Achieving large-scale clinician adoption of AI-enabled decision support.

Computerised decision support (CDS) tools enabled by artificial intelligence (AI) seek to enhance accuracy and efficiency of clinician decision-making at the point of care. Statistical models developed using machine learning (ML) underpin most current tools. However, despite thousands of models and hundreds of regulator-approved tools internationally, large-scale uptake into routine clinical practice has proved elusive. While underdeveloped system readiness and investment in AI/ML within Australia and perhaps other countries are impediments, clinician ambivalence towards adopting these tools at scale could be a major inhibitor. We propose a set of principles and several strategic enablers for obtaining broad clinician acceptance of AI/ML-enabled CDS tools.

Development of a scoring system to quantify errors from semantic characteristics in incident reports.

OBJECTIVES: Incident reporting systems are widely used to identify risks and enable organisational learning. Free-text descriptions contain important information about factors associated with incidents. This study aimed to develop error scores by extracting information about the presence of error factors in incidents using an original decision-making model that partly relies on natural language processing techniques. METHODS: We retrospectively analysed free-text data from reports of incidents between January 2012 and December 2022 from Nagoya University Hospital, Japan. The sample data were randomly allocated to equal-sized training and validation datasets. We conducted morphological analysis on free text to segment terms from sentences in the training dataset. We calculated error scores for terms, individual reports and reports from staff groups according to report volume size and compared these with conventional classifications by patient safety experts. We also calculated accuracy, recall, precision and F-score values from the proposed 'report error score'. RESULTS: Overall, 114 013 reports were included. We calculated 36 131 'term error scores' from the 57 006 reports in the training dataset. There was a significant difference in error scores between reports of incidents categorised by experts as arising from errors (p<0.001, d=0.73 (large)) and other incidents. The accuracy, recall, precision and F-score values were 0.8, 0.82, 0.85 and 0.84, respectively. Group error scores were positively associated with expert ratings (correlation coefficient, 0.66; 95% CI 0.54 to 0.75, p<0.001) for all departments. CONCLUSION: Our error scoring system could provide insights to improve patient safety using aggregated incident report data.

Objectives for algorithmic decision-making systems in childhood asthma: Perspectives of children, parents, and physicians.

Objectives: To identify with children, parents and physicians the objectives to be used as parameters for algorithmic decision-making systems (ADMSs) adapting treatments in childhood asthma. Methods: We first conducted a qualitative study based on semi-structured interviews to explore the objectives that children aged 8-17 years, their parents, and their physicians seek to achieve when taking/giving/prescribing a treatment for asthma. Following the grounded theory approach, each interview was independently coded by two researchers; reconciled codes were used to assess code frequency, categories were defined, and the main objectives identified. We then conducted a quantitative study based on questionnaires using these objectives to determine how children/parents/physicians ranked these objectives and whether their responses were aligned. Results: We interviewed 71 participants (31 children, 30 parents and 10 physicians) in the qualitative study and identified seven objectives associated with treatment uptake and five objectives associated with treatment modalities. We included 291 participants (137 children, 137 parents, and 17 physicians) in the quantitative study. We found little correlation between child, parent, and physician scores for each of the objectives. Each child's asthma history influenced the choice of scores assigned to each objective by the child, parents, and physician. Conclusion: The identified objectives are quantifiable and relevant to the management of asthma in the short and long term. They can therefore be incorporated as parameters for future ADMS. Shared decision-making seems essential to achieve consensus among children, parents, and physicians when choosing the weight to assign to each of these objectives.

Performance of large language models on advocating the management of meningitis: a comparative qualitative study.

