Search

Background/Aims: The COVID-19 pandemic caused substantial disruption to elective surgery, with NHS trusts still trying to work through their backlogs and reduce waiting lists. This study aimed to design, implement and evaluate a novel one-stop general surgical outpatient clinic to tackle long waiting lists post COVID-19. Methods: General surgical outpatient clinics were held on 6 days between April and October 2021. The impact of these clinics was retrospectively measured using data collected from the trust's electronic database on the length of waiting lists, number of weeks before appointment and financial expenditure associated with the project. Results: There were 1440 fewer patients on the outpatient waiting list after the clinics were implemented compared to before, with a 100% reduction in the number of patients waiting longer than 52 weeks for an appointment. Across the six clinic days, 93.7% of available slots were booked and patients attended 90.5% of booked slots. A net financial benefit of £128 700 was calculated. Conclusions: Lessons from this project could be applied in other specialties and NHS trusts to tackle waiting lists and outpatient clinic backlog post COVID-19. [ABSTRACT FROM AUTHOR]

Background/Aims: Joint replacement services in Wales were particularly affected by the COVID-19 pandemic, but waiting times had been high for several years before this. This study aimed to explore the hip and knee replacement activity in NHS Wales between 2000 and 2019 to indicate whether an existing gap between capacity and demand exacerbated the impact of the pandemic on waiting times. Methods: A retrospective review was performed using publicly available databases, including the Patient Episode Database Wales and Stats Wales. Data relating to the number of hip and knee replacement operations performed between 2000 and 2019 and average waiting times were extracted and analysed. Data on the number of patients who underwent a hip or knee replacement during the study period after waiting over 36 weeks from referral were obtained by request to the Patient Episode Database Wales. Data were analysed descriptively. Results: The 36-week referral to treatment target was not achieved for any year during the study period. Activity increased each year from 2000 to 2009, but varied substantially year to year afterwards. Average waiting times were at their lowest in 2009, but rose substantially after this point. Conclusions: There had been a long-term lack of capacity for hip and knee replacements in NHS Wales for 20 years before 2020, which may have exacerbated the impact of the COVID-19 pandemic on waiting times for these services. Implementing more nuanced waiting time targets, based on the clinical effectiveness of procedures, may be more appropriate than blanket waiting time guarantees. [ABSTRACT FROM AUTHOR]

Background/Aims: Identifying health inequalities can require substantial data and analytical resources. A healthcare setting that may be particularly exposed to inequality is elective care, where hospital waiting times have substantially lengthened since the COVID-19 pandemic. This study aimed to investigate how routine health data and standard analytical methods could be used to identify inequalities in waiting times relating to age, sex, ethnicity and socioeconomic deprivation. Methods: Decision trees were fitted to data on waiting times for 78 510 completed elective treatments from a large NHS organisation in England for the calendar year of 2021. Data were sourced from the Waiting List Minimum Dataset and combined with a local dataset on patient attributes. Analysis was stratified by treatment specialty and whether the treatment was delivered in an outpatient or inpatient setting. A decision tree was fitted to the data at each stratum to assess three questions: To what extent can variation in waiting times be explained by age, sex, ethnicity and socioeconomic deprivation? Which variables are most explanatory? In what 'direction' is this explanation (eg for sex, did male or female patients wait longer?). Results: Across the elective hospital specialties assessed, a maximum of 12% of variation in waiting times could be explained by age, sex, ethnicity and socioeconomic deprivation. For all decision trees, age appeared as the most important explanative 'branching' variable in 54% of cases, followed by socioeconomic deprivation (2%) and sex (1%). Ethnicity was not a statistically significant explanatory variable. Where variation did exist, waiting times were longer for younger patients, female patients and those from areas with greater socioeconomic deprivation. Conclusions: According to the approach taken in this study, there is little evidence of significant waiting time inequality dependent on sex, socioeconomic deprivation and ethnicity. Analysis of this nature does not confirm a causal association between younger age and longer waiting time, but instead highlights the need for further explorative analysis. Healthcare managers should be cautious about the use of routine health data and standard analytical methods in efforts to identify health inequalities. [ABSTRACT FROM AUTHOR]

