Feasibility, accuracy, and usability analysis of MapAML, a first-in-class app for integrated diagnosis in acute myeloid leukemia.

Performing a comprehensive diagnosis of acute myeloid leukemia (AML) is complex and involves the integration of clinical information, bone marrow morphology, immunophenotyping, cytogenetic, and molecular analysis, which can be challenging to the general hematologist. The aim of this study was to evaluate the usability and accuracy of MapAML, a smartphone app for integrated diagnosis in AML, created to aid the hematologist in its clinical practice. App performance was evaluated in dedicated sessions, in which 21 hematologists or fellows in hematology performed an integrated diagnosis of deidentified real-world clinical AML cases, first without and posteriorly with MapAML use. Diagnosis accuracy increased after MapAML utilization, with the average score going from 7.08 without app to 8.88 with app use (on a scale from 0 to 10), representing a significant accuracy improvement (p = .002). Usability evaluation was very favorable, with 81% of users considering the app very or extremely simple to use. There was also a significant increase in confidence to perform a complete and accurate diagnosis in AML after app use, with 61.9% of the participants willing to use the app in their clinical practice. In this study, MapAML increased accuracy with excellent usability for integrated diagnosis in AML.

Automatic dental age calculation from panoramic radiographs using deep learning: a two-stage approach with object detection and image classification.

BACKGROUND: Dental age is crucial for treatment planning in pediatric and orthodontic dentistry. Dental age calculation methods can be categorized into morphological, biochemical, and radiological methods. Radiological methods are commonly used because they are non-invasive and reproducible. When radiographs are available, dental age can be calculated by evaluating the developmental stage of permanent teeth and converting it into an estimated age using a table, or by measuring the length between some landmarks such as the tooth, root, or pulp, and substituting them into regression formulas. However, these methods heavily depend on manual time-consuming processes. In this study, we proposed a novel and completely automatic dental age calculation method using panoramic radiographs and deep learning techniques. METHODS: Overall, 8,023 panoramic radiographs were used as training data for Scaled-YOLOv4 to detect dental germs and mean average precision were evaluated. In total, 18,485 single-root and 16,313 multi-root dental germ images were used as training data for EfficientNetV2 M to classify the developmental stages of detected dental germs and Top-3 accuracy was evaluated since the adjacent stages of the dental germ looks similar and the many variations of the morphological structure can be observed between developmental stages. Scaled-YOLOv4 and EfficientNetV2 M were trained using cross-validation. We evaluated a single selection, a weighted average, and an expected value to convert the probability of developmental stage classification to dental age. One hundred and fifty-seven panoramic radiographs were used to compare automatic and manual human experts' dental age calculations. RESULTS: Dental germ detection was achieved with a mean average precision of 98.26% and dental germ classifiers for single and multi-root were achieved with a Top-3 accuracy of 98.46% and 98.36%, respectively. The mean absolute errors between the automatic and manual dental age calculations using single selection, weighted average, and expected value were 0.274, 0.261, and 0.396, respectively. The weighted average was better than the other methods and was accurate by less than one developmental stage error. CONCLUSION: Our study demonstrates the feasibility of automatic dental age calculation using panoramic radiographs and a two-stage deep learning approach with a clinically acceptable level of accuracy.

Best practices for multimodal clinical data management and integration: An atopic dermatitis research case.

BACKGROUND: In clinical research on multifactorial diseases such as atopic dermatitis, data-driven medical research has become more widely used as means to clarify diverse pathological conditions and to realize precision medicine. However, modern clinical data, characterized as large-scale, multimodal, and multi-center, causes difficulties in data integration and management, which limits productivity in clinical data science. METHODS: We designed a generic data management flow to collect, cleanse, and integrate data to handle different types of data generated at multiple institutions by 10 types of clinical studies. We developed MeDIA (Medical Data Integration Assistant), a software to browse the data in an integrated manner and extract subsets for analysis. RESULTS: MeDIA integrates and visualizes data and information on research participants obtained from multiple studies. It then provides a sophisticated interface that supports data management and helps data scientists retrieve the data sets they need. Furthermore, the system promotes the use of unified terms such as identifiers or sampling dates to reduce the cost of pre-processing by data analysts. We also propose best practices in clinical data management flow, which we learned from the development and implementation of MeDIA. CONCLUSIONS: The MeDIA system solves the problem of multimodal clinical data integration, from complex text data such as medical records to big data such as omics data from a large number of patients. The system and the proposed best practices can be applied not only to allergic diseases but also to other diseases to promote data-driven medical research.

