Implementation of interventions to reduce preventable hospital admissions for cardiovascular or respiratory conditions: an evidence map and realist synthesis
Bibliographic record
Abstract
Background In 2012, a series of systematic reviews summarised the evidence regarding interventions to reduce preventable hospital admissions. Although intervention effects were dependent on context, the reviews revealed a consistent picture of reduction across different interventions targeting cardiovascular and respiratory conditions. The research reported here sought to provide an in-depth understanding of how interventions that have been shown to reduce admissions for these conditions may work, with a view to supporting their effective implementation in practice. Objectives To map the available evidence on interventions used in the UK NHS to reduce preventable admissions for cardiovascular and respiratory conditions and to conduct a realist synthesis of implementation evidence related to these interventions. Methods For the mapping review, six databases were searched for studies published between 2010 and October 2017. Studies were included if they were conducted in the UK, the USA, Canada, Australia or New Zealand; recruited adults with a cardiovascular or respiratory condition; and evaluated or described an intervention that could reduce preventable admissions or re-admissions. A descriptive summary of key characteristics of the included studies was produced. The studies included in the mapping review helped to inform the sampling frame for the subsequent realist synthesis. The wider evidence base was also engaged through supplementary searching. Data extraction forms were developed using appropriate frameworks (an implementation framework, an intervention template and a realist logic template). Following identification of initial programme theories (from the theoretical literature, empirical studies and insights from the patient and public involvement group), the review team extracted data into evidence tables. Programme theories were examined against the individual intervention types and collectively as a set. The resultant hypotheses functioned as synthesised statements around which an explanatory narrative referenced to the underpinning evidence base was developed. Additional searches for mid-range and overarching theories were carried out using Google Scholar (Google Inc., Mountain View, CA, USA). Results A total of 569 publications were included in the mapping review. The largest group originated from the USA. The included studies from the UK showed a similar distribution to that of the map as a whole, but there was evidence of some country-specific features, such as the prominence of studies of telehealth. In the realist synthesis, it was found that interventions with strong evidence of effectiveness overall had not necessarily demonstrated effectiveness in UK settings. This could be a barrier to using these interventions in the NHS. Facilitation of the implementation of interventions was often not reported or inadequately reported. Many of the interventions were diverse in the ways in which they were delivered. There was also considerable overlap in the content of interventions. The role of specialist nurses was highlighted in several studies. The five programme theories identified were supported to varying degrees by empirical literature, but all provided valuable insights. Limitations The research was conducted by a small team; time and resources limited the team’s ability to consult with a full range of stakeholders. Conclusions Overall, implementation appears to be favoured by support for self-management by patients and their families/carers, support for services that signpost patients to consider alternatives to seeing their general practitioner when appropriate, recognition of possible reasons why patients seek admission, support for health-care professionals to diagnose and refer patients appropriately and support for workforce roles that promote continuity of care and co-ordination between services. Future work Research should focus on understanding discrepancies between national and international evidence and the transferability of findings between different contexts; the design and evaluation of implementation strategies informed by theories about how the intervention being implemented might work; and qualitative research on decision-making around hospital referrals and admissions. Funding The National Institute for Health Research Health Services and Delivery Research programme.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.134 | 0.297 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.009 | 0.011 |
| Bibliometrics | 0.047 | 0.029 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.015 | 0.012 |
| Open science | 0.005 | 0.009 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".