Importance of Balanced Attention Toward Coronavirus Disease 2019 and Neglected Tropical Diseases.

Coronavirus disease 2019 (COVID-19), caused by SARS-CoV-2, has been spreading since 2019, causing a worldwide pandemic. Amid the COVID-19 pandemic, tuberculosis, AIDS, and malaria have adversely affected the quality of life of patients and killed millions of people. In addition, COVID-19 continues to impede the delivery of health services, including those for the control of neglected tropical diseases (NTDs). Furthermore, NTDs have been reported as possible co-pathogens among patients infected with COVID-19. However, studies regarding parasitic co-infection among these patients have been limited. This review aimed to explore and describe the cases and reports of parasitic infections in the backdrop of COVID-19 to provide comprehensive knowledge regarding this aspect. We reviewed seven cases of patients who had parasitic co-infection and tested positive for COVID-19, and summarized the literature on the importance of controlling parasitic diseases. In addition, we identified recommendations for the control of parasitic diseases under possible difficulties, such as declining funding for parasitic diseases in 2020. This review highlights the growing burden of NTDs under COVID-19 that may be caused by the deficiency of healthcare infrastructure and human resources as the main reasons. Clinicians should remain vigilant for possible co-infections with parasites in COVID-19 patients, while policymakers are urged to reinforce a balanced and long-term health strategy that addresses both NTDs and COVID-19.

Evaluation of medical decision errors during the transition period to telemedicine

The context of the Coronavirus pandemic has fundamentally changed the way we approach medical services. Beyond setting up new technological possibilities, it has propelled telemedicine to become a reality, bringing undeniable practical benefits. The questions that arise are both justified and worrying: can digitalization replace a direct interpersonal relationship that involves a physical examination, while preserving the quality of the medical act and the degree of patient satisfaction? Isn't there a risk that the digitization of the medical record will cancel out the deep human character of classical medicine that has evolved since the time of Hippocrates? Should the implementation of telemedicine be "the state-of-art" of modern medicine, in accordance with the co-evolution of digital technology? It is hard to believe that once used in this period, telemedicine will be abandoned. However, telemedicine must be analyzed not only in the short term but also in the long term, in order to be able to evaluate both its usefulness and possible deficiencies.

Hospitalization for child maltreatment and other types of injury during the COVID-19 pandemic.

BACKGROUND: The possibility that child maltreatment was misclassified as unintentional injury during the COVID-19 pandemic has not been evaluated. OBJECTIVE: We assessed if child maltreatment hospitalizations changed during the pandemic, and if the change was accompanied by an increase in unintentional injuries. PARTICIPANTS AND SETTING: This study included children aged 0-4 years who were admitted for maltreatment or unintentional injuries between April 2006 and March 2021 in hospitals of Quebec, Canada. METHODS: We used interrupted time series regression to estimate the effect of the pandemic on hospitalization rates for maltreatment, compared with unintentional transport accidents, falls, and mechanical force injuries. We assessed if the change in maltreatment hospitalization was accompanied by an increase in specific types of unintentional injury. RESULTS: Hospitalizations for child maltreatment decreased from 16.3 per 100,000 (95 % CI 9.1-23.4) the year before the pandemic to 13.2 per 100,000 (95 % CI 6.7-19.7) during the first lockdown. Hospitalizations for most types of unintentional injury also decreased, but injuries due to falls involving another person increased from 9.0 to 16.5 per 100,000. Hospitalization rates for maltreatment and unintentional injury remained low during the second lockdown, but mechanical force injuries involving another person increased from 3.8 to 8.1 per 100,000. CONCLUSIONS: Hospitalizations for child maltreatment may have been misclassified as unintentional injuries involving another person during the pandemic. Children admitted for these types of unintentional injuries may benefit from closer assessment to rule out maltreatment.

Delayed Diagnosis of Pulmonary Thromboembolism Due to Overfocus on COVID-19.

The coronavirus disease 2019 (COVID-19) pandemic has made it difficult for physicians to follow their usual diagnostic processes. We present the case of a 25-year-old man with adjustment disorder who developed dyspnoea. He was concerned about COVID-19, but his test result was negative. After excluding COVID-19, the physician concluded that his symptoms were related to his psychiatric condition. However, the patient was diagnosed with pulmonary thromboembolism by another physician. To avoid missing a diagnosis, physicians must practice zero-based thinking, regardless of COVID-19 concerns, and not be distracted from the patient's core problems. LEARNING POINTS: During the COVID-19 pandemic, significant diagnostic errors can occur because physicians are so concerned about COVID-19 that they may ignore other diagnoses.To avoid missing a life-threatening condition during the COVID-19 pandemic, physicians must consider zero-based thinking, regardless of COVID-19 concerns, and not be distracted from the patient's core problems.Measuring vital signs after a short walk can be helpful for investigating suspected pulmonary thromboembolism.

