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Background: Streptococcus pneumoniae, as an important human pathogen, is a leading cause of serious infections (e.g., sepsis, pneumonia and meningitis). Nasopharyngeal colonization is often the first step in the development of pneumococcal infections. The aim of this study was to determine the prevalence of S. pneumoniae serotypes in the nasopharynx of students in Kashan and its related risk factors. Materials and Methods: This cross-sectional study was conducted on 1289 students in Kashan from November 2011 to December 2012. Demographic and clinical data were collected from patients using a questionnaire. Samples were cultured on a selective media. Moreover, S. pneumoniae strains were identified using gram staining, detecting alpha hemolysis, catalase, optochin susceptibility and bile solubility tests. Results: One-hundred eighty one out of 1289 (14%) students were carriers for nasopharyngeal S. pneumoniae. There was a significant correlation between sex, age, previous respiratory infections, asthma, runny nose and previous hospitalization with an increased rate of the pneumococcal carriers. Conclusion: According to the high prevalence of pneumococcal carriers and a significant correlation between increased rates of colonization and sex, age, previous respiratory infections, asthma, runny nose and previous hospitalization, applying preventive strategies such as student vaccination seems to be necessary. [ABSTRACT FROM AUTHOR]

Background: Group B Streptococcus (GBS) has been described as an important pathogen in newborns and pregnant women. Maternal vaccination against GBS can reduce maternal GBS colonization and enhance antibody transfer to the fetus and also prevent the subsequent infections. Nine serotypes can be identified based on capsular polysaccharide: Ia, Ib, II-VIII. Due to the changes in serotypes' distribution pattern over time and also variation in different geographic areas, production of a universally optimal vaccine is impossible. This study aimed to evaluate the serotype distribution of GBS using the multiplex PCR among the pregnant women. Materials and Methods: This study was performed on 382 pregnant women. Vaginal swab samples were placed in the LIM selective medium and incubated at 37°C for 24 h. Then the samples were cultured in blood Agar medium and the GBS was identified and confirmed using the standard tests and gene encoding dlts, respectively. Capsular typing was performed using the multiplex PCR method to identify the Ia,Ib,II-VIII serotypes. Results: Thirty-six (9.4 %) out of 382 pregnant women were carriers of GBS. The most common types were III (32.14%), V (21.43%), and IV (14.3%), respectively. Types II and VIII were not identified in this study. Conclusion: Considering the high prevalence of III, V and IV serotypes in this study, they are potential sources for the production of multivalent GBS vaccines in near future. [ABSTRACT FROM AUTHOR]

Coronary artery spasms (CAS) can manifest in various forms, from silent ischemia to severe cardiac events like myocardial infarction and sudden death. This case involves a 56-year-old male with recurrent ischemic chest pain and varying ECG signs. Cardiac catheterization revealed multiple coronary spasms that resolved spontaneously or with intracoronary nitroglycerin. The report emphasizes the severe presentations of multiple CAS and the importance of thorough diagnostic evaluation to avoid unnecessary interventions, highlighting the diagnostic challenges in managing such cases., (© 2024 The Author(s). Annals of Noninvasive Electrocardiology published by Wiley Periodicals LLC.)

The integration of Electronic Medical Records (EMRs) revolutionized healthcare but often retained limitations from paper-based structures. This study proposes a framework for developing dynamic medical content specifically adapted to the clinical context including medical specialty and diseases. Tailoring content to this dynamic context offers several benefits, including improved access to relevant information, streamlined workflows, and potentially better patient outcomes. We applied our framework to develop neurosurgical content, focusing on brain tumors. The method involves defining the medical specialty, outlining user journeys, and iteratively developing artifacts like assessment forms, dashboards, and order sets. Standardized terminologies ensure consistency and interoperability. Our results demonstrate a successful development of content meeting user needs and clinical relevance. While initial implementation focused on neurosurgery, exploring scalability and AI integration offers promising avenues for further advancement. Future studies could quantitatively evaluate the impact of this method on user satisfaction and patient outcomes.

Traditionally, Electronic Medical Records (EMR) have been designed to mimic paper records. Organizing and presenting medical information along the lines that evolved for non-digital records over the decades, reduced change management for medical users, but failed to make use of the potential of organizing digital data. We proposed a method to create clinical dashboards to increase the usability of information in the medical records. Official clinical guidelines were studied by a working group, including dashboard target users. Necessary clinical concepts contained in the medical records were identified according to the clinical context and finally, dedicated technical tools with standard terminologies were used to represent categories of information. We used this method to generate and implement a dashboard for sepsis. The dashboard was found to be appropriate and easy to use by the target users.

