Prolonged wait time is associated with increased mortality for Chilean waiting list patients with non-prioritized conditions

Waiting times spillovers in a National Health Service hospital network: a little organizational diversity can go a long way

BACKGROUND

The objective of this study is to assess if waiting times for elective surgeries within the Portuguese National Health Service (NHS) are influenced by the waiting times at neighboring hospitals. Recognizing these interdependencies, and their extent, is crucial for understanding how hospital network dynamics affect healthcare delivery efficiency and patient access.

METHODS

We utilized patient-level data from all elective surgeries conducted in Portuguese NHS hospitals to estimate a hospital-specific index for waiting times. This index served as the dependent variable in our analysis. We applied a spatial lag model to examine the potential strategic interactions between hospitals concerning their waiting times.

RESULTS

Our analysis revealed a significant positive endogenous spatial dependence, indicating that waiting times in NHS hospitals are strategic complements. Furthermore, we found that NHS contracts with private not-for-profit hospitals not only reduce waiting times within these hospitals but also exert positive spillover effects on other NHS hospitals.

CONCLUSIONS

The findings suggest that diversifying the organization of the NHS hospital network, particularly through contracts with private entities for marginal patients, can significantly enhance competitive dynamics and reduce waiting times. This effect persists even when patient choice is confined to a small fraction of the patient population, highlighting a strategic avenue for policy optimization in healthcare service delivery.

Insights into how universal, tax-funded, single payer health systems manage their waiting lists: A review of the literature

Background: A conspicuous consequence of gatekeeping arrangements in universal, tax-funded, single-payer health care systems is the long waiting times. Besides limiting equal access to care, long waiting times can have a negative impact on health outcomes. Long waiting times can create obstacles in a patient's care pathway. Organization for Economic Co-operation and Development (OECD) countries have implemented various strategies to tackle this issue, but there is little evidence for which approach is the most effective. This literature review examined waiting times for ambulatory care. Objective: The aim was to identify the main policies or combinations of policies universal, tax-funded, and single-payer healthcare systems have implemented to improve the governance of outpatient waiting times. Methods: Starting from 1040 potentially eligible articles, a total of 41 studies were identified via a 2-step selection process. Findings: Despite the relevance of the issue, the literature is limited. A set of 15 policies for the governance of ambulatory waiting time was identified and categorized by the type of intervention: generation of supply capacity, control of demand, and mixed interventions. Even if a primary intervention was always identifiable, rarely a policy was implemented solo. The most frequent primary strategies were: guidelines implementation and/or clinical pathways, including triage, guidelines for referral and maxim waiting times (14 studies), task shifting (9 studies), and telemedicine (6 studies). Most studies were observational, with no data on costs of intervention and impact on clinical outcomes.

The Synergetic Effect of 3D Printing and Electrospinning Techniques in the Fabrication of Bone Scaffolds

The primary goal of bone tissue engineering is to restore and rejuvenate bone defects by using a suitable three-dimensional scaffold, appropriate cells, and growth hormones. Various scaffolding methods are used to fabricate three-dimensional scaffolds, which provide the necessary environment for cell activity and bone formation. Multiple materials may be used to create scaffolds with hierarchical structures that are optimal for cell growth and specialization. This study examines a notion for creating an optimal framework for bone regeneration using a combination of the robocasting method and the electrospinning approach. Research indicates that the integration of these two procedures enhances the benefits of each method and provides a rationale for addressing their shortcomings via this combination. The hybrid approach is anticipated to provide a manufactured scaffold that can effectively replace bone defects while possessing the necessary qualities for bone regeneration.

Decentralizing referral prioritization to general practitioners at the primary care level: A qualitative case study based on the Grounded Theory

BACKGROUND

Patient referral prioritizations is an essential process in coordinating healthcare delivery, since it organizes the waiting lists according to priorities and availability of resources.

OBJECTIVE

This study aims to highlight the consequences of decentralizing ambulatory patient referrals to general practitioners that work as family physicians in primary care clinics.