Detection of COVID-19 in diagnostic tests carried out on patients

Diagnostic tests to detect the coronavirus allow the identification of the virus in addition to the antibodies generated by the body of the person who has previously been infected, so the objective of the research is to detect COVID-19 in diagnostic tests carried out in patients. It is a quantitative, descriptive experimental cross-sectional study, which was carried out with a total population of 560 patients from hospital centers. In its results, we observed that 83.4% (n=467) of patients were non-reactive to the Antigen Test and 66.8% (n=374) of patients were negative in the PCR test. In conclusion, the strengths of the COVID-19 detection tests should be complemented since it allows for an accurate and timely diagnosis of patients. © 2022 The Authors.

Data Classification Model for COVID-19 Pandemic

A significant worldwide pandemic disease that has shut the whole world's economy and put the health care services personnel into anxiety is COronaVIrus Disease 2019 (COVID-19). It is difficult to model as it shared closely related characteristics/symptoms with other pneumonia diseases like SARS, MERS, ARDS, and Pulmonary Tuberculosis (PTB). Health practitioners use images (CT scan, Chest X-Ray (CXR)), timely occurrences (daily), audio (Cough), text (clinical and laboratory data) to detect, predict and treat patients with this disease. But machine learning has been proven by researchers when it can effectively and precisely detect, predict, classify, recommend treatment. This chapter discusses and implements a data classification task for early diagnosis and prognosis of the COVID-19 pandemic using CXR image. Classification is a supervised learning task that uses labeled data to assign items to different classes. The indicators that define a good classification task and assess classification models' performance are Receiver Operating Characteristic (ROC), Precision-Recall Curve (PRC), Recall, F1-Score Precision.

The consequences Sars-CoV-2 pandemic on medical education in the combat leprosy / As consequências da pandemia de Sars-CoV-2 sobre a educação médica no combate à hanseníase / Las consecuencias de la pandemia de Sars-CoV-2 en educación médica en combate à lepra

Background and objectives: Leprosy is an infectious disease in which early diagnosis is a decisive factor to prevent disability and disabilities. This study sought to analyze the panorama of leprosy between 2016 and 2021 in the state of Rio Grande do Sul and unveil the importance of medical education in the context of Neglected Tropical Diseases during the Sars-CoV-2 pandemic. Methods: Cross-sectional study using the State Center database of Health Surveillance of Rio Grande do Sul. In the data collection, were included leprosy data of individuals residents in the state of Rio Grande do Sul (RS), in the 2016 period 2021. The variables analyzed were confirmed leprosy cases, notified cases, the number of cases in terms of operational classifications of leprosy, the therapeutic scheme, and the number of cases according to the degrees of physical disability. Results: Over this period, 725 cases were confirmed as leprosy, 70% in the years 2016, 2017 and 2018. Of the total number of cases, 88% were Multibacillary form of the disease, 50% had some degree of disability at diagnosis time and 80% underwent the standard treatment regimen. Conclusion: There is a delay in leprosy diagnosis, and there is underdiagnosis of the disease in the state of Rio Grande do Sul: which highlights the need to reaffirm educational practices on mycobacteriosis.(AU)

Justificativa e objetivos: A hanseníase é uma doença infectocontagiosa na qual o diagnóstico precoce é fator decisivo para prevenir incapacidade e deficiências. O presente estudo buscou analisar o panorama da hanseníase entre os anos de 2016 e 2021 no estado do Rio Grande do Sul, desvelando a importância da educação médica no contexto das Doenças Tropicais Negligenciadas durante a pandemia da Sars-CoV-2. Métodos: Estudo transversal por meio da base de dados do Centro Estadual de Vigilância em Saúde do Rio Grande do Sul. Na coleta de dados, foram incluídos os dados de hanseníase em indivíduos residentes do estado do Rio Grande do Sul (RS), no período de 2016 a 2021. As variáveis analisadas foram os casos confirmados de hanseníase, os casos notificados, o número de casos quanto às classificações operacionais de hanseníase, o esquema terapêutico e o número de casos de acordo com os graus de incapacidade física. Resultados: No período analisado, foram confirmados 725 casos de hanseníase, sendo 70% nos anos de 2016, 2017 e 2018. Do número total de casos, 88% eram a forma multibacilar da doença, 50% apresentaram algum grau de incapacidade física no momento do diagnóstico e 80% realizaram o esquema terapêutico padrão. Conclusão: Existe atraso no diagnóstico de hanseníase e há subdiagnóstico da doença no estado do Rio Grande do Sul, o que evidencia a necessidade de reafirmação das práticas educacionais sobre a micobacteriose.(AU)

