Hasil untuk "Computer applications to medicine. Medical informatics"

Jing Jing Su, Chi-Keung Chan, Ladislav Batalik et al.

Immersive virtual reality (IVR) is an emerging therapeutic modality that engages older adults in psychological therapeutically oriented activities developed to improve their psychological well-being. This systematic review aims to investigate the effects of IVR psychological intervention on psychological symptoms and well-being. A systematic review and meta-analysis was conducted following the Cochrane Handbook for Systematic Reviews of Interventions. Six databases were searched, including Embase, PubMed, Web of Science, Scopus, CINAHL, and PsycINFO, covering the period from 2010 to December 2024. RevMan 5.3 was utilized for meta-analysis, and the Cochrane Risk of Bias tool was employed for quality assessment. Ten randomized controlled trials of 746 older adults were included. The IVR interventions employed reminiscence (40%), garden/forest therapy (40%), cognitive stimulation (10%), and multi-sensory stimulation to reduce psychological symptoms and improve self-perception (10%). Data pooling suggested that IVR interventions have significantly reduced depressive symptoms [n = 5; SMD = -0.83, 95%CI (-1.05, -0.60), I2 = 21%, p < .001]; anxiety [n = 5, SMD = -0.77, 95% CI (-1.32, -0.22), I2 = 70%, p = .006]. Synthesis without meta-analysis (SWiM) was conducted for stress and affect outcomes following SWiM guidance. In all three studies (100%), IVR produced statistically significant reductions in stress versus usual/standard care, and in both studies (100%), it yielded statistically significant improvements in affect-higher positive and lower negative affect-compared with the respective control conditions. IVR-based interventions could be an alternative method for alleviating the psychological symptoms of older adults. Registration: PROSPERO CRD42024575387.

Kazumasa Kishimoto, Tadamasa Takemura, Osamu Sugiyama et al.

Abstract BackgroundAlthough an increasing number of bedside medical devices are equipped with wireless connections for reliable notifications, many nonnetworked devices remain effective at detecting abnormal patient conditions and alerting medical staff through auditory alarms. Staff members, however, can miss these notifications, especially when in distant areas or other private rooms. In contrast, the signal-to-noise ratio of alarm systems for medical devices in the neonatal intensive care unit is 0 dB or higher. A feasible system for automatic sound identification with high accuracy is needed to prevent alarm sounds from being missed by the staff. ObjectiveThe purpose of this study was to design a method for classifying multiple alarm sounds collected with a monaural microphone in a noisy environment. MethodsFeatures of 7 alarm sounds were extracted using a mel filter bank and incorporated into a classifier using convolutional and recurrent neural networks. To estimate its clinical usefulness, the classifier was evaluated with mixtures of up to 7 alarm sounds and hospital ward noise. ResultsThe proposed convolutional recurrent neural network model was evaluated using a simulation dataset of 7 alarm sounds mixed with hospital ward noise. At a signal-to-noise ratio of 0 dB, the best-performing model (convolutional neural network 3+bidirectional gate recurrent unit) achieved an event-based F1 ConclusionsThe proposed classifier was found to be highly accurate in detecting alarm sounds. Although the performance of the proposed classifier in a clinical environment can be improved, the classifier could be incorporated into an alarm sound detection system. The classifier, combined with network connectivity, could improve the notification of abnormal status detected by unconnected medical devices.

Anshu Ankolekar

Suad J. Ghaben, Arimi Fitri Mat Ludin, Nazlena Mohamad Ali et al.