METHODS

A qualitative case study was carried out in the municipality of Rio de Janeiro. The ten health regions of Rio de Janeiro were visited during fieldwork, totalizing 35 hours of semi-structured interviews and approximately 70 hours of analysis based on the Grounded Theory.

RESULTS

The findings of this study show that the obstacles to adequate referrals are beyond the management of vacancies, ranging from the standardization of prioritization criteria to ensuring the proper employment of referral protocols in diverse locations assisted by overloaded health workers with different backgrounds and perceptions. Efforts in decentralizing patient referral to primary care still face the growing dilemmas and challenges of expanding the coverage of health services while putting pressure on risk assessment, as well as sustaining the autonomy of physicians' work while respecting the eligibility when ordering waiting lists.

CONCLUSION

A major strength of this work is on the method to organize and aggregate qualitative data using visual representations. Limitations concerning the reach of fieldwork in vulnerable and hardly accessible areas were overcame using snowball sampling techniques, making more participants accessible.

Automatic Detection of Distant Metastasis Mentions in Radiology Reports in Spanish

PURPOSE

A critical task in oncology is extracting information related to cancer metastasis from electronic health records. Metastasis-related information is crucial for planning treatment, evaluating patient prognoses, and cancer research. However, the unstructured way in which findings of distant metastasis are often written in radiology reports makes it difficult to extract information automatically. The main aim of this study was to extract distant metastasis findings from free-text imaging and nuclear medicine reports to classify the patient status according to the presence or absence of distant metastasis.

MATERIALS AND METHODS

We created a distant metastasis annotated corpus using positron emission tomography-computed tomography and computed tomography reports of patients with prostate, colorectal, and breast cancers. Entities were labeled M1 or M0 according to affirmative or negative metastasis descriptions. We used a named entity recognition model on the basis of a bidirectional long short-term memory model and conditional random fields to identify entities. Mentions were subsequently used to classify whole reports into M1 or M0.

RESULTS

The model detected distant metastasis mentions with a weighted average F1 score performance of 0.84. Whole reports were classified with an F1 score of 0.92 for M0 documents and 0.90 for M1 documents.

CONCLUSION

These results show the usefulness of the model in detecting distant metastasis findings in three different types of cancer and the consequent classification of reports. The relevance of this study is to generate structured distant metastasis information from free-text imaging reports in Spanish. In addition, the manually annotated corpus, annotation guidelines, and code are freely released to the research community.

An innovative model of access and triage to reduce waiting in an outpatient epilepsy clinic: an intervention study

BACKGROUND

Delayed access to outpatient care may negatively impact on health outcomes. We aimed to evaluate implementation of the Specific Timely Appointments for Triage (STAT) model of access in an epilepsy clinic to reduce a long waitlist and waiting time.

METHODS

This study is an intervention study using pre-post comparison and an interrupted time series analysis to measure the effect of implementation of the STAT model to an epilepsy clinic. Data were collected over 28 months to observe the number of patients on the waitlist and the waiting time over three time periods: 12 months prior to implementation of STAT, ten months during implementation and six months post-intervention. STAT combines one-off backlog reduction with responsive scheduling that protects time for new appointments based on historical data. The primary outcomes were the number of patients on the waitlist and the waiting time across the three time periods. Secondary outcomes evaluated pre- and post-intervention changes in number of appointments offered weekly, non-arrival and discharge rates.

RESULTS

A total of 938 patients were offered a first appointment over the study period. The long waitlist was almost eliminated, reducing from 616 during the pre-intervention period to 11 post-intervention (p = 0.002), but the hypothesis that waiting time would decrease was not supported. The interrupted time series analysis indicated a temporary increase in waiting time during the implementation period but no significant change in slope or level in the post- compared to the pre-intervention period. Direct comparison of the cohort of patients seen in the pre- and post-intervention periods suggested an increase in median waiting time following the intervention (34 [IQR 25-86] to 46 [IQR 36-61] days (p = 0.001)), but the interquartile range reduced indicating less variability in days waited and more timely access for the longest waiters.

CONCLUSIONS

The STAT model was implemented in a specialist epilepsy outpatient clinic and reduced a large waitlist. Reductions in the waitlist were achieved with little or no increase in waiting time. The STAT model provides a framework for an alternative way to operate outpatient clinics that can help to ensure that all people referred are offered an appointment in a timely manner.

