The Psychiatric Pediatric Issues Task Force of the International League Against Epilepsy (ILAE) aimed to develop recommendations for the diagnosis and treatment of anxiety and depression in children and adolescents with epilepsy. The Task Force conducted a systematic review and identified two studies that assessed the accuracy of four screening measures for depression and anxiety symptoms compared with a psychiatric interview. Nine studies met the eligibility criteria for treatment of anxiety and depressive disorders or symptoms. The risk of bias and certainty of evidence were assessed. The evidence generated by this review followed by consensus where evidence was missing generated 47 recommendations. Those with a high level of agreement (≥80%) are summarized. Diagnosis: (1) Universal screening for anxiety and depression is recommended. Closer surveillance is recommended for children after 12 years, at higher risk (e.g., suicide-related behavior), with subthreshold symptoms, and experiencing seizure worsening or therapeutic modifications. (2) Multiple sources of ascertainment and a formal screening are recommended. Clinical interviews are recommended whenever possible. The healthcare provider must always explain that symptom recognition is essential to optimize treatment outcomes and reduce morbidity. (3) Questioning about the relationship between symptoms of anxiety or depression with seizure worsening/control and behavioral adverse effects of antiseizure medications is recommended. Treatment: (1) An individualized treatment plan is recommended. (2) For mild depression, active monitoring must be considered. (3) Referral to a mental health care provider must be considered for moderate to severe depression and anxiety. (4) Clinical care pathways must be developed. (5) Psychosocial interventions must be tailored and age-appropriate. (6) Healthcare providers must monitor children with epilepsy who are prescribed antidepressants, considering symptoms and functioning that may not improve simultaneously. (7) Caregiver education is essential to ensure treatment adherence. (8) A shared-care model involving all healthcare providers is recommended for children and adolescents with epilepsy and mental health disorders. We identified clinical decisions in the management of depression and anxiety that lack solid evidence and provide consensus-based guidance to address the care of children and adolescents with epilepsy.

Ethnopharmacological relevance: Tanreqing injection (TRQI) is an intravenous herbal preparation derived from 5 types of traditional Chinese medicines including Scutellariae Radix, Lonicerae Japonicae Flos, Forsythiae Fructus, bear bile powder and goral horn, incorporating baicalin, chlorogenic acid, ursodeoxycholic acid, and goose deoxycholic acid and other compounds known for anti-inflammatory properties, is widely used in China to treat cough caused by acute trachea-bronchitis disease (ATB). Aim of the study: To investigate the clinical efficacy and safety of Tanreqing injection (TRQI) with and without Western medicine (WM) for cough caused by acute trachea-bronchitis (ATB). Materials and methods: We systematically searched eight databases, including CENTRAL, Embase, PubMed, Science Direct, Wiley, China National Knowledge Infrastructure, Chinese Biomedical Literature Database and WanFang, from inception to August 2023 for randomized clinical trials (RCTs) on TRQI for cough caused by ATB. The critical outcomes of interest were time to symptom disappearance, including time for cough symptom to disappear and time to improve cough and sputum production. Important outcomes included symptom disappearance rate, adverse events (AEs) and lung function. We carried out random-effects meta-analysis using Review Manager 5.4 and assessed the certainty of evidence utilizing the Grading of Recommendations Assessment, Development and Evaluation (GRADE) approach. Results: A total of 2872 citations were identified by our search, of which 26 eligible RCTs enrolled 2731 participants. Low to moderate certainty evidence showed that when compared with WM, TRQI plus WM treatment was associated with a favorable effect on the time for cough symptom to disappear (MD-2.21 d, 95% CI-2.64 to-1.78), time to improve cough and sputum production (MD-0.68 d, 95% CI-0.83 to-0.53), symptom disappearance rate (RR 1.37, 95% CI 1.20 to 1.55), forced vital capacity, and forced expiratory volume in 1 s (MD 0.38 L, 95% CI 0.26 to 0.50; MD 2.92%, 95% CI 1.29 to 4.56, respectively). In terms of AEs, there was no association between TRQI plus WM and WM (RR 0.55, 95% CI 0.14 to 2.21; low-certainty evidence). Very low certainty evidence showed that TRQI alone was associated with reduced time to improve cough and sputum (MD-0.14 d, 95% CI-0.26 to-0.02) and increased symptom disappearance rate (RR 1.89, 95% CI 1.24 to 2.88; low certainty evidence) compared to WM.

