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.

BACKGROUND: People forced to leave their homes, such as refugees and internally displaced persons, are exposed to various stressors during their forced displacement, putting them at risk for mental disorders. OBJECTIVE: To summarize evidence on the efficacy of psychosocial interventions aiming to promote mental health and/or to prevent mental symptoms by fostering transdiagnostic skills in forcibly displaced persons of all ages. METHOD: Four databases and reference lists were searched for randomized controlled trials on interventions in this population on 11 March 2022. Thirty-six studies were eligible, 32 studies (comprising 5299 participants) were included in random-effects multilevel meta-analyses examining the effects of interventions on mental symptoms and positive mental health (e.g. wellbeing) as well as moderators to account for heterogeneity. OSF Preregistration-ID: 10.17605/OSF.IO/XPMU3 RESULTS: Our search resulted in 32 eligible studies, with 10 reporting on children/adolescents and 27 on adult populations. There was no evidence for favourable intervention effects in children/adolescents, with 44.4% of the effect sizes pointing to potentially negative effects yet remaining non-significant. For adult populations, our meta-analyses showed a close-to-significant favourable effect for mental symptoms, M(SMD) = 0.33, 95% CI [-0.03, 0.69], which was significant when analyses were limited to high-quality studies and larger for clinical compared to non-clinical populations. No effects emerged for positive mental health. Heterogeneity was considerable and could not be explained by various moderators (e.g. type of control, duration, setting, theoretical basis). Certainty of evidence was very low across all outcomes limiting the generalizability of our findings. CONCLUSION: The present review provides at most weak evidence for an effect favouring transdiagnostic psychosocial interventions over control conditions for adult populations but not for children and adolescents. Future research should combine the imperative of humanitarian aid in face of major crises with studying the diverse needs of forcibly displaced persons to improve and tailor future interventions.

Introduction: Despite common, serious, costly, and often fatal conditions affecting up to 50 % of older patients, delirium is often unrecognized and overlooked. We examine the accuracy of the 4AT for detecting older patients with delirium.Methods: We performed a systematic search of PubMed, Web of Science, PsycINFO, and EMBASE databases from inception to April 2020 and updated to January 2022. Four independently reviewers extracted study data and assessed the methodological quality using the revised quality assessment of diagnostic accuracy studies tool (QUADAS-2). Pooled estimates of sensitivity and specificity were generated using a bivariate random effects model. All statistical analyses were performed with STATA version 15.1 and Meta-DiSc version 1.4 software. Results: Eleven studies with 2789 participants were included. The pooled sensitivity and specificity were 0.87 (95 % CI: 0.81-0.91) and 0.87 (95 % CI: 0.79-0.92), respectively, and the positive and negative likelihood ratios were 6.66 (95 % CI: 4.12-10.74) and 0.15 (95 % CI: 0.10-0.23), respectively. Deeks' test indicated no significant publication bias (t = 0.83, P = 0.43). Univariable meta-regression showed that patient selection and flow and timing significantly influenced the pooled sensitivity (P < 0.05), settings significantly influenced the pooled specificity (P < 0.05).Conclusion: Our meta-analysis demonstrates that 4AT is a sensitive and specific screening tool for delirium in older patients. Its brevity and simplicity support its use in routine clinical practice, particularly in time-poor settings. Clinicians should come to a conclusion based largely on the 4AT findings in conjunction with clinical judgment.

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: 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.

BACKGROUND: One-stop clinics provide comprehensive diagnostic testing in one outpatient appointment. They could benefit patients with conditions, such as cancer, whose outcomes are improved by early diagnosis, and bring efficiency savings for health systems. OBJECTIVE: To assess the use and outcomes of one-stop clinics for symptoms where cancer is a possible diagnosis. DESIGN AND SETTING: Systematic review of studies reporting use and outcomes of one-stop clinics in primary care patients. METHOD: We searched MEDLINE, Embase, and Cochrane Library for studies assessing one-stop clinics for adults referred after presenting to primary care with any symptom that could be indicative of cancer. Study selection was carried out independently in duplicate with disagreements resolved through discussion. RESULTS: Twenty-nine studies were identified, most were conducted in the UK and observational in design. Few included a comparison arm. A pooled comparison of the cancer conversion rate of one-stop and multi-stop clinics was only possible for breast symptoms, and we found no significant difference. One-stop clinics were associated with significant reductions in the interval from referral to testing (15 versus 75 days) and more patients diagnosed on the same day (79% versus 25%) compared to multi-stop pathways. The majority of patients and GPs found one-stop clinics to be acceptable. CONCLUSION: This review found one-stop clinics were associated with reduced time from referral to testing, increased same day diagnoses, and were acceptable to patients and GPs. Our conclusions are limited by high levels of heterogeneity, scarcity of comparator groups, and the overwhelmingly observational nature of included studies.

