Ward staff practices
Ethnographic observation and interviews with staff (as above) were conducted at nine and 18 months where PIE implementation occurred.
Patient and relative/caregiver experience of care
Sources of data were ethnographic observation and patient case studies.
Clinical outcomes
We sought to collect quantitative data on delirium incidence and days in delirium; and falls at baseline and at 9 and 18 months. Delirium and falls are the commonest unintended adverse events affecting older people admitted to hospital. We hypothesised that the rate of falls and days in delirium would be affected by improvements in care practice.
Delirium is a common, serious and an unrecognised condition on acute wards;13,36,92,93 dementia is a risk factor for delirium36,94,95 and delirium accelerates cognitive decline.96 Delirium incidence is associated with environmental (e.g. setting, lighting, sensory overload) and care related factors (e.g. fluid and nutritional intake, mobilisation, cognitive stimulation) which interact with patient vulnerability,36,97 and is regarded as a critical marker of care quality.36,97 Delirium incidence and occurrence is not routinely collected in hospital, in part reflecting poor knowledge of the condition.93,97,98 In this study, we employed the validated Delirium Observation Screening (DOS) scale,99 a 13-item scale developed to facilitate early recognition of delirium and based on the
Diagnostic and Statistical Manual-IV criteria. It comprises a single sheet completed at least once
daily by nurses as they attend to patients, with a score of >3 indicating delirium.
We negotiated for DOS to be incorporated into ward practice so as to prospectively collect information to monitor delirium rates and days spent with incident delirium. Although completion of DOS was reported to take less than one minute and therefore not resource-intensive, staff found it took considerably longer. It was not consistently collected; wards citing staffing difficulties. Several wards where staff were already sensitive to observational cues of ‘acute confusion’ and valued the information to inform practice (including both dementia wards) completed DOS . High delirium incidence and days in delirium on these wards did not (from observation) reflect poor care quality.
Regarding falls, evidence suggests a modest reduction in the falls rate with a multifaceted intervention in hospital settings.100,101 The National Patient Safety Agency,102 concluded that the
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most useful measure of falls is the number per 1000 occupied bed days. Reported falls for patients admitted during each phase of data collection were recorded as one of the patient profile variables on the Excel datasheet and a calculation made of the rate of falls per 1000 occupied bed days. Unfortunately, there was considerable missing patient profile data, including falls for the two wards that successfully implemented PIE. Where such data was available, we have reported it. However, interpretation of the data is not straightforward. Risk factors for falls not only relate to the actions of ward staff, but there are also patient specific factors that give rise to variability in rates. Thus, risk increases with advanced age, presence of dementia, delirium and frailty so that ward patient profile will affect the falls rate.103 Similarly, since hospital-related falls occur as a consequence of mobilising and recovering from illness, practices regarding the balance of risk, promoting rehabilitation and respecting autonomy, will also affect the falls rate.103 Overall, data obtained on falls was patchy across sites. On both clinical outcome measures then, we were unsuccessful in securing reliable and consistent data for all wards. The attempt to do so has raised useful questions regarding feasibility, considered in Chapter six.
Service outcomes (length of stay, re-admissions within 30 days and discharge destination) were collected as part of the patient profile dataset.
Analysis
Qualitative data (interview transcripts and ethnographic field notes) were analysed using grounded theory methods;104 including simultaneous data collection and analysis, constant comparison, search for negative cases and memo-writing. Descriptive and analytic codes were developed into higher order categories through processes of data reduction and re-assembly. Analysis was pursued firstly for each dataset (interviews, observations, patient case studies) within individual wards (the case study). We then compared analytic themes across these datasets, drawing out for example, similarities and differences between staff interviews and observation on understanding and practice of person-centred care. Through regular research team meetings the emerging data from individual case studies and reflections on them, were discussed. Similarities and differences between wards were drawn out, topics for more focused observation and ideas of analytic interest to pursue, identified. Hypotheses were generated to explore and account for variation between cases; these were tested out through cross case comparison and search for alternative explanations.
Regarding ward practices in respect of patients with dementia, we proceeded as follows. From initial observations, we focused on aspects of practice relating to the work of communication; routine care tasks; responses to different types of distress; the strategies adopted; and the attitudes and knowledge that informed them. For each case study, all incidents and episodes relating to this
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work were mapped, alongside the environmental, organisational and interactional contexts in which they occurred; and the physical and emotional responses of patients. Through comparison between incidents and episodes captured in memos, we identified patterns of action and interaction. These patterns and what shaped them were refined through cross-case comparison. For example, the open code ‘distress’ was explored through perusal of all incidents and episodes pertaining to it. Through constant comparison, different forms of distress were identified which varied by how the distress was manifested, the perceived need giving rise to it, and the persistence of its expression. Staff responses were similarly coded and categorised based on the type of response and the context in which it occurred. The relationship between the varied forms of distress and the responses to them were interrogated to identify patterns, which were further refined through comparison between cases.
A similar analytic process was pursued in relation to PIE implementation. Qualitative data from interviews, workshop notes, observation of action planning and review meetings, and examination of completed documentation, were drawn together for each ward in chronological order. This provided a within case descriptive account of implementation over time; the nature of the engagement of staff with each step in the cycle and the barriers encountered, using NPT as a sensitising framework. Emphasis was on delineating the sequence of implementation steps over time and in context of events within the hospital and Trust; the conditions that impacted the temporal flow of action, and their consequences, whether persisting with or abandoning PIE. We then examined implementation processes through cross-case comparison to discern generalisable features that might account for variation. Analysis of qualitative data was conducted manually moving iteratively between the empirical data, sense making in relation to it and review of the literature.
Quantitative data was analysed using descriptive statistics.