Verifying measurements on Siemens Atellica® instruments using clinically acceptable analytical performance specifications.

Measurements on clinical chemistry analysers must be verified to demonstrate applicability to their intended clinical use. We verified the performance of measurements on the Siemens Atellica® Solution chemistry analysers against the clinically acceptable analytical performance specifications, CAAPS, including the component of intra-individual biological variation, CVI. The relative standard uncertainty of measurement, i.e. analytical variation, CVA, was estimated for six example measurands, haemoglobin A1c in whole blood (B-HbA1c), albumin in urine (U-Alb), and the following measurands in plasma: sodium (P-Na), pancreatic amylase (P-AmylP), low-density lipoprotein cholesterol (P-LDL-C), and creatinine (P-Crea). Experimental CVA was calculated from single-instrument imprecision using control samples, variation between measurements on parallel instruments, and estimation of bias with pooled patient specimens. Each obtained CVA was compared with previously developed CAAPS. The calculated CVA was 1.4% for B-HbA1c (CAAPS 1.9% for single diagnostic testing, CAAPS 2.0% for monitoring after duplicate tests; IFCC units), 10.9% for U-Alb (CAAPS 44.9%), 1.2% for P-Na (CAAPS 0.6%, after triplicate testing 1.5%), 8.2% for P-AmylP (CAAPS 22.9%). The CVA was 4.9% for P-LDL-C (CAAPS for cardiovascular risk stratification 4.9% after four replicates), and 4.2% for P-Crea (CAAPS 8.0%). Three of the six measurands fulfilled the estimated clinical need. Results from P-Na measurements indicate a general need for improving the P-Na assays for emergency patients. It is necessary to consider CVI when creating diagnostic targets for laboratory tests, as emphasised by the CAAPS estimates of B-HbA1c and P-LDL-C.

Accuracy and Comprehensiveness in Recording Information of a Web-Based Application for Preoperative Assessment: A Prospective Observational Study.

PURPOSE: Preanestes@s is a web-based application that includes a smart computer-based self-assessment preoperative questionnaire (PreQuest). Preanestes@s enables remote non-telephonic preoperative assessment via a virtual visit. We aimed to determine if the American Society of Anesthesiologists (ASA) physical status classification assigned by PreQuest and virtual visit is comparable with that assigned by face-to-face assessment, and to determine the accuracy of Preanestes@s in recording complementary preoperative information. DESIGN: Prospective, observational, paired study. METHODS: This study was conducted in a tertiary teaching hospital. A total of 510 adult patients scheduled for surgery were consecutively recruited. Of these, 220 patients were included in the analysis of ASA grade agreement. FINDINGS: PreQuest and virtual visit showed higher predictive value than face-to-face assessment in detecting patients with ASA grades 1 to 2. Face-to-face assessment showed the highest rate of false negatives (ASA 3-4 misclassified as ASA 1-2), with a sensitivity of 44.2% versus 69.8% and 50% for PreQuest and virtual visit, respectively. Virtual visit showed the highest agreement with the ASA grade assigned by a panel of experts (kappa index 0.52, 95% confidence interval 0.15-0.89). PreQuest and virtual visit offered a more comprehensive registry of anthropometric data, more detailed record of chronic diseases condition, and more accurate registry of patients' treatments (virtual visit > PreQuest > face-to-face assessment). CONCLUSIONS: The combined use of PreQuest and virtual visit offers a better performance in assigning the ASA grade for non-complex patients and a more accurate and detailed record of complementary information. This finding suggests the feasibility of Preanestes@ as a tool for preoperative assessment.

The HEARTS app: a clinical tool for cardiovascular risk and hypertension management in primary health care.

HEARTS in the Americas is the regional adaptation of the World Health Organization's Global Hearts Initiative, which will be the model for risk management for cardiovascular disease (CVD) in primary health care in the Region of the Americas by 2025. It has already been implemented in 21 countries and 1045 primary health care centers throughout Latin America and the Caribbean. It takes a public health and health systems approach to systematically introduce simplified interventions at the primary health care level and focuses on hypertension as a clinical entry point. This paper introduces a new, improved application (app), the main component of which is the calculator for CVD risk and hypertension management. The paper summarizes the risk assessment approach and the methodology used by the World Health Organization to update its cardiovascular risk charts in 2019; describes the app, its use, functionality and validation process; and provides a set of practical recommendations for optimizing CVD risk and hypertension management by using the app in clinical practice. The HEARTS app is a powerful tool to improve the quality of care provided in primary health settings. The creation and dissemination of the HEARTS app is an essential step in the journey towards eliminating preventable CVD in the Americas.

