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Personal health records designed for shared decision making (SDM) have the potential to engage patients and provide opportunities for positive health outcomes. Given the limited number of published interventions that become normal practice, this preimplementation evaluation of an integrated SDM personal health record system (e-PHR) was underpinned by Normalization Process Theory (NPT). The theory provides a framework to analyze cognitive and behavioral mechanisms known to influence implementation success. A mixed-methods investigation was utilized to explain the work required to implement e-PHR and its potential to integrate into practice. Patients, care providers, and electronic health record (EHR) and clinical leaders (n = 27) offered a rich explanation of the implementation work. Reliability tests of the NPT-based instrument negated the use of scores for two of the four mechanisms. Participants indicated that e-PHR made sense as explained by two qualitative themes game-changing technology and sensibility o action gaps to inform priorities and approaches for future implementation success. This research has implications for patients, care providers, EHR vendors, and the healthcare system for improving the effectiveness and efficiency of patient-centric services. Findings confirm the usefulness of NPT for planning and understanding implementation success of PHRs.Chemotherapy-Induced Peripheral Neuropathy (CIPN) is a common dose-limiting side-effect of taxane-based chemotherapy, causing progressive and often irreversible pain/sensitivity in the hands and feet. Prevention/treatments for CIPN are not well-developed and urgently needed. Limb cryocompression during chemotherapy has demonstrated promising early data of preventing/reducing CIPN severity. Currently there are no medical devices available that are dedicated to the specific requirements of CIPN prevention. As part of our ongoing development of a dedicated CIPN-prevention limb cryocompression system, this study documents the design & development of the wearable arm wrap, a central component of the system, from initial concept to a trial-ready prototype. A collaborative and multidisciplinary approach was adopted to address the complex and high-risk nature of this SME (Small Medium Enterprise)-centered medical device design & development process. The complementary collaboration unites multidisciplinary expertise shes facilitated stakeholder-feedback through expert focus groups, informing further design & development and projecting the design into the next stage, Beta prototyping, for use in large-scale efficacy trials and upscaling manufacturing. This paper demonstrates a novel mixed-methods approach, which promotes cross-sector multidisciplinary collaboration, to address the complex multi-layered challenges posed by an early-stage medical device design & development process.Improving cancer survival rates globally requires improvements in disease detection and monitoring, with the aim of improving early diagnosis and prediction of disease relapse. Traditional means of detecting and monitoring cancers rely largely on imaging and, where possible, blood-based protein biomarkers, many of which are non-specific. Treatments are being improved by identification of inherited and acquired genomic aberrations in tumors, some of which can be targeted by newly developed therapeutic interventions. Treatment of gynecological malignancy is progressively moving toward personalized therapy, as exemplified by application of PARP-inhibition for patients with BRCA-deficient tubo-ovarian cancers, or checkpoint inhibition in patients with mismatch repair-deficient disease. However, the more recent discovery of a group of biomarkers described under the umbrella term of "liquid biopsy" promises significant improvement in our ability to detect and monitor cancers. The term "liquid biopsy" is used to describe an array of tumor-derived material found in blood plasma and other bodily fluids such as ascites, pleural fluid, saliva, and urine. Tanespimycin datasheet It includes circulating tumors cells (CTCs), circulating nucleic acids including DNA, messenger RNA and micro RNAs, and extracellular vesicles (EVs). In this review, we discuss recent advancements in liquid biopsy for biomarker detection to help in diagnosis, prognosis, and planning of treatment of ovarian and endometrial cancer.In this paper, we quantify the joint acoustic emissions (JAEs) from the knees of children with juvenile idiopathic arthritis (JIA) and support their use as a novel biomarker of the disease. JIA is the most common rheumatic disease of childhood; it has a highly variable presentation, and few reliable biomarkers which makes diagnosis and personalization of care difficult. The knee is the most commonly affected joint with hallmark synovitis and inflammation that can extend to damage the underlying cartilage and bone. During movement of the knee, internal friction creates JAEs that can be non-invasively measured. We hypothesize that these JAEs contain clinically relevant information that could be used for the diagnosis and personalization of treatment of JIA. In this study, we record and compare the JAEs from 25 patients with JIA-10 of whom were recorded a second time 3-6 months later-and 18 healthy age- and sex-matched controls. We compute signal features from each of those record cycles of flexion/extension and train a logistic regression classification model. The model classified each cycle as having JIA or being healthy with 84.4% accuracy using leave-one-subject-out cross validation (LOSO-CV). When assessing the full JAE recording of a subject (which contained at least 8 cycles of flexion/extension), a majority vote of the cycle labels accurately classified the