External and service factors influencing outcomes in a model of stratified mental health services for Young People

Background: Stratified care aims at matching the intensity and setting of mental health interventions to the needs of help-seeking Young People. In Australia, a 5-tiered system of mental health services is in operation. To aid patient triage to the most appropriate tier, a Decision Support Tool (DST) has been developed and is being rolled out nationally Methods: We analysed outcome data pre-and post-enrolment of about 1500 Young People (aged 16-25) referred to a Youth Mental Health Service delivering medium- and high intensity psychological treatment programs (tiers 3 and 4). We compared outcomes in both tiers during three 12-month periods: (a) in the inaugural phase of tier 4, prior to service saturation and stringent triaging, and prior to the COVID-19 pandemic (2019);(b) during the COVID-19 pandemic when all services were delivered remotely over phone- and video facilities, and when DST triaging was introduced (2020);(c) following return of face-to-face consultations, in a situation of service saturation and stringent DST triaging (2021) Findings: About 22% of Young People in the tier 3 program experienced reliable improvement according to their Kessler-10 (K-10) scale ratings, regardless of changing circumstances. In contrast, 40% of people in the tier 4 program reliably improved during the inaugural phase When circumstances and service delivery changed (COVID-19 restrictions service saturation, DST triaging), the rate of reliable improvement halved to about 20% Conclusion(s): Access to higher intensity psychological programs improves treatment outcomes for help-seeking Young People. However high-intensity services are more sensitive to external and service factors than less intense treatment models.

A Hybrid Deep Fused Learning Approach to Segregate Infectious Diseases

Humankind is facing another deadliest pandemic of all times in history, caused by COVID-19. Apart from this challenging pandemic, World Health Organization (WHO) considers tuberculosis (TB) as a preeminent infectious disease due to its high infection rate. Generally, both TB and COVID-19 severely affect the lungs, thus hardening the job of medical practitioners who can often misidentify these diseases in the current situation. Therefore, the time of need calls for an immediate and meticulous automatic diagnostic tool that can accurately discriminate both diseases. As one of the preliminary smart health systems that examine three clinical states (COVID-19, TB, and normal cases), this study proposes an amalgam of image filtering, data-augmentation technique, transfer learning-based approach, and advanced deep-learning classifiers to effectively segregate these diseases. It first employed a generative adversarial network (GAN) and Crimmins speckle removal filter on X-ray images to overcome the issue of limited data and noise. Each pre-processed image is then converted into red, green, and blue (RGB) and Commission Internationale de l'Elcairage (CIE) color spaces from which deep fused features are formed by extracting relevant features using DenseNet121 and ResNet50. Each feature extractor extracts 1000 most useful features which are then fused and finally fed to two variants of recurrent neural network (RNN) classifiers for precise discrimination of three-clinical states. Comparative analysis showed that the proposed Bi-directional long-short-term-memory (Bi-LSTM) model dominated the long-short-term-memory (LSTM) network by attaining an overall accuracy of 98.22% for the three-class classification task, whereas LSTM hardly achieved 94.22% accuracy on the test dataset.

Development of a model of care based on the chronic care and collaborative care models to improve cardiometabolic health in low-to- middle income countries

Background: Non-communicable diseases (NCDs) are rising in low middle income countries (LMICs) mainly driven by cardiometabolic disease (cardiovascular disease, diabetes, and hypertension). Aim(s): To develop a model of care, based on the chronic care model and collaborative care model, to improve care, outcomes and risk factor control for adults with cardio metabolic disease in LMICs in the Covid-19 era. This will contribute to the sustainable development goals of promoting good health, well-being and reducing inequalities. Method(s): Using an iterative consultative approach with healthcare workers, clients, and community leaders in Kenya, Ghana and Mozambique, we developed a model of care, which includes core features from chronic care models: self-management support;decision support;clinical information systems;delivery system design;and community linkages. Result(s): We produced a culturally adapted self-management education programme, a training package for educators delivering the programme, as well as a training package for community and healthcare professional leaders to increase awareness and self-care for cardiometabolic disease. Given the lack of a robust health information system, we are offering a global registry to provide real world data on patient management and quality of care for people with type 2 diabetes, hypertension, heart failure and chronic kidney disease. Conclusion(s): This intervention will be tested in a mixed-methods single-arm feasibility study in five sites across three African countries: Kenya, Ghana, Mozambique.

