A survey of automatic contact tracing approaches using bluetooth low energy

To combat the ongoing Covid-19 pandemic, many new ways have been proposed on how to automate the process of finding infected people, also called contact tracing. A special focus was put on preserving the privacy of users. Bluetooth Low Energy as base technology has the most promising properties, so this survey focuses on automated contact tracing techniques using Bluetooth Low Energy. We define multiple classes of methods and identify two major groups: systems that rely on a server for finding new infections and systems that distribute this process. Existing approaches are systematically classified regarding security and privacy criteria.Copyright © 2021 ACM.

Changes in medication safety indicators in England throughout the covid-19 pandemic using OpenSAFELY: population based, retrospective cohort study of 57 million patients using federated analytics.

Objective: To implement complex, PINCER (pharmacist led information technology intervention) prescribing indicators, on a national scale with general practice data to describe the impact of the covid-19 pandemic on safe prescribing. Design: Population based, retrospective cohort study using federated analytics. Setting: Electronic general practice health record data from 56.8 million NHS patients by use of the OpenSAFELY platform, with the approval of the National Health Service (NHS) England. Participants: NHS patients (aged 18-120 years) who were alive and registered at a general practice that used TPP or EMIS computer systems and were recorded as at risk of at least one potentially hazardous PINCER indicator. Main outcome measure: Between 1 September 2019 and 1 September 2021, monthly trends and between practice variation for compliance with 13 PINCER indicators, as calculated on the first of every month, were reported. Prescriptions that do not adhere to these indicators are potentially hazardous and can cause gastrointestinal bleeds; are cautioned against in specific conditions (specifically heart failure, asthma, and chronic renal failure); or require blood test monitoring. The percentage for each indicator is formed of a numerator of patients deemed to be at risk of a potentially hazardous prescribing event and the denominator is of patients for which assessment of the indicator is clinically meaningful. Higher indicator percentages represent potentially poorer performance on medication safety. Results: The PINCER indicators were successfully implemented across general practice data for 56.8 million patient records from 6367 practices in OpenSAFELY. Hazardous prescribing remained largely unchanged during the covid-19 pandemic, with no evidence of increases in indicators of harm as captured by the PINCER indicators. The percentage of patients at risk of potentially hazardous prescribing, as defined by each PINCER indicator, at mean quarter 1 (Q1) 2020 (representing before the pandemic) ranged from 1.11% (age ≥65 years and non-steroidal anti-inflammatory drugs) to 36.20% (amiodarone and no thyroid function test), while Q1 2021 (representing after the pandemic) percentages ranged from 0.75% (age ≥65 years and non-steroidal anti-inflammatory drugs) to 39.23% (amiodarone and no thyroid function test). Transient delays occurred in blood test monitoring for some medications, particularly angiotensin-converting enzyme inhibitors (where blood monitoring worsened from a mean of 5.16% in Q1 2020 to 12.14% in Q1 2021, and began to recover in June 2021). All indicators substantially recovered by September 2021. We identified 1 813 058 patients (3.1%) at risk of at least one potentially hazardous prescribing event. Conclusion: NHS data from general practices can be analysed at national scale to generate insights into service delivery. Potentially hazardous prescribing was largely unaffected by the covid-19 pandemic in primary care health records in England.

Information Technology, Healthcare Data and Analytics, and Clinical Engineering

During a health crisis and a pandemic, information technology, analytics, and clinical engineering departments within an acute-care hospital setting play a significant role in the delivery of healthcare services. Electronic health record systems have become equally as important as the technology infrastructure that underpins them and the healthcare service itself. Both healthcare workers and patients require network access for effective communications, both within the hospital and beyond. Collaboration tools within and across departments at all levels have become essential for business continuity and clinical care. During the COVID-19 crisis, the virtual workspace became the "new normal” for healthcare workers, and virtual care through telehealth platforms, for patients and caregivers, enabled a quality of care to be maintained while still protecting both patients and healthcare workers throughout the infectious pandemic surge. Providing such services required agile project planning, along with a collaborative team effort, to quickly and effectively respond to expanded patient capacity within SBH. This chapter documents how the IT department at SBH Health System was able to successfully adapt to the demanding requirements of the initial COVID-19 surge in New York City, and it further highlights the key lessons learned to help recognize the tools needed to assist enhanced clinical innovations during a health crisis, especially an infectious pandemic. © SBH Health System 2022.

