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• We study the effect of emergency department crowding due to low severity patients. • We unpack welfare implications for high severity patients along their pathway in ED. • We show the slack in capacity by looking at the distribution of unplanned demand. • Emergency departments efficiently prioritise high severity patients. • Care rationing at very high levels of demand explains crowding effects on outcomes. Unexpected peaks in volumes of attendances at hospital emergency departments (EDs) have been found to affect waiting times, intensity of care and outcomes. We ask whether these effects of ED crowding on patients are caused by poor clinical prioritisation or a quality-quantity trade-off generated by a binding capacity constraint. We study the effects of crowding created by lower-severity patients on the outcomes of approximately 13 million higher-severity patients attending the 140 public EDs in England between April 2016 and March 2017. Our identification approach relies on high-dimensional fixed effects to account for planned capacity. Unexpected demand from low-severity patients has very limited effects on the care provided to higher-severity patients throughout their entire pathway in ED. Detrimental effects of crowding caused by low-severity patients materialise only at very high levels of unexpected demand, suggesting that binding resource constraints impact patient care only when demand greatly exceeds the ED's expectations. These effects are smaller than those caused by crowding induced by higher-severity patients, suggesting an efficient prioritisation of incoming patients in EDs. [ABSTRACT FROM AUTHOR]

Introduction: All the surgeries carry risks, which may lead to readmission at a later date. At present, there is limited Trauma and Orthopaedic (T&O) specific data in the literature. As a result, a prospective regional service evaluation aimed to discover the current complication and readmission rates across all T&O procedures and identify any factors associated with these outcomes.Methods: Data were collected at six sites across Yorkshire and Humber for all T&O procedures during October 2016. Patient demographics and procedure-specific data were collected. Post-operative complications and length of stay were recorded. All the patients were then followed up for 30 days post-discharge to determine if they experienced complications which resulted in readmission and further surgical intervention.Results: 1411 patients having a total of 64 operations were recorded with 1391 completing follow-up (98.5%). Overall in-patient complication rate was 8.4% with the readmission rate being 4.4%. An ASA grade of three or more was found to be associated with readmission. Procedure-related factors such as the use of VTE prophylaxis and prophylactic antibiotics, as well as the elective nature of certain operations were negatively associated with readmission. The largest subgroup of patients was those undergoing total hip (THR) or knee replacements (TKR). For these 234 patients, the readmission rate for TKR and THR being 3.77% and 3.13%, respectively.Conclusions: This large, multi-centre project describes readmission rates following trauma and orthopaedic surgery. In the presented study, the elective nature of the procedure was associated with a reduced risk of readmission. [ABSTRACT FROM AUTHOR]

Objective: To develop methods to model the relationship between cancer detection and recall rates to inform professional standards.Methods: Annual screening programme information for each of the 80 English NHSBSP units (totalling 11.3 million screening tests) for the seven screening years from 1 April 2009 to 31 March 2016 and some Dutch screening programme information were used to produce linear and non-linear models. The non-linear models estimated the modelled maximum values (MMV) for cancers detected at different grades and estimated how rapidly the MMV was reached (the modelled 'slope' (MS)). Main outcomes include the detection rate for combined invasive/micro-invasive and high-grade DCIS (IHG) detection rate and the low/intermediate grade DCIS (LIG) detection rate.Results: At prevalent screens for IHG cancers, 99% of the MMV was reached at a recall rate of 7.0%. The LIG detection rate had no discernible plateau, increasing linearly at a rate of 0.12 per 1000 for every 1% increase in recall rate. At incident screens, 99% of the MMV for IHG cancer detection was 4.0%. LIG DCIS increased linearly at a rate of 0.18 per 1000 per 1% increase in recall rate.Conclusions: Our models demonstrate the diminishing returns associated with increasing recall rates. The screening programme in England could use the models to set recall rate ranges, and other countries could explore similar methodology.Key Points: • Question: How can we determine optimum recall rates in breast cancer screening? • Findings: In this large observational study, we show that increases in recall rates above defined levels are almost exclusively associated with false positive recalls and a very small increase in low/intermediate grade DCIS. • Meaning: High recall rates are not associated with increases in detection of life-threatening cancers. The models developed in this paper can be used to help set recall rate ranges that maximise benefit and minimise harm. [ABSTRACT FROM AUTHOR]