HEARTS en las Américas es la adaptación regional de la Iniciativa Global Hearts de la Organización Mundial de la Salud, que será el modelo para el manejo del riesgo de la enfermedad cardiovascular (ECV) en la atención primaria de la salud en la Región de las Américas para el año 2025. Ya se ha implementado en 21 países y 1045 centros de atención primaria de salud en toda América Latina y el Caribe. Adopta un enfoque de salud pública y sistemas de salud para introducir sistemáticamente intervenciones simplificadas en el nivel de atención primaria de salud y se centra en la hipertensión como punto de entrada clínico. En este artículo se presenta una aplicación (app) nueva y mejorada cuyo componente principal es la calculadora de riesgo de ECV y de manejo de la hipertensión. Se resume el enfoque de evaluación del riesgo y la metodología utilizada por la Organización Mundial de la Salud para actualizar sus tablas de riesgo cardiovascular en 2019; se describe la app, su uso, su funcionalidad y su proceso de validación; y se proporciona un conjunto de recomendaciones prácticas para optimizar el manejo del riesgo de ECV y de la hipertensión mediante el uso de la app en la práctica clínica. La app HEARTS es una herramienta robusta para mejorar la calidad de la atención prestada en los centros de atención primaria. La creación y difusión de la aplicación HEARTS es un paso esencial en el camino hacia la eliminación de la ECV prevenible en las Américas.

HEARTS nas Américas é a adaptação regional da Iniciativa Global Hearts da Organização Mundial da Saúde, que será o modelo para o gerenciamento de risco de doenças cardiovasculares (DCV) na atenção primária à saúde na Região das Américas até 2025. Ele já foi implementado em 21 países e 1045 centros de saúde primária em toda a América Latina e Caribe. Adota uma abordagem de saúde pública e sistemas de saúde para introduzir sistematicamente intervenções simplificadas no nível da atenção primária à saúde e concentra-se na hipertensão como um ponto de entrada clínica. Este documento introduz uma nova e melhor aplicação (app), cujo principal componente é a calculadora de risco de DCV e gerenciamento de hipertensão. O artigo resume a abordagem de avaliação de risco e a metodologia usada pela Organização Mundial da Saúde para atualizar seus gráficos de risco cardiovascular em 2019; descreve o aplicativo, seu uso, funcionalidade e processo de validação; e fornece um conjunto de recomendações práticas para otimizar o gerenciamento do risco de DCV e da hipertensão, usando o aplicativo na prática clínica. O aplicativo HEARTS é uma ferramenta potente para melhorar a qualidade dos cuidados prestados em ambientes de saúde primária. A criação e disseminação do aplicativo HEARTS é um passo essencial para eliminar a DCV evitável nas Américas.

[The HEARTS app: a clinical tool for cardiovascular risk and hypertension management in primary health careO aplicativo HEARTS: uma ferramenta clínica para o gerenciamento de risco cardiovascular e hipertensão na atenção primária à saúde]. / La aplicación HEARTS: una herramienta clínica para el manejo del riesgo cardiovascular y la hipertensión en la atención primaria de salud.

HEARTS in the Americas is the regional adaptation of the World Health Organization's Global Hearts Initiative, which will be the model for risk management for cardiovascular disease (CVD) in primary health care in the Region of the Americas by 2025. It has already been implemented in 21 countries and 1045 primary health care centers throughout Latin America and the Caribbean. It takes a public health and health systems approach to systematically introduce simplified interventions at the primary health care level and focuses on hypertension as a clinical entry point. This paper introduces a new, improved application (app), the main component of which is the calculator for CVD risk and hypertension management. The paper summarizes the risk assessment approach and the methodology used by the World Health Organization to update its cardiovascular risk charts in 2019; describes the app, its use, functionality and validation process; and provides a set of practical recommendations for optimizing CVD risk and hypertension management by using the app in clinical practice. The HEARTS app is a powerful tool to improve the quality of care provided in primary health settings. The creation and dissemination of the HEARTS app is an essential step in the journey towards eliminating preventable CVD in the Americas.