Impact of Provider Nudges on Herpes Zoster Vaccine Series Completion during the COVID-19 Pandemic in the United States

Objectives: To determine the impact of a pharmacy-based, clinical decision support (CDS) tool on herpes zoster (HZ) vaccine series completion during the initial months of the COVID-19 pandemic across the US. Method(s): In partnership with Kroger Health, a pharmacy CDS tool alerted staff of patients due for their second HZ vaccine dose, which had been accompanied previously by a timed text message. Once operations changed due to COVID-19, the system limited outreach to only patients visiting the pharmacy. Primary outcomes included the proportion of patients receiving both doses within a Kroger-owned pharmacy (n=2,293) and the number of days between doses, both within and across two 32-week periods before and after the pandemic hit the US. Generalized estimating equation-based (GEE) logistic and linear regression models determined differences in completion rates and time to completion. Result(s): During the observation period, 38,937 adults received at least one HZ vaccine dose, with 77.2% receiving both doses. Patients engaged by the CDS tool achieved 80.5% dose completion, versus 65.4% of those not intervened (p<0.0001), which was lower than in the period immediately before the pandemic (85.8%, p<0.0001). The dosing window averaged 119.4 days (SD: 26.91), which was the longest timeframe between doses since the HZ vaccine was launched and nearly one month longer than before the pandemic (93.0 days [SD: 28.02], p<0.0001). The odds of dose completion increased in areas of higher health literacy (OR: 1.01;95% CI: 1.007-1.014), but decreased in areas of higher poverty (OR: 0.992;95% CI: 0.988-0.995). Time to completion was slightly shorter (B=-0.04, p<0.05) in areas of higher health literacy. Conclusion(s): Despite changes in clinical processes, a nationwide community pharmacy was successful in completing HZ vaccine dose series for adults during the pandemic, suggesting that processes in community pharmacies can protect staff while remaining committed to providing preventive health services during public health crises.Copyright © 2023

Outcomes of a Pilot Contribution Database for the ICU Pharmacy Team at the University Hospitals of Leicester NHS Trust

Introduction It has long been felt that many contributions made by the ICU Pharmacy team, are not well showcased by the yearly regional network multi-speciality contributions audit. Themes specific to ICU are diluted amongst Trust and region wide data, and valuable learning for the multi-disciplinary team (MDT) is subsequently overlooked. Objective(s): The aims of this project were to: * Develop and pilot a MicrosoftTM Access © database for the ICU pharmacy team to record significant contributions. * Enable the production of reports to the ICU Quality & Safety board, to raise awareness, disseminate concerns, and influence future quality improvement projects. * Provide examples to contribute to the training of the whole MDT. * Generate evidence of team effectiveness and encourage further investment. * Provide team members with a means to recall contributions, for revalidation, appraisal, prescribing re-affirmation and framework mapping. Method(s): * A database was built with a user-friendly data-entry form to prevent overwriting. Fields were agreed with peers who would be using the database. * The team were invited to voluntarily enter their contributions which they thought added value and provided useful learning. * The pilot phase ceased with the emergence of the Omicron SARS-CoV-2 variant, due to staffing pressures and surge planning. Result(s): * Between 12/07/2021 and 25/11/2021, a total of 211 contributions were recorded. * Pharmacists entered 88.6% and a single technician entered 11.4% of these. * Independent Prescribing was utilised in 52.13% of contributions, and deprescribing in 25.12%. * Figure 1 demonstrates the contributions by drug group * The top 5 drugs associated with contributions were: ? Dalteparin ? Vancomycin ? Voriconazole ? Meropenem ? Co-trimoxazole * Treatment optimisation was an outcome for 76.3% of all contributions. Figure 2 stratifies these by type. Contributions. * Drug suitability was a cause for intervention in 12.8% of all contributions, encompassing allergies, contraindications, cautions and interactions and routes. * Medicines reconciliation accounted for 17.54% of all contributions, which almost half were Technician led. Admission was the most common stage to intervene (81.08%), followed by transcription. * Of all contributions, 37.91% were classified as patient safety incidents. Reassuringly 76.25% of these were prevented by the Pharmacy team. Themes have been extracted from these incidents and are presented in Table 1. Conclusion(s): PROTECTED-UK1 demonstrated the value pharmacists contribute to the quality and safety of patient care on ICU. Studies of similar quality and scale including Pharmacy Technicians are lacking, but even in this pilot study, it is evident how important their input is. Independent prescribing is a fundamental and well utilised part of our ICU Pharmacist skillset, supporting the GPICS2 recommendation that ICU pharmacists should be encouraged to become prescribers. Compiling a team interventions database is a useful tool to highlight local priority areas for guideline development;training;and ensuring that appropriate decision support is built into electronic prescribing systems. To improve the usefulness of the data, further stratification of contributions according to the Eadon Criteria3 may be worthwhile, to expand its use as a medication safety thermometer for ICU.