OBJECTIVES: We aimed to examine the adherence of large language models (LLMs) to bacterial meningitis guidelines using a hypothetical medical case, highlighting their utility and limitations in healthcare. METHODS: A simulated clinical scenario of a patient with bacterial meningitis secondary to mastoiditis was presented in three independent sessions to seven publicly accessible LLMs (Bard, Bing, Claude-2, GTP-3.5, GTP-4, Llama, PaLM). Responses were evaluated for adherence to good clinical practice and two international meningitis guidelines. RESULTS: A central nervous system infection was identified in 90% of LLM sessions. All recommended imaging, while 81% suggested lumbar puncture. Blood cultures and specific mastoiditis work-up were proposed in only 62% and 38% sessions, respectively. Only 38% of sessions provided the correct empirical antibiotic treatment, while antiviral treatment and dexamethasone were advised in 33% and 24%, respectively. Misleading statements were generated in 52%. No significant correlation was found between LLMs' text length and performance (r=0.29, p=0.20). Among all LLMs, GTP-4 demonstrated the best performance. DISCUSSION: Latest LLMs provide valuable advice on differential diagnosis and diagnostic procedures but significantly vary in treatment-specific information for bacterial meningitis when introduced to a realistic clinical scenario. Misleading statements were common, with performance differences attributed to each LLM's unique algorithm rather than output length. CONCLUSIONS: Users must be aware of such limitations and performance variability when considering LLMs as a support tool for medical decision-making. Further research is needed to refine these models' comprehension of complex medical scenarios and their ability to provide reliable information.

Early Prediction of Poststroke Rehabilitation Outcomes Using Wearable Sensors.

OBJECTIVE: Inpatient rehabilitation represents a critical setting for stroke treatment, providing intensive, targeted therapy and task-specific practice to minimize a patient's functional deficits and facilitate their reintegration into the community. However, impairment and recovery vary greatly after stroke, making it difficult to predict a patient's future outcomes or response to treatment. In this study, the authors examined the value of early-stage wearable sensor data to predict 3 functional outcomes (ambulation, independence, and risk of falling) at rehabilitation discharge. METHODS: Fifty-five individuals undergoing inpatient stroke rehabilitation participated in this study. Supervised machine learning classifiers were retrospectively trained to predict discharge outcomes using data collected at hospital admission, including patient information, functional assessment scores, and inertial sensor data from the lower limbs during gait and/or balance tasks. Model performance was compared across different data combinations and was benchmarked against a traditional model trained without sensor data. RESULTS: For patients who were ambulatory at admission, sensor data improved the predictions of ambulation and risk of falling (with weighted F1 scores increasing by 19.6% and 23.4%, respectively) and maintained similar performance for predictions of independence, compared to a benchmark model without sensor data. The best-performing sensor-based models predicted discharge ambulation (community vs household), independence (high vs low), and risk of falling (normal vs high) with accuracies of 84.4%, 68.8%, and 65.9%, respectively. Most misclassifications occurred with admission or discharge scores near the classification boundary. For patients who were nonambulatory at admission, sensor data recorded during simple balance tasks did not offer predictive value over the benchmark models. CONCLUSION: These findings support the continued investigation of wearable sensors as an accessible, easy-to-use tool to predict the functional recovery after stroke. IMPACT: Accurate, early prediction of poststroke rehabilitation outcomes from wearable sensors would improve our ability to deliver personalized, effective care and discharge planning in the inpatient setting and beyond.

ChatGPT in Iranian medical licensing examination: evaluating the diagnostic accuracy and decision-making capabilities of an AI-based model.

INTRODUCTION: Large language models such as ChatGPT have gained popularity for their ability to generate comprehensive responses to human queries. In the field of medicine, ChatGPT has shown promise in applications ranging from diagnostics to decision-making. However, its performance in medical examinations and its comparison to random guessing have not been extensively studied. METHODS: This study aimed to evaluate the performance of ChatGPT in the preinternship examination, a comprehensive medical assessment for students in Iran. The examination consisted of 200 multiple-choice questions categorised into basic science evaluation, diagnosis and decision-making. GPT-4 was used, and the questions were translated to English. A statistical analysis was conducted to assess the performance of ChatGPT and also compare it with a random test group. RESULTS: The results showed that ChatGPT performed exceptionally well, with 68.5% of the questions answered correctly, significantly surpassing the pass mark of 45%. It exhibited superior performance in decision-making and successfully passed all specialties. Comparing ChatGPT to the random test group, ChatGPT's performance was significantly higher, demonstrating its ability to provide more accurate responses and reasoning. CONCLUSION: This study highlights the potential of ChatGPT in medical licensing examinations and its advantage over random guessing. However, it is important to note that ChatGPT still falls short of human physicians in terms of diagnostic accuracy and decision-making capabilities. Caution should be exercised when using ChatGPT, and its results should be verified by human experts to ensure patient safety and avoid potential errors in the medical field.