Exploring the topic structure and evolutionary trends of health informatics research in library and information science

PurposeThis study aims to reveal the topic structure and evolutionary trends of health informatics research in library and information science.Design/methodology/approachUsing publications in Web of Science core collection, this study combines informetrics and content analysis to reveal the topic structure and evolutionary trends of health informatics research in library and information science. The analyses are conducted by Pajek, VOSviewer and Gephi.FindingsThe health informatics research in library and information science can be divided into five subcommunities: health information needs and seeking behavior, application of bibliometrics in medicine, health information literacy, health information in social media and electronic health records. Research on health information literacy and health information in social media is the core of research. Most topics had a clear and continuous evolutionary venation. In the future, health information literacy and health information in social media will tend to be the mainstream. There is room for systematic development of research on health information needs and seeking behavior.Originality/valueTo the best of the authors' knowledge, this is the first study to analyze the topic structure and evolutionary trends of health informatics research based on the perspective of library and information science. This study helps identify the concerns and contributions of library and information science to health informatics research and provides compelling evidence for researchers to understand the current state of research.

Virtual assistant: a tool for health co-production in coping with covid-19 / Asistente virtual: una herramienta para co-producción en salud con el fin de hacer frente al covid-19 / Assistente virtual: ferramenta para coprodução em saúde no enfrentamento à covid-19

ABSTRACT Objective: to describe the development of a virtual assistant as a potential tool for health co-production in coping with COVID-19. Method: this is an applied technological production research study developed in March and April 2020 in five stages: 1) literature review, 2) content definition, 3) elaboration of the dialog, 4) test of the prototype, and 5) integration with the social media page. Results: the literature review gathered diverse scientific evidence about the disease based on the Brazilian Ministry of Health publications and by consulting scientific articles. The content was built from the questions most asked by the population, in March 2020, evidenced by Google Trends, in which the following topics emerged: concept of the disease, prevention means, transmission of the disease, main symptoms, treatment modalities, and doubts. Elaboration of the dialog was based on Natural Language Processing, intentions, entities and dialog structure. The prototype was tested in a laboratory with a small number of user computers on a local network to verify the functionality of the set of apps, technical and visual errors in the dialog, and whether the answers provided were in accordance with the user's question, answering the questions correctly and integrated into Facebook. Conclusion: the virtual assistant proved to be a health education tool with potential to combat "Fake News". It also represents a patient-centered form of health communication that favors the strengthening of the bond and interaction between health professionals and patients, promoting co-production in health.

RESUMEN Objetivo: describir el desarrollo de un asistente virtual como posible herramienta para la co-producción en salud a fin de hacer frente al COVID-19. Método: trabajo de investigación aplicado de producción tecnológica, desarrollado en marzo y abril de 2020 en cinco etapas: 1) revisión de la literatura, 2) definición del contenido, 3) elaboración del diálogo, 4) prueba del prototipo y 5) integración con la página web del medio social. Resultados: en la revisión de la literatura se reunieron evidencias científicas sobre la enfermedad a partir de las publicaciones del Ministerio de Salud de Brasil, al igual que sobre la base de consultas en artículos científicos. El contenido se elaboró a partir de las preguntas más frecuentes de la población, en marzo de 2020, puestas en evidencia por medio de Google Trends, donde surgieron los siguientes temas: concepto de la enfermedad, formas de prevención, transmisión de la enfermedad, principales síntomas, modalidades de tratamiento y dudas. La elaboración del diálogo se basó en el Procesamiento de Lenguaje Natural, en intenciones, en entidades y en la estructura del diálogo. El prototipo se puso a prueba en un laboratorio con una cantidad reducida de computadoras usuario en una red local para verificar la funcionalidad del conjunto de aplicaciones, errores técnicos y visuales acerca del diálogo, y si las respuestas proporcionadas estaban de acuerdo con la pregunta del usuario, respondiendo correctamente los interrogantes e integrado a Facebook. Conclusión: el asistente virtual demostró ser una herramienta de educación en salud con potencial para combatir Fake News. También representa una forma de comunicación en salud centrada en el paciente que favorece el fortalecimiento del vínculo y la interacción entre profesionales de la salud y pacientes, promoviendo así la coproducción en salud.

