Availability and Accessibility of Mental Health Services for Youth: A Descriptive Survey of Safety-Net Health Centers During the COVID-19 Pandemic.

Community Mental Health Centers (CMHCs) and Federally Qualified Health Centers (FQHCs) are critical access points for families with adolescents needing mental health care, especially those enrolled in Medicaid. However, barriers exist which may reduce their accessibility. This study aims to describe the availability and accessibility of outpatient mental health services for children and adolescents at safety-net health centers in a large metropolitan county. Approximately one year after the COVID-19 pandemic began in the U.S., a comprehensive sample of 117 CMHCs and 117 FQHCs were called and administered a 5-minute survey. Approximately 10% of health centers were closed, and 20% (28.2% of FQHCs and 7.7% of CMHCs) reported not offering outpatient mental health services. Despite CMHCs having 5.4 more clinicians on staff on average, reported wait times were longer at CMHCs than FQHCs. These findings indicate that online directories intended to be a comprehensive and accessible resource, such as the SAMHSA Treatment Locator, are often inaccurate or out-of-date.

Community diagnostic centres: what, where and why?

The British Journal of Healthcare Management's editor discusses the potential benefits of community diagnostic centres for patients and the wider NHS, along with key considerations to drive this model forward.

Natural language processing for clinical text in Spanish: The case of waiting lists in Chile

The waiting lists not covered by the Explicit Health Guarantee Plan for new specialty consultation in Chile increased due to the effects of the SARS-CoV-2 coronavirus (COVID-19) pandemic. This represents a problem derived from the delay in the resolution and prioritization of each case. This paper aims to describe the issue of the waiting lists in the Chilean health system and present an example of the application of Natural Language Processing (NLP). Specifically, a methodology for recognizing key information in medical narratives is described. Currently, we have a set of manually annotated medical referrals in the development of the Chilean Waiting List Corpus, with a fraction of 2,000 referrals in which the annotated medical entities were automatically normalized to the Unified Medical Language System concepts using the lexicon MedLexSp. The clinical NLP Group of the Center for Mathematical Modeling of the University of Chile, and other national NLP groups, are developing several tools and resources in medicine that can be transferred to the Chilean health system to support managing clinical text in Spanish. © 2022

Ophthalmology service recovery with 'general anaesthesia to local anaesthesia conversion' or GALA initiative: a new surgical service pilot.

We report on the establishment of "general anaesthesia (GA) to local anaesthesia (LA) conversion" or GALA initiative at Sheffield Teaching Hospitals NHS Foundation Trust. Through GALA initiative, long waiters for cataract surgery under GA or LA with intravenous sedation were counselled for LA surgery. With careful surgical planning and personalised solutions to LA barriers, LA-converted patients reported good visual and surgical experience outcomes. The GALA initiative reduced cataract surgery waiting times of participants by 11 weeks and increased GA list capacity by three to four theatre lists in its four months of pilot run. This initiative did not limit training opportunities for ophthalmology trainees whose training was affected significantly during the pandemic.

The effect of COVID-19 on surgical training.

The COVID-19 pandemic has had a huge impact on society, healthcare in general and also on training in surgery. Cancellation of elective procedures, redeployment and establishment of green sites have combined with other factors to create significant gaps in training experience in operative and all other areas of surgery. There are nearly a million cases which have been lost to training since March 2020 and recovery means that tens of thousands of extra training cases have to be performed every month to recover that experience. There are pressures to address huge waiting list backlogs which may squeeze out time for training unless training is considered at the heart of any recovery plan. #NoTrainingTodayNoSurgeonsTomorrow. New, no blame, COVID ARCP outcomes have helped recognize the impact of the pandemic on progression and significant trainee and trainer organizations are united in raising the profile of the training crisis and offering a suite of suggestions on how to speed recovery. Disruption caused by the pandemic has allowed existing simulation and conferencing platforms to finally be widely accepted and the importance of the wider surgical team in supporting surgical training to be realized. New, outcomes-based curricula, with better feedback at their centre, will speed recovery of training trajectories. We should embrace the opportunity for change to help short and medium term recovery and improve the delivery of surgical training into the future.

The impact of the post- COVID 19 pandemic elective waiting list delays on patient reported symptoms and intention to proceed with planned upper limb surgeries-A prospective cohort study.

