As discussed inChapter 3, we drew on the realist model and the‘five simple rules of large-scale transformation’ (designated and distributed leadership, presence and use of feedback, attention to historical factors, provider engagement, and PPI)7as a framework to understand the process of implementation of these two aspects of the SICP. We also drew on NPT and psychological models of self-management and patient centredness to guide analysis.
Study methods: multidisciplinary groups
Multidisciplinary group fieldwork took place from March to December 2015, with fieldwork largely based on non-participatory attendance at neighbourhood MDG meetings. Data collection included 72 hours observing MDG meetings, with sequential fortnightly observations in one neighbourhood MDG for each of the three waves of roll-out (Table 5). Additional observations were undertaken at other meetings supporting the MDG process (including the working group meetings, joint chairpersons’meetings and administrator meetings) as well as engagement events. Further observations were conducted with MDG nurse and social care co-chairpersons to outline how the work of referring and prioritising patients for discussion and pre-MDG meetings was enacted.
TABLE 5 Data collection implementation 2: MDGs
Data collection method Number Further information
Interviews 37 l 27 with MDG staff
l 5 with non-MDG staff
l 5 patients/carers
Observations 36 (approximately 72 hours) Observations included:
l MDG meetings in seven neighbourhoods
l care home MDG meeting
l pre-MDG preparation
l MDG working group
l MDG joint chairpersons
By agreement, field notes made during the MDG meetings did not contain any identifiable patient data. Initials, sex and the general practice were recorded, permitting further questioning around individual cases with the relevant general practitioners (GPs) and to identify potential patients to be invited to participate in qualitative interviews.
Thirty-two face-to-face interviews were carried out with professionals participating in the MDG meetings or those whose work was associated with them. Maximum variation sampling was used to ensure that representatives from all staff groups participating in MDGs were interviewed.
We used routine data (workload and throughput, patient characteristics, links with other services) to contextualise our data. Operational documents were collected from the MDG processes and meetings around them and used to provide information about the implementation.
Study methods: integrated contact centre
Fieldwork took place between October 2015 and July 2016, during which time the single integrated referral point (SIRP) was based within a Salford City Council facility. Colocation with the council corporate team unfortunately meant that permission to carry out observational work within the SIRP was declined. Data collection was therefore based mainly on interviews with 11 ICC staff during which in-depth descriptions of their work were provided in lieu of observations (Table 6).
We explored the various services provided by the centre through individual interviews with participating staff and managers to assess the development of the service over time, how fidelity to the model was achieved and the potential for unintended consequences. We used routine data reported by respondents (workload, patient characteristics, referrals) to contextualise the data.
We described the characteristics of the centre, its staffing and technology, and how the existence and function of the centre is communicated to patients. At the level of the patient, we described the
interaction between the staff and patients, through individual interviews with six patients/carers who had direct experience. Observations included 11 hours of non-participant observations of meetings directly related to the centre, including a short visit to the SIRP, observing the locality base, a care homes meeting and initial engagement events promoting the wider SICP. In addition, documents providing evidence of the implementation process and allowing a comparison with the initial plans for the ICC at the start of the SICP were collated. Health coaching data are presented inAppendix 2.
Qualitative analysis methods
Qualitative data from both the ICC and MDG observations and interview transcripts were organised using NVivo 10. Techniques from grounded theory were used for the thematic analysis.76Analytical memos were written and discussed to develop a collective understanding of the issues represented in the data. Members of the qualitative team met monthly to discuss emerging themes and to agree subthemes.
TABLE 6 Data collection implementation 2: ICC
Data collection method Number Further information
Interviews 17 l 11 ICC staff
l 6 patients/carers
Observations 5 (approximately 11 hours) Observations included:
l SIRP base
l Locality base working
l Telecare/telehealth workshop
l Care homes meeting
l Engagement events
l Health coaching
qualitative interviews. We considered how data mapped onto the framework, and although there were some connections between concepts, these were limited. We therefore adopted a more responsive approach, using iterative sampling and analysis of data until no new information emerged. This prevented the background framework from constraining the interviews and allowed us to learn from, and develop, the topic guide as the interviews were conducted.
Qualitative data from both the ICC and MDGs observations and interview transcripts were organised using NVivo 10. We conducted a thematic analysis drawing on some techniques from a grounded theory approach, including open coding and the creation of analytical memos as a basis for iterative analysis and sampling as outlined previously. Members of the qualitative team met monthly to discuss emerging themes and subthemes, any unusual cases and to agree the final stage of‘selective’coding. These processes of coding and iterative analysis enabled core themes to emerge inductively from the data consistent with a grounded theory approach.76
Chapter 7
Methods of outcomes 1
A
core SICP aim was to reduce emergency admissions. Although all mechanisms of integration in the SICP have a potential role in reducing admissions, the MDGs are most clearly focused on providing a rapid reduction in the use of hospital services through intervention with patients at high risk of admission.Multidisciplinary groups and linked case management interventions have an important place in the NHS as a core mechanism of integration. Since the Evercare pilots,29,30studies have cast some doubt on the evidence that this model can achieve reductions in hospital admissions.18,19,33,77,78However, an unpublished survey of CCGs reported that 80% included some variant in their integration plans.79There are a number of different models of MDGs and some of the ways in which they vary are outlined inTable 2. In line with the realist model (seeChapter 3), there is also an argument that the general case management‘mechanism’is effective only in certain contexts, such as a history of previous joint working among staff in an integrated care service.
Methods
The SICP was targeted at all general practices. Therefore, the primary analysis for the effects of the MDGs compared data from practices in Salford with suitable comparators in other parts of England. However, the introduction of MDGs was staged, and we used this to assess any differential impact relating to the staged introduction.
We adopted lagged dependent variable approaches to estimate the effect of the MDGs.80This approach does not require assumptions of parallel trends between intervention and comparator groups imposed by a difference-in-differences specification. The lagged dependent variable approach uses a fixed vector of lagged values of the outcomes prior to the intervention as explanatory variables. The analysis is conducted only on the time points following the intervention.80
If the parallel trends assumption does not hold, the lagged dependent variable approach is less prone to bias and is more efficient than alternatives such as the creation of synthetic controls.80The superiority of the lagged dependent variable approach is increased when data are available on more pre-intervention periods, as is the case in this setting.
Data
Data were HES from NHS Digital, stratified by financial quarter and general practice (financial years 2009/10–2015/16), for populations aged≥65 years:
1. the number of accident and emergency (A&E) attendances per person
2. the number of A&E attendances referred by health and social care providers per person 3. the number of self-referred A&E attendances per person
4. the number of emergency admissions per person
5. the number of emergency admissions via A&E per person 6. the number of direct emergency admissions per person
7. the number of ambulatory care-sensitive emergency admissions per person 8. the proportion of patients discharged to usual place of residence.
We also obtained general practice patient registration lists for persons aged≥65 years from two sources: (1) the Personal Demographic Service for the financial years 2009/10 and 2010/11 and (2) NHS Digital for the financial years 2013/14 to 2015/16. For the financial years 2008/9, 2011/12 and 2012/13, we used the closest year of data available.