Background The increasing prevalence and burden of chronic obstructive pulmonary disorder (COPD), the challenges in implementing pulmonary rehabilitation (PR) programs and the limited availability of alternatives and supportive programs to serve patients with COPD necessitate the development of pulmonary telerehabilitation (PTR) systems to provide patients with COPD with PR programs. Objective This study aimed to design and develop the ChestCare mobile Health app using user-centred design (UCD) approach. Thus, it provided PTR for patients with COPD, enhancing their self-management of symptoms and improving their compliance with PR programs. Methods In this mixed-methods sequential research, we deployed the UCD iterative design through the prototype app design and development sequence. The first phase was built based on the results of a previous needs assessment study and an analysis of related apps. This produced the initial mock-up, the foundation for the focus group discussions with physiotherapists and patients. Six physiotherapists with cardiorespiratory specialisation evaluated each app module and item of the latest mock-up using the content validity index (CVI) document. The I-CVI (S-CVI/Ave) and (S-CVI/UA) were computed. Qualitative and quantitative data were integrated, and decisions were made by comparing their results. Results The UCD iterative design through sequential MMR has generated four mock-up app versions. The latest version identified 13 modules through 150 items validated by six experts using a CVI document. The I-CVI calculation of 145 items was 1, while 0.83 for the remaining items, was within accepted values. The S-CVI scored 99.4, indicating an overall validity of the ChestCare app as a PTR system for patients with COPD. Conclusions The development and validation of the ChestCare app resulted from conducting UCD iterative design and sequential MMR, which identified 13 functionalities, including symptom assessment, tracking lung volume, functional capacity test, action plan, intervention program, COPD education, COPD community, monitoring and reminders.

Emmanuella Magriplis, Sotiria Kotopoulou, Signe Adamberg et al.

BackgroundFermented foods vary significantly by food substrate and regional consumption patterns. Although they are consumed worldwide, their intake and potential health benefits remain understudied. Europe, in particular, lacks specific consumption recommendations for most fermented foods. ObjectiveThis project, which is under the framework of the Promoting Innovation Of Fermented Foods (PIMENTO) Cooperation in Science and Technology (COST) Action (CA20128), aims to develop a validated tool to quantitatively estimate fermented food intake across 4 European regions. MethodsThe Fermented Food Frequency Questionnaire (3FQ) was designed to quantify fermented food intake in terms of frequency and quantity. Fermented foods were categorized into broad groups (eg, dairy, plant-based, meat, beverages) based on product classifications, ensuring that the foods included were genuinely fermented through ingredient analysis according to the International Scientific Association for Probiotics and Prebiotics consensus for fermented foods as a guide. For each main fermented food group, subcategories were determined after detailed discussions by a scientific expert panel that provided country-specific examples. For example, for hard cheeses, Parmigiano was chosen in the Italian version, and Graviera in the Greek version. The questionnaire was developed in English (universal version) and then translated into multiple languages using the back-translation method. Each version was pilot-tested for clarity, and data for the prospective validation were gathered. This included two key steps: (1) assessing repeatability by having participants retake the questionnaire after 6 weeks and (2) confirming accuracy by comparing 3FQ results against 24-hour dietary recalls from a subsample of participants. Statistical analyses will be used to confirm agreement between the methods. Representative sample calculations were performed for 4 groups by biological sex and age group (between 18 and 49.9 years and 50+ years). To ensure representative sample obtainment, participants aged 18+ years were recruited via the internet using multiple strategies, including social media platforms in all countries, snowball sampling, and potential supplementation with panels provided by the survey platform. Prior to all responses, participants were asked to provide informed consent and agree to data collection under ethical guidelines using a General Data Protection Regulation–compliant platform. ResultsA representative sample of 1536 participants per European region was targeted, ensuring diversity in age and sex, with the goal of achieving a 60% response rate. A multilingual questionnaire was developed and pilot-tested for clarity. The upcoming steps will include final validation for accuracy and repeatability using 24-hour dietary recalls and specific statistical techniques of analysis to ensure reliability. ConclusionsThe validated web-based 3FQ aims to address the current gaps in fermented food intake to help improve future research in this important area. International Registered Report Identifier (IRRID)DERR1-10.2196/69212

Deborah Passey, Hasan Ferdous, Kara Burns et al.

Abstract There is currently no single resource for UI/UX guidelines and design standards that encapsulates all the requirements for young stroke survivors (<55 years) and their carers. We reviewed 25 studies to provide a summary of recommendations for designing stroke rehabilitation and self-management apps and digital platforms for young stroke survivors. The findings highlight the need for participatory codesign and research to build consensus on UI/UX guidelines and design standards.