A cost-effectiveness analysis of patiromer in the UK: evaluation of hyperkalaemia treatment and lifelong RAASi maintenance in chronic kidney disease patients with and without heart failure

BACKGROUND

Chronic kidney disease (CKD) patients with and without heart failure (HF) often present with hyperkalaemia (HK) leading to increased risk of hospitalisations, cardiovascular related events and cardiovascular-related mortality. Renin-angiotensin-aldosterone system inhibitor (RAASi) therapy, the mainstay treatment in CKD management, provides significant cardiovascular and renal protection. Nevertheless, its use in the clinic is often suboptimal and treatment is frequently discontinued due to its association with HK. We evaluated the cost-effectiveness of patiromer, a treatment known to reduce potassium levels and increase cardiorenal protection in patients receiving RAASi, in the UK healthcare setting.

METHODS

A Markov cohort model was generated to assess the pharmacoeconomic impact of patiromer treatment in regulating HK in patients with advanced CKD with and without HF. The model was generated to predict the natural history of both CKD and HF and quantify the costs and clinical benefits associated with the use of patiromer for HK management from a healthcare payer's perspective in the UK.

RESULTS

Economic evaluation of patiromer use compared to standard of care (SoC) resulted in increased discounted life years (8.93 versus 8.67) and increased discounted quality-adjusted life years (QALYs) (6.36 versus 6.16). Furthermore, patiromer use resulted in incremental discounted cost of £2,973 per patient and an incremental cost-effectiveness ratio (ICER) of £14,816 per QALY gained. On average, patients remained on patiromer therapy for 7.7 months, and treatment associated with a decrease in overall clinical event incidence and delayed CKD progression. Compared to SoC, patiromer use resulted in 218 fewer HK events per 1,000 patients, when evaluating potassium levels at the 5.5-6 mmol/l; 165 fewer RAASi discontinuation episodes; and 64 fewer RAASi down-titration episodes. In the UK, patiromer treatment was predicted to have a 94.5% and 100% chance of cost-effectiveness at willingness-to-pay thresholds (WTP) of £20,000/QALY and £30,000/QALY, respectively.

CONCLUSION

This study highlights the value of both HK normalisation and RAASi maintenance in CKD patients with and without HF. Results support the guidelines which recommend HK treatment, e.g., patiromer, as a strategy to enable the continuation of RAASi therapy and improve clinical outcomes in CKD patients with and without HF.

Impact of Socio-demographic Characteristics on Time in Outpatient Cardiology Clinics: A Retrospective Analysis

Inequitable access to health services influences health outcomes. Some studies have found patients of lower socio-economic status (SES) wait longer for surgery, but little data exist on access to outpatient services. This study analyzed patient-level data from outpatient public cardiology clinics and assessed whether low SES patients spend longer accessing ambulatory services. Retrospective analysis of cardiology clinic encounters across 3 public hospitals between 2014 and 2019 was undertaken. Data were linked to age, gender, Indigenous status, country of birth, language spoken at home, number of comorbidities, and postcode. A cox proportional hazards model was applied adjusting for visit type (new/follow up), clinic, and referral source. Higher hazard ratio (HR) indicates shorter clinic time. Overall, 22 367 patients were included (mean [SD] age 61.4 [15.2], 14 925 (66.7%) male). Only 7823 (35.0%) were born in Australia and 8452 (37.8%) were in the lowest SES quintile. Median total clinic time was 84 min (IQR 58-130). Visit type, clinic, and referral source were associated with clinic time (R² = 0.23, 0.35, 0.20). After adjusting for these variables, older patients spent longer in clinic (HR 0.94 [0.90-0.97]), though there was no difference according to SES (HR 1.02 [0.99-1.06]) or other variables of interest. Time spent attending an outpatient clinic is substantial, amplifying an already significant time burden faced by patients with chronic health conditions. SES was not associated with longer clinic time in our analysis. Time spent in clinics could be used more productively to optimize care, improve health outcomes and patient experience.