RESUMO Objetivo: descrever o desenvolvimento de um assistente virtual como ferramenta potencial para a coprodução em saúde no enfrentamento à COVID-19. Método: trata-se de uma pesquisa aplicada de produção tecnológica, desenvolvida nos meses de março e abril de 2020 em cinco etapas: 1) revisão de literatura, 2) definição de conteúdo, 3) construção do diálogo, 4) teste do protótipo e 5) integração com página de mídia social. Resultados: a revisão de literatura reuniu evidências científicas sobre a doença a partir das publicações do Ministério da Saúde, no Brasil, e de consultas em artigos científicos. O conteúdo foi construído a partir das perguntas mais realizadas pela população, em março de 2020, evidenciadas por meio do Google Trends, em que emergiram os seguintes temas: conceito da doença, formas de prevenção, transmissão da doença, principais sintomas, formas de tratamento e dúvidas. A construção do diálogo foi baseada em Processamento de Linguagem Natural, intenções, entidades e estrutura de diálogo. O protótipo foi testado em laboratório com um número reduzido de computadores usuários em uma rede local para verificar a funcionalidade do conjunto de aplicações, erros técnicos e visuais acerca do diálogo e se as respostas fornecidas estavam de acordo com a pergunta do usuário, respondendo de forma correta os questionamentos e integrado ao Facebook. Conclusão: o assistente virtual mostrou-se uma ferramenta de educação em saúde e com potencial para combater fake news. Também representa uma forma de comunicação em saúde centrada no paciente, que favorece o fortalecimento de vínculo e interação entre profissionais de saúde e pacientes, promovendo a coprodução em saúde.

Insights into the FAIRness of the German Network University Medicine: A Survey.

The need to harness large amounts of data, possibly within a short period of time, became apparent during the Covid-19 pandemic outbreak. In 2022, the Corona Data Exchange Platform (CODEX), which had been developed within the German Network University Medicine (NUM), was extended by a number of common components, including a section on FAIR science. The FAIR principles enable research networks to evaluate how well they comply with current standards in open and reproducible science. To be more transparent, but also to guide scientists on how to improve data and software reusability, we disseminated an online survey within the NUM. Here we present the outcomes and lessons learnt.

Proposing a Novel Hybrid Short-Term Exchange Program in Biomedical and Health Informatics Education.

International student exchange is a valuable opportunity for Biomedical and Health Informatics students to gain new perspectives and experiences. In the past, such exchanges have been made possible through international partnerships between universities. Unfortunately, numerous obstacles such as housing, financial concerns, and environmental implications related to travel, have made it difficult to continue international exchange. Experiences with hybrid and online education during covid-19 paved the way for a new approach that allows for short international exchange with a hybrid online-offline supervision model. This will be initiated with an exploration project between two international universities , each related to their respective institute's research focus.

Effect of computerized physician order entry inactivation to order complementary studies in an emergency department / [Efecto de la inactivación de las órdenes múltiples para solicitud de estudios complementarios en una central de emergencias.]

Introduction: The computerized provider order entry (CPOE) is a computing tool that could lead to unintended consequences despite its myriad benefits. We aimed to explore the effect of its inactivation on requests for complementary studies and the associated costs. Methods: Cross sectional study at the Emergency Department of Hospital Italiano de Buenos Aires, which included a consecutive sample of pre-intervention (January-February 2020) and post-intervention (2021) consultations. Using secondary bases, the variables included were administrative debits and their respective billing prices. Results: There were 27,671 consultations in 2020 with a total median value of $474, and 20,819 with $1,639 in 2021. After the analysis restricted to the area of ​​moderately complex clinics (excluding COVID-19 consultations), the following was found: a decrease in the median number of practices per consultation (median of 11 vs. 10, p=0.001), a decrease in the request for at least one laboratory practice (45% vs. 39%, p=0.001), without finding significant changes in global costs (median $1,419 vs. $1,081; p=0.122) or in specific laboratory costs (median $1,071 vs. $1,089, p=0.710). Conclusion: Despite inflation, a significant reduction in the number of practices was achieved and overall costs per consultation were maintained. These findings demonstrate the effectiveness of the intervention, but an educational intervention aimed at reminding the potential harm of overuse and the health costs of unnecessary studies will be necessary.