subjects as having JIA or being healthy 100% of the time. Using the output probabilities of a JIA class as a basis for a joint health score and test it on the follow-up patient recordings. In all 10 of our 6-week follow-up recordings, the score accurately tracked with successful treatment of the condition. Our proposed JAE-based classification model of JIA presents a compelling case for incorporating this novel joint health assessment technique into the clinical work-up and monitoring of JIA.Background AI-driven digital health tools often rely on estimates of disease incidence or prevalence, but obtaining these estimates is costly and time-consuming. We explored the use of machine learning models that leverage contextual information about diseases from unstructured text, to estimate disease incidence. Methods We used a class of machine learning models, called language models, to extract contextual information relating to disease incidence. We evaluated three different language models BioBERT, Global Vectors for Word Representation (GloVe), and the Universal Sentence Encoder (USE), as well as an approach which uses all jointly. The output of these models is a mathematical representation of the underlying data, known as "embeddings." We used these to train neural network models to predict disease incidence. The neural networks were trained and validated using data from the Global Burden of Disease study, and tested using independent data sourced from the epidemiological literature. Findings A varieuggest it complements existing modeling efforts, where data is required more rapidly or at larger scale. This may particularly benefit AI-driven digital health products where the data will undergo further processing and a validated approximation of the disease incidence is adequate.Artificial intelligence (AI) digital health systems have drawn much attention over the last decade. However, their implementation into medical practice occurs at a much slower pace than expected. This paper reviews some of the achievements of first-generation AI systems, and the barriers facing their implementation into medical practice. The development of second-generation AI systems is discussed with a focus on overcoming some of these obstacles. Second-generation systems are aimed at focusing on a single subject and on improving patients' clinical outcomes. A personalized closed-loop system designed to improve end-organ function and the patient's response to chronic therapies is presented. The system introduces a platform which implements a personalized therapeutic regimen and introduces quantifiable individualized-variability patterns into its algorithm. The platform is designed to achieve a clinically meaningful endpoint by ensuring that chronic therapies will have sustainable effect while overcoming compensatory mechanisms associated with disease progression and drug resistance. Second-generation systems are expected to assist patients and providers in adopting and implementing of these systems into everyday care.Background The integration of genetic testing into eHealth applications holds great promise for the personalization of disease prevention guidelines. However, relatively little is known about the impact of eHealth applications on an individual's behavior. Aim The aim of the pilot study was to investigate the effect of the personalized eHealth application approach to behavior change in a 1-month follow-up period on groups with previously known and unknown caffeine impacts. Method We created a direct-to-consumer approach that includes providing relevant information and personalized reminders and goals on the digital device regarding the caffeine intake for two groups of individuals the intervention group (IG) with the genetic raw data available and the control group (CG) to test the impact of the same content (article about caffeine metabolism) on participants without the genetic test. Study participants were all Estonians (n = 160). Results The study suggests that eHealth applications work for short-term behavior change. Participants in the genetic IG tended to increase caffeine intake if they were informed about caffeine not being harmful. They reported feeling better physically and/or mentally after their behavioral change decision during the period of the study. Conclusions Our pilot study revealed that eHealth applications may have a positive effect for short-term behavior change, regardless of a prior genetic test. Further studies among larger study groups are required to achieve a better understanding about behavior change of individuals in the field of personalized medicine and eHealth interventions.This review focuses on virtual coaching systems that were designed to enhance healthcare interventions, combining the available sensing and system-user interaction technologies. In total, more than 1,200 research papers have been retrieved and evaluated for the purposes of this review, which were obtained from three online databases (i.e.,PubMed, Scopus and IEEE Xplore) using an extensive set of search keywords. After applying exclusion criteria, the remaining 41 research papers were used to evaluate the status of virtual coaching systems over the past 10 years and assess current and future trends in this field. The results suggest that in home coaching systems were mainly focused in promoting physical activity and a healthier lifestyle, while a wider range of medical domains was considered in systems that were evaluated in lab environment. In home patient monitoring with IoT devices and sensors was mostly limited to activity trackers, pedometers and heart rate monitoring. Real-time evaluations and personalized patient feedback was also found to be rather lacking in home coaching systems and this is the most alarming find of this analysis.