Effective medical center finding during COVID-19 pandemic using a spatial DSS centered on ontology engineering.

The global spread of the coronavirus has generated one of the most critical circumstances forcing healthcare systems to deal with it everywhere in the world. The complexity of crisis management, particularly in Iran, the unfamiliarity of the disease, and a lack of expertise, provided the foundation for researchers and implementers to propose innovative solutions. One of the most important obstacles in COVID-19 crisis management is the lack of information and the need for immediate and real-time data on the situation and appropriate solutions. Such complex problems fall into the category of semi-structured problems. In this respect, decision support systems use people's mental resources with computer capabilities to improve the quality of decisions. In synergetic situations, for instance, healthcare domains cooperating with spatial solutions, coming to a decision needs logical reasoning and high-level analysis. Therefore, it is necessary to add rich semantics to different classes of involved data, find their relationships, and conceptualize the knowledge domain. For the COVID-19 case in this study, ontologies allow for querying over such established relationships to find related medical solutions based on description logic. Bringing such capabilities to a spatial decision support system (SDSS) can help with better control of the COVID-19 pandemic. Ontology-based SDSS solution has been developed in this study due to the complexity of information related to coronavirus and its geospatial aspect in the city of Tehran. According to the results, ontology can rationalize different classes and properties about the user's clinical information, various medical centers, and users' priority. Then, based on the user's requests in a web-based SDSS, the system focuses on the inference made, advises the users on choosing the most related medical center, and navigates the user on a map. The ontology's capacity for reasoning, overcoming knowledge gaps, and combining geographic and descriptive criteria to choose a medical center all contributed to promising outcomes and the satisfaction of the sample community of evaluators.

Federated Learning for Healthcare Domain - Pipeline, Applications and Challenges

Federated learning is the process of developing machine learning models over datasets distributed across data centers such as hospitals, clinical research labs, and mobile devices while preventing data leakage. This survey examines previous research and studies on federated learning in the healthcare sector across a range of use cases and applications. Our survey shows what challenges, methods, and applications a practitioner should be aware of in the topic of federated learning. This paper aims to lay out existing research and list the possibilities of federated learning for healthcare industries.© 2022 Copyright held by the owner/author(s).

An intelligent platform of services based on multimedia understanding and telehealth for supporting the management of SARS-CoV-2 multi-pathological patients

The combination of pervasive sensing and multimedia understanding with the advances in communications makes it possible to conceive platforms of services for providing telehealth solutions responding to the current needs of society. The recent outbreak has indeed posed several concerns on the management of patients at home, urging to devise complex pathways to address the Severe Acute Respiratory Syndrome (SARS) in combination with the usual diseases of an increasingly elder population. In this paper, we present TiAssisto, a project aiming to design, develop, and validate an innovative and intelligent platform of services, having as its main objective to assist both Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) multi-pathological patients and healthcare professionals. This is achieved by researching and validating new methods to improve their lives and reduce avoidable hospitalisations. TiAssisto features telehealth and telemedicine solutions to enable high-quality standards treatments based on Information and Communication Technologies (ICT), Artificial Intelligence (AI) and Machine Learning (ML). Three hundred patients are involved in our study: one half using our telehealth platform, while the other half participate as a control group for a correct validation. The developed AI models and the Decision Support System assist General Practitioners (GPs) and other healthcare professionals in order to help them in their diagnosis, by providing suggestions and pointing out possible presence or absence of signs that can be related to pathologies. Deep learning techniques are also used to detect the absence or presence of specific signs in lung ultrasound images. © 2022 IEEE.