Justificación y objetivos: La lepra es una enfermedad infecciosa en la que el diagnóstico precoz es un factor decisivo para prevenir la incapacidad y las discapacidades. Este estudio buscó analizar el panorama de la lepra entre 2016 y 2021 en el estado de Rio Grande do Sul y develar la importancia de la educación médica en el contexto de las Enfermedades Tropicales Desatendidas durante la pandemia Sars-CoV-2. Métodos: Estudio transversal con datos del Centro Estatal de Vigilancia en Salud de Rio Grande do Sul. La recolección de datos incluyó datos sobre lepra en individuos residentes en el estado de Rio Grande do Sul (RS), de 2016 a 2021. Las variables analizadas fueron casos confirmados de lepra, casos notificados, el número de casos en términos de clasificaciones operativas de lepra, el esquema terapéutico y el número de casos según los grados de discapacidad física. Resultados: En el período analizado se confirmaron 725 casos de lepra, 70% en los años 2016, 2017 y 2018. Del total de casos, 88% fueron la forma multibacilar de la enfermedad, 50% tenían algún grado de discapacidad física en el momento del diagnóstico y el 80% realizó el régimen terapéutico padrón. Conclusiones: Hay un retraso en el diagnóstico de la lepra y hay un infradiagnóstico de la enfermedad en el estado de Rio Grande do Sul: lo que pone de relieve la necesidad de reafirmar las prácticas educativas sobre micobacteriosis.(AU)

Three cases of clinically significant inaccurate carboxyhemoglobin measurement.

Background: Clinicians often rely on measurement of carboxyhemoglobin (COHb) to confirm or rule out a diagnosis of carbon monoxide (CO) poisoning. Methods: We report two cases of false negative COHb in patients with CO poisoning and one case of false positive COHb in a patient without CO poisoning. Results: In the first case, a 20-year-old male developed headache, confusion, and near-syncope while operating a gasoline-powered pressure washer in an enclosed space. In the emergency department (ED), his COHb was 1.8%, but this level was disregarded, and he was referred for hyperbaric oxygen. His COHb just before hyperbaric oxygen was 4.1%, and later analysis of his blood collected at ED arrival revealed a COHb of 20.1%. The referral ED blood gas machine calibration and controls were within specification. In the second case, a 45-year-old male presented with several others to the ED with symptoms of CO poisoning after exposure at a conference. All others had elevated COHb levels, but his COHb was 2%. He was discharged but returned shortly with continued symptoms and requested his COHb be repeated. The repeat COHb was 17% (84 minutes after the first). After three hours of oxygen, his COHb was 7%. In the final case, an 83-year-old non-smoking male presented to an ED with breathlessness and tachypnea and was diagnosed with COVID-19 pneumonia. His COHb was 7.1%, but he reported living in an all-electric home. Another adult who lived with him and rode with him to the ED was asymptomatic and had a COHb of 3%. Later, COHb of 1.9% was measured from blood collected at ED arrival, and gas chromatography/mass spectrometry confirmed this result (2%). Conclusions: COHb levels are not always accurate. Clinicians should use clinical judgment to manage their patients, including rejecting laboratory values that do not fit the clinical situation.

Unwell Women: Misdiagnosis and Myth in a Man-Made World

It is no laughing matter when endometriosis - a disease that has obviously existed for thousands of years and afflicts 1 in 10 women across the world - is called "a career woman's disease." The history of medicine, of illness, is every bit as social and cultural as it is scientific. - Elinor Cleghorn, in I Unwell Women i I Unwell Women i provides an alternative overview of women's illnesses since the time of Hippocrates. [Extracted from the article] Copyright of American Journal of Clinical Pathology is the property of Oxford University Press / USA and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full . (Copyright applies to all s.)