The recovery of elective waiting lists represents a major challenge and priority for the health services of many countries. In England's National Health Service (NHS), the waiting list has increased by 45% in the two years since the COVID-19 pandemic was declared in March 2020. Long waits associate with worse patient outcomes and can deepen inequalities and lead to additional demands on healthcare resources. Modelling the waiting list can be valuable for both estimating future trajectories and considering alternative capacity allocation strategies. However, there is a deficit within the current literature of scalable solutions that can provide managers and clinicians with hospital and specialty level projections on a routine basis. In this paper, a model representing the key dynamics of the waiting list problem is presented alongside its differential equation based solution. Versatility of the model is demonstrated through its calibration to routine publicly available NHS data. The model has since been used to produce regular monthly projections of the waiting list for every hospital trust and specialty in England. [ABSTRACT FROM AUTHOR]

In an early stage of the COVID-19 outbreak, hospitals in England were asked to postpone elective treatments in order to accommodate the expected demand for COVID-19 related admissions. This study aims to forecast the extent to which waiting times could increase as a result of these measures, and estimate the level of effort required to restore performance to pre COVID-19 levels. A time-driven simulation is configured and calibrated based upon conditions in England as of February 2020. As a worst case scenario, where restrictions on elective care extend to twelve months and elective treatment rates are halved, results suggest performance could drop to levels not seen since 2007 and the size of the waiting list could double. Restoring performance would take two years assuming additional capacity injections of 12.5%, costing an estimated £14.7b. The modelling presented here offers clinicians and managers an insight into the outcomes that could result under a range of scenarios considered plausible at the early stage of the outbreak. Freely available as open source code, the model may be locally-calibrated for regional healthcare systems and used more widely in countries where similar elective performance measures exist. [ABSTRACT FROM AUTHOR]

Background: Patients with schizophrenia need continuous integrated healthcare, but many discontinue their treatment, often experiencing adverse outcomes. The first objective of this study is to assess whether patient characteristics or treatment history are associated with discontinuity of psychiatric elective care. The second objective is to assess whether practice variation between providers of psychiatric care contributes to discontinuity of elective care. Methods: A large registry-based retrospective cohort of 9194 schizophrenia patients, who were included if they received elective psychiatric care in December 2014–January 2015. Logistic regression models were used to identify predictive factors of discontinuity of care. The dependent variable was the binary variable discontinuity of care in 2016. Potential independent predictive variables were: age, sex, urbanization, and treatment history in 2013–2014. Practice variation between providers was assessed, adjusting for the case mix of patients regarding their demographic and care utilization characteristics. Results: 12.9% of the patients showed discontinuity of elective psychiatric care in the follow-up year 2016. The risk of discontinuity of care in 2016 was higher in younger patients (between age 18 and 26), patients with a history of receiving less elective psychiatric care, more acute psychiatric care, more quarters with elective psychiatric care without antipsychotic medication, or receiving no elective treatment at all. No evidence for practice variation between providers was found. Conclusions: Our findings show that the pattern of previous care consumption is an important prognostic factor of future discontinuity of elective care. We propose that previous care consumption can be used to design strategies to improve treatment retention and focus resources on those most at risk of dropping out. [ABSTRACT FROM AUTHOR]