Training and intrinsic evaluation of lightweight word embeddings for the clinical domain in Spanish

Resources for Natural Language Processing (NLP) are less numerous for languages different from English. In the clinical domain, where these resources are vital for obtaining new knowledge about human health and diseases, creating new resources for the Spanish language is imperative. One of the most common approaches in NLP is word embeddings, which are dense vector representations of a word, considering the word's context. This vector representation is usually the first step in various NLP tasks, such as text classification or information extraction. Therefore, in order to enrich Spanish language NLP tools, we built a Spanish clinical corpus from waiting list diagnostic suspicions, a biomedical corpus from medical journals, and term sequences sampled from the Unified Medical Language System (UMLS). These three corpora can be used to compute word embeddings models from scratch using Word2vec and fastText algorithms. Furthermore, to validate the quality of the calculated embeddings, we adapted several evaluation datasets in English, including some tests that have not been used in Spanish to the best of our knowledge. These translations were validated by two bilingual clinicians following an ad hoc validation standard for the translation. Even though contextualized word embeddings nowadays receive enormous attention, their calculation and deployment require specialized hardware and giant training corpora. Our static embeddings can be used in clinical applications with limited computational resources. The validation of the intrinsic test we present here can help groups working on static and contextualized word embeddings. We are releasing the training corpus and the embeddings within this publication¹.

A predictive model for the post-pandemic delay in elective treatment

The COVID-19 pandemic had a major impact on healthcare systems across the world. In the United Kingdom, one of the strategies used by hospitals to cope with the surge in patients infected with SARS-Cov-2 was to cancel a vast number of elective treatments planned and limit its resources for non-critical patients. This resulted in a 30% drop in the number of people joining the waiting list in 2020-2021 versus 2019-2020. Once the pandemic subsides and resources are freed for elective treatment, the expectation is that the patients failing to receive treatment throughout the pandemic would trigger a significant backlog on the waiting list post-pandemic with major repercussions to patient health and quality of life. As the nation emerges from the worst phase of the pandemic, hospitals are focusing on strategies to prioritise patients for elective treatments. A key challenge in this context is the ability to quantify the expected backlog and predict the delays experienced by patients as an outcome of the prioritisation policies. This study presents an approach based on discrete-event simulation to predict the elective waiting list backlog along with the delay in treatment based on a predetermined prioritisation policy. The model is demonstrated using data on the endoscopy waiting list at Cambridge University Hospitals. The model shows that 21% of the patients on the waiting list will experience a delay less than 18-weeks, the acceptable threshold set by the National Health Service (NHS). A longer-term scenario analysis based on the model reveals investment in NHS resources will have a significant positive outcome for addressing the waiting lists. The model presented in this paper has the potential to be an invaluable tool for post-pandemic planning for hospitals around the world that are facing a crisis of treatment backlog.

Creation of a Pilot Surgical Program for the Comprehensive Management of Patients with Congenital Urological Malformations

Objectives Congenital malformations constitute the first cause of morbidity and mortality in childhood in Latin America. That is why, since 2001, a surveillance system for congenital malformations has been implemented in Bogota - Colombia. However, despite the increase in detection, an impact on treatment has not been achieved. The present study describes our experience with a novel social program focused on congenital urologic disorders.

Methods The present manuscript is a retrospective observational study. We reviewed two national databases containing patients with congenital malformations. Patients were actively contacted to verify the status of the malformations. Children in whom the malformation was confirmed were offered a free consultation with a multidisciplinary group. After screening for surgical indications, patients were scheduled for surgery.

Results Between November 2018 and December 2019, 60 patients were identified. In total 44, attended the consultation; the remaining did not attend due to financial or travel limitations. The most common condition assessed was hypospadias. In total, 29 patients underwent surgery. The total cost of care was of US$ 5,800.

Conclusions Active search improves attention times and reduces the burden of disease. The limitations to be resolved include optimizing the transportation of patients and their families, which is a frequent limitation to access health care.