INTRODUCTION: The aim of this study was to evaluate patient reported outcome measures using the EQ5D-5L and EQ5D-5L visual analogue scale (VAS) following elective shoulder and elbow orthopaedic list delays. This was further correlated with patients' intention to proceed with the planned surgery. METHODS: Patients on the waiting list for more than 26 weeks were included in the study. Telephone interviews were conducted utilising the EQ5D-5L and the EQ5D-5L VAS at the time of addition to the waiting list and presently. RESULTS: 75 out of 324 screened patients were eligible. 62 (82.7%) patients still wanted to proceed with their planned procedure while 13 (17.3%) patients in the other group no longer wanted to proceed. There was no statistically significant difference in the mean age, gender, initial trial of conservative treatment and limb laterality between these groups (p < 0.05). There was a statistically significant difference in the mean duration of being on the waiting list between these groups (40.4 ± 19 vs. 62.9 ± 17.5 weeks respectively). Furthermore, statistically significant differences (p < 0.05) in the current EQ5D-5L VAS scores were observed between these groups (52.4 vs. 65.8 respectively). CONCLUSION: This study has shown that majority of patients on elective shoulder and elbow orthopaedic lists with prolonged waiting list delays and improved EQ5D-5L scores are likely to decline the planned procedure and vice versa. Nevertheless, the unplanned 'watchful waiting' caused by the COVID-19 pandemic and leading to patients deciding to decline surgery, is not a substitute for timely planned surgery to alleviate patients' suffering.

Supporting COVID-19 elective recovery through scalable wait list modelling: Specialty-level application to all hospitals in England.

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.

Caution against the new normal

The article reports on the need to improve plans for care of patients with cardiovascular disease even after the COVID-19 pandemic in Great Britain.

Improving access to Hackney Integrated Learning Disability Service

Aims Improving access to Hackney Integrated Learning Disability Service (ILDS) using quality improvement (QI) methodology by reducing the time taken to complete eligibility assessment by 50% by April 2021, while improving service user and staff experience. Background Referrals to ILDS require assessment of eligibility. It was noted that there was significant waiting time between referral and eligibility assessment, exacerbated by the COVID-19 pandemic. Quality Network for Community Learning Disability Services guidelines suggest waiting times for those accessing adult learning disability (LD) community services be locally agreed, although there is limited literature on this topic. Methods All staff members across the multidisciplinary team were invited to participate in the QI project. We defined outcome measures as days from referral to allocation of eligibility assessment to staff member and to completion of eligibility assessment, comparing referrals received before and after start of the project. The key change ideas tested using Plan-Do-Study-Act cycles were: (1) eligibility screening checklist, (2) eligibility assessments drop-in sessions for staff, (3) formal training for ILDS staff, (4) eligibility screening allocation system, (5) template letters for eligibility decisions, (6) new ILDS referral form, (7) workshops for local general practitioners. Results Time taken to eligibility assessment allocation decreased from median of 184 (mean=183.5 +/- 109.8) to 13 days (mean=19.9 +/- 26.4) (93% reduction). Time to completion of eligibility assessment decreased from a median of 271 (mean=296.0 +/- 133.8) to 63 days (mean=75.7 +/- 34.8) (77% reduction). We received positive feedback from staff and service users regarding the new eligibility process. Conclusions We report waiting times for accessing our community adult LD services and effective strategies for reducing this. We recommend similar services use a QI methodology to reduce waiting times and improve the experience of staff and service users.

Modeling the Recovery of Elective Waiting Lists Following COVID-19: Scenario Projections for England.

OBJECTIVES: A significant indirect impact of COVID-19 has been the increasing elective waiting times observed in many countries. In England's National Health Service, the waiting list has grown from 4.4 million in February 2020 to 5.7 million by August 2021. The objective of this study was to estimate the trajectory of future waiting list size and waiting times up to December 2025. METHODS: A scenario analysis was performed using computer simulation and publicly available data as of November 2021. Future demand assumed a phased return of various proportions (0%, 25%, 50%, and 75%) of the estimated 7.1 million referrals "missed" during the pandemic. Future capacity assumed 90%, 100%, and 110% of that provided in the 12 months immediately before the pandemic. RESULTS: As a worst-case scenario, the waiting list would reach 13.6 million (95% confidence interval 12.4-15.6 million) by Autumn 2022, if 75% of missed referrals returned and only 90% of prepandemic capacity could be achieved. The proportion of patients waiting under 18 weeks would reduce from 67.6% in August 2021 to 42.2% (37.4%-46.2%) with the number waiting over 52 weeks reaching 1.6 million (0.8-3.1 million) by Summer 2023. At this time, 29.0% (21.3%-36.8%) of patients would be leaving the waiting list before treatment. Waiting lists would remain pressured under even the most optimistic of scenarios considered, with 18-week performance struggling to maintain 60%. CONCLUSIONS: This study reveals the long-term challenge for the National Health Service in recovering elective waiting lists and potential implications for patient outcomes and experience.

An observational study on IAPT waiting times before, during and after the COVID-19 pandemic using descriptive time-series data

Purpose: The purpose of this paper is to explore waiting times in improving access to psychological therapies (IAPT) services before and throughout the COVID-19 pandemic. The paper aims to help develop a better understanding of waiting times in IAPT so that interventions can be developed to address them. Design/methodology/approach: IAPT national data reports was analysed to determine access and in-treatment waiting times before, during and after the COVID-19 pandemic. Time-series data was used to examine referral patterns, waiting list size and waiting times between the period of November 2018 and January 2022. The data covers all regions in England where an IAPT service has been commissioned. Findings: There was a dramatic drop in referrals to IAPT services when lockdown started. Waiting list size for all IAPT services in the country reduced, as did incomplete and completed waits. The reduction in waiting times was short-lived, and longer waits are returning. Practical implications: This paper aims to contribute to the literature on IAPT waiting times both in relation to, and outside of, COVID-19. It is hoped that the conclusions will generate discussion about addressing long waits to treatment for psychological therapy and encourage further research. Originality/value: To the best of the authors’ knowledge, there is no published research examining the performance of IAPT waiting times to second appointment. The paper also contributes to an understanding of how IAPT waiting times are measured and explores challenges with the system itself. Finally, it offers an overview on the impact of the COVID-19 pandemic on waiting time performance nationally. © 2022, Emerald Publishing Limited.