Navigating the machine learning pipeline: a scoping review of inpatient delirium prediction models.

OBJECTIVES: Early identification of inpatients at risk of developing delirium and implementing preventive measures could avoid up to 40% of delirium cases. Machine learning (ML)-based prediction models may enable risk stratification and targeted intervention, but establishing their current evolutionary status requires a scoping review of recent literature. METHODS: We searched ten databases up to June 2022 for studies of ML-based delirium prediction models. Eligible criteria comprised: use of at least one ML prediction method in an adult hospital inpatient population; published in English; reporting at least one performance measure (area under receiver-operator curve (AUROC), sensitivity, specificity, positive or negative predictive value). Included models were categorised by their stage of maturation and assessed for performance, utility and user acceptance in clinical practice. RESULTS: Among 921 screened studies, 39 met eligibility criteria. In-silico performance was consistently high (median AUROC: 0.85); however, only six articles (15.4%) reported external validation, revealing degraded performance (median AUROC: 0.75). Three studies (7.7%) of models deployed within clinical workflows reported high accuracy (median AUROC: 0.92) and high user acceptance. DISCUSSION: ML models have potential to identify inpatients at risk of developing delirium before symptom onset. However, few models were externally validated and even fewer underwent prospective evaluation in clinical settings. CONCLUSION: This review confirms a rapidly growing body of research into using ML for predicting delirium risk in hospital settings. Our findings offer insights for both developers and clinicians into strengths and limitations of current ML delirium prediction applications aiming to support but not usurp clinician decision-making.

Anticipating artificial intelligence in mammography screening: views of Swedish breast radiologists.

OBJECTIVES: Artificial intelligence (AI) is increasingly tested and integrated into breast cancer screening. Still, there are unresolved issues regarding its possible ethical, social and legal impacts. Furthermore, the perspectives of different actors are lacking. This study investigates the views of breast radiologists on AI-supported mammography screening, with a focus on attitudes, perceived benefits and risks, accountability of AI use, and potential impact on the profession. METHODS: We conducted an online survey of Swedish breast radiologists. As early adopter of breast cancer screening, and digital technologies, Sweden is a particularly interesting case to study. The survey had different themes, including: attitudes and responsibilities pertaining to AI, and AI's impact on the profession. Responses were analysed using descriptive statistics and correlation analyses. Free texts and comments were analysed using an inductive approach. RESULTS: Overall, respondents (47/105, response rate 44.8%) were highly experienced in breast imaging and had a mixed knowledge of AI. A majority (n=38, 80.8%) were positive/somewhat positive towards integrating AI in mammography screening. Still, many considered there to be potential risks to a high/somewhat high degree (n=16, 34.1%) or were uncertain (n=16, 34.0%). Several important uncertainties were identified, such as defining liable actor(s) when AI is integrated into medical decision-making. CONCLUSIONS: Swedish breast radiologists are largely positive towards integrating AI in mammography screening, but there are significant uncertainties that need to be addressed, especially regarding risks and responsibilities. The results stress the importance of understanding actor-specific and context-specific challenges to responsible implementation of AI in healthcare.

Novel machine learning model for predicting multiple unplanned hospitalisations.