Telehealth Strategies to Support Referral Management to Secondary Care in Brazil: A Cost-Effectiveness Analysis

OBJECTIVES

This study aimed to assess the cost-effectiveness of a remotely operated referral management system (RORMS) compared with a conventional referral management system (CRMS) in Brazil.

METHODS

This is a model-based cost-effectiveness analysis under the perspective of the Unified Healthcare System (Sistema Único de Saúde [SUS]) in Brazil. A Markov microsimulation model was developed to compare costs and referral outcomes of the RORMS and the CRMS. Model consisted of 4 states representative of sequential stepwise assessments of referral suitability, 3 states representative of referral outcomes, and 1 exit model state. Target population represented cases being referred from primary healthcare units to specialized care in SUS. Model inputs related to costs and effectiveness in the RORMS arm were obtained from the data set of a RORMS between July and December 2019. Model inputs for the CRMS model arm were obtained from administrative data sets of 2 Brazilian localities for the year 2019. Relative effect size of RORMS in comparison with CRMS in SUS was obtained from published studies. Effectiveness outcome was unnecessary referrals averted. The incremental cost-effectiveness ratio was calculated for the base case. Probabilistic sensitivity analysis was conducted.

RESULTS

In the base-case analyses, RORMS dominated CRMS, with expected cost-savings from $50.42 to $80.62 per unnecessary referral averted. RORMS was the dominant strategy in 83.7% of 100 000 simulations in the probabilistic sensitivity analysis. In 16.2% of simulations, incremental cost-effectiveness ratio was between $0 and $222 per unnecessary referral averted.

CONCLUSIONS

Model-based simulations indicate that the RORMS is likely to be cost saving in comparison with the CRMS.

How scheduling systems with automated appointment reminders improve health clinic efficiency

Missed clinic appointments or no-shows burden health care systems through inefficient use of staff time and resources. Scheduling software with automatic appointment reminders shows promise to improve clinics' management through timely cancellations and re-scheduling, but at-scale evidence is missing. We study a nationwide text message appointment reminder program in Chile implemented at primary care clinics for patients with chronic disease. Using longitudinal clinic-level data, we find that the program did not change the number of visits by chronic patients eligible to receive the reminder but visits from other patients ineligible to receive reminders increased by 5.0% in the first year and 7.4% in the second. Clinics treating more chronic patients and those with a relatively younger patient population benefited more from the program. Scheduling systems with automatic appointment reminders were effective in increasing clinics' ability to care for more patients, likely due to timely cancellations and re-scheduling.

Chatbot for Health Care and Oncology Applications Using Artificial Intelligence and Machine Learning: Systematic Review

Background

Chatbot is a timely topic applied in various fields, including medicine and health care, for human-like knowledge transfer and communication. Machine learning, a subset of artificial intelligence, has been proven particularly applicable in health care, with the ability for complex dialog management and conversational flexibility.

Objective

This review article aims to report on the recent advances and current trends in chatbot technology in medicine. A brief historical overview, along with the developmental progress and design characteristics, is first introduced. The focus will be on cancer therapy, with in-depth discussions and examples of diagnosis, treatment, monitoring, patient support, workflow efficiency, and health promotion. In addition, this paper will explore the limitations and areas of concern, highlighting ethical, moral, security, technical, and regulatory standards and evaluation issues to explain the hesitancy in implementation.

Methods

A search of the literature published in the past 20 years was conducted using the IEEE Xplore, PubMed, Web of Science, Scopus, and OVID databases. The screening of chatbots was guided by the open-access Botlist directory for health care components and further divided according to the following criteria: diagnosis, treatment, monitoring, support, workflow, and health promotion.

Results

Even after addressing these issues and establishing the safety or efficacy of chatbots, human elements in health care will not be replaceable. Therefore, chatbots have the potential to be integrated into clinical practice by working alongside health practitioners to reduce costs, refine workflow efficiencies, and improve patient outcomes. Other applications in pandemic support, global health, and education are yet to be fully explored.

Conclusions

Further research and interdisciplinary collaboration could advance this technology to dramatically improve the quality of care for patients, rebalance the workload for clinicians, and revolutionize the practice of medicine.

