BackgroundIn 2013 NHS England set out its strategy for the development of an emergency and urgent care system that is more responsive to patients’ needs, improves outcomes and delivers clinically excellent and safe care. Knowledge about the current evidence base on models for provision of safe and effective urgent care, and the gaps in evidence that need to be addressed, can support this process.ObjectiveThe purpose of the evidence synthesis is to assess the nature and quality of the existing evidence base on delivery of emergency and urgent care services and identify gaps that require further primary research or evidence synthesis.Data sourcesMEDLINE, EMBASE, The Cochrane Library, the Cumulative Index to Nursing and Allied Health Literature (CINAHL) and the Web of Science.MethodsWe have conducted a rapid, framework-based, evidence synthesis approach. Five separate reviews linked to themes in the NHS England review were conducted. One general and five theme-specific database searches were conducted for the years 1995–2014. Relevant systematic reviews and additional primary research papers were included and narrative assessment of evidence quality was conducted for each review.ResultsThe review was completed in 6 months. In total, 45 systematic reviews and 102 primary research studies have been included across all five reviews. The key findings for each review are as follows: (1) demand – there is little empirical evidence to explain increases in demand for urgent care; (2) telephone triage – overall, these services provide appropriate and safe decision-making with high patient satisfaction, but the required clinical skill mix and effectiveness in a system is unclear; (3) extended paramedic roles have been implemented in various health settings and appear to be successful at reducing the number of transports to hospital, making safe decisions about the need for transport and delivering acceptable, cost-effective care out of hospital; (4) emergency department (ED) – the evidence on co-location of general practitioner services with EDs indicates that there is potential to improve care. The attempt to summarise the evidence about wider ED operations proved to be too complex and further focused reviews are needed; and (5) there is no empirical evidence to support the design and development of urgent care networks.LimitationsAlthough there is a large body of evidence on relevant interventions, much of it is weak, with only very small numbers of randomised controlled trials identified. Evidence is dominated by single-site studies, many of which were uncontrolled.ConclusionsThe evidence gaps of most relevance to the delivery of services are (1) a requirement for more detailed understanding and mapping of the characteristics of demand to inform service planning; (2) assessment of the current state of urgent care network development and evaluation of the effectiveness of different models; and (3) expanding the current evidence base on existing interventions that are viewed as central to delivery of the NHS England plan by assessing the implications of increasing interventions at scale and measuring costs and system impact. It would be prudent to develop a national picture of existing pilot projects or interventions in development to support decisions about research commissioning.FundingThe National Institute for Health Research Health Services and Delivery Research Programme.
Background The primary objective of this review is to assess the accuracy of machine learning methods in their application of triaging the acuity of patients presenting in the Emergency Care System (ECS). The population are patients that have contacted the ambulance service or turned up at the Emergency Department. The index test is a machine-learning algorithm that aims to stratify the acuity of incoming patients at initial triage. This is in comparison to either an existing decision support tool, clinical opinion or in the absence of these, no comparator. The outcome of this review is the calibration, discrimination and classification statistics. Methods Only derivation studies (with or without internal validation) were included. MEDLINE, CINAHL, PubMed and the grey literature were searched on the 14th December 2019. Risk of bias was assessed using the PROBAST tool and data was extracted using the CHARMS checklist. Discrimination (C-statistic) was a commonly reported model performance measure and therefore these statistics were represented as a range within each machine learning method. The majority of studies had poorly reported outcomes and thus a narrative synthesis of results was performed. Results There was a total of 92 models (from 25 studies) included in the review. There were two main triage outcomes: hospitalisation (56 models), and critical care need (25 models). For hospitalisation, neural networks and tree-based methods both had a median C-statistic of 0.81 (IQR 0.80-0.84, 0.79-0.82). Logistic regression had a median C-statistic of 0.80 (0.74-0.83). For critical care need, neural networks had a median C-statistic of 0.89 (0.86-0.91), tree based 0.85 (0.84-0.88), and logistic regression 0.83 (0.79-0.84). Conclusions Machine-learning methods appear accurate in triaging undifferentiated patients entering the Emergency Care System. There was no clear benefit of using one technique over another; however, models derived by logistic regression were more transparent in reporting model performance. Future studies should adhere to reporting guidelines and use these at the protocol design stage. Registration and funding This systematic review is registered on the International prospective register of systematic reviews (PROSPERO) and can be accessed online at the following URL: https://www.crd.york.ac.uk/PROSPERO/display_record.php?ID=CRD42020168696 This study was funded by the NIHR as part of a Clinical Doctoral Research Fellowship.