BACKGROUND: In the context of a deepening global shortage of health workers and, in particular, the COVID-19 pandemic, there is growing international interest in, and use of, online symptom checkers (OSCs). However, the evidence surrounding the triage and diagnostic accuracy of these tools remains inconclusive. OBJECTIVE: This systematic review aimed to summarize the existing peer-reviewed literature evaluating the triage accuracy (directing users to appropriate services based on their presenting symptoms) and diagnostic accuracy of OSCs aimed at lay users for general health concerns. METHODS: Searches were conducted in MEDLINE, Embase, CINAHL, Health Management Information Consortium (HMIC), and Web of Science, as well as the citations of the studies selected for full-text screening. We included peer-reviewed studies published in English between January 1, 2010, and February 16, 2022, with a controlled and quantitative assessment of either or both triage and diagnostic accuracy of OSCs directed at lay users. We excluded tools supporting health care professionals, as well as disease- or specialty-specific OSCs. Screening and data extraction were carried out independently by 2 reviewers for each study. We performed a descriptive narrative synthesis. RESULTS: A total of 21,296 studies were identified, of which 14 (0.07%) were included. The included studies used clinical vignettes, medical records, or direct input by patients. Of the 14 studies, 6 (43%) reported on triage and diagnostic accuracy, 7 (50%) focused on triage accuracy, and 1 (7%) focused on diagnostic accuracy. These outcomes were assessed based on the diagnostic and triage recommendations attached to the vignette in the case of vignette studies or on those provided by nurses or general practitioners, including through face-to-face and telephone consultations. Both diagnostic accuracy and triage accuracy varied greatly among OSCs. Overall diagnostic accuracy was deemed to be low and was almost always lower than that of the comparator. Similarly, most of the studies (9/13, 69 %) showed suboptimal triage accuracy overall, with a few exceptions (4/13, 31%). The main variables affecting the levels of diagnostic and triage accuracy were the severity and urgency of the condition, the use of artificial intelligence algorithms, and demographic questions. However, the impact of each variable differed across tools and studies, making it difficult to draw any solid conclusions. All included studies had at least one area with unclear risk of bias according to the revised Quality Assessment of Diagnostic Accuracy Studies-2 tool. CONCLUSIONS: Although OSCs have potential to provide accessible and accurate health advice and triage recommendations to users, more research is needed to validate their triage and diagnostic accuracy before widescale adoption in community and health care settings. Future studies should aim to use a common methodology and agreed standard for evaluation to facilitate objective benchmarking and validation.

The global rise in deaths caused by pulmonary tuberculosis (TB) has placed increased pressure on overburdened healthcare systems to provide TB diagnostic services. Artificial intelligence-based computer-aided diagnosis (AI-based CAD) promises to be a powerful tool in responding to this health challenge by providing actionable outputs which support the diagnostic accuracy and efficiency of clinicians. However, these technologies must first be extensively evaluated to understand their impact and risks before pursuing wide-scale deployment. Yet, health technology assessments for them in real world settings have been limited. Comprehensive evaluation demands consideration of technical safety, human factors, and health impacts to generate robust evidence and understand what is needed for long-term sustainable benefit realisation. This work-in progress study presents a three-stage methodological approach that will be used to guide the data collection and analysis process for evaluating the impact of implementing a commercial AI-based CAD system for TB diagnosis in a real-world radiological setting.

Numerous factors contribute to the low adherence to lung cancer screening (LCS) programs. A theory-informed approach to identifying the obstacles and facilitators to LCS uptake is required. This study aimed to identify, assess, and synthesize the available literature at the individual and healthcare provider (HCP) levels based on a social-ecological model and identify gaps to improve practice and policy decision-making. Systematic searches were conducted in nine electronic databases from inception to December 31, 2020. We also searched Google Scholar and manually examined the reference lists of systematic reviews to include relevant articles. Primary studies were scored for quality assessment. Among 3938 potentially relevant articles, 36 studies, including 25 quantitative and 11 qualitative studies, were identified for inclusion in the review. Fifteen common factors were extracted from 34 studies, including nine barriers and six facilitators. The barriers included individual factors (n = 5), health system factors (n = 3), and social/environmental factors (n = 1). The facilitators included only individual factors (n = 6). However, two factors, age and screening harm, remain mixed. This systematic review identified and combined barriers and facilitators to LCS uptake at the individual and HCP levels. The interaction mechanisms among these factors should be further explored, which will allow the construction of tailored LCS recommendations or interventions for the Chinese context.