The paper presents a collaborative approach employed to identify and examine the obstacles faced by telehealth solutions. The study involved the active participation of health start-ups, telehealth providers, and healthcare professionals delivering telehealth services. By harnessing the collective expertise and diverse perspectives of these stakeholders, the research led to develop an open platform, entitled Digital Connecting for Health, that has the potential to overcome the challenges impeding the widespread adoption and effectiveness of digital health services including telehealth in delivery of care. The developed platform shed light on various obstacles faced by telehealth solutions and provide valuable infrastructures for enhancing the implementation and efficacy of various digital health solutions, including telehealth applications, from various providers.

Standardized order sets are a pragmatic type of clinical decision support that can improve adherence to clinical guidelines with a list of recommended orders related to a specific clinical context. We developed a structure facilitating the creation of order sets and making them interoperable, to increase their usability. Various orders contained in electronic medical records in different hospitals were identified and included in different categories of orderable items. Clear definitions were provided for each category. A mapping to FHIR resources was performed to relate these clinically meaningful categories to FHIR standards to assure interoperability. We used this structure to implement the relevant user interface in the Clinical Knowledge Platform. The use of standard medical terminologies and the integration of clinical information models like FHIR resources are key factors for creating reusable decision support systems. The content authors should be provided with a clinically meaningful system to use in a non-ambiguous context.

A large number of Electronic Medical Records (EMR) are currently available with a variety of features and architectures. Existing studies and frameworks presented some solutions to overcome the problem of specification and application of clinical guidelines toward the automation of their use at the point of care. However, they could not yet support thoroughly the dynamic use of medical knowledge in EMRs according to the clinical contexts and provide local application of international recommendations. This study presents the development of the Clinical Knowledge Platform (CKP): a collaborative interoperable environment to create, use, and share sets of information elements that we entitled Clinical Use Contexts (CUCs). A CUC could include medical forms, patient dashboards, and order sets that are usable in various EMRs. For this purpose, we have identified and developed three basic requirements: an interoperable, inter-mapped dictionary of concepts leaning on standard terminologies, the possibility to define relevant clinical contexts, and an interface for collaborative content production via communities of professionals. Community members work together to create and/or modify, CUCs based on different clinical contexts. These CUCs will then be uploaded to be used in clinical applications in various EMRs. With this method, each CUC is, on the one hand, specific to a clinical context and on the other hand, could be adapted to the local practice conditions and constraints. Once a CUC has been developed, it could be shared with other potential users that can consume it directly or modify it according to their needs.

As a leading cause of morbidity and premature mortality, obesity has become a major global public health problem. It is therefore important to investigate the spatial variation of obesity prevalence and its associations with environmental and behavioral factors (e.g., food environment, physical activity), to optimize the targeting of scarce public health resources. In this study, the geographic clustering of obesity in the Netherlands was explored by analyzing the local spatial autocorrelation of municipal-level prevalence rates of adulthood obesity (aged ≥19 years) in 2016. The potential influential factors that may be associated with obesity prevalence were first selected from five categories of healthrelated factors through binary and Least Absolute Shrinkage and Selection Operator (LASSO) regressions. Geographically Weighted Regression (GWR) was then used to investigate the spatial variations of the associations between those selected factors and obesity prevalence. The results revealed marked geographic variations in obesity prevalence, with four clusters of high prevalence in the north, south, east, and west, and three clusters of low prevalence in the north and south of the Netherlands. Lack of sports participation, risk of anxiety, falling short of physical activity guidelines, and the number of restaurants around homes were found to be associated with obesity prevalence across municipalities. Our findings show that effective, region-specific strategies are needed to tackle the increasing obesity in the Netherlands.

Background and Aim: One of the prerequisites to develop Computerised Decision Support Systems is Clinical Practice Guidelines (CPGs) which provide a systematic aid to make complex medical decisions. In order to provide an automated CPG, it is needed to have a unique structure for the CPGs. This study aims to propose a unique framework for the Persian guidelines., Materials and Methods: 20 Persian CPGs were selected and divided into the creation and validation sets (n=10 for each). The first group was studied independently and their headings were listed; wherever possible, the headings were merged into a new heading that was applicable to all the guidelines. The developed framework was validated by the second group of the guidelines., Results: Studied guidelines had a very heterogeneous structure. The number of original headings was 249; they were reduced to 14 main headings with 16 subheadings in a unique developed framework. The framework is able to represent and cover 100% of the guidelines., Conclusion: The heterogeneity of guidelines was high as they were not developed based on the unique framework. The proposed framework provides a layout for designing the CPGs with a homogeneous structure. Guideline developers can use this framework to develop structured CPGs. This will facilitate the integration of the guidelines into electronic medical records as well as clinical decision support systems., Competing Interests: Competing interests: None declared., (© Author(s) (or their employer(s)) 2020. No commercial re-use. See rights and permissions. Published by BMJ.)