Predicting COVID-19 Severity Integrating RNA-Seq Data Using Machine Learning Techniques

A fundamental challenge in the fight against COVID-19 is the development of reliable and accurate tools to predict disease progression in a patient. This information can be extremely useful in distinguishing hospitalized patients at higher risk for needing UCI from patients with low severity. How SARS-CoV-2 infection will evolve is still unclear. Method(s): A novel pipeline was developed that can integrate RNA-Seq data from different databases to obtain a genetic biomarker COVID-19 severity index using an artificial intelligence algorithm. Our pipeline ensures robustness through multiple cross-validation processes in different steps. Result(s): CD93, RPS24, PSCA, and CD300E were identified as COVID-19 severity gene signatures. Furthermore, using the obtained gene signature, an effective multi-class classifier capable of discrimi-nating between control, outpatient, inpatient, and ICU COVID-19 patients was optimized, achieving an accuracy of 97.5%. Conclusion(s): In summary, during this research, a new intelligent pipeline was implemented to develop a specific gene signature that can detect the severity of patients suffering COVID-19. Our approach to clinical decision support systems achieved excellent results, even when processing unseen samples. Our system can be of great clinical utility for the strategy of planning, organizing and managing human and material resources, as well as for automatically classifying the severity of patients affected by COVID-19.Copyright © 2023 Bentham Science Publishers.

Understanding Airline Passengers during Covid-19 Outbreak to Improve Service Quality: Topic Modeling Approach to Complaints with Latent Dirichlet Allocation Algorithm.

The COVID-19 pandemic has deeply affected the airline industry, as it has many sectors, and has created tremendous financial pressure on companies. Flight bans, new regulations, and restrictions increase consumer complaints and are emerging as a big problem for airline companies. Understanding the main reasons triggering complaints and eliminating service failures in the airline industry will be a vital strategic priority for businesses, while reviewing the dimensions of service quality during the COVID-19 pandemic provides an excellent opportunity for academic literature. In this study, 10,594 complaints against two major airlines that offer full-service and low-cost options were analyzed with the Latent Dirichlet Allocation algorithm to categorize them by essential topics. Results provide valuable information for both. Furthermore, this study fills the gap in the existing literature by proposing a decision support system to identify significant service failures through passenger complaints in the airline industry utilizing e-complaints during an unusual situation such as the COVID-19 pandemic.

EpiDesktop-A Spatial Decision Support System for Simulating Epidemic Spread and Human Mobility Trends Under Different Scenarios

Human mobility has been recognized as one of the critical factors determining the spread of contagious diseases, such as SARS-CoV-2, a highly contagious and elusive virus. This virus disrupts the normal lives of more than half of the global population in one way or another, claiming the lives of millions. In such cases, mobility should be managed via the imposition of certain policies. This proposed study presents a newly developed spatial platform aimed at simulating and mapping the spread of infectious diseases and mobility patterns under different scenarios based on different epidemiological models. In addition to the "business as usual" scenario, other response scenarios can be defined to reflect real-world situations, taking into consideration various parameters, including the daily rise in infections and deaths, among others. The developed system provides insights to decision-makers about strategies to be implemented and measures for controlling the spread of the virus.

Decision support systems in healthcare: systematic review, meta- analysis and prediction, with example of COVID-19

Objective: The objective of this study was to provide an overview of Decision Support Systems (DSS) applied in healthcare used for diagnosis, monitoring, prediction and recommendation in medicine. Methods: We conducted a systematic review using PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analysis) guidelines of articles published until September 2022 from PubMed, Cochrane, Scopus and web of science databases. We used KH coder to analyze included research. Then we categorized decision support systems based on their types and medical applications. Results: The search strategy provided a total of 1605 articles in the studied period. Of these, 231 articles were included in this qualitative review. This research was classified into 4 categories based on the DSS type used in healthcare: Alert Systems, Monitoring Systems, Recommendation Systems and Prediction Systems. Under each category, domain applications were specified according to the disease the system was applied to. Conclusion: In this systematic review, we collected CDSS studies that use ML techniques to provide insights into different CDSS types. We highlighted the importance of ML to support physicians in clinical decision-making and improving healthcare according to their purposes.