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

Introduction: Lyme Disease (LD) is the most common tick-borne disease in North America. With the number of cases increasing yearly, Canadian healthcare professionals (HCP) rely on up-to-date and evidence-informed guidelines, instruction, and resources to effectively prevent, diagnose, and treat Lyme disease (LD). This review is the first of its kind to examine gray literature and analyze the diversity of recommendations provided to Canadian HCP about the prevention, diagnosis, and treatment of Lyme disease. Methods: A gray literature review consisting of 4 search strategies was conducted to retrieve materials targeted to Canadian HCP. Searches within targeted websites, targeted Google searches, and gray literature databases, and consultation with content experts were done to look for continuing medical education (CME) events, clinical flow charts, webinars, videos, and reference documents that discussed the prevention, diagnosis, and treatment of Lyme disease. Results: A total of 115 resources were included in this study. Recommendations surrounding prevention strategies were less varied between materials, whereas diagnosis and treatment recommendations were more varied. Our findings suggest that Canadian HCP are met with varying and sometimes contradictory recommendations for diagnosing and treating LD. Conclusions: Due to the increasing incidence of LD in Canada, there is a greater need for resource consistency. Providing this consistency may help mitigate LD burden, standardize approaches to prevention, diagnosis and treatment, and improve patient outcomes.

Background: There has been a growing interest in using diagnosis-related groups (DRGs) payment to reimburse inpatient care worldwide. But its effects on healthcare and health outcomes are controversial, and the evidence from low- and middle- income countries (LMICs) is especially scarce. The objective of this study is to evaluate the effects of DRGs payment on healthcare and health outcomes in China. Method: A systematic review was conducted. We searched literature databases of PubMed, Cochrane Library, EMBASE, Web of Science, Chinese National Knowledge Infrastructure and SinoMed for empirical studies examining the effects of DRGs payment on healthcare in mainland China. We performed a narrative synthesis of outcomes regarding expenditure, efficiency, quality and equity of healthcare, and assessed the quality of evidence. Results: Twenty-three publications representing thirteen DRGs payment studies were included, including six controlled before after studies, two interrupted time series studies and five uncontrolled before-after studies. All studies compared DRGs payment to fee-for-service, with or without an overall budget, in settings of tertiary (7), secondary (7) and primary care (1). The involved participants varied from specific groups to all inpatients. DRGs payment mildly reduced the length of stay. Impairment of equity of healthcare was consistently reported, especially for patients exempted from DRGs payment, including: patient selection, cost-shifting and inferior quality of healthcare. However, findings on total expenditure, out of pocket payment (OOP) and quality of healthcare were inconsistent. The quality of the evidence was generally low or very low due to the study design and potential risk of bias of included studies. Conclusion: DRGs payment may mildly improve the efficiency but impair the equity and quality of healthcare, especially for patients exempted from this payment scheme, and may cause up-coding of medical records. However, DRGs payment may or may not contain the total expenditure or OOP, depending on the components design of the payment. Policymakers should very carefully consider each component of DRGs payment design against policy goals. Well-designed randomised trials or comparative studies are warranted to consolidate the evidence of the effects of DRGs payment on healthcare and health outcomes in LMICs to inform policymaking.

Backgrounds: Pulmonary tuberculosis (PTB) is a major health and economic burden. Accurate PTB detection is an important step to eliminating TB globally. Interferon gamma-induced protein 10 (IP-10) has been reported as a potential diagnostic marker for PTB since 2007. In this study, a meta-analysis approach was used to assess diagnostic value of IP-10 for PTB. Methods: Web of Science, PubMed, the Cochrane Library, and Embase databases were searched for studies published in English up to February 2019. The pooled sensitivity, specificity, positive likelihood ratio (PLR), negative likelihood ratio (NLR), diagnostic odds ratio (DOR), the area under the curve (AUC) and hierarchical summary receiver operating characteristic (HSROC) curve were estimated by the HSROC model and random effect model. Results: Eighteen studies including 2836 total participants met our inclusion criteria. The pooled sensitivity, specificity, PLR, and NLR of IP-10 for PTB detection were 86, 88%, 7.00, and 0.16, respectively. The pooled DOR was 43.01, indicating a very powerful discriminatory ability of IP-10. The AUC was 0.93 (95% CI: 0.91-0.95), showed the accuracy of IP-10 was good. Meta-regression showed that there was no heterogeneity with respect to TB burden, study design type, age, IP-10 assay method, IP-10 condition and HIV-infection status. Conclusions: Our results showed that IP-10 is a promising marker for differentiating PTB from non-TB.