Addressing backlogs and managing waiting lists during and beyond the COVID-19 pandemic

In order to restore services to pre-pandemic levels and catch up on care, we need to understand and act on what we have learned from the pandemic, including investing in the health workforce, increasing funding for the health infrastructure of the future, and maintaining the innovative forms of service delivery that proved useful in reaching out to key groups affected by the pandemic. This brief is a valuable resource for policy-makers seeking to understand the extent of disruption to health services caused by COVID-19, the reasons behind this, and what different countries are doing in response. Its aim is to provide options to reduce service backlogs for those who are addressing this challenge in their national contexts.

Recovering staff, recovering services: massive-online support for recovering a paediatric service using Lean and compassionate communication.

The COVID-19 pandemic has infected tens of millions worldwide. Healthcare systems have been stretched caring for the most seriously ill and healthcare workers have struggled to maintain non-COVID services leading to backlogs.Strategies proposed to support the recovery of backlogs include additional administration support; waiting list data validation; enhanced patient communication; and use of systematic improvement methods to make rapid incremental improvements.As part of COVID-19 recovery, a hospital trust in northern England used the Lean systematic improvement approach to recover the waiting list of a paediatric service to pre-COVID levels. The intervention strategy used a massive-open-online-course (Lean Fundamentals) to support the improvement project lead to follow a structured improvement routine to apply Lean improvement techniques.By acknowledging that staff were overburdened by the requirements of COVID-19 and that patients were stuck in a system of disconnected processes, administrative activities were redesigned around an ethos of compassionate communication that put patients first.Over a period of 8 weeks, the project reduced the waiting list from 1109 to 212. Waiting times were reduced from a maximum of 36 months to a 70-day average.Lean is often described in terms of increasing process efficiency and productivity. It is not often associated with staff benefits. However, when seen in the context of unburdening staff to deliver patient care, Lean has potential to support the recovery of both staff and services. Lean Fundamentals, with its accessible massive-online design, may provide a means of supporting such improvement at scale.

How the First Year of COVID-19 Affected Elective Pediatric Urology Patients: A Longitudinal Study Based on Waiting Lists and Surveys From 10 European Centers.

Introduction: COVID-19 impacted healthcare systems worldwide, and elective surgical activity was brought to a minimum. Although children were not primarily affected by the disease, pediatric urology was halted by clinical closedown and staff allocation. We aimed to document how these prioritizations affected waiting lists, and to investigate how European centers dealt with the challenge of these logistical and financial prioritizations. Materials and Methods: This was a 1-year prospective study, starting March 2020. Participants were surveyed at 3-month intervals about waiting lists for several common procedures as well as OR capacity and funding. Further, centers retrospectively reported on surgical and outpatient activity rates during 2019-2021. Waiting list tendencies were evaluated in relation to study baseline. Results: A marked decrease in surgical and outpatient activity was seen in the spring of 2020. Some included pediatric urology centers were able to increase their budget (15%) and staff working hours (20%) during part of the study period. Still, at the end of the study, the centers had increased the total number of patients on waiting lists with 11%, whereas the average days on waiting lists had accumulated with 73%, yielding a total of 6,102 accumulated waiting days in the study population. Centers with decreased resources had markedly negative effects on waiting lists. Conclusions: Correlations between COVID-19 derived burdening of healthcare systems and the availability of pediatric urology greatly depends on the prioritizations made at individual centers. Ongoing monitoring of these correlations is warranted to safely avoid unnecessary negative impact on the pediatric population.

Modeling of Waiting Lists for Chronic Heart Failure in the Wake of the COVID-19 Pandemic

The Covid-19 pandemic has disrupted access to health services globally for patients with non-Covid-19 conditions. We consider the condition of heart failure and describe a discrete event simulation model built to describe the impact of the pandemic and associated societal lockdowns on access to diagnosis procedures. The number of patients diagnosed with heart failure fell during the pandemic and in the UK, the number of GP referrals for diagnostic tests in November 2020 were at 20% of their pre-pandemic levels. While the numbers in the system have fallen clinicians believe that this is not reflective of a change in need, suggesting that many patients are delaying accessing care during pandemic peaks. While the effect of this is uncertain, it is thought that this could have a significant impact on patient survival. Initial results reproduce the observed increase in the number of patients waiting. © 2021 IEEE.