W. Stead

Artificial intelligence (AI) and deep learning are entering the mainstream of clinical medicine. For example, in December 2016, Gulshan et al1 reported development and validation of a deep learning algorithm for detection of diabetic retinopathy in retinal fundus photographs. An accompanying editorial by Wong and Bressler2 pointed out limits of the study, the need for further validation of the algorithm in different populations, and unresolved challenges (eg, incorporating the algorithm into clinical work flows and convincing clinicians and patients to “trust a ‘black box’”). Sixteen months later, the Food and Drug Administration (FDA)3 permitted marketing of the first medical device to use AI to detect diabetic retinopathy. FDA reduced the risk of releasing the device by limiting the indication for use to screening adults who do not have visual symptoms for greater than mild retinopathy, to refer them to an eye care specialist. This issue of JAMA contains 2 Viewpoints on deep learning in health care. Hinton4 explains the technology underlying AI and deep learning, using clinical examples. AI is the general term for imitating human intelligence with computer systems. Early AI systems represented human reasoning with symbolic logic. As computer processing and storage became more powerful, researchers developed machine-learning techniques to imitate the way the human brain learns. The first machine learning continued to rely on human experts to label the data the system trained on (eg, the diagnosis) and to identify the significant features (eg, findings). Machine learning weighted the features from the data. With continued advances in computational power and with larger data sets, researchers began to develop deep learning techniques. The first deep learning algorithms were “supervised” in that human experts continued to label the training data, and the deep learning algorithms learned the features and weights directly from the data. The retinopathy screening algorithms are an example of supervised deep learning. Hinton4 describes continuing development of new deep learning techniques, including ones that are completely unsupervised. He also points out that it is not feasible to see the features learned by deep learning to explain how the system reaches a conclusion. Naylor5 identifies 7 factors driving adoption of AI and deep learning in health care: (1) the strengths of digital imaging over human interpretation; (2) the digitization of health-related records and data sharing; (3) the adaptability of deep learning to analysis of heterogeneous data sets; (4) the capacity of deep learning for hypothesis generation in research; (5) the promise of deep learning to streamline clinical workflows and empower patients; (6) the rapid-diffusion open-source and proprietary deep learning programs; and (7) of the adequacy of today’s basic deep learning technology to deliver improved performance as data sets get larger. Factors 3, 4, and 6 are specific to deep learning; the other factors apply to other AI techniques as well. Artificial intelligence is a family of technical techniques in the same way the radiologic imaging tool kit includes flat images, computed tomography scans, and functional imaging such as magnetic resonance imaging. Advances in computational technology, computer science, informatics, and statistics improve existing techniques and make new techniques possible. The addition of deep learning to the AI family of techniques represents an advance similar in magnitude to the addition of the computed tomography scanner to the radiology tool kit. Each AI technique has strengths and weaknesses. Symbolic logic is self-explaining but difficult to scale.6 For example, knowledge engineers extract the logic by interviewing or observing human experts. Statistical techniques such as supervised deep learning scale, but are subject to bias in the training data, and the reasoning cannot be explained. Since deep learning systems are trained on data from the past, they are not prepared to reason in the way humans do about conditions that have not been seen before. In the future, unsupervised deep learning may reduce this gap between human intelligence and AI. The potential applications of AI in health care present a range of computational difficulty. Narrow tasks, in which the context is predefined, are relatively easy. Imageprocessing tasks such as recognizing the border of an organ to suggest where to cut off a scan, or highlighting a suspicious area in an image for the radiologist or pathologist, are examples of narrow tasks. Image analysis and diagnostic prediction tasks such as the diabetic retinopathy example are broader and harder, but doable with today’s technology. Very broad data analysis and pattern prediction tasks such as analyzing heterogeneous data sets from diverse sources to suggest novel associations are feasible today because the purpose is limited to hypothesis generation. Thinking in the way humans do—reasoning, for example, from a few observations to suggest a novel scientific framework as Einstein did with the theory of relativity—is beyond technology on the horizon. Clinicians should view the output of AI programs or devices as statistical predictions. They should maintain an index of suspicion that the prediction may be wrong, just as they Viewpoint pages 1099 and 1101 Opinion

Reija Paananen, Sakari Kainulainen, Varpu Wiens et al.

