, waiting for transfers, and they’ve suffered, because they’re not always prioritised by the therapists, has been a SU [stroke unit] patient, by the fact that they’re geographically still on the HASU patient, even though they should have moved into SU mode.

Consultant, H3

The temporal expectation of the HASU to repatriate patients within a short time frame was frustrated by how the pathway can be inhibited by the availability of beds at the destination of the patient:

As long as they’re not um needing an investigation for the Monday then they will go home, um, we will make referrals but obviously we can’t follow them up um until the Monday, because obviously with the community teams aren’t working, if anyone needs a package of care restart or anything, then they’ll have to stay until Monday as well.

Physiotherapist, H7

Temporal structures acting on the patient pathway

How temporal structures have an impact on measurement of temporal variation

The third section of this chapter describes how the temporal structures outlined above result in measured temporal variation. Temporal variation in stroke has mainly been assessed using the SSNAP data set. This has the advantage that it was a stroke-specific database relating to nationally agreed standards of care, but carries the risk that the metrics imposed as a measure of quality in this database could become concretised as definitive of care quality more widely.

Rotas and temporal rhythms are more deterministic than time of admission on longer time-related indicators

Looking further at the limitation of the longer binary temporal variables (e.g. whether or not a patient was seen by a consultant within 24 hours, whether or not the patient saw a therapist within 72 hours), the time of admission is no longer fully relevant. Figure 18 illustrates this problem: the lines shown are based on the median time intervals for patients arriving in the time slots indicated from our SSNAP data set. Patients A and B both arrive on a weekday in hours. However, patient A sees a therapist after 24 hours and patient B sees a therapist after 60 hours because there are no therapists on the HASU over the weekend. Patient C arrives on a Sunday (out of hours), but sees a therapist relatively quickly when they return to work on Monday.

FIGURE 18.

Exemplars of patient trajectories and temporal variation in relation to the time and day of admission based on median SSNAP data for the time periods indicated.

Therefore, looking at outcomes such as therapist contact as a binary variable in relation to the time or day of admission was meaningless, for two reasons: (1) the SSNAP and London stroke standards accept that therapists do not work over the weekend, showing a cultural expectation that this state of affairs was normative and did not affect patient outcomes; and (2) the trajectory of the patient pathway in relation to therapist ward rotas dictates the length of time taken to see a therapist, not the time of admission. Similarly, the time taken to see the consultant was directly related to the patient pathway in relation to the morning ward round, not whether their time of admission was in hours or out of hours.

Implications for whether or not acute stroke services can be temporally consistent (24/7)

London HASUs were designed to be 24/7 services, but were built out of ‘old-fashioned’ models of team practice in a way that was minimally disruptive to previous practices. This can be likened to ‘sedimentation’, whereby elements of the new culture merely sediment over the values and structures of the old culture. 94 This has been recognised as a limitation to aspirations for ‘transformational’ change in health care. 95

Hyperacute stroke units have been able to produce temporal consistency in provision of some crucial interventions and clinical outcomes; however, we found deep-rooted patterns of variation on HASUs in a number of key areas:

• Medical and nursing staff were reduced in number and seniority at night, and junior staff had to ‘act up’ to fulfil senior responsibilities.

• Therapy provision was a contentious issue, with some HASUs attempting 7-day provision and others not (see Chapter 4, Variations in delivering therapist assessments).

• Those that did attempt 7-day provision found that their staff were stretched thin and only basic assessments were completed over the weekend.

• Many patterns of working on the HASU still have observable variation in the way that rotas are constructed in different professional groups. For example, the rotas of all key staffing groups (doctors, nurses and therapists) remain based around conventional working-week models.

• This is seen most strongly with the therapy teams, who work only conventional working hours 5 days per week, and least seen with the nursing teams, in which staffing is more often timetabled to meet peaks in activity.

• Many hospital departments had no access to testing at night or only with consultant-to-consultant request, in a stark contrast to the facilitated pathways during the week.

• Repatriation to social services and rehabilitation centres is bottlenecked by entrenched 5-day working patterns.

For stroke and other services to achieve greater gains in temporal consistency, they will need to overcome cultural acceptance of ‘difference’ at weekends and at night. Otherwise quality improvement will continue to be hampered and lead to frustration among patients who cannot progress to repatriation or make therapeutic advances.

We gathered data in each London HASU at different times of day, both during the week and at weekends, providing a rich picture of the realities of organising and providing a high-performing acute care system. Our study has provided detailed information about the organisation and management of the HASU, particularly in terms of nursing (see Chapter 5). Our study suggests the following:

• Senior nursing management is crucial to a well-managed ward and high-quality patient care.

• Both HASUs and EDs are dynamic and, if the clinician responsible for organising an interaction between the CT scanner and these two environments is in charge of both the patient in the ED and the preparation of the potential bed-space in HASU after brain scanning, this task may well be performed more efficiently.

• In addition to the urgent patient assessments, our observations identify that those patients presenting to the ED who do not need an immediate input could be deprioritised, either by junior nurses in the tPA role or by the team in the radiology department.

• Senior stroke nurses working in the ED (and controlling the HASU bed space) may be more aware of the potential for this deprioritisation to result in poorer and slower care, and so manage both the urgent and the non-urgent pathways more effectively than junior nurses with less awareness.

• Unsurprisingly, a nurse who is acting as both ward manager and tPA nurse is able to manage transitions well, but they neglect the management of essential interventions, such as dysphagia screening.

• Patients who are not eligible for thrombolysis could be perceived as less urgent, either by junior nurses in a tPA role or by, for example, radiology: we showed that a senior nurse manages this process better for non-thrombolysis patients.