HEARTS nas Américas é a adaptação regional da Iniciativa Global Hearts da Organização Mundial da Saúde, que será o modelo para o gerenciamento de risco de doenças cardiovasculares (DCV) na atenção primária à saúde na Região das Américas até 2025. Ele já foi implementado em 21 países e 1045 centros de saúde primária em toda a América Latina e Caribe. Adota uma abordagem de saúde pública e sistemas de saúde para introduzir sistematicamente intervenções simplificadas no nível da atenção primária à saúde e concentra-se na hipertensão como um ponto de entrada clínica. Este documento introduz uma nova e melhor aplicação (app), cujo principal componente é a calculadora de risco de DCV e gerenciamento de hipertensão. O artigo resume a abordagem de avaliação de risco e a metodologia usada pela Organização Mundial da Saúde para atualizar seus gráficos de risco cardiovascular em 2019; descreve o aplicativo, seu uso, funcionalidade e processo de validação; e fornece um conjunto de recomendações práticas para otimizar o gerenciamento do risco de DCV e da hipertensão, usando o aplicativo na prática clínica. O aplicativo HEARTS é uma ferramenta potente para melhorar a qualidade dos cuidados prestados em ambientes de saúde primária. A criação e disseminação do aplicativo HEARTS é um passo essencial para eliminar a DCV evitável nas Américas.

Mobile application as a learning tool for improving dental students' knowledge regarding dental trauma.

INTRODUCTION: Current evidence suggests an important knowledge gain of health sciences' students and professionals with mobile devices (m-learning). This study aimed to verify whether different strategies of teaching (traditional lecture classroom, m-learning and association of both methods) would present greater efficacy in knowledge improvement regarding dental trauma in primary dentition. MATERIALS AND METHODS: An action research study using a participatory approach was conducted with 36 undergraduate Brazilian dental students. A structured questionnaire composed of six clinical cases of traumatic injuries was initially applied to all students (pre-test). Participants were then randomly distributed into three experimental groups: TC-students who had access exclusively to traditional lecture classroom; APP-access exclusively to the mobile application; and TC-APP-access to both approaches. The traditional and technological methods had evidence-based content and visual resources of the subject. After interventions, all students responded to the same previous questionnaire (post-test). Wilcoxon and Kruskal-Wallis tests were performed for statistical analysis. RESULTS: All tested groups obtained statistically significant improvement between their pre-test and post-test scores, for both diagnosis and treatment of dental trauma (p < .005). Nonetheless, no intergroup differences were observed amongst the scores of pre- and post-test for diagnosis (p = .159) and treatment (p = .206). CONCLUSION: All teaching approaches applied obtained similar knowledge improvement of undergraduate dental students regarding traumatic injuries in primary teeth.

Frustration With Technology and its Relation to Emotional Exhaustion Among Health Care Workers: Cross-sectional Observational Study.

BACKGROUND: New technology adoption is common in health care, but it may elicit frustration if end users are not sufficiently considered in their design or trained in their use. These frustrations may contribute to burnout. OBJECTIVE: This study aimed to evaluate and quantify health care workers' frustration with technology and its relationship with emotional exhaustion, after controlling for measures of work-life integration that may indicate excessive job demands. METHODS: This was a cross-sectional, observational study of health care workers across 31 Michigan hospitals. We used the Safety, Communication, Operational Reliability, and Engagement (SCORE) survey to measure work-life integration and emotional exhaustion among the survey respondents. We used mixed-effects hierarchical linear regression to evaluate the relationship among frustration with technology, other components of work-life integration, and emotional exhaustion, with adjustment for unit and health care worker characteristics. RESULTS: Of 15,505 respondents, 5065 (32.7%) reported that they experienced frustration with technology on at least 3-5 days per week. Frustration with technology was associated with higher scores for the composite Emotional Exhaustion scale (r=0.35, P<.001) and each individual item on the Emotional Exhaustion scale (r=0.29-0.36, P<.001 for all). Each 10-point increase in the frustration with technology score was associated with a 1.2-point increase (95% CI 1.1-1.4) in emotional exhaustion (both measured on 100-point scales), after adjustment for other work-life integration items and unit and health care worker characteristics. CONCLUSIONS: This study found that frustration with technology and several other markers of work-life integration are independently associated with emotional exhaustion among health care workers. Frustration with technology is common but not ubiquitous among health care workers, and it is one of several work-life integration factors associated with emotional exhaustion. Minimizing frustration with health care technology may be an effective approach in reducing burnout among health care workers.

Iterative heuristic design of temporal graphic displays with clinical domain experts.