Introducción: La plantilla de órdenes múltiples es una herramienta informática que podría producir consecuencias inadvertidas pese a sus innumerables beneficios. Nos propusimos explorar el efecto de su inactivación sobre las solicitudes de estudios complementarios y los costos asociados. Métodos: Corte transversal en la Central de Emergencias de Adultos del Hospital Italiano de Buenos Aires, que incluyó muestra consecutiva de consultas pre-intervención (Enero-Febrero 2020) y post-intervención (2021). Mediante el uso de bases secundarias, las variables incluidas fueron los débitos administrativos y sus respectivos precios de facturación. Resultados: Hubo 27.671 consultas en 2020 con una mediana de valor total de 474$, y 20.819 con 1.639$ en 2021. Tras el análisis restringido al área de consultorios de moderada complejidad (excluyendo consultas por COVID-19), se encontró: una disminución en la mediana del número de prácticas por consulta (mediana de 11 vs 10, p=0,001), una disminución en la solicitud de al menos una práctica de laboratorio (45% versus 39%, p=0,001), sin encontrar cambios significativos en costos globales (mediana 1.419$ vs 1.081$; p=0,122) ni en costos específicos de laboratorio (mediana 1.071$ vs 1.089$, p=0,710). Conclusión: Pese a la inflación interanual, se logró una reducción significativa en el número de prácticas y se mantuvieron los costos globales por consulta. Estos hallazgos demuestran la efectividad de la intervención, pero serán necesarias medidas educativas que apunten al recordatorio de los potenciales daños en la sobreutilización, y los costos sanitarios de los estudios innecesarios.

Intelligent Information System for Detection of COVID-19 Based on AI

The pandemic is changing the clinical needs and potential for AI-driven computer-assisted diagnoses (CDS). Since the beginning, rapid identification of COVID-19 patients has been a significant difficulty, especially in areas with limited diagnostic testing capacity. Intelligent Information System (IIS) represents the knowledge progression of available data. It has been directed by recent technological integration, data processing, and distribution in multiple computational environments. Intelligent Information Systems are aimed to work like an advanced human brain, where, as per the requirement of changing circumstances, the optimal decision can be evolved. IIS tools are expected to be adaptive, which may vary according to their processing data. As a result, the goal of this study was to provide a complete analysis of various technologies for combating COVID-19, with a focus on their features, problems, and domiciliation nation. Our findings demonstrate the performance of developing technologies. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

Forecasting of COVID-19 Outbreak in India: A Time Series Analysis

The World Health Organization (WHO) declared the status of coronavirus disease 2019 (COVID-19) to a global pandemic on March 11, 2020. Since then, numerous statistical, epidemiological and mathematical models have been used and investigated by researchers across the world to predict the spread of this pandemic in different geographical locations. The data for COVID-19 outbreak in India has been collated on daily new confirmed cases from March 12, 2020 to April 10, 2021. A time series analysis using Auto Regressive Integrated Moving Average (ARIMA) model was used to investigate the dataset and then forecast for the next 30-day time-period from April 11, 2021, to May 10, 2021. The selected model predicts a surge in the number of daily new cases and number of deaths. An investigation into the daily infection rate for India has also been done. © 2023 The authors and IOS Press.

A Novel Ontological Approach to Track Social Determinants of Health in Primary Care

The associated morbidity and mortality from COVID-19 and the public health response to prevent the spread of the virus has repeatedly demonstrated the significant impact of social determinants of health (SDoH) and social inequities on health outcomes. Social prescriptions are interventions aimed at tackling SDoH. In 2019, NHS-England committed to support the use of social prescribing across England. NHS-England commissioned the Oxford-Royal College of General Practitioners (RCGP) Research and Surveillance Centre (RSC) sentinel network to monitor the distribution of social prescribing services within English primary care and, within that, monitor the impact of the COVID-19 pandemic response on SDoH. To track incidence of people presenting to primary care with SDoH-related issues, we implemented an ontological approach to curate SDoH indicators in computerised medical records (CMR) using the Systematized Nomenclature of Medicine - Clinical Terms (SNOMED CT). These indicators were then extracted from the RCGP-RSC sentinel network database to present weekly incidence rates per 10,000 people to assess the impact of the pandemic on these SDoH. Pre- versus peri-pandemic, we observed an increase in the recording of several of our SDoH indicators;namely issues related to homelessness, unemployment, mental health, harmful substance use and financial difficulties. As far as we are aware, this is the first time that routinely collected primary care CMR data has been utilised for the monitoring and surveillance of SDoH and demonstrates the feasibility of this approach for future surveillance. © 2022, Springer Nature Switzerland AG.