This study's practical objective was to determine the reliability and feasibility of the service guidance based on the Traffic Light model used in the Zekki digital service. The equivalence between the numerical answers to the 3X10D Survey questions in Zekki and the young people's life situations was studied, revealing how well the Traffic Light model defined by the researchers matched the respondents' views. The equivalence of people's descriptions of their life situations was compared to their given quantitative assessments (N = 185). Based on written descriptions, the researcher classified each answer as a number. Equivalence was evaluated by cross-tabulating the researchers' and survey respondents' assessments of their life situations and testing the results with a chi-squared test. The respondents' assessments of satisfaction with different life spheres were compared with those made by the researchers, who read the written descriptions. Overall, the respondents' numerical assessments were slightly more positive than the researchers' assessments. However, two-thirds of the researchers' and respondents' assessments matched exactly within the same Traffic Light category. The assessments differed by no more than one category in about one-third of the cases. Only 3.5% of the assessments were considered completely incorrect. The 3X10D Survey produces numerical information strongly connected to a respondent's real life, making it a reliable basis for digital service guidance. The Zekki digital service recommends appropriate support for those in need. Based on these findings, other user-oriented digital service guidance platforms can be developed.

Carl V.L. Olson, David Kachlík, Azzat Al-Redouan

Background: Angular projection measurements have long been an established approach in anatomical morphometry. However, many described projection angles are in reference to inherently curved structures, often oversimplifying their topologies. Aims: The objective of this study is to develop a quick, quantitative method for determining structural curvature from digital images. We aim to utilize readily-available software and statistical methods to extrapolate curvature from images and compare this new method to established angular measurements. Methods: Projection angulation and curvature was modeled on and assessed by the acromia of 50 dry scapulae. Digital images were taken at a known scale, perpendicular to the acromion, and then processed with ImageJ software. Angles were measured by the angle tool and for curvature, seven markers were placed along the external and internal margins of the acromion. Utilizing Excel's Solver function, the coordinate points were passed through a rotation matrix and optimized for second order regression. Solver was instructed to minimize the sum of squared estimated error between our measured and calculated coordinate values by manipulating the angle of point rotation and regression equation coefficients. Results: Significant differences were found between external, internal, and midline acromion measurements in both angles and curvatures. External angle = 80.8 (14.2)°; internal angle = 130.3 (13.6)°; midline angle = 105.6 (10.5)°; [reported as mean (SD)]. External curvature = 0.055 (0.015) mm−1; internal curvature = 0.035 (0.025) mm−1; midline curvature = 0.046 (0.017) mm−1; [reported as median (IQR)]. Conclusions: Solver allows for researchers and clinicians to quickly characterize morphometric courses and properties of a given structure. Paired with other scalar measurements, curvature can complete the picture of an anatomical structure's pattern.

Jinsung Yoon, Michel Mizrahi, Nahid Farhady Ghalaty et al.

Abstract Privacy concerns often arise as the key bottleneck for the sharing of data between consumers and data holders, particularly for sensitive data such as Electronic Health Records (EHR). This impedes the application of data analytics and ML-based innovations with tremendous potential. One promising approach for such privacy concerns is to instead use synthetic data. We propose a generative modeling framework, EHR-Safe, for generating highly realistic and privacy-preserving synthetic EHR data. EHR-Safe is based on a two-stage model that consists of sequential encoder-decoder networks and generative adversarial networks. Our innovations focus on the key challenging aspects of real-world EHR data: heterogeneity, sparsity, coexistence of numerical and categorical features with distinct characteristics, and time-varying features with highly-varying sequence lengths. Under numerous evaluations, we demonstrate that the fidelity of EHR-Safe is almost-identical with real data (<3% accuracy difference for the models trained on them) while yielding almost-ideal performance in practical privacy metrics.