Background: We undertook a prospective qualitative survey to ascertain the perceptions and experience of National Health Service patients in the United Kingdom who underwent planned or elective procedures and surgery at alternate 'clean' hospital sites during the coronavirus disease 2019 (COVID-19) pandemic. These alternate 'clean' hospital sites were independent hospitals running active staff and patient testing programmes for COVID-19 and which did not admit or treat patients suffering with COVID-19. Methods: A prospective survey was undertaken to include patients at least 30 days after a planned surgery or procedure conducted at a 'clean' alternate hospital site during the COVID-19 pandemic. The study was conducted using structured interviews undertaken by trained assessors. A 20% sample group of patients were randomly selected to participate in this study. Qualitative data related to confidence, safety and perceptions of safety were collected. Results: Ninety-five patients (60%) reported that they had prior worries or concerns about undergoing an elective procedure during the COVID-19 pandemic. A total of 47 patients (30%) had delayed their surgery at least once because of these concerns. A total of 150 patients (95%) felt that the precautions in place to protect their safety in the setting of an alternate 'clean' hospital site were well thought out and proportionate. Patients reported high levels of confidence in the measures undertaken. Separation of patient pathways using the independent sector and patient testing were identified by patients as having the greatest impact on their perception of safety. Conclusions: Patient confidence will be key to ensuring uptake of planned and elective procedures and surgery during the COVID-19 pandemic. Perceptions of safety will be key to this confidence and efforts to demonstrably enhance safety are well received by patients. In particular, patients felt that a dedicated programme of patient testing and separation of patient pathways provided the greatest levels of confidence in the safety of their treatment. [ABSTRACT FROM AUTHOR]

Purpose: The primary aim of our study was to investigate elective orthopaedic care during the first wave government-imposed COVID-19 lockdown and at four weeks and 21 weeks after resuming elective care. The secondary aim of our study was to evaluate the implementation of the European Society of Sports Traumatology, Knee Surgery and Arthroscopy (ESSKA) COVID-19 Guidelines and Recommendations for Resuming Elective Surgery in the clinical practice of Belgian knee surgeons. Methods: We sent three anonymous online surveys to 102 Belgian Knee Society members (BKS) at times mentioned above. Addressed topics were: (1) participant demographics, (2) elective surgeries, (3) outpatient visits, (4) ESSKA Guidelines, (5) patient and surgeon safety. Results: During the COVID-19 lockdown, there was a decrease of 97% in elective knee surgeries and 91% in outpatient visits. At four and 21 weeks after resuming elective care, volumes were respectively 67% and 89% for elective surgeries and 81% and 91% for outpatient visits. Regarding ESSKA guidelines, 91% of surgeons had no COVID-19 testing prior to resuming elective care. Ninety-two per cent reported preoperative (< 72 h) patient PCR testing, and 45% gave preference to young patients without comorbidities. Seventy-two per cent did not use additional personal protective equipment (PPE) if a patient PCR test was negative. Forty-nine per cent continued to give preference to general anaesthesia. Conclusion: Our study shows that elective surgeries and outpatient visits were almost completely interrupted during the COVID-19 lockdown and were still below normal at four and 21 weeks after resuming elective care. Regarding ESSKA COVID-19 guidelines, our study observes good compliance in preoperative patient COVID-19 testing, but lower compliance for preoperative health care personnel testing, patient selection, use of PPE, and locoregional anaesthesia. Level of Evidence: V [ABSTRACT FROM AUTHOR]

Rising admissions from emergency departments (EDs) to hospitals are a primary concern for many healthcare systems. The issue of how to differentiate urgent admissions from non-urgent or even elective admissions is crucial. We aim to develop a model for classifying inpatient admissions based on a patient's primary diagnosis as either emergency care or elective care and predicting urgency as a numerical value. We use supervised machine learning techniques and train the model with physician-expert judgments. Our model is accurate (96%) and has a high area under the ROC curve (>.99). We provide the first comprehensive classification and urgency categorization for inpatient emergency and elective care. This model assigns urgency values to every relevant diagnosis in the ICD catalog, and these values are easily applicable to existing hospital data. Our findings may provide a basis for policy makers to create incentives for hospitals to reduce the number of inappropriate ED admissions. [ABSTRACT FROM AUTHOR]

Seven-day working is an extremely topical and much-debated subject. The findings of Sir Bruce Keogh's Seven Days a Week Forum (NHS England, 2013) were published last year and subsequently widely reported in t he medical and popular press. Organisational and workforce development considerations were outlined and included rota and shift planning for junior and senior doctors, along with the need to establish the right balance between training and service provision. Unfortunately, how such complex changes in working patterns would be implemented remains unclear. This article details three potential options from the trainee perspective and how they will impact on doctor-to-patient ratios throughout the week. [ABSTRACT FROM AUTHOR]