BACKGROUND: The acute respiratory distress syndrome (ARDS) results in substantial mortality but remains underdiagnosed in clinical practice. Automated ARDS "sniffer" systems, tools that can automatically analyze electronic medical record data, have been developed to improve recognition of ARDS in clinical practice. OBJECTIVE: To perform a systematic review examining the evidence underlying automated sniffer systems for ARDS detection. DATA SOURCES: MEDLINE and Scopus databases through October 2018 to identify studies of tools using routinely available clinical data to detect patients with ARDS. DATA EXTRACTION: Study design, tool description, and diagnostic performance were extracted by two reviewers. The Quality Assessment of Diagnostic Accuracy Studies (QUADAS-2) was used to evaluate each study for risk of bias in 4 domains: patient selection, index test, reference standard, and study flow and timing. SYNTHESIS: Among 480 studies identified, nine met inclusion criteria, evaluating six unique ARDS sniffer tools. Eight studies were derivation and/or temporal validation designs, with one also evaluating the effect of implementing a tool in clinical practice. A single study performed an external validation of previously published ARDS sniffer tools. Studies reported a wide range of sensitivities (43% to 98%) and positive predictive values (26% to 90%) for detection of ARDS. Most studies had potential for high risks of bias identified in their study design, including patient selection (5 of 9), reference standard (4 of 9), and flow and timing (3 of 9). In the single external validation without any perceived risks of biases, performance of ARDS sniffer tools were worse. CONCLUSIONS: Sniffer systems developed to detect ARDS had moderate to high predictive value in their derivation cohorts, although most studies had potential for high risks of bias in study design. Methodological issues may explain some of the variability in tool performance. There remains an ongoing need for robust evaluations of ARDS sniffer systems and their impact on clinical practice.

PURPOSE: Ultrasound examinations are currently being implemented in general practice. This study aimed to systematically review the literature on the training in and use of point-of-care ultrasound (POCUS) by general practitioners. METHODS: We followed the Cochrane guidelines for conduct and the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines for reporting. We searched the databases MEDLINE (via PubMed), EMBASE, CINAHL, Web of Science, and Cochrane Central Register of Controlled Trials using the key words ultrasonography and general practice in combination and using thesaurus terms. Two reviewers independently screened articles for inclusion, extracted data, and assessed the quality of included studies using an established checklist. RESULTS: We included in our review a total of 51 full-text articles. POCUS was applied for a variety of purposes, with the majority of scans focused on abdominal and obstetric indications. The length of training programs varied from 2 to 320 hours. Competence in some types of focused ultrasound scans could be attained with only few hours of training. Focused POCUS scans were reported to have a higher diagnostic accuracy and be associated with less harm than more comprehensive scans or screening scans. The included studies were of a low quality, however, mainly because of issues with design and reporting. CONCLUSIONS: POCUS has the potential to be an important tool for the general practitioner and may possibly reduce health care costs. Future research should aim to assess the quality of ultrasound scans in broader groups of general practitioners, further explore how these clinicians should be trained, and evaluate the clinical course of patients who undergo scanning by general practitioners.

BACKGROUND: Health information exchange (HIE), the electronic sharing of clinical information across the boundaries of health care organizations, has been promoted to improve the efficiency, cost-effectiveness, quality, and safety of health care delivery. OBJECTIVE: To systematically review the available research on HIE outcomes and analyze future research needs. METHODS: Data sources included citations from selected databases from January 1990 to February 2015. We included English-language studies of HIE in clinical or public health settings in any country. Data were extracted using dual review with adjudication of disagreements. RESULTS: We identified 34 studies on outcomes of HIE. No studies reported on clinical outcomes (eg, mortality and morbidity) or identified harms. Low-quality evidence generally finds that HIE reduces duplicative laboratory and radiology testing, emergency department costs, hospital admissions (less so for readmissions), and improves public health reporting, ambulatory quality of care, and disability claims processing. Most clinicians attributed positive changes in care coordination, communication, and knowledge about patients to HIE. CONCLUSIONS: Although the evidence supports benefits of HIE in reducing the use of specific resources and improving the quality of care, the full impact of HIE on clinical outcomes and potential harms are inadequately studied. Future studies must address comprehensive questions, use more rigorous designs, and employ a standard for describing types of HIE. TRIAL REGISTRATION: PROSPERO Registry No CRD42014013285; http://www.crd.york.ac.uk/PROSPERO/ display_record.asp?ID=CRD42014013285 (Archived by WebCite at http://www.webcitation.org/6dZhqDM8t)

Background: Many healthcare professionals use smartphones and tablets to inform patient care. Contemporary research suggests that handheld computers may support aspects of clinical diagnosis and management. This systematic review was designed to synthesise high quality evidence to answer the question; Does healthcare professionals' use of handheld computers improve their access to information and support clinical decision making at the point of care? Methods: A detailed search was conducted using Cochrane, MEDLINE, EMBASE, PsycINFO, Science and Social Science Citation Indices since 2001. Interventions promoting healthcare professionals seeking information or making clinical decisions using handheld computers were included. Classroom learning and the use of laptop computers were excluded. Two authors independently selected studies, assessed quality using the Cochrane Risk of Bias tool and extracted data. High levels of data heterogeneity negated statistical synthesis. Instead, evidence for effectiveness was summarised narratively, according to each study's aim for assessing the impact of handheld computer use. Results: We included seven randomised trials investigating medical or nursing staffs' use of Personal Digital Assistants. Effectiveness was demonstrated across three distinct functions that emerged from the data: accessing information for clinical knowledge, adherence to guidelines and diagnostic decision making. When healthcare professionals used handheld computers to access clinical information, their knowledge improved significantly more than peers who used paper resources. When clinical guideline recommendations were presented on handheld computers, clinicians made significantly safer prescribing decisions and adhered more closely to recommendations than peers using paper resources. Finally, healthcare professionals made significantly more appropriate diagnostic decisions using clinical decision making tools on handheld computers compared to colleagues who did not have access to these tools. For these clinical decisions, the numbers need to test/screen were all less than 11. Conclusion: Healthcare professionals' use of handheld computers may improve their information seeking, adherence to guidelines and clinical decision making. Handheld computers can provide real time access to and analysis of clinical information. The integration of clinical decision support systems within handheld computers offers clinicians the highest level of synthesised evidence at the point of care. Future research is needed to replicate these early results and to identify beneficial clinical outcomes.