Stephen P. Fortin, Jenna Reps, Patrick Ryan

Abstract Objectives The Charlson comorbidity index (CCI), the most ubiquitous comorbid risk score, predicts one-year mortality among hospitalized patients and provides a single aggregate measure of patient comorbidity. The Quan adaptation of the CCI revised the CCI coding algorithm for applications to administrative claims data using the International Classification of Diseases (ICD). The purpose of the current study is to adapt and validate a coding algorithm for the CCI using the SNOMED CT standardized vocabulary, one of the most commonly used vocabularies for data collection in healthcare databases in the U.S. Methods The SNOMED CT coding algorithm for the CCI was adapted through the direct translation of the Quan coding algorithms followed by manual curation by clinical experts. The performance of the SNOMED CT and Quan coding algorithms were compared in the context of a retrospective cohort study of inpatient visits occurring during the calendar years of 2013 and 2018 contained in two U.S. administrative claims databases. Differences in the CCI or frequency of individual comorbid conditions were assessed using standardized mean differences (SMD). Performance in predicting one-year mortality among hospitalized patients was measured based on the c-statistic of logistic regression models. Results For each database and calendar year combination, no significant differences in the CCI or frequency of individual comorbid conditions were observed between vocabularies (SMD ≤ 0.10). Specifically, the difference in CCI measured using the SNOMED CT vs. Quan coding algorithms was highest in MDCD in 2013 (3.75 vs. 3.6; SMD = 0.03) and lowest in DOD in 2018 (3.93 vs. 3.86; SMD = 0.02). Similarly, as indicated by the c-statistic, there was no evidence of a difference in the performance between coding algorithms in predicting one-year mortality (SNOMED CT vs. Quan coding algorithms, range: 0.725–0.789 vs. 0.723–0.787, respectively). A total of 700 of 5,348 (13.1%) ICD code mappings were inconsistent between coding algorithms. The most common cause of discrepant codes was multiple ICD codes mapping to a SNOMED CT code (n = 560) of which 213 were deemed clinically relevant thereby leading to information gain. Conclusion The current study repurposed an important tool for conducting observational research to use the SNOMED CT standardized vocabulary.

Ghulam Bhat, Ishfaq Bhat, Sobiya Sobiya

With the introduction of the Competency-Based Medical Education (CBME) curriculum in India, many new concepts like the Foundation course, Self-Directed Learning (SDL), Early Clinical Exposure, Family Adoption, etc., were included in the curriculum. In SDL, a learner has to plan, develop, adapt, and change in a digital, interactive, and global society. For that purpose, the faculty has to be trained, and learners’ readiness is to be ensured before the start of the SDL sessions. This write-up aims not to find all the literature related to SDL but to emphasize the basic knowledge of SDL required for medical educators.

Mavadat Saidi, Motahareh Afshari

Lin Li, Hao Dai, Zhaoyuan Fang et al.

The rapid advancement of single-cell technologies has shed new light on the complex mechanisms of cellular heterogeneity. However, compared to bulk RNA sequencing (RNA-seq), single-cell RNA-seq (scRNA-seq) suffers from higher noise and lower coverage, which brings new computational difficulties. Based on statistical independence, cell-specific network (CSN) is able to quantify the overall associations between genes for each cell, yet suffering from a problem of overestimation related to indirect effects. To overcome this problem, we propose the c-CSN method, which can construct the conditional cell-specific network (CCSN) for each cell. c-CSN method can measure the direct associations between genes by eliminating the indirect associations. c-CSN can be used for cell clustering and dimension reduction on a network basis of single cells. Intuitively, each CCSN can be viewed as the transformation from less “reliable” gene expression to more “reliable” gene–gene associations in a cell. Based on CCSN, we further design network flow entropy (NFE) to estimate the differentiation potency of a single cell. A number of scRNA-seq datasets were used to demonstrate the advantages of our approach. 1) One direct association network is generated for one cell. 2) Most existing scRNA-seq methods designed for gene expression matrices are also applicable to c-CSN-transformed degree matrices. 3) CCSN-based NFE helps resolving the direction of differentiation trajectories by quantifying the potency of each cell. c-CSN is publicly available at https://github.com/LinLi-0909/c-CSN.

Jun Young An, Hoseok Seo, Young-Gon Kim et al.