The ApiAppS ongoing project aims to provide physicians with a decision support system for the prescription / recommendation of mHealth technologies. We describe the context and the components of the project which includes: 1) a technical part on modelling and implementing the decision support system, and 2) a psychosocial investigation part designed to have a better knowledge of general practitioners (GPs) and patients' expectations, beliefs and practices.

Objective: Review the existing studies including an assessment tool/method to assess the quality of mHealth apps; extract their criteria; and provide a classification of the collected criteria., Methods: In accordance with the PRISMA statement, a literature search was conducted in MEDLINE, EMBase, ISI and Scopus for English language citations published from January 1, 2008 to December 22, 2016 for studies including tools or methods for quality assessment of mHealth apps. Two researchers screened the titles and abstracts of all retrieved citations against the inclusion and exclusion criteria. The full text of relevant papers was then individually examined by the same researchers. A senior researcher resolved eventual disagreements and confirmed the relevance of all included papers. The authors, date of publication, subject fields of target mHealth apps, development method, and assessment criteria were extracted from each paper. The extracted assessment criteria were then reviewed, compared, and classified by an expert panel of two medical informatics specialists and two health information management specialists., Results: Twenty-three papers were included in the review. Thirty-eight main classes of assessment criteria were identified. These were reorganized by expert panel into 7 main classes (Design, Information/Content, Usability, Functionality, Ethical Issues, Security and Privacy, and User-perceived value) with 37 sub-classes of criteria., Conclusions: There is a wide heterogeneity in assessment criteria for mHealth apps. It is necessary to define the exact meanings and degree of distinctness of each criterion. This will help to improve the existing tools and may lead to achieve a better comprehensive mHealth app assessment tool.

Mobile health (mHealth) could offer unprecedented opportunity to provide medical support closer to the users. We have selected some relevant criteria to describe 100 apps from Google Play store and Apple's App Store's top suggestions in medical category. These characteristics were compared based on the paid or free nature of the apps, the target users: consumers or healthcare professionals, and the platform: Android or iOS. Seventeen provided functionalities and 27 medical subjects covered by these apps were also extracted. Our study shows that even in top rated mHealth apps, a high proportion lacks some basic criteria regarding the quality of the apps including the presence of a privacy policy, describing content sources, participation of the target users in the app development, etc. Paid apps did not ensure better quality compared to free apps. The current mHealth market is not mature enough to be used widely and recommended by healthcare professionals.

A large number of biometric connected devices are currently available with a variety of designs. Healthcare users cannot easily choose the reliable ones that correspond the best to their healthcare problems. The existing evaluation methods do not consider at the same time aspects of connectivity and healthcare usage. In this study, a collaborative evaluation framework for biometric connected devices in healthcare usage is proposed. This framework contains six dimensions: medical validity, technical reliability, usability, ergonomy, legal compliance, and accuracy of measurements. In a first step, these dimensions were assessed by designing a self administered questionnaire answered by the stakeholders (patients, health professionals, payers, and manufacturers). A case study was then carried out in a second step to test this framework in a project of telemonitoring for heart failure patients. The results are in favor of the efficiency of the proposed framework as a decision making tool in healthcare usage.

Successful medication adherence particularly in elderly with chronic diseases will improve their self-management. Medication reminder systems could be useful to improve this adherence. This study consists of two phases, designing a mobile medical app based on Android platform and then its evaluation. To develop this application, first, the use case scenarios have been hypothesized in partnership with health professionals and patients used to take medications daily. Unified Modeling Language was used to model the use cases. The evaluation was performed with usability testing and efficacy testing. The results show that the app was well accepted both in young people and older adults. Engaging target users and health professionals in the conception and development of a health-related app could have better results in the usability and the efficacy of the app.

Mobile health apps play an important role in healthcare processes and health promotion. In recent years many Persian mhealth apps were developed and are available in various national app markets. Cafebazaar is the largest Persian app store that contains more than 3500 android apps in medical and health & fitness categories. In this study some characteristics of 200 top Persian medical apps of Cafebazaar were investigated and then categorized by their use cases. Results showed that only 6% of apps declare the involvement of at least one health professional in the conception or development of the apps. In 35% of studied apps, no contact information was provided for the users and 10.5% applied reliable sources for their content. 13 distinct use cases were found in all 200 apps of which two were new to an already published use-case model. This study shows that Persian mHealth apps, like other existing apps in the world, have a long way to improve and reach some basic standards. Lack of regulatory agencies and absence of a dynamic evaluation system for mHealth apps might be the main reason of these defects. This study also shows that 20 use cases existing in international health related apps are not yet used in Persian apps and therefore there is a reach potential of creating new apps in mHealth field.