BACKGROUND: In the Australian public healthcare system, hospitals are funded based on the number of inpatient discharges and types of conditions treated (casemix). Demand for services is increasing faster than public funding and there is a need to identify and support patients that have high service usage. In 2016, the Victorian Department of Health and Human Services developed an algorithm to predict multiple unplanned admissions as part of a programme, Health Links Chronic Care (HLCC), that provided capitation funding instead of activity based funding to support patients with high admissions. OBJECTIVES: The aim of this study was to determine whether an algorithm with higher performance than previously used algorithms could be developed to identify patients at high risk of three or more unplanned hospital admissions 12 months from discharge. METHODS: The HLCC and Hospital Unplanned Readmission Tool (HURT) models were evaluated using 34 801 unplanned inpatient episodes (27 216 patients) from 2017 to 2018 with an 8.3% prevalence of 3 or more unplanned admissions in the following year of discharge. RESULTS: HURT had a higher AUROC (84%, 95% CI 83.4% to 84.9% vs 71%, 95% CI 69.4% to 71.8%) than HLCC, that was statistically significant using Delong test at p<0.05. DISCUSSION: We found features that appear to be strong predictors of admission risk that have not been previously used in models, including socioeconomic status and social support. CONCLUSION: The high AUROC, moderate sensitivity and high specificity for the HURT algorithm suggests it is a very good predictor of future multi-admission risk and that it can be used to provide targeted support for at-risk individual.

Aplicación de la dinámica de sistemas en el análisis de la evolución de la hipoacusia y la evaluación del impacto de estrategias sanitarias

Objetivos: Aplicar la dinámica de sistemas para estimar la evolución de la incidencia y la prevalencia de hipoacusia en personas mayores en países de bajos, medios y altos ingresos, así como el acceso al tratamiento, y evaluar la influencia de la implementación de estrategias sanitarias sobre estos indicadores. Metodología: Los análisis se realizaron mediante simulación con dinámica de sistemas según parámetros globales. Para ello, se desarrolló un diagrama de bucles causal, integrando la incidencia, la prevalencia y el tratamiento de hipoacusia con el nivel de desigualdad, factores de riesgo, uso de dispositivos de ayuda auditiva, fuerza laboral de audiólogos y otorrinolaringólogos según el nivel de ingresos del país. Luego, se construyó un diagrama de flujo para ejecutar las simulaciones durante un período de 100 años. Además, se ejecutaron cuatro simulaciones con estrategias sanitarias (reducción de factores de riesgo, mejora en el uso dispositivos de ayuda auditiva, aumento del número de audiólogos y otorrinolaringólogos) y se estimó el porcentaje de cambio respecto al modelo basal. Resultados: Los países de bajos ingresos mostraron una mayor incidencia y prevalencia de hipoacusia, menor acceso a tratamiento adecuado y una mayor prevalencia de hipoacusia sin tratar o con tratamiento inadecuado. La reducción de factores de riesgo creció en un 15 y 33 % la población con audición normal en los próximos 50 y 100 años, respectivamente. Además, la mejora en el uso de dispositivos de ayuda auditiva logró una reducción del 60 % de la población con tratamientos inadecuados o sin tratamiento, y el aumento de audiólogos y otorrinolaringólogos incrementó un 250 % el acceso a un tratamiento adecuado. Conclusiones: La evolución de la salud auditiva está condicionada por factores económicos, donde los entornos más desfavorecidos muestran peores indicadores. Además, la implementación de estrategias combinadas favorecería la salud auditiva en el futuro.

Objectives: To estimate the evolution of the incidence and prevalence of hearing loss in the elderly in low-, middle- and high-income countries by means of system dynamics simulation according to global parameters and to analyze the influence of the implementation of health strategies. Methodology: A causal loop diagram was developed to relate the incidence, prevalence and treatment of hearing loss to the level of inequality, risk factors (RF), use of hearing aids (HA), audiologist and otolaryngologist (ENT) workforce by country income level. A flow chart was then constructed to run the simulations over a 100-year period. In addition, four simulations were run with health strategies (reduction of RF, improvement in HA use, increase in the number of audiologists and ENT specialists) and the percentage change from the baseline model was estimated. Results: Low-income countries showed a higher incidence and prevalence of hearing loss, less access to adequate treatment, and a higher prevalence of untreated or inadequately treated hearing loss. The reduction of RF increased the population with normal hearing by 15% and 33% over the next 50 and 100 years, respectively. In addition, the improvement in the use of ha achieved a 60% reduction in the population with inadequate or untreated treatment, and the increase in audiologists and ENT specialists improved the access to adequate treatment by 250%. Conclusions: The evolution of hearing health is conditioned by economic factors, where the most disadvantaged environments show worse indicators. In addition, the implementation of combined strategies would favor hearing health in the future. System dynamics is a very useful methodology for health managers because it enables to understand how a disease evolves and define what are the best health interventions considering different scenarios.