Conventional electronic health record information displays are not optimized for efficient information processing. Graphical displays that integrate patient information can improve information processing, especially in data-rich environments such as critical care. We propose an adaptable and reusable approach to patient information display with modular graphical components (widgets). We had two study objectives. First, reduce numerous widget prototype alternatives to preferred designs. Second, derive widget design feature recommendations. Using iterative human-centered design methods, we interviewed experts to hone design features of widgets displaying frequently measured data elements, e.g., heart rate, for acute care patient monitoring and real-time clinical decision-making. Participant responses to design queries were coded to calculate feature-set agreement, average prototype score, and prototype agreement. Two iterative interview cycles covering 64 design queries and 86 prototypes were needed to reach consensus on six feature sets. Interviewers agreed that line graphs with a smoothed or averaged trendline, 24-h timeframe, and gradient coloring for urgency were useful and informative features. Moreover, users agreed that widgets should include key functions: (1) adjustable reference ranges, (2) expandable timeframes, and (3) access to details on demand. Participants stated graphical widgets would be used to identify correlating patterns and compare abnormal measures across related data elements at a specific time. Combining theoretical principles and validated design methods was an effective and reproducible approach to designing widgets for healthcare displays. The findings suggest our widget design features and recommendations match critical care clinician expectations for graphical information display of continuous and frequently updated patient data.

[Electronic medical record and prescription: risks and benefits detected since its implementation. Safe designing, rollout and use]. / Historia clínica y receta electrónica: riesgos y beneficios detectados desde su implantación. Diseño, despliegue y usos seguros.

New information technologies have transformed the way care is delivered within health services, permeating almost every aspect of health care. As the complexity of the system increases, it becomes more difficult to work optimally without the assistance of new technologies. Although its implementation represents a breakthrough, either because of the advancement involved in the proper use of any technology in health care, or because of the development of specific applications that improve patient safety, other factors such as incorrect design, implementation and poor maintenance, inadequate training, along with overconfidence and dependency, can make technologies compromise patient safety. This article describes the beneficial effects, and those that are not so, of the introduction in our country of the electronic medical record and the electronic prescription on the healthcare quality and safety.

Disaster eHealth: Scoping Review.

BACKGROUND: Although both disaster management and disaster medicine have been used for decades, their efficiency and effectiveness have been far from perfect. One reason could be the lack of systematic utilization of modern technologies, such as eHealth, in their operations. To address this issue, researchers' efforts have led to the emergence of the disaster eHealth (DEH) field. DEH's main objective is to systematically integrate eHealth technologies for health care purposes within the disaster management cycle (DMC). OBJECTIVE: This study aims to identify, map, and define the scope of DEH as a new area of research at the intersection of disaster management, emergency medicine, and eHealth. METHODS: An extensive scoping review using published materials was carried out in the areas of disaster management, disaster medicine, and eHealth to identify the scope of DEH. This review procedure was iterative and conducted in multiple scientific databases in 2 rounds, one using controlled indexed terms and the other using similar uncontrolled terms. In both rounds, the publications ranged from 1990 to 2016, and all the appropriate research studies discovered were considered, regardless of their research design, methodology, and quality. Information extracted from both rounds was thematically analyzed to define the DEH scope, and the results were evaluated by the field experts through a Delphi method. RESULTS: In both rounds of the research, searching for eHealth applications within DMC yielded 404 relevant studies that showed eHealth applications in different disaster types and disaster phases. These applications varied with respect to the eHealth technology types, functions, services, and stakeholders. The results led to the identification of the scope of DEH, including eHealth technologies and their applications, services, and future developments that are applicable to disasters as well as to related stakeholders. Reference to the elements of the DEH scope indicates what, when, and how current eHealth technologies can be used in the DMC. CONCLUSIONS: Comprehensive data gathering from multiple databases offered a grounded method to define the DEH scope. This scope comprises concepts related to DEH and the boundaries that define it. The scope identifies the eHealth technologies relevant to DEH and the functions and services that can be provided by these technologies. In addition, the scope tells us which groups can use the provided services and functions and in which disaster types or phases. DEH approaches could potentially improve the response to health care demands before, during, and after disasters. DEH takes advantage of eHealth technologies to facilitate DMC tasks and activities, enhance their efficiency and effectiveness, and enhance health care delivery and provide more quality health care services to the wider population regardless of their geographical location or even disaster types and phases.

Web-Based Technology for Remote Viewing of Radiological Images: App Validation.