Study of coronary artery bypass grafting admission in Covid-19 era: A bicentric study

The Covid-19 pandemic that began in December 2019 and is still underway in 2022 has changed many habits and protocols in different economic industries including healthcare. In the specific case, the change in protocols and management of work activities also affected the health sector. Among the sectors in which the pandemic has influenced the flow of events is the cardiology sector. In the specific case, the present work will present how coronary bypass interventions have been influenced in their different aspects by the Covid-19 pandemic. The work will be based on a comparison between the 2019 data in the period prior to the pandemic and in 2020 in the post-pandemic period for two major public hospitals in the Campania region: The university hospital of Salerno (Italy) "San Giovanni di Dio and Ruggi D'Aragona"and AORN "A. Cardarelli "of Naples (Italy). Both the structures considered have an Emergency Department and First Aid Acceptance for surgical pathologies. © 2022 ACM.

A Course Design and a Flexible Platform for Learning Programming Languages in Health Informatics Courses Flexible Platform for Learning Programming Languages

This paper addresses problems of learning programming languages for healthcare professionals. Learners may have limitation of time to understand and apply their knowledge and skills of these topics. This study aimed to create a course design and a flexible learning platform to increase the flexibility learning platform for programming languages in both the normal and the COVID-19 situations. The way in which learners learn was discussed and designed. After a lecture-based session, students could use any of their computing devices including computers with several operating systems, tablets, and smartphones to practice the exercise. Students may workout whenever they wanted and from any locations when the internet connection was provided. In the course design, it began with the programming module and then follow by the computer system module. In this module, a flexible learning platform was constructed to teach the PHP. The real server was installed with Ubuntu Linux, and environments for running the PHP. For the client, only a web browser was enough to learn coding. However, some useful editors with the ability of remote syncing files on the server, were introduced. With this platform, instructors could easily review and modify codes written by learners. Learners also gained some experiences in the computer systems module, i.e., operating systems and network. This concept was applied on a course namely "Computer Systems and Principles of Programming for Pharmaceutical and Health Informatics” in Master Degree in Pharmacy (Health Informatics) in academic year of 2021 during the COVID-19 situation. The results from the questionnaire suggested that the course design and the platform were very useful and flexible. Students had more knowledge and skills by using this learning environment. © 2022 Association for Computing Machinery.

Using Deep Learning Techniques for Diagnosis & Treatment with COVID-19 & AI Technology

The COVID-19 epidemic has caused an unprecedented level of difficulty for the entire world, stopping life and taking thousands of lives. Since COVID-19 has spread to 212 countries and territories and has resulted in 5,212,172 infected cases and 334,915 fatalities, it continues to pose a serious threat to public health. This study proposes a solution to battle the infection using Artificial Intelligence. It has been shown that some Deep Learning techniques, including Long-Short Term Memory, Extreme Learning Machines, and Generative Adversarial Networks, can accomplish this goal. It is informatics techniques in various informational facets from numerous structured & unstructured Data-Sources are combined to produce user-friendly platforms for medical professionals & researchers. The primary benefit of these AI-based platforms is that they speed up the process of diagnosing and treating COVID-19 illness. The most recent related publications and medical reports were examined in order to identify network sources & objectives that might assist in the construction of a feasible Artificial Neural Network based solution for COVID-19 issues. © 2022 IEEE.

COVID-19 and Minimizing Micro-Spatial Interactions

COVID-19, the novel coronavirus that has disrupted lives around the world, continues to challenge how humans interact in public and shared environments. Repopulating the micro-spatial setting of an office building, with virus spread and transmission mitigation measures, is critical for a return to normalcy. Advice from public health experts, such as maintaining physical distancing from others and well-ventilated spaces, are essential, yet there is a lack of sound guidance on configuring office usage that allows for a safe return of workers. This paper highlights the potential for decision-making and planning insights through location analytics, particularly within an office setting. Proposed is a spatial analytic framework addressing the need for physical distancing and limiting worker interaction, supported by geographic information systems, network science, and spatial optimization. The developed modeling approach addresses dispersion of assigned office spaces as well as associated movement within the office environment. This can be used to support the design and utilization of offices in a manner that minimizes the risk of COVID-19 transmission. Our proposed model produces two main findings: (1) that the consideration of minimizing potential interaction as an objective has implications for the safety of work environments, and (2) that current social distancing measures may be inadequate within office settings. Our results show that leveraging exploratory spatial data analyses through the integration of geographic information systems, network science, and spatial optimization, enables the identification of workspace allocation alternatives in support of office repopulation efforts. © 2022 held by the owner/author(s).