The 2019 crisis in Chile: fundamental change needed, not just technical fixes to the health system

Chile has been viewed as an exemplar of social and economic progress in Latin America, with its health system attracting considerable attention. Eruption of widespread civil disorder marred this image in 2019. We trace the evolution of Chilean health policy and place it in context with developments in other sectors, pensions and education. We argue that much has been achieved, but further progress will necessitate politicians tackling the enduring power of elites that has prevented reform of a two-tier system enshrined in policies of the dictatorship.

The Waiting Game - How Cooperation Between Public and Private Hospitals Can Help Reduce Waiting Lists

Prolonged waiting to access health care is a primary concern for nations aiming for comprehensive effective care, due to its adverse effects on mortality, quality of life, and government approval. Here, we propose two novel bargaining frameworks to reduce waiting lists in two-tier health care systems with local and regional actors. In particular, we assess the impact of 1) trading patients on waiting lists among hospitals, the 2) introduction of the role of private hospitals in capturing unfulfilled demand, and the 3) hospitals’ willingness to share capacity on the system performance. We calibrated our models with 2008–2018 Chilean waiting list data. If hospitals trade unattended patients, our game-theoretic models indicate a potential reduction of waiting lists of up to 37%. However, when private hospitals are introduced into the system, we found a possible reduction of waiting lists of up to 60%. Further analyses revealed a trade-off between diagnosing unserved demand and the additional expense of using private hospitals as a back-up system. In summary, our game-theoretic frameworks of waiting list management in two-tier health systems suggest that public–private cooperation can be an effective mechanism to reduce waiting lists. Further empirical and prospective evaluations are needed.

Supporting the classification of patients in public hospitals in Chile by designing, deploying and validating a system based on natural language processing

Background

In Chile, a patient needing a specialty consultation or surgery has to first be referred by a general practitioner, then placed on a waiting list. The Explicit Health Guarantees (GES in Spanish) ensures, by law, the maximum time to solve 85 health problems. Usually, a health professional manually verifies if each referral, written in natural language, corresponds or not to a GES-covered disease. An error in this classification is catastrophic for patients, as it puts them on a non-prioritized waiting list, characterized by prolonged waiting times.

Methods

To support the manual process, we developed and deployed a system that automatically classifies referrals as GES-covered or not using historical data. Our system is based on word embeddings specially trained for clinical text produced in Chile. We used a vector representation of the reason for referral and patient's age as features for training machine learning models using human-labeled historical data. We constructed a ground truth dataset combining classifications made by three healthcare experts, which was used to validate our results.

Results

The best performing model over ground truth reached an AUC score of 0.94, with a weighted F1-score of 0.85 (0.87 in precision and 0.86 in recall). During seven months of continuous and voluntary use, the system has amended 87 patient misclassifications.

Conclusion

This system is a result of a collaboration between technical and clinical experts, and the design of the classifier was custom-tailored for a hospital's clinical workflow, which encouraged the voluntary use of the platform. Our solution can be easily expanded across other hospitals since the registry is uniform in Chile.

The J-IDEA Pandemic Planner: A Framework for Implementing Hospital Provision Interventions During the COVID-19 Pandemic

BACKGROUND

Planning for extreme surges in demand for hospital care of patients requiring urgent life-saving treatment for coronavirus disease 2019 (COVID-19), while retaining capacity for other emergency conditions, is one of the most challenging tasks faced by health care providers and policymakers during the pandemic. Health systems must be well-prepared to cope with large and sudden changes in demand by implementing interventions to ensure adequate access to care. We developed the first planning tool for the COVID-19 pandemic to account for how hospital provision interventions (such as cancelling elective surgery, setting up field hospitals, or hiring retired staff) will affect the capacity of hospitals to provide life-saving care.

METHODS

We conducted a review of interventions implemented or considered in 12 European countries in March to April 2020, an evaluation of their impact on capacity, and a review of key parameters in the care of COVID-19 patients. This information was used to develop a planner capable of estimating the impact of specific interventions on doctors, nurses, beds, and respiratory support equipment. We applied this to a scenario-based case study of 1 intervention, the set-up of field hospitals in England, under varying levels of COVID-19 patients.