Objetivos: Aplicar a dinâmica do sistema para estimar a evolução da incidência e prevalência da perda auditiva em pessoas idosas em países de baixo, médio e alto rendimento, bem como o acesso ao tratamento, e avaliar a influência da implementação de estratégias de saúde sobre estes indicadores. Metodologia: As análises foram conduzidas utilizando simulação da dinâmica do sistema com base em parâmetros globais. Para tal, foi desenvolvido um diagrama do laço causal, integrando a incidência, prevalência e tratamento da perda auditiva com o nível de desigualdade, fatores de risco, utilização de aparelhos auditivos, mão-de-obra de audiologistas e otorrinolaringologistas por nível de rendimento nacional. Foi então construído um fluxograma para executar as simulações ao longo de um período de 100 anos. Além disso, foram realizadas quatro simulações com estratégias de saúde (reduzindo os fatores de risco, melhorando a utilização de aparelhos auditivos, aumentando o número de audiologistas e otorrinolaringologistas) e foi estimada a mudança percentual em relação ao modelo de base. Resultados: Os países de baixos rendimentos mostraram maior incidência e prevalência de perda auditiva, menor acesso a tratamento apropriado e maior prevalência de perda auditiva não tratada ou tratada de forma inadequada. A redução dos fatores de risco aumentou a população com audição normal em 15 e 33% durante os próximos 50 e 100 anos, respectivamente. Além disso, uma melhor utilização de aparelhos auditivos permitiu uma redução de 60% na população mal tratada ou não tratada, e o aumento do número de audiologistas e especialistas em ORL aumentou em 250% o acesso ao tratamento adequado. Conclusões: A evolução da saúde auditiva é condicionada por fatores económicos, com os ambientes mais desfavorecidos a apresentarem indicadores piores. Além disso, a implementação de estratégias combinadas favoreceria a saúde auditiva no futuro.

[Artificial intelligence for medical information departments : construction and evaluation of a decision-making tool to identify and prioritize stays of which the PMSI coding could be optimized, and to ensure the revenues generated by activity-based pricing]. / L'intelligence artificielle au service des départements d'information médicale : construction et évaluation d'un outil d'aide à la décision pour cibler et prioriser les séjours à contrôler et fiabiliser les recettes hospitalières générées par la tarification à l'activité.

BACKGROUND: Medical Information Departments help to optimize the hospital revenues generated by activity-based pricing. A review of medical files, selected after the targeting of coding summaries, is organized. The aim is to make any corrections to the diagnoses or coded procedures with a potential impact on the pricing of the stay. Targeting is of major importance as a means of concentrating resources on the files for which coding can be effectively improved. The tools available for targeting can be optimized. We have developed a decision-making support tool to make targeting more efficient. The objective of our study was to evaluate the performance of this tool. METHODS: The tool combines an artificial intelligence module with a rule-based expert module. A predictive score is assigned to each coding summary that reflects the probability of a revalued stay. Evaluation of the performance of this tool was based on a sample of 400 stays of at least 3 nights of patients hospitalized at the Paris Saint-Joseph Hospital from 1st November to 31st December 2019. Each stay was reviewed by a coding expert, without knowledge of the score assigned and without help from expert queries. Two main assessment criteria were used: area under the ROC curve and positive predictive value (PPV). RESULTS: The area under the ROC curve was 0.70 (CI 95% [0.64-0.76]). With a revalued coding rate of 32%, PPV was 41% for scores above 5, 65% for scores above 8, 88% for scores above 9. CONCLUSION: The study made it possible to validate the performance of the tool. The implementation of new variables could further increase its performance. This is an area of development to be considered, particularly with in view of generalizing individual invoicing in hospitals.

Opening the black box of machine learning in radiology: can the proximity of annotated cases be a way?