Natural language processing for clinical text in Spanish: The case of waiting lists in Chile

The waiting lists not covered by the Explicit Health Guarantee Plan for new specialty consultation in Chile increased due to the effects of the SARS-CoV-2 coronavirus (COVID-19) pandemic. This represents a problem derived from the delay in the resolution and prioritization of each case. This paper aims to describe the issue of the waiting lists in the Chilean health system and present an example of the application of Natural Language Processing (NLP). Specifically, a methodology for recognizing key information in medical narratives is described. Currently, we have a set of manually annotated medical referrals in the development of the Chilean Waiting List Corpus, with a fraction of 2,000 referrals in which the annotated medical entities were automatically normalized to the Unified Medical Language System concepts using the lexicon MedLexSp. The clinical NLP Group of the Center for Mathematical Modeling of the University of Chile, and other national NLP groups, are developing several tools and resources in medicine that can be transferred to the Chilean health system to support managing clinical text in Spanish. © 2022

Introduction to the Special Issue on Understanding the Spread of COVID-19, Part 1

Infectious diseases are transmitted between human hosts when in close contact over space and time. Recently, an unprecedented amount of spatial and spatiotemporal data have been made available that can be used to improve our understanding of the spread of COVID-19 and other infectious diseases. This understanding will be paramount to prepare for future pandemics through spatial algorithms and systems to collect, capture, curate, and analyze complex, multi-scale human movement data to solve problems such as infectious diseases prediction, contact tracing, and risk assessment. In exploring and deepening the conversation around this topic, the eight articles included in the first volume of this special issue employ diverse theoretical perspectives, methodologies, and frameworks, including but not limited to infectious diseases simulation, risk prediction, response policy design, mobility analysis, and case diagnosis. Rather than focusing on a narrow set of problems, these articles provide a glimpse into the diverse possibilities of leveraging spatial and spatiotemporal data for pandemic preparedness. © 2022 held by the owner/author(s).

EpiGNN: Exploring Spatial Transmission with Graph Neural Network for Regional Epidemic Forecasting

Epidemic forecasting is the key to effective control of epidemic transmission and helps the world mitigate the crisis that threatens public health. To better understand the transmission and evolution of epidemics, we propose EpiGNN, a graph neural network-based model for epidemic forecasting. Specifically, we design a transmission risk encoding module to characterize local and global spatial effects of regions in epidemic processes and incorporate them into the model. Meanwhile, we develop a Region-Aware Graph Learner (RAGL) that takes transmission risk, geographical dependencies, and temporal information into account to better explore spatial-temporal dependencies and makes regions aware of related regions' epidemic situations. The RAGL can also combine with external resources, such as human mobility, to further improve prediction performance. Comprehensive experiments on five real-world epidemic-related datasets (including influenza and COVID-19) demonstrate the effectiveness of our proposed method and show that EpiGNN outperforms state-of-the-art baselines by 9.48% in RMSE. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

Electronic monitoring of potential adverse drug events related to lopinavir/ritonavir and hydroxychloroquine during the first wave of COVID-19.

During Switzerland's first wave of COVID-19, clinical pharmacy activities during medical rounds in Geneva University Hospitals were replaced by targeted remote interventions. We describe using the electronic PharmaCheck system to screen high-risk situations of adverse drug events (ADEs), particularly targeting prescriptions of lopinavir/ritonavir (LPVr) and hydroxychloroquine (HCQ) in the presence of contraindications or prescriptions outside institutional guidelines. Of 416 patients receiving LPVr and/or HCQ, 182 alerts were triggered for 164 (39.4%) patients. The main associated risk factors of ADEs were drug-drug interactions, QTc interval prolongation, electrolyte disorder and inadequate LPVr dosage. Therapeutic optimisation recommended by a pharmacist or proposals for additional monitoring were accepted in 80% (n=36) of cases. Combined with pharmacist contextualisation to the clinical context, PharmaCheck made it possible to successfully adapt clinical pharmacist activities by switching from a global to a targeted analysis mode in an emergency context.