Improved healthcare disaster decision-making utilizing information extraction from complementary social media data during the COVID-19 pandemic.

Managing an extreme event like a healthcare disaster requires accurate information about the event's circumstances to comprehend the full consequences of acting. However, information quality is rarely optimal since it takes time to determine the information of relevance. The COVID-19 pandemic showed that even official data sources are far from optimal since they suffer from reporting delays that slow decision-making. To support decision-makers with timely information, we utilize data from online social networks to propose an adaptable information extraction solution to create indices helping to forecast COVID-19 case numbers and hospitalization rates. We show that combining heterogeneous data sources like Twitter and Reddit can leverage these sources' inherent complementarity and yield better predictions than those using a single data source alone. We further show that the predictions run ahead of the official COVID-19 incidences by up to 14 days. Additionally, we highlight the importance of model adjustments whenever new information becomes available or the underlying data changes by observing distinct changes in the presence of specific symptoms on Reddit.

Use of artificial intelligence for point-of-care echocardiographic assessment of left ventricular ejection fraction among COVID-19 patients

Background: The association between COVID-19 infection and the cardiovascular system has been well described. Isolation precautions limit the use of formal echocardiography in this setting. Artificial intelligence (AI) utilization using a hand-held device in these patients can be a reliable tool for left ventricular ejection fraction (LVEF) assessment. Aim(s): To prospectively investigate the accuracy of AI-base tool for LVEF assessment using a hand-held echocardiogram in patients with COVID-19. Method(s): From April-28 through July-26, 2020, consecutive patients with COVID-19 underwent a real-time LVEF assessment within 48-h of admission using a hand-held echocardiogram evaluation (Vscan Extend) equipped with LVivoEF, an AI-based tool that automatically evaluates LVEF. The examinations were further analyzed off-line by a blinded fellowshiptrained echocardiographer for LVEF as a gold standard. Result(s): Among 42 patients, 21 (50%) were male (aged 53.3+/-17.8 years, mean BMI 27.6+/-5.1 kg/m2). Seven (16.7%) patients couldn't turn on their left side and three (7.1%) couldn't maintain effective communication. The mean length of each echocardiogram study was 6.8+/-2.2 minutes, battery usage was 13.4+/-4.9%, and mean operator-to-patient proximity was 64.5+/-9.3 cm. A fair to good correlation was demonstrated between the AI and the echocardiographer LVEF assessment (Pearson's correlation of 0.691, p<0.001). An almost perfect agreement was demonstrated between the AI and the echocardiographer for LVEF using a threshold of 45% (kappa=0.806, p<0.001). The sensitivity of focused echocardiogram for 45% LVEF threshold is 85.7%, specificity is 97.1% with a PPV of 85.7% and NPV of 97.1%. Conclusion(s): An AI-based algorithm incorporated into an existing handheld echocardiogram device can be reliably utilized as a decision support tool for automatic real-time LVEF assessment among COVID-19 patients.

Association of macrophage inflammatory protein-1beta with the recovery process in COVID-19

Background: Recovery from coronavirus disease 19 (COVID-19) is a gradual process that depends on the disease severity. Immunologic changes that precede and relate clinical symptoms may predict the course of COVID-19 and final outcome. Our goal was to determine prognostic markers of COVID-19 improvement. Method(s): The study included hospitalized patients from the ages 31-72 with moderate to severe COVID-19. All biomarkers were assessed at three checkpoints starting from the first day of hospitalization (day 0), continuing on day 8, and between 40-50 day. Luminex xMAP technology and the Bio-Plex Pro Human Cytokine 17-plex assay was used for quantitative evaluation cytokines and chemokines in peripheral blood of COVID-19 patients. The comparative study was done in combination with clinical data. Univariate and multivariate analyses of data were delivered. Finally, a fuzzy logic model for decision support was proposed and validated for explored data. Result(s): Macrophage inflammatory protein-1beta (MIP1b) was inversely related to COVID-19 evolution. MIP1b significantly higher on day 8 compared to day 0 (p < 0.0001) correlated with clinical improvement and predicted a successful course of the disease. It was also associated with the significant increase in TNF-alpha (p = 0.03), and decrease in IL-10 (p < 0.0001), and IL-6 (p = 0.01). The increase in MIP1b on day 8 correlated positively with eosinophil and lymphocytes counts and negatively with inflammatory mediators (ferritin, procalcitonin, fibrinogen, CRP). Moderately positive correlation between MIP-1b and TNF-alpha was noted, in parallel. Tested the statistical and machine learning predictors exhibited sensitivity to MIP1b input, improving the ROC curve compared to the classification models trained without MIP1b. Conclusion(s): This finding next to already known indicators such as IL-6, eosinophil and lymphocytes counts, highlight a role of MIP1b as a marker of good prognosis in COVID-19 and provide a novel insight into this as a potential diagnostic and therapeutic target.