Serious games could be used to improve cognitive functions in the elderly. We evaluated the adoption of a new tablet application dedicated to cognitive stimulation in the elderly. The Stim'Art application offers various serious games to work different cognitive functions (memory, attention, concentration, etc.). The usage of fifteen older adults was followed for six months. The type of the game, the number of launches for each game, the time spent on each game, the difficulty level, the success rate and perceived well-being of users have been studied and compared at the end of the first and the sixth months. The participants have played half an hour per day on average. The average time of playing per day in the sixth month was significantly higher than the average time of playing during the first month (p value < 7 * 10(-4)). The same result was found for the average number of game launches per day (p value < 7 * 10(-4)). However, older people seem not to launch more difficult levels in the last month. The success rate at sixth months was significantly higher than the success rate at the end of the first month (p value < 6.4 * 1010(-4)). Generally, seniors have had an improvement in their wellbeing score judged by themselves. Our study showed that the mobile application receives a good admission from users. The results are promising and can pave the way for improving cognitive function in the elderly patients. The use of tablets and the constitution of serious games in close cooperation with health professionals and elderly patients (the end user), are likely to provide satisfactory results to improve healthcare provided for elderly patients suffering from cognitive disorders.

A large number of mobile health applications (apps) are currently available with a variety of functionalities. The user ratings in the app stores seem not to be reliable to determine the quality of the apps. The traditional methods of evaluation are not suitable for fast paced nature of mobile technology. In this study, we propose a collaborative multidimensional scale to assess the quality of mHealth apps. During our process, the app quality is assessed in various aspects including medical reliability, legal consistency, ethical consistency, usability aspects, personal data privacy and IT security. A hypothetico-deductive approach was used in various working groups to define the audit criteria based on the various use cases that an app could provide. These criteria were then implemented into a web based self-administered questionnaires and the generation of automatic reports were considered. This method is on the one hand specific to each app because it allows to assess each health app according to its offered functionalities. On the other hand, this method is automatic, transferable to all apps and adapted to the dynamic nature of mobile technology.

Smartphones are growing in number and mobile health applications (apps) are becoming a commonly used way for improving the quality of health and healthcare delivery. Health related apps are mainly centralized in Medical and health&fitness categories in Google and Apple app stores. However, these apps are not easily accessible by the users. We decided to develop a system facilitating the access to these apps, to increase their visibility and usability. Various use cases for 567 health related apps in French were identified and listed incrementally. UML modeling was then used to represent these use cases and their relationships with each other and with the potential users of these apps. Thirty one different use cases were found that were then regrouped into six major categories: consulting medical information references, communicating and/or sharing the information, fulfilling a contextual need, educational tools, managing professional activities, health related management. A classification of this type would highlight the real purpose and functionalities of these apps and offers the user to search for the right app rapidly and to find it in a non-ambiguous context.

Health-related mobile applications (apps) have been shown to improve the quality of health and patient care. Their use in clinical and health-related environments is becoming more considerable. The number of health-related apps available for download has considerably increased, while the regulatory position of this new industry is not well known. Despite this lack of regulation, measuring the usability score of these apps is not difficult. We compared two samples of twenty health-related applications each. One of the samples contained the apps with top-rated usability scores, and the other contained the apps with lowest-rated usability scores. We found that a good usability score correlates with a better medical reliability of the app's content (p&lt;0.005). In the period in which a valid regulation is still lacking, calculation and attribution of usability scores to mobile applications could be used to identify apps with better medical quality. However, the usability score method ought to be rigorous and should not be rounded off with a simple five stars rating (as is the case in the classic app stores).