Objectives This paper proposes a method for computer-assisted diagnosis of coronavirus disease 2019 (COVID-19) through chest X-ray imaging using a deep learning model without writing a single line of code using the Konstanz Information Miner (KNIME) analytics platform. Methods We obtained 155 samples of posteroanterior chest X-ray images from COVID-19 open dataset repositories to develop a classification model using a simple convolutional neural network (CNN). All of the images contained diagnostic information for COVID-19 and other diseases. The model would classify whether a patient was infected with COVID-19 or not. Eighty percent of the images were used for model training, and the rest were used for testing. The graphic user interface-based programming in the KNIME enabled class label annotation, data preprocessing, CNN model training and testing, performance evaluation, and so on. Results 1,000 epochs training were performed to test the simple CNN model. The lower and upper bounds of positive predictive value (precision), sensitivity (recall), specificity, and f-measure are 92.3% and 94.4%. Both bounds of the model’s accuracies were equal to 93.5% and 96.6% of the area under the receiver operating characteristic curve for the test set. Conclusions In this study, a researcher who does not have basic knowledge of python programming successfully performed deep learning analysis of chest x-ray image dataset using the KNIME independently. The KNIME will reduce the time spent and lower the threshold for deep learning research applied to healthcare.

David Riaño, Mor Peleg, A. T. Teije

BACKGROUND In the last ten years, the international workshop on knowledge representation for health care (KR4HC) has hosted outstanding contributions of the artificial intelligence in medicine community pertaining to the formalization and representation of medical knowledge for supporting clinical care. Contributions regarding modeling languages, technologies and methodologies to produce these models, their incorporation into medical decision support systems, and practical applications in concrete medical settings have been the main contributions and the basis to define the evolution of this field across Europe and worldwide. OBJECTIVES Carry out a review of the papers accepted in KR4HC in the 2009-2018 decade, analyze and characterize the topics and trends within this field, and identify challenges for the evolution of the area in the near future. METHODS We reviewed the title, the abstract, and the keywords of the 112 papers that were accepted to the workshop, identified the medical and technological topics involved in these works, provided a classification of these papers in medical and technological perspectives and obtained the timeline of these topics in order to determine interest growths and declines. The experience of the authors in the field and the evidences after the review were the basis to propose a list of challenges of knowledge representation in health care for the future. RESULTS The most generic knowledge representation methods are ontologies (31%), semantic web related formalisms (26%), decision tables and rules (19%), logic (14%), and probabilistic models (10%). From a medical informatics perspective, knowledge is mainly represented as computer interpretable clinical guidelines (43%), medical domain ontologies (26%), and electronic health care records (22%). Within the knowledge lifecycle, contributions are found in knowledge generation (38%), knowledge specification (24%), exception detection and management (12%), knowledge enactment (8%), temporal knowledge and reasoning (7%), and knowledge sharing and maintenance (7%). The clinical emphasis of knowledge is mainly related to clinical treatments (27%), diagnosis (13%), clinical quality indicators (13%), and guideline integration for multimorbid patients (12%). According to the level of development of the works presented, we distinguished four maturity levels: formal (22%), implementation (52%), testing (13%), and deployment (2%) levels. Some papers described technologies for specific clinical issues or diseases, mainly cancer (22%) and diseases of the circulatory system (20%). Chronicity and comorbidity were present in 10% and 8% of the papers, respectively. CONCLUSIONS KR4HC is a stable community, still active after ten years. A persistent focus has been knowledge representation, with an emphasis on semantic-web ontologies and on clinical-guideline based decision-support. Among others, two topics receive growing attention: integration of computer-interpretable guideline knowledge for the management of multimorbidity patients, and patient empowerment and patient-centric care.

A. Idri, Houda Benhar, J. Fernández-Alemán et al.

BACKGROUND AND OBJECTIVE Datamining (DM) has, over the last decade, received increased attention in the medical domain and has been widely used to analyze medical datasets in order to extract useful knowledge and previously unknown patterns. However, historical medical data can often comprise inconsistent, noisy, imbalanced, missing and high dimensional data. These challenges lead to a serious bias in predictive modeling and reduce the performance of DM techniques. Data preprocessing is, therefore, an essential step in knowledge discovery as regards improving the quality of data and making it appropriate and suitable for DM techniques. The objective of this paper is to review the use of preprocessing techniques in clinical datasets. METHODS We performed a systematic map of studies regarding the application of data preprocessing to healthcare and published between January 2000 and December 2017. A search string was determined on the basis of the mapping questions and the PICO categories. The search string was then applied in digital databases covering the fields of computer science and medical informatics in order to identify relevant studies. The studies were initially selected by reading their titles, abstracts and keywords. Those that were selected at that stage were then reviewed using a set of inclusion and exclusion criteria in order to eliminate any that were not relevant. This process resulted in 126 primary studies. RESULTS Selected studies were analyzed and classified according to their publication years and channels, research type, empirical type and contribution type. The findings of this mapping study revealed that researchers have paid a considerable amount of attention to preprocessing in medical DM in last decade. A significant number of the selected studies used data reduction and cleaning preprocessing tasks. Moreover, the disciplines in which preprocessing have received most attention are: cardiology, endocrinology and oncology. CONCLUSIONS Researchers should develop and implement standards for an effective integration of multiple medical data types. Moreover, we identified the need to perform literature reviews.