A type-2 fuzzy rule-based model for diagnosis of COVID-19

In this study, a type-2 fuzzy logic-based decision support system comprising clinical examination and blood test results that health professionals can use in addition to existing methods in the diagnosis of COVID-19 has been developed. The developed system consists of three fuzzy units. The first fuzzy unit produces COVID-19 positivity as a percentage according to the respiratory rate, loss of smell, and body temperature values, and the second fuzzy unit according to the C-reactive protein, lymphocyte, and D-dimer values obtained as a result of the blood tests. In the third fuzzy unit, the COVID-19 positivity risks according to the clinical examination and blood analysis results, which are the outputs of the first and second fuzzy units, are evaluated together and the result is obtained. As a result of the evaluation of the trials with 60 different scenarios by physicians, it has been revealed that the system can detect COVID-19 risk with 86.6% accuracy. © 2023 TÜBÍTAK.

A Decision Support System using Machine Learning -IoT to tackle the Covid-19 pandemic in Uttarakhand

Covid-19 spread is worldwide;India is now at the second place where this epidemic is spreading with high rate. The state of Uttarakhand, a hilly state of India also has a significant impact of Covid-19. This paper suggests that machine learning techniques with IOT can equipped the doctors, and lab technicians to deal with this pandemic. Here, we also design a prediction system to help the doctors so that they can keep the records of infected patients. We used IoT, machine learning and ensemble methods for healthcare to store infected patient's data in the cloud database, and enable doctors/others to screen patient's data about their disease. We developed a decision support system to detect the diseases quickly and the treatment can be initiated immediately. © 2022 IEEE.

A decision support system for selecting the most suitable machine learning in healthcare using user parameters and requirements

The application of machine learning in the medical field is still limited. The main reason behind the lack of use is the unavailability of an easy-to-use machine learning system that targets non-technical users. The objective of this paper is to propose an automated machine learning system to aid non-technical users. The proposed system provides the user with simple choices to provide suggestions to the system. The system uses the combination of the user's choices and performance evaluation to select the most suited model from available options. In this study, we employed the system on a Parkinson's disease dataset. The templates for support vector machine and random forest algorithms are provided to the system. Support vector machines and random forests were able to produce 80% and 75% accuracy, respectively. The system used performance parameters of the system and user choices to select the most suited models for each test case. The support vector machine was selected as the most suited model in three test cases, while random forest was selected as the most suited for one test case. The test cases also showed that the weighted time parameter impacted the results heavily.Copyright © 2022 The Author(s)

A business retrieval model using scenario planning and analytics for life during and after the pandemic crisis

The COVID-19 pandemic crisis has fundamentally changed the way we live and work forever. The business sector is forecasting and formulating different scenarios associated with the impact of the pandemic on its employees, customers, and suppliers. Various business retrieval models are under construction to cope with life after the COVID-19 Pandemic Crisis. However, the proposed plans and scenarios are static and cannot address the dynamic pandemic changes worldwide. They also have not considered the peripheral in-between scenarios to propel the shifting paradigm of businesses from the existing condition to the new one. Furthermore, the scenario drivers in the current studies are generally centered on the economic aspects of the pandemic with little attention to the social facets. This study aims to fill this gap by proposing scenario planning and analytics to study the impact of the Coronavirus pandemic on large-scale information technology-led Companies. The primary and peripheral scenarios are constructed based on a balanced set of business continuity and employee health drivers. Practical action plans are formulated for each scenario to devise plausible responses. Finally, a damage management framework is developed to cope with the mental disorders of the employees amid the disease.Copyright © 2021 The Author(s)