Machine learning (ML) and deep learning (DL) systems, currently employed in medical image analysis, are data-driven models often considered as black boxes. However, improved transparency is needed to translate automated decision-making to clinical practice. To this aim, we propose a strategy to open the black box by presenting to the radiologist the annotated cases (ACs) proximal to the current case (CC), making decision rationale and uncertainty more explicit. The ACs, used for training, validation, and testing in supervised methods and for validation and testing in the unsupervised ones, could be provided as support of the ML/DL tool. If the CC is localised in a classification space and proximal ACs are selected by proper metrics, the latter ones could be shown in their original form of images, enriched with annotation to radiologists, thus allowing immediate interpretation of the CC classification. Moreover, the density of ACs in the CC neighbourhood, their image saliency maps, classification confidence, demographics, and clinical information would be available to radiologists. Thus, encrypted information could be transmitted to radiologists, who will know model output (what) and salient image regions (where) enriched by ACs, providing classification rationale (why). Summarising, if a classifier is data-driven, let us make its interpretation data-driven too.

[Machine learning in anesthesiology]. / "Machine learning" in der Anästhesiologie.

The application of artificial intelligence (AI) is currently changing very different areas of life. Artificial intelligence involves the emulation of human behavior with the aid of methods from mathematics and informatics. Machine learning (ML) represents a subdivision of AI. Algorithms for ML have the potential to optimize patient care, in that they can be utilized in a supportive way in personalized medicine, decision making and risk prediction. Although the majority of the applications in medicine are still limited to data analysis and research, it is certain that ML will become increasingly more important in scientific and clinical aspects in this supportive function. Therefore, it is necessary for clinicians to have at least a basic understanding of the functional principles, strengths and weaknesses of ML.

Requirements Analysis and Specification for a Molecular Tumor Board Platform Based on cBioPortal.

Clinicians in molecular tumor boards (MTB) are confronted with a growing amount of genetic high-throughput sequencing data. Today, at German university hospitals, these data are usually handled in complex spreadsheets from which clinicians have to obtain the necessary information. The aim of this work was to gather a comprehensive list of requirements to be met by cBioPortal to support processes in MTBs according to clinical needs. Therefore, oncology experts at nine German university hospitals were surveyed in two rounds of interviews. To generate an interview guideline a scoping review was conducted. For visual support in the second round, screenshot mockups illustrating the requirements from the first round were created. Requirements that cBioPortal already meets were skipped during the second round. In the end, 24 requirements with sometimes several conceivable options were identified and 54 screenshot mockups were created. Some of the identified requirements have already been suggested to the community by other users or are currently being implemented in cBioPortal. This shows, that the results are in line with the needs expressed by various disciplines. According to our findings, cBioPortal has the potential to significantly improve the processes and analyses of an MTB after the implementation of the identified requirements.

Improving Team-Based Decision Making Using Data Analytics and Informatics: Protocol for a Collaborative Decision Support Design.

BACKGROUND: According to the September 2015 Institute of Medicine report, Improving Diagnosis in Health Care, each of us is likely to experience one diagnostic error in our lifetime, often with devastating consequences. Traditionally, diagnostic decision making has been the sole responsibility of an individual clinician. However, diagnosis involves an interaction among interprofessional team members with different training, skills, cultures, knowledge, and backgrounds. Moreover, diagnostic error is prevalent in the interruption-prone environment, such as the emergency department, where the loss of information may hinder a correct diagnosis. OBJECTIVE: The overall purpose of this protocol is to improve team-based diagnostic decision making by focusing on data analytics and informatics tools that improve collective information management. METHODS: To achieve this goal, we will identify the factors contributing to failures in team-based diagnostic decision making (aim 1), understand the barriers of using current health information technology tools for team collaboration (aim 2), and develop and evaluate a collaborative decision-making prototype that can improve team-based diagnostic decision making (aim 3). RESULTS: Between 2019 to 2020, we are collecting data for this study. The results are anticipated to be published between 2020 and 2021. CONCLUSIONS: The results from this study can shed light on improving diagnostic decision making by incorporating diagnostics rationale from team members. We believe a positive direction to move forward in solving diagnostic errors is by incorporating all team members, and using informatics. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): DERR1-10.2196/16047.