S. Kabanda, H. Rother

BackgroundMobile health is a fast-developing field. The use of mobile health applications by healthcare professionals (HCPs) globally has increased considerably. While several studies in high income countries have investigated the use of mobile applications by HCPs in clinical practice, few have been conducted in low- and middle-income countries. The University of Cape Town developed a pesticide notification guideline which has been adapted and embedded into a South African Essential Medical Guidance mobile application. This study evaluated the usefulness of the guideline within a mobile application for improving the ability of HCPs to diagnose and notify on acute pesticide poisonings (APPs).MethodsA descriptive online questionnaire, with 15 open- and 20 closed-ended questions, was completed by 50 South African emergency medicine physicians and registrars (i.e. medical doctors training as specialists) between December 2015 to February 2016. Descriptive statistics were used to calculate response frequencies and percentages using SPSS version 23. Texts from the open-ended questions were thematically analysed. Fisher’s exact test was applied to determine associations.ResultsA significant association was found between participants’ knowledge that APP is a notifiable condition, and ever reporting the poisoning to the National Department of Health (p = 0.005). Thirty four percent of the participants were aware of the guideline within the Essential Medical Guidance application despite only seven participants having used it. Those who used the guideline found it provided useful information for the identification of unlabelled pesticides products and promoted reporting these cases to the National Department of Health for surveillance purposes. In addition, it appeared to facilitate the prompt diagnosis and treatment of APP cases, and most intended to continue using it for training and educational purposes.ConclusionsMobile health applications appear to support overburdened medical education programmes and promote better patient care. However, since most participants were not aware of the existence of the pesticide guideline within the studied essential medicine application, there is potential for the use of healthcare applications to play a more central role in healthcare systems and medical training. Furthermore, the field of medical informatics could support HCPs through mobile applications in improving reporting of APP.

M. Herman

Who are we? An excerpt from the AAPM Scope of Practice of Medical Physics says that we are medical professionals and “The essential role of the Qualified Medical Physicist is to assure the safe and effective use of radiation in medicine. The QMP performs or oversees the scientific and technical aspects of procedures necessary to achieve this objective.” More broadly, the AAPM position statement on our role in providing quality medical care states that Medical Physicists have a unique combination of scientific and clinical education and training in physics principles, radiation physics applications, dosimetry planning, radiobiological principles, human anatomy, radiology and oncology principles, as well as safety analysis and quality control methods. Medical Physicists working in clinical, research or educational environments, due to their training and professional focus, are crucial to the delivery of quality radiation therapy, performance of quality medical imaging, and protection of healthcare workers, patients and the general public from the potentially harmful effects of radiation and other physical phenomena such as magnetic fields and ultrasound. The medical physicist is a key member of research and development teams for both basic and applied work related to medical devices used in the above procedures. We have a common objective of contributing to the betterment of the human condition, with a commitment to improving patient care through research, education, clinical practice, and administration. No matter what we do, our ultimate focus is on developing better tools, processes, procedures, technologies, and teams for health care applications. We play on, contribute to and lead small and large teams of medical professionals often collaborating with physicians, other physicists, engineers, dosimetrists, radiation therapists, radiologic technologists, nurses, technicians, information technology professionals, and administrative staff. Successful and productive participation as a member of the healthcare team requires that a medical physicist have comprehensive knowledge of many aspects of patient care, to best understand where and how we can contribute most effectively. The medical physicist ends up participating in or overseeing activities with a broad array of responsibilities in the areas of: • Administration • Clinical service • Education • Research/development • Informatics • Equipment performance evaluation • Quality • Safety