Background Quality improvement collaboratives (QICs) bring together multidisciplinary teams in a structured process to improve care quality. How QICs can be used to support healthcare improvement in care homes is not fully understood. Methods A realist evaluation to develop and test a programme theory of how QICs work to improve healthcare in care homes. A multiple case study design considered implementation across 4 sites and 29 care homes. Observations, interviews and focus groups captured contexts and mechanisms operating within QICs. Data analysis classified emerging themes using context-mechanism-outcome configurations to explain how NHS and care home staff work together to design and implement improvement. Results QICs will be able to implement and iterate improvements in care homes where they have a broad and easily understandable remit; recruit staff with established partnership working between the NHS and care homes; use strategies to build relationships and minimise hierarchy; protect and pay for staff time; enable staff to implement improvements aligned with existing work; help members develop plans in manageable chunks through QI coaching; encourage QIC members to recruit multidisciplinary support through existing networks; facilitate meetings in care homes and use shared learning events to build multidisciplinary interventions stepwise. Teams did not use measurement for change, citing difficulties integrating this into pre-existing and QI-related workload. Conclusions These findings outline what needs to be in place for health and social care staff to work together to effect change. Further research needs to consider ways to work alongside staff to incorporate measurement for change intoQI.

Introduction care home residents are often unable to complete health-related quality of life questionnaires for themselves because of prevalent cognitive impairment. This study compared care home resident and staff proxy responses for two measures, the EQ-5D-5L and HowRU. Methods a prospective cohort study recruited residents ≥60 years across 24 care homes who were not receiving short stay, respite or terminal care. Resident and staff proxy EQ-5D-5L and HowRu responses were collected monthly for 3 months. Weighted kappa statistics and intra-class correlation coefficients (ICCs) adjusted for clustering at the care home level were used to measure agreement between resident and proxies for each time point. The effect of staff and resident baseline variables on agreement was considered using a multilevel mixed effect regression model. Results 117, 109 and 104 matched pairs completed the questionnaires at 1, 2 and 3 months, respectively. When clustering was controlled for, agreement between resident and staff proxy EQ-5D-5L responses was fair for mobility (ICC: 0.29) and slight for all other domains (ICC ≤ 0.20). EQ-5D Index and Quality-Adjusted Life Year scores (proxy scores higher than residents) showed better agreement than EQ-5D-VAS (residents scores higher than proxy). HowRU showed only slight agreement (ICC ≤ 0.20) between residents and proxies. Staff and resident characteristics did not influence level of agreement for either index. Discussion the levels of agreement for EQ-5D-5L and HowRU raise questions about their validity in this population.

IntroductionThis protocol describes a study of a quality improvement collaborative (QIC) to support implementation and delivery of comprehensive geriatric assessment (CGA) in UK care homes. The QIC will be formed of health and social care professionals working in and with care homes and will be supported by clinical, quality improvement and research specialists. QIC participants will receive quality improvement training using the Model for Improvement. An appreciative approach to working with care homes will be encouraged through facilitated shared learning events, quality improvement coaching and assistance with project evaluation.Methods and analysisThe QIC will be delivered across a range of partnering organisations which plan, deliver and evaluate health services for care home residents in four local areas of one geographical region. A realist evaluation framework will be used to develop a programme theory informing how QICs are thought to work, for whom and in what ways when used to implement and deliver CGA in care homes. Data collection will involve participant observations of the QIC over 18 months, and interviews/focus groups with QIC participants to iteratively define, refine, test or refute the programme theory. Two researchers will analyse field notes, and interview/focus group transcripts, coding data using inductive and deductive analysis. The key findings and linked programme theory will be summarised as context-mechanism-outcome configurations describing what needs to be in place to use QICs to implement service improvements in care homes.Ethics and disseminationThe study protocol was reviewed by the National Health Service Health Research Authority (London Bromley research ethics committee reference: 205840) and the University of Nottingham (reference: LT07092016) ethics committees. Both determined that the Proactive HEAlthcare of Older People in Care Homes study was a service and quality improvement initiative. Findings will be shared nationally and internationally through conference presentations, publication in peer-reviewed journals, a graphical illustration and a dissemination video.

Backgroundmeasuring the complex needs of care home residents is crucial for resource allocation. Hospital patient administration systems (PAS) may not accurately identify admissions from care homes.Objectiveto develop and validate an accurate, practical method of identifying care home resident hospital admission using routinely collected PAS data.Methodadmissions data between 2011 and 2012 (n = 103,105) to an acute Trust were modelled to develop an automated tool which compared the hospital PAS address details with the Care Quality Commission’s (CQC) database, producing a likelihood of care home residency. This tool and the Nuffield method (CQC postcode match only) were validated against a manual check of a random sample of admissions (n = 2,000). A dataset from a separate Trust was analysed to assess generalisability.Resultsthe hospital PAS was inaccurate; none of the admissions from a care home identified on manual check had a care home source of admission recorded on the PAS.Both methods performed well; the automated tool had a higher positive predictive value than the Nuffield method (100% 95% confidence interval (CI) 98.23–100% versus 87.10% 95%CI 82.28–91.00%), meaning those coded as care home residents were more likely to actually be from a care home. Our automated tool had a high level of agreement 99.2% with the second Trust’s data (Kappa 0.86 P < 0.001).Conclusionscare home status is not routinely or accurately captured. Automated matching offers an accurate, repeatable, scalable method to identify care home residency and could be used as a tool to benchmark how care home residents use acute hospital resources across the National Health Service.