BackgroundThe purpose of this rapid evidence synthesis is to support the current NHS England service review on organisation of services for congenital heart disease (CHD). The evidence synthesis team was asked to examine the evidence on relationships between organisational features and patient outcomes in CHD services and, specifically, any relationship between (1) volume of cases and patient outcomes and (2) proximity of colocated services and patient outcomes. A systematic review published in 2009 had confirmed the existence of this relationship, but cautioned this was not sufficient to make recommendations on the size of units needed.ObjectivesTo identify and synthesise the evidence on the relationship between organisational features and patient outcomes for adults and children with CHD.Data sourcesA systematic search of medical- and health-related databases [MEDLINE, EMBASE, Cumulative Index to Nursing and Allied Health Literature (CINAHL), The Cochrane Library and Web of Science] was undertaken for 2009–14 together with citation searching, reference list checking and stakeholder recommendations of evidence from 2003 to 2014.Review methodsThis was a rapid review and, therefore, the application of the inclusion and exclusion criteria to retrieved records was undertaken by one reviewer, with 10% checked by a second reviewer. Five reviewers extracted data from included studies using a bespoke data extraction form which was subsequently used for evidence synthesis. No formal quality assessment was undertaken, but the usefulness of the evidence was assessed together with limitations identified by study authors.ResultsThirty-nine papers were included in the review. No UK-based studies were identified and 36 out of 39 (92%) studies included only outcomes for paediatric patients. Thirty-two (82%) studies investigated the relationship between volume and mortality and seven (18%) investigated other service factors or outcomes. Ninety per cent were from the USA, 92% were multicentre studies and all were retrospective observational studies. Twenty-five studies (64%) included all CHD conditions and 14 (36%) included single conditions or procedures. Although the evidence does demonstrate a relationship between volume and outcome in the majority of studies, this relationship is not consistent. The relationship was stronger for single-complex conditions or procedures. A mixed picture emerged revealing a range of factors as well as volume that influence outcome, including condition severity, individual centre and surgeon effects and clinical advances over time. We found limited (seven studies) evidence about the impact of proximity and colocation of services on outcomes, and about volume on non-mortality outcomes.LimitationsThis was a rapid review that followed standard methods to ensure transparency and reproducibility. The main limitations of the included studies were the retrospective nature, reliance on routine data sets, completeness, selection bias and lack of data on key clinical and service-related processes.ConclusionsThis review identified a substantial number of studies reporting a positive relationship between volume and outcome, but the complexity of the evidence requires careful interpretation. The heterogeneity of findings from observational studies suggests that, while a relationship between volume and outcome exists, this is unlikely to be a simple, independent and directly causal relationship. The effect of volume on outcome relative to the effect of other as yet undetermined health system factors remains a complex and unresolved research question.FundingThe National Institute for Health Research Health Services and Delivery Research programme.
Background Demand for both the ambulance service and the emergency department (ED) is rising every year and when this demand is excessive in both systems, ambulance crews queue at the ED waiting to hand patients over. Some transported ambulance patients are ‘low-acuity’ and do not require the treatment of the ED. However, paramedics can find it challenging to identify these patients accurately. Decision support tools have been developed using expert opinion to help identify these low acuity patients but have failed to show a benefit beyond regular decision-making. Predictive algorithms may be able to build accurate models, which can be used in the field to support the decision not to take a low-acuity patient to an ED. Methods and analysis All patients in Yorkshire who were transported to the ED by ambulance between July 2019 and February 2020 will be included. Ambulance electronic patient care record (ePCR) clinical data will be used as candidate predictors for the model. These will then be linked to the corresponding ED record, which holds the outcome of a ‘non-urgent attendance’. The estimated sample size is 52,958, with 4767 events and an EPP of 7.48. An XGBoost algorithm will be used for model development. Initially, a model will be derived using all the data and the apparent performance will be assessed. Then internal-external validation will use non-random nested cross-validation (CV) with test sets held out for each ED (spatial validation). After all models are created, a random-effects meta-analysis will be undertaken. This will pool performance measures such as goodness of fit, discrimination and calibration. It will also generate a prediction interval and measure heterogeneity between clusters. The performance of the full model will be updated with the pooled results. Discussion Creating a risk prediction model in this area will lead to further development of a clinical decision support tool that ensures every ambulance patient can get to the right place of care, first time. If this study is successful, it could help paramedics evaluate the benefit of transporting a patient to the ED before they leave the scene. It could also reduce congestion in the urgent and emergency care system. Trial Registration This study was retrospectively registered with the ISRCTN: 12121281
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