BACKGROUND: Internet technologies can create advanced and rich web-based apps that allow radiologists to easily access teleradiology systems and remotely view medical images. However, each technology has its own drawbacks. It is difficult to balance the advantages and disadvantages of these internet technologies and identify an optimal solution for the development of medical imaging apps. OBJECTIVE: This study aimed to compare different internet platform technologies for remotely viewing radiological images and analyze their advantages and disadvantages. METHODS: Oracle Java, Adobe Flash, and HTML5 were each used to develop a comprehensive web-based medical imaging app that connected to a medical image server and provided several required functions for radiological interpretation (eg, navigation, magnification, windowing, and fly-through). Java-, Flash-, and HTML5-based medical imaging apps were tested on different operating systems over a local area network and a wide area network. Three computed tomography colonography data sets and 2 ordinary personal computers were used in the experiment. RESULTS: The experimental results demonstrated that Java-, Flash-, and HTML5-based apps had the ability to provide real-time 2D functions. However, for 3D, performances differed between the 3 apps. The Java-based app had the highest frame rate of volume rendering. However, it required the longest time for surface rendering and failed to run surface rendering in macOS. The HTML5-based app had the fastest surface rendering and the highest speed for fly-through without platform dependence. Volume rendering, surface rendering, and fly-through performances of the Flash-based app were significantly worse than those of the other 2 apps. CONCLUSIONS: Oracle Java, Adobe Flash, and HTML5 have individual strengths in the development of remote access medical imaging apps. However, HTML5 is a promising technology for remote viewing of radiological images and can provide excellent performance without requiring any plug-ins.

Paving the Way for the Implementation of a Decision Support System for Antibiotic Prescribing in Primary Care in West Africa: Preimplementation and Co-Design Workshop With Physicians.

BACKGROUND: Suboptimal use of antibiotics is a driver of antimicrobial resistance (AMR). Clinical decision support systems (CDSS) can assist prescribers with rapid access to up-to-date information. In low- and middle-income countries (LMIC), the introduction of CDSS for antibiotic prescribing could have a measurable impact. However, interventions to implement them are challenging because of cultural and structural constraints, and their adoption and sustainability in routine clinical care are often limited. Preimplementation research is needed to ensure relevant adaptation and fit within the context of primary care in West Africa. OBJECTIVE: This study examined the requirements for a CDSS adapted to the context of primary care in West Africa, to analyze the barriers and facilitators of its implementation and adaptation, and to ensure co-designed solutions for its adaptation and sustainable use. METHODS: We organized a workshop in Burkina Faso in June 2019 with 47 health care professionals representing 9 West African countries and 6 medical specialties. The workshop began with a presentation of Antibioclic, a publicly funded CDSS for antibiotic prescribing in primary care that provides personalized antibiotic recommendations for 37 infectious diseases. Antibioclic is freely available on the web and as a smartphone app (iOS, Android). The presentation was followed by a roundtable discussion and completion of a questionnaire with open-ended questions by participants. Qualitative data were analyzed using thematic analysis. RESULTS: Most of the participants had access to a smartphone during their clinical consultations (35/47, 74%), but only 49% (23/47) had access to a computer and none used CDSS for antibiotic prescribing. The participants considered that CDSS could have a number of benefits including updating the knowledge of practitioners on antibiotic prescribing, improving clinical care and reducing AMR, encouraging the establishment of national guidelines, and developing surveillance capabilities in primary care. The most frequently mentioned contextual barrier to implementing a CDSS was the potential risk of increasing self-medication in West Africa, where antibiotics can be bought without a prescription. The need for the CDSS to be tailored to the local epidemiology of infectious diseases and AMR was highlighted along with the availability of diagnostic tests and antibiotics using national guidelines where available. Participants endorsed co-design involving all stakeholders, including nurses, midwives, and pharmacists, as central to any introduction of CDSS. A phased approach was suggested by initiating and evaluating CDSS at a pilot site, followed by dissemination using professional networks and social media. The lack of widespread internet access and computers could be circumvented by a mobile app with an offline mode. CONCLUSIONS: Our study provides valuable information for the development and implementation of a CDSS for antibiotic prescribing among primary care prescribers in LMICs and may, in turn, contribute to improving antibiotic use, clinical outcomes and decreasing AMR.

Factors associated with the performance of routine health information system in Yaoundé-Cameroon: a cross-sectional survey.