Implementation and Evaluation of a Web-Based Distribution System For Anesthesia Department Guidelines and Standard Operating Procedures: Qualitative Study and Content Analysis.

BACKGROUND: Digitization is spreading exponentially in medical care, with improved availability of electronic devices. Guidelines and standard operating procedures (SOPs) form an important part of daily clinical routine, and adherence is associated with improved outcomes. OBJECTIVE: This study aimed to evaluate a digital solution for the maintenance and distribution of SOPs and guidelines in 2 different anesthesiology departments in Switzerland. METHODS: A content management system (CMS), WordPress, was set up in 2 tertiary-level hospitals within 1 year: the Department of Anesthesiology and Pain Medicine at the Kantonsspital Lucerne in Lucerne, Switzerland, as an open-access system, followed by a similar system for internal usage in the Department of Anaesthesiology and Pain Medicine of the Inselspital, Bern University Hospital, in Bern, Switzerland. We analyzed the requirements and implementation processes needed to successfully set up these systems, and we evaluated the systems' impact by analyzing content and usage. RESULTS: The systems' generated exportable metadata, such as traffic and content. Analysis of the exported metadata showed that the Lucerne website had 269 pages managed by 44 users, with 88,124 visits per month (worldwide access possible), and the Bern website had 341 pages managed by 35 users, with 1765 visits per month (access only possible from within the institution). Creation of an open-access system resulted in third-party interest in the published guidelines and SOPs. The implementation process can be performed over the course of 1 year and setup and maintenance costs are low. CONCLUSIONS: A CMS, such as WordPress, is a suitable solution for distributing and managing guidelines and SOPs. Content is easily accessible and is accessed frequently. Metadata from the system allow live monitoring of usage and suggest that the system be accepted and appreciated by the users. In the future, Web-based solutions could be an important tool to handle guidelines and SOPs, but further studies are needed to assess the effect of these systems.

[Artificial intelligence in medicine-the wrong track or promise of cure?] / Künstliche Intelligenz in der Medizin ­ Holzweg oder Heilversprechen?

Artificial intelligence (AI) has attained a new level of maturity in recent years and is developing into the driver of digitalization in all areas of life. AI is a cross-sectional technology with great importance for all branches of medicine employing imaging as well as text and biodata. There is no field of medicine that remains unaffected by AI, with AI-assisted clinical decision-making assuming a particularly important role. AI methods are becoming established in medial workflow management and for prediction of therapeutic success or treatment outcome. AI systems are already able to lend support to imaging-based diagnosis and patient management, but cannot suggest critical decisions. The corresponding preventive or therapeutic measures can be more rationally assessed with the help of AI, although the number of diseases covered is currently far too low for the creation of robust systems for clinical routine. Prerequisite for the comprehensive use of AI systems is appropriate training to enable physicians to decide when computer-assisted decision-making can be relied upon.

A systematic review of spatial decision support systems in public health informatics supporting the identification of high risk areas for zoonotic disease outbreaks.

BACKGROUND: Zoonotic diseases account for a substantial portion of infectious disease outbreaks and burden on public health programs to maintain surveillance and preventative measures. Taking advantage of new modeling approaches and data sources have become necessary in an interconnected global community. To facilitate data collection, analysis, and decision-making, the number of spatial decision support systems reported in the last 10 years has increased. This systematic review aims to describe characteristics of spatial decision support systems developed to assist public health officials in the management of zoonotic disease outbreaks. METHODS: A systematic search of the Google Scholar database was undertaken for published articles written between 2008 and 2018, with no language restriction. A manual search of titles and abstracts using Boolean logic and keyword search terms was undertaken using predefined inclusion and exclusion criteria. Data extraction included items such as spatial database management, visualizations, and report generation. RESULTS: For this review we screened 34 full text articles. Design and reporting quality were assessed, resulting in a final set of 12 articles which were evaluated on proposed interventions and identifying characteristics were described. Multisource data integration, and user centered design were inconsistently applied, though indicated diverse utilization of modeling techniques. CONCLUSIONS: The characteristics, data sources, development and modeling techniques implemented in the design of recent SDSS that target zoonotic disease outbreak were described. There are still many challenges to address during the design process to effectively utilize the value of emerging data sources and modeling methods. In the future, development should adhere to comparable standards for functionality and system development such as user input for system requirements, and flexible interfaces to visualize data that exist on different scales. PROSPERO registration number: CRD42018110466.