RESULTS

The Abdul Latif Jameel Institute for Disease and Emergency Analytics pandemic planner is a hospital planning tool that allows hospital administrators, policymakers, and other decision-makers to calculate the amount of capacity in terms of beds, staff, and crucial medical equipment obtained by implementing the interventions. Flexible assumptions on baseline capacity, the number of hospitalizations, staff-to-beds ratios, and staff absences due to COVID-19 make the planner adaptable to multiple settings. The results of the case study show that while field hospitals alleviate the burden on the number of beds available, this intervention is futile unless the deficit of critical care nurses is addressed first.

DISCUSSION

The tool supports decision-makers in delivering a fast and effective response to the pandemic. The unique contribution of the planner is that it allows users to compare the impact of interventions that change some or all inputs.

Adapting hospital capacity to meet changing demands during the COVID-19 pandemic

BACKGROUND

To calculate hospital surge capacity, achieved via hospital provision interventions implemented for the emergency treatment of coronavirus disease 2019 (COVID-19) and other patients through March to May 2020; to evaluate the conditions for admitting patients for elective surgery under varying admission levels of COVID-19 patients.

METHODS

We analysed National Health Service (NHS) datasets and literature reviews to estimate hospital care capacity before the pandemic (pre-pandemic baseline) and to quantify the impact of interventions (cancellation of elective surgery, field hospitals, use of private hospitals, deployment of former medical staff and deployment of newly qualified medical staff) for treatment of adult COVID-19 patients, focusing on general and acute (G&A) and critical care (CC) beds, staff and ventilators.

RESULTS

NHS England would not have had sufficient capacity to treat all COVID-19 and other patients in March and April 2020 without the hospital provision interventions, which alleviated significant shortfalls in CC nurses, CC and G&A beds and CC junior doctors. All elective surgery can be conducted at normal pre-pandemic levels provided the other interventions are sustained, but only if the daily number of COVID-19 patients occupying CC beds is not greater than 1550 in the whole of England. If the other interventions are not maintained, then elective surgery can only be conducted if the number of COVID-19 patients occupying CC beds is not greater than 320. However, there is greater national capacity to treat G&A patients: without interventions, it takes almost 10,000 G&A COVID-19 patients before any G&A elective patients would be unable to be accommodated.

CONCLUSIONS

Unless COVID-19 hospitalisations drop to low levels, there is a continued need to enhance critical care capacity in England with field hospitals, use of private hospitals or deployment of former and newly qualified medical staff to allow some or all elective surgery to take place.

Tempo de espera e absenteísmo na atenção especializada: um desafio para os sistemas universais de saúde

RESUMO O objetivo do estudo foi analisar o absenteísmo em relação ao tempo de espera por consultas e exames especializados nos 20 municípios que compõem a Região de Saúde Metropolitana do estado do Espírito Santo (RSM-ES), Brasil. Estudo descritivo retrospectivo realizado a partir da análise do Banco de Dados da Secretaria de Estado da Saúde do Espírito Santo (Sesa). Foram considerados 1.002.719 encaminhamentos dos usuários residentes na RSM-ES para consultas/exames especializados fora do município no período de janeiro de 2014 a dezembro de 2016, que correspondem a todos os agendamentos. O tempo médio de espera pela consulta foi de 419 dias (desvio padrão = 29,3, mediana = 17,0) em 2014, de 687 dias (desvio padrão = 70,5, mediana = 16,0) em 2015, de 1.077 dias (desvio padrão = 140,3, mediana = 20,0) em 2016, aumento progressivo da espera com o passar dos anos. As análises de correlação do estudo apontaram que o tempo de espera e o porte municipal são fatores correlacionados às taxas de absenteísmo em consultas e exames especializados (p-valor<5%). O impacto do absenteísmo nos serviços ambulatoriais, influenciado pelo tempo de espera, constitui-se em um grande desafio para a estruturação de um sistema público de saúde no Brasil. Conhecer como certos fatores impactam o comportamento de não comparecimento a compromissos agendados em municípios pode subsidiar mudanças nas políticas de agendamento de consultas/exames especializados.