BACKGROUND: Routine Health Information Systems (RHIS) of low-income countries function below the globally expected standard, characterised by the production and use of poor-quality data, or the non-use of good quality data for informed decision making. This has negatively influenced the health service delivery and uptake. This study focuses on identifying the factors associated with the performance of RHIS of the health facilities (HF) in Yaoundé, so as to guide targeted RHIS strengthening. METHODS: A HF-based cross-sectional study in the 6 health districts (HDs) of Yaoundé was conducted. HFs were chosen using stratified sampling with probability proportional to size per HD. Data were collected, entered into Microsoft Excel 2013 and analysed with IBM- SPSS version 25. Consistency of the questionnaire was measured using Cronbach's alpha coefficient. Pearson's chi-square (and Fisher exact where relevant) tests were used to establish relationships between qualitative variables. Associations were further quantified using unadjusted Odd ratio (OR) for univariable analysis and adjusted odds ratio (aOR) for multivariable analysis with 95% confidence interval (CI). A p-value of less than 0.05 was considered statistically significant. RESULTS: Of 111 selected HFs; 16 (14.4%) were public and 95 (85.6%) private. Respondents aged 24-60 years with an average of 38.3 ± 9.3 years; 58 (52.3%) males and 53(47.7%) females. Cronbach's alpha was 0.96 (95%CI: 0.95-0.98, p < 0.001), proving that the questionnaire was reliable in measuring RHIS performances. At univariable level, the following factors were positively associated with good performances: supportive supervision (OR = 3.03 (1.1, 8.3); p = 0.02), receiving feedback from hierarchy (OR = 3.6 (0.99, 13.2); p = 0.05), having received training on health information (OR = 5.0 (1.6, 16.0); p = 0.003), and presence of a performance evaluation plan (OR = 3.3 (1.4, 8.2), p = 0.007). At multivariable level, the only significantly associated factor was having received training on health information (aOR = 3.3 (1.01, 11.1), p = 0.04). CONCLUSION: Training of health staff in the RHIS favors RHIS good performance. Hence, emphasis should be laid on training and empowering staff, frequent and regular RHIS supervision, and frequent and regular feedback, for an efficient RHIS strengthening in Yaoundé.

Determining Follow-Up Imaging Study Using Radiology Reports.

Radiology reports often contain follow-up imaging recommendations. Failure to comply with these recommendations in a timely manner can lead to delayed treatment, poor patient outcomes, complications, unnecessary testing, lost revenue, and legal liability. The objective of this study was to develop a scalable approach to automatically identify the completion of a follow-up imaging study recommended by a radiologist in a preceding report. We selected imaging-reports containing 559 follow-up imaging recommendations and all subsequent reports from a multi-hospital academic practice. Three radiologists identified appropriate follow-up examinations among the subsequent reports for the same patient, if any, to establish a ground-truth dataset. We then trained an Extremely Randomized Trees that uses recommendation attributes, study meta-data and text similarity of the radiology reports to determine the most likely follow-up examination for a preceding recommendation. Pairwise inter-annotator F-score ranged from 0.853 to 0.868; the corresponding F-score of the classifier in identifying follow-up exams was 0.807. Our study describes a methodology to automatically determine the most likely follow-up exam after a follow-up imaging recommendation. The accuracy of the algorithm suggests that automated methods can be integrated into a follow-up management application to improve adherence to follow-up imaging recommendations. Radiology administrators could use such a system to monitor follow-up compliance rates and proactively send reminders to primary care providers and/or patients to improve adherence.

Repurposing Clinical Decision Support System Data to Measure Dosing Errors and Clinician-Level Quality of Care.

We aimed to develop and validate an instrument to detect hospital medication prescribing errors using repurposed clinical decision support system data. Despite significant efforts to eliminate medication prescribing errors, these events remain common in hospitals. Data from clinical decision support systems have not been used to identify prescribing errors as an instrument for physician-level performance. We evaluated medication order alerts generated by a knowledge-based electronic prescribing system occurring in one large academic medical center's acute care facilities for patient encounters between 2009 and 2012. We developed and validated an instrument to detect medication prescribing errors through a clinical expert panel consensus process to assess physician quality of care. Six medication prescribing alert categories were evaluated for inclusion, one of which - dose - was included in the algorithm to detect prescribing errors. The instrument was 93% sensitive (recall), 51% specific, 40% precise, 62% accurate, with an F1 score of 55%, positive predictive value of 96%, and a negative predictive value of 32%. Using repurposed electronic prescribing system data, dose alert overrides can be used to systematically detect medication prescribing errors occurring in an inpatient setting with high sensitivity.