Background: Artificial intelligence (AI) has been extensively used in a range of medical fields to promote therapeutic development. The development of diverse AI techniques has also contributed to early detections, disease diagnoses, and referral management. However, concerns about the value of advanced AI in disease diagnosis have been raised by health care professionals, medical service providers, and health policy decision makers. Objective: This review aimed to systematically examine the literature, in particular, focusing on the performance comparison between advanced AI and human clinicians to provide an up-to-date summary regarding the extent of the application of AI to disease diagnoses. By doing so, this review discussed the relationship between the current advanced AI development and clinicians with respect to disease diagnosis and thus therapeutic development in the long run. Methods: We systematically searched articles published between January 2000 and March 2019 following the Preferred Reporting Items for Systematic reviews and Meta-Analysis in the following databases: Scopus, PubMed, CINAHL, Web of Science, and the Cochrane Library. According to the preset inclusion and exclusion criteria, only articles comparing the medical performance between advanced AI and human experts were considered. Results: A total of 9 articles were identified. A convolutional neural network was the commonly applied advanced AI technology. Owing to the variation in medical fields, there is a distinction between individual studies in terms of classification, labeling, training process, dataset size, and algorithm validation of AI. Performance indices reported in articles included diagnostic accuracy, weighted errors, false-positive rate, sensitivity, specificity, and the area under the receiver operating characteristic curve. The results showed that the performance of AI was at par with that of clinicians and exceeded that of clinicians with less experience. Conclusions: Current AI development has a diagnostic performance that is comparable with medical experts, especially in image recognition-related fields. Further studies can be extended to other types of medical imaging such as magnetic resonance imaging and other medical practices unrelated to images. With the continued development of AI-assisted technologies, the clinical implications underpinned by clinicians' experience and guided by patient-centered health care principle should be constantly considered in future AI-related and other technology-based medical research.

Objective: The aim of this study was to determine the diagnostic accuracy of different computer-aided diagnostic (CAD) systems for thyroid nodules classification. Methods: A systematic search of the literature was conducted from inception until March, 2019 using the PubMed, EMBASE, Web of science, and Cochrane library. Literature selection and data extraction were conducted by 2 independent reviewers. Numerical values for sensitivity and specificity were obtained from false negative (FN), false positive (FP), true negative (TN), and true positive (TP) rates, presented alongside graphical representations with boxes marking the values and horizontal lines showing the confidence intervals (CIs). Summary receiver operating characteristic (SROC) curves were applied to assess the performance of diagnostic tests. Data were processed using Review Manager 5.3 and Stata 15. The methodological quality of included studies was assessed using Quality Assessment of Diagnostic Accuracy Studies (QUADAS-2) tool. Trial registration number: PROSPERO CRD42019132540

BACKGROUND: Screening high-risk patients for invasive aspergillosis (IA) has the potential to decrease the use of empiric antifungal agents. However, the performance of different screening methods has not been studied. METHODS: We performed a meta-analysis of published studies to assess the diagnostic performance of galactomannan (GM) and PCR as weekly screening tests in high risk populations. The sensitivity and specificity of six approaches combining GM and PCR were estimated using the bivariate model. RESULTS: Thirteen studies with 1,670 patients met our inclusion criteria. Single positive test results had modest sensitivity and specificity for screening (92% and 90% for GM, 84% and 76% for PCR, respectively). The screening approach with the highest sensitivity was the one that used at least one GM or PCR positive result to define a positive episode, achieving a sensitivity of 99%, significantly higher than any single test (p=0.0018 compared to GM and p<0.0001 compared to PCR). Meanwhile, when both GM and PCR were positive for the same patient, the specificity increased to 98%, which was not significantly different compared to the specificity of at least two positive GM (95%, p=0.56 for the comparison) or PCR results (93%, p=0.07 for the comparison). CONCLUSIONS: When screening high risk patients for IA with GM and PCR tests, the absence of any positive test can obviate the need for antifungal agents with a negative predictive value of 100%, whereas presence of at least two positive results are highly suggestive of an active infection with a positive predictive value of 88%