A Review: Application of Machine Learning Algorithm in Medical Diagnosis

After Covid 19 Pandemic people are more focusing on healthcare. Every person wants to get the solution related to any health issue from their doorstep, this is the reason that Machine learning techniques has been adopted very fast in the field of medical diagnosis which can provide fast and accurate diagnosis results at the time of disease diagnosis step this system will assist physician to predict the diseases in early stage. Using Machine learning the correct diagnosis can be done when the system will get the complete, sufficient and proper information with respect to the problem. Because of if the system will not get the proper information related to the disease this will leads to some diagnostic error by this adverse impact on the treatment of the patient. Machine learning works upon the concept of train and test the machine with the required algorithm which can provide efficient result for execution of this process first we need to train the machine with respect to the data collected and after collecting the data, data cleaning processing to be done efficiently so that we get the correct feature extraction when we follow the test step. In this research paper we are presenting comparative analysis of various machine learning algorithm ie. Linear regression. Decision tree, SVM, Random Forest etc. Applied in the field of medical diagnosis our analysis in focusing on the criteria with respect to the accuracy, performance and algorithm is applied for medical diagnosis. © 2021 IEEE.

Understanding safety-netting in remote consulting

The COVID-19 pandemic has changed the way health care is delivered. Paul Silverston explains the importance of providing appropriate safety-netting advice in remote consultations In primary care, the COVID-19 pandemic caused a rapid switch from face-to-face to remote consulting, which presented few opportunities for nurses who were unfamiliar with remote consulting to undergo any training in this skill. The clinical assessment and clinical decision-making skills required in remote consulting are different from those in face-to-face consulting and there is also a higher risk of diagnostic and decision-making errors in remote consulting than in face-to-face consulting. Safety-netting is an essential part of safe practice in primary care to reduce the risk of serious harm to patients from these errors. This article discusses the principles and practices of safety-netting in remote consulting.

Hemolysis and blood gas analysis: it's time for a change!

Modern blood gas analyzers are not able to identify hemolysis, lipemia and icterus; therefore, the aim of this study was to assess the influence of hemolysis on blood gas samples. Blood gas analysis represents an essential part in the diagnosis and treatment of critically ill patients, including those affected by the pandemic coronavirus disease 2019 (COVID-19). Hemolysis, lipemia, and icterus, are causes of clinical misinterpretation of laboratory tests. A total of 1244 blood gas specimens were collected over a one-week period from different clinical wards, including the Emergency Department, and were assessed for serum indices on Cobas C6000 CE (Roche Diagnostics, Mannheim, Germany). The prevalence of hemolysis, lipemia, and icterus were 5%, 12%, and 14%, respectively. Sample storage at room temperature, delivery to central laboratory using pneumatic tube system, as well as small sample size, strongly affected blood gas parameters (p < .01). Hemolysis led to an increase in analytical bias for pH, pO2, and potassium, and a significant decrease for pCO2, HCO3-, sodium, and Ca2+ (p <.01). Currently, hemolysis detection systems are not yet widespread, and a rapid centrifugation of samples after blood gas analysis along with the assessment of serum indices represent the only prompt approach to identify unsuitable results, avoiding pitfalls in clinical decision-making, although it cannot be applied to the Emergency Department routine. Blood gas analyzers manufacturers and suppliers should implement automated built-in serum indices detection systems.

M-CAD: Towards Multi-Categorical Auto Diagnosis of Varied Diseases using Deep Learning

The economic burden and the number of lives lost due to diagnostic errors are higher than ever due to the onset of pandemics and new viruses, Specially in medium and low-economic status nations (including India) are affected heavily in terms of capital and human resources. Due to limited expertise in diagnostic technologies in remote parts of India and many low-economic nations of Africa, autonomous diagnostics can save millions of lives and lower the costs. To accomplish this goal we propose a method that uses modern developments in Deep Learning in semantic segmentation and classification to predict multiple diseases from multiple medical images. To conduct the study we test the model with Dermoscopy images and CT-Scans to predict 8 classes relating to Melanoma cancer, Covid-19 virus and different types of Carcinoma. The setup is tested on largest publicly available ISIC Dermoscopy dataset, 1061 CT-scan images combined for the classification and Segmentation(only for Melanoma). Classification model(M-CAD) is progressively tested by increasing the number of classes and data that it trains on. This pilot study is conducted on a small subset of the complete data, segmentation of Melanoma images obtained an accuracy of 96.6% compared to human expert agreement which is 90.9%. we were able to produce average accuracy of 81.5% and AUC of 0.94 for 6 classes using CT-Scans whereas accuracy and AUC for all the 8 classes is 80.2% and 0.97 respectively. These results were quite promising for a model that classifies different images with no apparent relation at all. © 2021 IEEE.