Development of a smartphone app for neuromuscular facial training.

BACKGROUND: Several studies have demonstrated a significant benefit of neuromuscular facial training in the rehabilitation of patients with facial palsy. However, printed instructions for home training are often not of optimum quality and associated with low adherence to therapy. Professional guidance, e.g., by occupational therapists, is regarded as being of high quality, but is associated with a high cost burden, particularly in chronic forms of disease. OBJECTIVE: The idea to develop a smartphone app for facial training arose from the above-described situation. The aim was to provide structured exercises for the mimic muscles in the sense of neuromuscular training with visual feedback via the front camera of the device. MATERIALS AND METHODS: A native app architecture in iOS was chosen to implement the graphical and content-related concept. In the Apple Xcode (Apple, Cupertino, CA, US) development environment, the app's code was written in the Swift programming language (Apple) and the graphical user interface was created. RESULTS: An app prototype was implemented that provides step-by-step instructions on selected mimic exercises via animated smileys. The duration and speed of the exercise can be varied within a limited range. In the development environment, the correct functionality of both physical and virtual devices was successfully tested. CONCLUSION: App-based facial training offers attractive opportunities to motivate patients for improved adherence to treatment, which could hypothetically lead to a better outcome. Evaluation of this question is planned in a clinical trial after completion of the development.

[Development of a smartphone app for neuromuscular facial training. German Version]. / Entwicklung einer Smartphone-App für neuromuskuläres Fazialistraining.

BACKGROUND: Several studies have demonstrated a significant benefit of neuromuscular facial training in the rehabilitation of patients with facial palsy. However, printed instructions for home training are often not of optimum quality and associated with low adherence to therapy. Professional guidance, e.g., by occupational therapists, is regarded as being of high quality, but is associated with a high cost burden, particularly in chronic forms of disease. OBJECTIVE: The idea to develop a smartphone app for facial training arose from the above-described situation. The aim was to provide structured exercises for the mimic muscles in the sense of neuromuscular training with visual feedback via the front camera of the device. MATERIALS AND METHODS: A native app architecture in iOS was chosen to implement the graphical and content-related concept. In the Apple Xcode (Apple, Cupertino, California, US) development environment, the app's code was written in the Swift programming language (Apple) and the graphical user interface was created. RESULTS: An app prototype was implemented that provides step-by-step instructions on selected mimic exercises via animated smileys. The duration and speed of the exercise can be varied within a limited range. In the development environment, the correct functionality of both physical and virtual devices was successfully tested. CONCLUSION: App-based facial training offers attractive opportunities to motivate patients for improved adherence to treatment, which could hypothetically lead to a better outcome. Evaluation of this question is planned in a clinical trial after completion of the development.

Automated Tracking of Follow-Up Imaging Recommendations.

OBJECTIVE. Radiology reports often contain follow-up imaging recommendations. Failure to comply with these recommendations in a timely manner can lead to poor patient outcomes, complications, and legal liability. As such, the primary objective of this research was to determine adherence rates to follow-up recommendations. MATERIALS AND METHODS. Radiology-related examination data, including report text, for examinations performed between June 1, 2015, and July 31, 2017, were extracted from the radiology departments at the University of Washington (UW) and Lahey Hospital and Medical Center (LHMC). The UW dataset contained 923,885 examinations, and the LHMC dataset contained 763,059 examinations. A 1-year period was used for detection of imaging recommendations and up to 14-months for the follow-up examination to be performed. RESULTS. On the basis of an algorithm with 97.9% detection accuracy, the follow-up imaging recommendation rate was 11.4% at UW and 20.9% at LHMC. Excluding mammography examinations, the overall follow-up imaging adherence rate was 51.9% at UW (range, 44.4% for nuclear medicine to 63.0% for MRI) and 52.0% at LHMC (range, 30.1% for fluoroscopy to 63.2% for ultrasound) using a matcher algorithm with 76.5% accuracy. CONCLUSION. This study suggests that follow-up imaging adherence rates vary by modality and between sites. Adherence rates can be influenced by various legitimate factors. Having the capability to identify patients who can benefit from patient engagement initiatives is important to improve overall adherence rates. Monitoring of follow-up adherence rates over time and critical evaluation of variation in recommendation patterns across the practice can inform measures to standardize and help mitigate risk.