Our finding that managerial nursing seniority varies between weekdays and weekends and between day and night is novel, and has broad implications. We did not find that nurse-in-charge seniority had an effect on 3-day and discharge mortality in consort with studies looking at medical specialist intensity,31 which could be interpreted as consistent quality. A contrasting interpretation of these findings, however, is that staff are able to ‘step up’ in junior roles or ‘rescue’ patients in particularly serious situations. However, some authors, such as Dixon-Woods,96 have cautioned that normalisation of these workarounds as a way of adapting to staff shortages and bed pressures can be an inadequate defence against patient safety threat when staff are inexperienced or undermanaged. Regular use of stretched junior staff may also lead to unforeseen consequences, such as that being constantly under strain is normal and valuable, rather than a sign of mismanagement. 96 Therefore, stroke services should not interpret our results as evidence that wards can be equally well managed by junior nurses, but that infrequent use of junior staff as subtitutes can be safe, but should not be normalised.

An example of this is to look at processes rather than outcomes. Indeed, we did find that senior nurses in charge of the ward were more likely to ensure dysphagia screen assessment unless also working as thrombolysis nurses (see Table 16) than junior colleagues. Other studies have also shown the important relationship between well-trained nursing staff, dysphagia and mortality. 87 Without long-term mortality data, our ability to perceive an impact on patient outcomes is limited. However, other studies have found that missed dysphagia screening predicts higher odds of pneumonia, severe disability, discharge to long-term care and 1-year mortality. 97 Furthermore, considered in conjunction with our findings about reduced therapist provision over the weekend, the risk of pneumonia and, therefore, long-term poor outcomes, may be further increased: a study of stroke patients found that delays in dysphagia screening and speech and language therapy dysphagia assessment were associated with a higher risk of pneumonia, with an absolute increase in the incidence of pneumonia of 1% per day of delay. 89

Our novel findings about this inpatient setting may have generalisable implications for 7-day working and bed management more widely. For example, the observed extension of nursing roles to create continuity could have widespread implications for NHS recruitment and training in the transition to 7-day services. Strategies that we found to be important included creating ward capacity, staff acting up to other roles and achieving a degree of self-sufficiency in making thrombolysis decisions. The flexible use of adjacent ward space to create free beds, and the staff prioritisation of new patients and those ready for discharge, is consistent with existing literature, suggesting that emergency room staff anchor working practice around admission and discharge possibilities. 98

The second implication is that we found evidence of no time-of-admission effect across the week on early outcomes in acute stroke patients admitted to a London HASU. We also found no evidence of an effect in terms of rapid access to brain scanning, stroke nursing care and thrombolysis in London HASUs. Other quality-of-care measures did significantly vary across the week in London HASUs. Therefore, in terms of the outcomes considered here, and for ‘front-door’ quality-of-care measures, the centralised model in London would seem to be more effective at providing 24/7 care. Several issues are worth bearing in mind. First, our study focuses on patients admitted to London HASUs only, not other hospitals in London; our data suggest that 6% of acute stroke patients in London are not treated in a HASU. Our focus on London HASUs was deliberate, as the aim of our study was to evaluate if they operate 24/7 care, but it means that our findings for London HASUs should not be generalised to all stroke patients in London. Indeed, there is evidence that quality of care is lower for the 6% of acute stroke patients in London not treated in a HASU than for those who are treated in a HASU. 41,99 Hence, better adherence to HASU referral protocols in these patients is likely to mean that the quality of care that they receive will increase. Another issue is that the centralisation of acute stroke services in London was not without cost; the additional cost required as a result of the higher cost per bed-day in a HASU was estimated at £20M. With this additional level of funding, it might be expected that there should be little or no variation in quality of care and outcomes across the week in London. There is some evidence that the reorganisation in London was cost-effective, but further analyses accounting for the size of the up-front investment, the relatively high costs per day of hyperacute stroke care, the impact on mortality and functional outcome, and the lifetime costs incurred by the NHS, social services and families caring for stroke survivors at different levels of functional outcome would be helpful.

Implications for the study of temporal variation in acute stroke services

There is a large literature examining weekend effects in health care across a range of clinical areas (see Chapter 1). In agreement with more recent studies22 we found that (1) there is no significant temporal variation in HASU care in London in all but a few clinical variables; (2) mortality is not affected by time and day of admission when controlling for baseline patient characteristics and case mix; and (3) temporal variation in a wider range of variables should be measured. 22 The study of temporal variation is restricted by reliance on routinely collected data sets, such as the SSNAP, that themselves enshrine some of the cultural norms of daytime, 5-day working in the metrics relating to consultant and therapist contact. In the case of stroke, the SSNAP data set provides a more accurate record of stroke patients’ clinical interventions and outcomes than other hospital data, but has not shown more severe variations in outcome than other hospital data sets. 100 We suggest that:

• other indexes of quality should be derived from patient-centred principles, and assessed for temporal variation

• staff in a successful system work to fulfil the commitments of their audited service standards, and this has driven temporal consistency in London’s HASUs in the metrics relating to ‘front-door’ provision

• temporal variation remains further along the patient pathway and may influence long-term patient outcomes

• assessment of temporal variation for improvement is limited when success or quality are being assessed by what happened, rather than assessing whether or not potential risks were taken, or whether or not the service was optimal.

This has been highlighted in cases of grave misconduct, such as the Morecambe Bay investigation, where the reliance on ‘high-level data may not reveal underlying risks’ (Reproduced with permission from Morecambe Bay Investigation copyright 2015. The Report of the Morecambe Bay Investigation101). Another way to characterise this is the over-reliance on ‘hard metrics’, such as incidents, and not enough attention to ‘soft metrics’, such as cultural impediments and social practices, as identified by senior leaders of health systems. 102 We hope that the results of this report contribute to soft intelligence in striving to meet the goals for 7-day working.