Background: Streptococcus pneumoniae is an important problem worldwide and nasopharyngeal colonization plays significant role in pneumococcal infections. The aims of this study were to determine the nasopharyngeal colonization rate, serotyping, antibiotics susceptibility and study the risk factors for nasopharyngeal colonization with S. pneumoniae in students in Kashan, Iran., Methods: A cross-sectional study was conducted on children aged 7 to 19 years from December 2011 to November 2012. Nasopharyngeal swabs were plated onto brain heart infusion agar plates with 5% sheep blood and 4µg/ml of gentamycin. Antimicrobial susceptibility profiles were determined on Mueller-Hinton agar in accordance with CLSI. S. pneumoniae strains were investigated for the presence of the most common pneumococcal serotypes using a multiplex polymerase chain reaction., Results: 13.9% were found to be carriers. The most prevalent serogroups were 19F (30%), 6A/B (18.9%), 15A (16.5%), 11 (11.3%), 23F (8.2%), 1 (6.2%), 19A (3.4%), and 35B (2.4%). Nine strains (3.1%) were non-typeable. The carrier rate was significantly higher in 12 to15 year old age group. Upper respiratory tract infections within the last month (OR=1.5, P<0.011), previous hospitalization (OR=1.6, P<0.001), previous antibiotic usage last two weeks (OR=1.89, P<0.001), rhinorea (OR=1.9 P<0.001), male sex (OR=3.5 P< 0.001) and passive smoking (OR=1.56, P< 0.001) have been determined to be risk factors for S. pneumoniae carriage. The highest pneumococcal resistance was to tetracycline (25.4%). All strains were susceptible to linezolid and levofloxacin., Conclusion: Our information leads to an important source to screen the future impact of pneumococcal vaccination on bacterial colonization.

Lack of interoperability between health information systems is a major obstacle in implementing Clinical decision supports systems (CDSS) and their widespread disseminations. Virtual Medical Record (vMR) proposed by HL7 is a common data model for representing clinical information Inputs and outputs that can be used by CDSS and local clinical systems. A CDSS called ASTI used a similar model to represent clinical data and therapeutic history of patient. In order to evaluate the compatibility of ASTI with vMR, we started to map the ASTI model of representing patient's therapeutic data to vMR. We compared the data elements and associated terminologies used in ASTI and vMR and we evaluated the semantic fidelity between the models. Only one data element the qualitative description of drug dosage, did not match the vMR model. However, it can be calculated in the execution engine. The semantic fidelity was satisfactorily preserved in 12 of 17 elements mapped between the models. This model of ASTI seems compatible to vMR. Further work is necessary to evaluate the compatibility of clinical data model of ASTI to vMR and the use of vMR in implementing practice guidelines.

Laboratory medicine is responsible for an important part of hospital expenditure. Providing appropriate decision support to laboratory test requesters at the point of care is one of the main incentives for implementing laboratory guidelines, which can improve medical care. Laboratory guidelines developed by local experts in the Parisian region and two national guidelines for dyslipidemia were analyzed to extract test ordering recommendations. Clinical conditions which can be a trigger to order or not to order laboratory tests were extracted and mapped with ICD10 and SNOMED CT: 43.1% of clinical conditions were matched by ICD10 whereas SNOMED CT covered 80.1% of these conditions. For the non-mapped conditions, the main problem was found to be the ambiguity of the terms used in the guidelines. Ordinal characteristics of some clinical conditions and using terms more specific than SNOMED CT were other causes of mapping failure. Applying consistent and explicit concepts in the development of guidelines would lead to better implementation. By resolving the guideline ambiguity, SNOMED CT is a good choice and covers almost all of the clinical conditions in laboratory guidelines which are needed to implement in a Clinical Decision Support System.

The culture of evidence-based practice includes also the field of laboratory medicine. Clinical laboratory expenditure is growing rapidly for various reasons including increased utilization. Delivering decision support to requesters at the point of care is one of the main incentives for implementing laboratory guidelines. Laboratory guidelines were analyzed to extract test-ordering rules. Each rule was explicated in at least one clinical situation with triggers that launch the execution of the implemented rule. The Unified Modeling Language was used to represent the categories of information elements found in the guidelines and underline the information elements that need to be structured and coded in the EHR. These information elements are related to conditions including clinical conditions, habits, family history, demographic information, medical treatments, laboratory tests, and non-laboratory test procedures. Timestamping of each event is also important for implementing laboratory prescription rules. A linkage between the conditions of this model and HL7 RIM was feasible. Use of this model facilitates the implementation of evidence-based test-ordering rules and clarifies the EHR requirements for successful implementation of guidelines.

Laboratory tests are not always prescribed appropriately. Guidelines for some important laboratory tests have been developed by expert panels in the Parisian region to maximize the appropriateness of laboratory medicine. However; these recommendations are not frequently consulted by physicians and nurses. We developed a system facilitating consultation of these guidelines, to increase their usability. Elements of information contained in these documents were identified and included in recommendations of different categories. UML modeling was used to represent these categories and their relationships to each other in the guidelines. We used the generated model to implement a computerized interface. The prototype interface, based on web-based technology was found to be rapid and easy to use. By clicking on provided keywords, information about the subject sought is highlighted whilst retaining the entire text of the guideline on-screen.