The Abrupt Expansion of Ambulatory Telemedicine: Implications for Patient Safety.

The exponential growth of telemedicine in ambulatory care triggered by the COVID-19 public health emergency has undoubtedly impacted the quality of care and patient safety. In particular, the increased adoption of remote care has impacted communication, care teams, and patient engagement, which are key factors that impact patient safety in ambulatory care. In this perspective, we draw on a scoping review of the literature, our own clinical experiences, and conversations with patient safety experts to describe how changes in communication, care teams, and patient engagement have impacted two high priority areas in ambulatory safety: diagnostic errors and medication safety. We then provide recommendations for research funders, researchers, healthcare systems, policy makers, and healthcare payors for how to improve patient safety in telemedicine based on what is currently known as well as next steps for how to advance understanding of the safety implications of telemedicine utilization.

Clinical reasoning in dire times. Analysis of cognitive biases in clinical cases during the COVID-19 pandemic.

Cognitive biases are systematic cognitive distortions, which can affect clinical reasoning. The aim of this study was to unravel the most common cognitive biases encountered in in the peculiar context of the COVID-19 pandemic. Case study research design. Primary care. Single centre (Division of General Internal Medicine, University Hospitals of Geneva, Geneva, Switzerland). A short survey was sent to all primary care providers (N = 169) taking care of hospitalised adult patients with COVID-19. Participants were asked to describe cases in which they felt that their clinical reasoning was "disrupted" because of the pandemic context. Seven case were sufficiently complete to be analysed. A qualitative analysis of the clinical cases was performed and a bias grid encompassing 17 well-known biases created. The clinical cases were analyzed to assess for the likelihood (highly likely, plausible, not likely) of the different biases for each case. The most common biases were: "anchoring bias", "confirmation bias", "availability bias", and "cognitive dissonance". The pandemic context is a breeding ground for the emergence of cognitive biases, which can influence clinical reasoning and lead to errors. Awareness of these cognitive mechanisms could potentially reduce biases and improve clinical reasoning. Moreover, the analysis of cognitive biases can offer an insight on the functioning of the clinical reasoning process in the midst of the pandemic crisis.

Systems engineering analysis of diagnostic referral closed-loop processes.

BACKGROUND: Closing loops to complete diagnostic referrals remains a significant patient safety problem in most health systems, with 65%-73% failure rates and significant delays common despite years of improvement efforts, suggesting new approaches may be useful. Systems engineering (SE) methods increasingly are advocated in healthcare for their value in studying and redesigning complex processes. OBJECTIVE: Conduct a formative SE analysis of process logic, variation, reliability and failures for completing diagnostic referrals originating in two primary care practices serving different demographics, using dermatology as an illustrating use case. METHODS: An interdisciplinary team of clinicians, systems engineers, quality improvement specialists, and patient representatives collaborated to understand processes of initiating and completing diagnostic referrals. Cross-functional process maps were developed through iterative group interviews with an urban community-based health centre and a teaching practice within a large academic medical centre. Results were used to conduct an engineering process analysis, assess variation within and between practices, and identify common failure modes and potential solutions. RESULTS: Processes to complete diagnostic referrals involve many sub-standard design constructs, with significant workflow variation between and within practices, statistical instability and special cause variation in completion rates and timeliness, and only 21% of all process activities estimated as value-add. Failure modes were similar between the two practices, with most process activities relying on low-reliability concepts (eg, reminders, workarounds, education and verification/inspection). Several opportunities were identified to incorporate higher reliability process constructs (eg, simplification, consolidation, standardisation, forcing functions, automation and opt-outs). CONCLUSION: From a systems science perspective, diagnostic referral processes perform poorly in part because their fundamental designs are fraught with low-reliability characteristics and mental models, including formalised workaround and rework activities, suggesting a need for different approaches versus incremental improvement of existing processes. SE perspectives and methods offer new ways of thinking about patient safety problems, failures and potential solutions.