Currinarcher9323

esearch and Development Command. The Service Member, Veteran, and Caregiver Community Stakeholders Group has been formed and the study questionnaire will be finalized once the panel reviews it. We anticipate the start of recruitment and primary data collection by January 2022.

New national initiatives aim to incorporate the caregiver into the veteran's treatment plan; however, we know little about the impact of caregiving-both positive and negative-on the caregivers themselves and on the veterans for whom they provide care. We will identify specific needs in this understudied population, which will inform clinicians, patients, families, and policy makers about the specific impact and needs to equip caregivers in caring for veterans at home.

PRR1-10.2196/30975.

PRR1-10.2196/30975.

The use of cloud computing (involving storage and processing of data on the internet) in health care has increasingly been highlighted as having great potential in facilitating data-driven innovations. Although some provider organizations are reaping the benefits of using cloud providers to store and process their data, others are lagging behind.

We aim to explore the existing challenges and barriers to the use of cloud computing in health care settings and investigate how perceived risks can be addressed.

We conducted a qualitative case study of cloud computing in health care settings, interviewing a range of individuals with perspectives on supply, implementation, adoption, and integration of cloud technology. Data were collected through a series of in-depth semistructured interviews exploring current applications, implementation approaches, challenges encountered, and visions for the future. The interviews were transcribed and thematically analyzed using NVivo 12 (QSR International). We coded the dat the implementation and exploitation of cloud-based infrastructures and to maximize returns on investment.

Implementations need to be viewed as a digitally enabled transformation of services, driven by skill development, organizational change management, and user engagement, to facilitate the implementation and exploitation of cloud-based infrastructures and to maximize returns on investment.

The sensitivity of teeth with molar incisor hypomineralization (MIH) can affect children's quality of life and is a challenging problem for dentists. Remineralizing agents such as sodium fluoride varnish seem to reduce the sensitivity of teeth with MIH, but long-term clinical trials with large samples are still needed for more evidence about its effectiveness as a desensitizing agent before its clinical recommendation.

This randomized clinical trial aims to compare three treatment interventions for teeth with MIH and hypersensitivity.

A total of 60 children aged 6-10 years presenting with at least one first permanent molar with sensitivity and no loss of enamel will be randomly assigned to three groups the control group (sodium fluoride varnish; Duraphat, Colgate); experimental group I (4% titanium tetrafluoride varnish); and experimental group II (a coating resin containing surface prereacted glass-ionomer filler; PRG Barrier Coat, Shofu). The sodium fluoride varnish and 4% titanium tetrafluoride varnire expected to be submitted for publication in 2022.

If found effective in reducing the patient's sensitivity long term, these agents can be considered as a treatment choice, and the findings will contribute to the development of a treatment protocol for teeth with sensitivity due to MIH.

Brazilian Registry of Clinical Trials Universal Trial Number U1111-1237-6720; https//tinyurl.com/mr4x82k9.

DERR1-10.2196/27843.

DERR1-10.2196/27843.

Chronic obstructive pulmonary disease (COPD) poses a large burden on health care. click here Severe COPD exacerbations require emergency department visits or inpatient stays, often cause an irreversible decline in lung function and health status, and account for 90.3% of the total medical cost related to COPD. Many severe COPD exacerbations are deemed preventable with appropriate outpatient care. Current models for predicting severe COPD exacerbations lack accuracy, making it difficult to effectively target patients at high risk for preventive care management to reduce severe COPD exacerbations and improve outcomes.

The aim of this study is to develop a more accurate model to predict severe COPD exacerbations.

We examined all patients with COPD who visited the University of Washington Medicine facilities between 2011 and 2019 and identified 278 candidate features. By performing secondary analysis on 43,576 University of Washington Medicine data instances from 2011 to 2019, we created a machine learning model to predict severe COPD exacerbations in the next year for patients with COPD.

The final model had an area under the receiver operating characteristic curve of 0.866. When using the top 9.99% (752/7529) of the patients with the largest predicted risk to set the cutoff threshold for binary classification, the model gained an accuracy of 90.33% (6801/7529), a sensitivity of 56.6% (103/182), and a specificity of 91.17% (6698/7347).

Our model provided a more accurate prediction of severe COPD exacerbations in the next year compared with prior published models. After further improvement of its performance measures (eg, by adding features extracted from clinical notes), our model could be used in a decision support tool to guide the identification of patients with COPD and at high risk for care management to improve outcomes.

RR2-10.2196/13783.

RR2-10.2196/13783.