[Condition and future of robotics in stomatology].

The successful research and application of robotic techniques will promote more dental practitioners to operate robotic simulation systems in various aspects as clinical practice, teaching and scientific research. This paper reviews the history and development of dental robotics in a systematic way. We specifically introduce the application conditions and estimate the future development of dental robotics. Besides, we also foresee the potential impact and change brought by dental robotic techniques.

Development of an app as a tool to support research and the prevention of osteoporosis / Desenvolvimento de aplicativo como ferramenta de apoio à investigação e prevenção de osteoporose

Abstract Objective: to develop an app for the investigation and prevention of osteoporosis for use by health professionals. Method: the development of the app was performed in six steps: definition of the target audience; survey of validated osteoporosis risk assessment tools in Brazil; programming of the app through the progressive web; selection of data for the preparation of sections of the evaluation form and clinical recommendations; automated auditing and evaluation of the app by health professionals. Results: three screens were prepared for the app. These were based on the data extracted using the Osteorisk, Sapori and Frax tools for sociodemographic data (age, gender, weight, height and ethnicity), health (use of glucocorticoids, hormone replacement therapy, arthritis rheumatoid arthritis, secondary osteoporosis, previous low impact fractures, parents with a history of hip fractures) and health related behaviors (physical activity, alcohol intake and smoking). The appl followed the design pattern and functionalities of the osteoporosis adviser tool (OPAD). Regarding guidelines relating to clinical recommendations, the guidelines on osteoporosis and fall prevention in the elderly of the Ministry of Health and the Brazilian Society of Geriatrics and Gerontology, respectively, were taken as a basis. Conclusion: the app allows the early identification of patients presenting risk factors for osteoporosis and, based on these results, provides guidance on the preventive measures to be adopted, aiming at reducing complications resulting from fractures, hospitalizations, disabilities and deaths. AU

Resumo Objetivo: desenvolver um aplicativo para a investigação e prevenção de osteoporose para profissionais de saúde. Método: o desenvolvimento do aplicativo foi realizado em seis etapas: definição do público-alvo; levantamento das ferramentas de avaliação de risco para osteoporose validadas no Brasil; programação do aplicativo por meio do web progressivo; seleção de dados para elaboração das seções do formulário de avaliação e das recomendações clínicas; auditoria automatizada e avaliação do aplicativo pelos profissionais de saúde. Resultados: foram elaboradas três telas para o aplicativo. Essas telas foram criadas com base nos dados extraídos por meio das ferramentas Osteorisk, Sapori e Frax no que se referem aos dados sociodemográficos (idade, sexo, peso, estatura e raça), de saúde (uso de glicocorticoides, terapia de reposição hormonal, artrite reumatoide, osteoporose secundária, fratura prévia por baixo impacto, pais com história de fratura do quadril) e de comportamentos relacionados à saúde (prática de atividade física, ingestão de bebida alcoólica e tabagismo). O aplicativo seguiu o padrão de design e funcionalidades da ferramenta osteoporosis adviser (OPAD). Com relação às orientações pertinentes às recomendações clínicas, tomaram-se por base as diretrizes sobre osteoporose e prevenção de quedas em idosos do Ministério da Saúde e da Sociedade Brasileira de Geriatria e Gerontologia, respectivamente. Conclusão: o aplicativo possibilita a identificação precoce de pacientes que apresentam fatores de risco à osteoporose e, com base nestes resultados, fornece a orientação sobre as medidas preventivas a serem adotadas, visando à diminuição de complicações decorrentes de fraturas, reduzindo internações, incapacidades e mortes. AU