Colorectal cancer survivors face multiple challenges after discharge. eHealth may potentially support them by providing tools such as smartphone apps. They have lots of capabilities to exchange information and could be used for remote monitoring of these patients.

In this study, we addressed the required features for apps designed to follow up colorectal cancer patients based on survivors' and clinical experts' views.

A mixed methods study was conducted. Features of related apps were extracted through the literature; the features were categorized, and then, they were modified. A questionnaire was designed containing the features listed and prioritized based on the MoSCoW (Must have, Should have, Could have, Won't have) technique and an open question for each category. The link to the questionnaire was shared among clinical experts in Iran. The answers were analyzed using the content validity ratio (CVR), and based on the value of this measure, the minimum feature set of a monitoring app to follow up patdesign an app for the targeted population or patients affected by other cancers. As the views from both survivors and clinical experts were considered in this study, the remote system may more adequately fulfill the need for follow-up of survivors. This eases the patients' and health care providers' communication and interaction.

The requirement set could be used to design an app for the targeted population or patients affected by other cancers. As the views from both survivors and clinical experts were considered in this study, the remote system may more adequately fulfill the need for follow-up of survivors. This eases the patients' and health care providers' communication and interaction.

Assessment of the physical frailty of older patients is of great importance in many medical disciplines to be able to implement individualized therapies. For physical tests, time is usually used as the only objective measure. To record other objective factors, modern wearables offer great potential for generating valid data and integrating the data into medical decision-making.

The aim of this study was to compare the predictive value of insole data, which were collected during the Timed-Up-and-Go (TUG) test, to the benchmark standard questionnaire for sarcopenia (SARC-F strength, assistance with walking, rising from a chair, climbing stairs, and falls) and physical assessment (TUG test) for evaluating physical frailty, defined by the Short Physical Performance Battery (SPPB), using machine learning algorithms.

This cross-sectional study included patients aged >60 years with independent ambulation and no mental or neurological impairment. A comprehensive set of parameters associated with physical fraithms trained with these parameters resulted in excellent results (AUROC of 0.801 and 0.919, respectively).

A gait analysis based on machine learning algorithms using sensor soles is superior to the SARC-F and the TUG test to identify physical frailty in orthogeriatric patients.

A gait analysis based on machine learning algorithms using sensor soles is superior to the SARC-F and the TUG test to identify physical frailty in orthogeriatric patients.

High blood pressure or hypertension is a vastly prevalent chronic condition among adults that can, if not appropriately treated, contribute to several life-threatening secondary diseases and events, such as stroke. In addition to first-line medication, self-management in daily life is crucial for tertiary prevention and can be supported by mobile health apps, including medication reminders. However, the prescription of medical apps is a relatively novel approach. There is limited information regarding the determinants of acceptance of such mobile health (mHealth) apps among patients as potential users and physicians as impending prescribers in direct comparison.

The present study aims to investigate the determinants of the acceptance of health apps (in terms of intention to use) among patients for personal use and physicians for clinical use in German-speaking countries. Moreover, we assessed patients' preferences regarding different delivery modes for self-care service (face-to-face services, apps, etc).aterial and self-management interventions to the needs and preferences of prospective users of hypertension apps in future research.

In summary, this study has identified performance expectancy as the most important determinant of the acceptance of mHealth apps for self-management of hypertension among patients and physicians. Concerning patients, we also identified mediating effects of performance expectancy on the relationships between effort expectancy and social influence and the acceptance of apps. Self-efficacy and protection motivation also contributed to an increase in the explained variance in app acceptance among patients, whereas eHealth literacy was a predictor in physicians. Our findings on additional determinants of the acceptance of health apps may help tailor educational material and self-management interventions to the needs and preferences of prospective users of hypertension apps in future research.

Sequential information in electronic medical records is valuable and helpful for patient outcome prediction but is rarely used for patient similarity measurement because of its unevenness, irregularity, and heterogeneity.

We aimed to develop a patient similarity framework for patient outcome prediction that makes use of sequential and cross-sectional information in electronic medical record systems.

Sequence similarity was calculated from timestamped event sequences using edit distance, and trend similarity was calculated from time series using dynamic time warping and Haar decomposition. We also extracted cross-sectional information, namely, demographic, laboratory test, and radiological report data, for additional similarity calculations. We validated the effectiveness of the framework by constructing k-nearest neighbors classifiers to predict mortality and readmission for acute myocardial infarction patients, using data from (1) a public data set and (2) a private data set, at 3 time points-at admission, on Day 7, and at discharge-to provide early warning patient outcomes.