Notes

Disparities in cancer malignancy prevalence, occurrence, and also fatality for imprisoned and in the past in prison people: A new scoping evaluate.
app.

The app evaluation study received a 3-year grant from the Agence Nationale de la Recherche contre le SIDA et les hépatites virales (ANRS) and from the Office Français de l'Immigration et Intégration (OFII). At the time of publication of this protocol, the initial qualitative study and systematic literature review were completed.

This study will develop an app that assists health providers in offering and explaining HIV and hepatitis screenings to migrants with a language barrier and measure its acceptability and effectiveness in terms of public health. When completed, this app could be distributed to numerous professionals carrying out screening with migrant populations in various health care settings.

PRR1-10.2196/22239.
PRR1-10.2196/22239.
Drug prescriptions are often recorded in free-text clinical narratives; making this information available in a structured form is important to support many health-related tasks. Although several natural language processing (NLP) methods have been proposed to extract such information, many challenges remain.

This study evaluates the feasibility of using NLP and deep learning approaches for extracting and linking drug names and associated attributes identified in clinical free-text notes and presents an extensive error analysis of different methods. This study initiated with the participation in the 2018 National NLP Clinical Challenges (n2c2) shared task on adverse drug events and medication extraction.

The proposed system (DrugEx) consists of a named entity recognizer (NER) to identify drugs and associated attributes and a relation extraction (RE) method to identify the relations between them. For NER, we explored deep learning-based approaches (ie, bidirectional long-short term memory with conditional challenging relation type.

The proposed end-to-end system achieved encouraging results and demonstrated the feasibility of using deep learning methods to extract medication information from free-text data.
The proposed end-to-end system achieved encouraging results and demonstrated the feasibility of using deep learning methods to extract medication information from free-text data.
Patient monitoring is vital in all stages of care. In particular, intensive care unit (ICU) patient monitoring has the potential to reduce complications and morbidity, and to increase the quality of care by enabling hospitals to deliver higher-quality, cost-effective patient care, and improve the quality of medical services in the ICU.

We here report the development and validation of ICU length of stay and mortality prediction models. The models will be used in an intelligent ICU patient monitoring module of an Intelligent Remote Patient Monitoring (IRPM) framework that monitors the health status of patients, and generates timely alerts, maneuver guidance, or reports when adverse medical conditions are predicted.

We utilized the publicly available Medical Information Mart for Intensive Care (MIMIC) database to extract ICU stay data for adult patients to build two prediction models one for mortality prediction and another for ICU length of stay. For the mortality model, we applied six commonly used machiations, and quantile percentages of the original features, which provided a richer dataset to achieve better predictive power in our models.
Orofacial cleft, one of the most common congenital deformities, presents with a plethora of defects, subjecting the patient to a multitude of treatments from a young age. Among the oral hard tissue problems, absence of a maxillary permanent tooth in the cleft region either due to congenital absence or extraction due to compromised prognosis is a common finding. Conventionally, the missing tooth is replaced using a removable or fixed partial denture; however, the treatment modality does not satisfactorily meet patient expectations. The most recent decade has seen increasing use of dental implants in the cleft region; however, the outcome of an immediately loaded dental implant is still elusive for orofacial cleft patients.

This protocol is for a single-arm clinical trial aimed at determining the treatment outcome of immediately loaded dental implants in patients with a nonsyndromic orofacial cleft.

Patients meeting the set criteria will be sequentially enrolled until a sample size of 30 dental implants iinicaltrials/showallp.php?mid1=47659&EncHid=&userName=dental%20implants.

PRR1-10.2196/25244.
PRR1-10.2196/25244.
Canadian public safety personnel (PSP) experience high rates of mental health disorders and face many barriers to treatment. Internet-delivered cognitive behavioral therapy (ICBT) overcomes many such barriers, and is effective for treating depression, anxiety, and posttraumatic stress disorder (PTSD) symptoms.

This study was designed to fill a gap in the literature regarding the use of ICBT tailored specifically for PSP. We examined the effectiveness of a tailored ICBT program for treating depression, anxiety, and PTSD symptoms among PSP in the province of Saskatchewan.

We employed a longitudinal single-group open-trial design (N=83) with outcome measures administered at screening and at 8 weeks posttreatment. Data were collected between December 5, 2019 and September 11, 2020. Primary outcomes included changes in depression, anxiety, and PTSD symptoms. Secondary outcomes included changes in functional impairment; symptoms of panic, social anxiety, and anger; as well as treatment satisfaction, working alliance, and program usage patterns.

Clients reported large symptom reductions on measures of depression and anxiety, as well as moderate reductions on measures of PTSD and secondary symptoms, except for social anxiety. Most clients who reported symptoms above clinical cut-offs on measures of depression, anxiety, and PTSD during screening experienced clinically significant symptom reductions. Results suggested good engagement, treatment satisfaction, and working alliance.

Tailored, transdiagnostic ICBT demonstrated promising outcomes as a treatment for depression, anxiety, and PTSD among Saskatchewan PSP and warrants further investigation.

Clinicaltrials.gov NCT04127032; https//www.clinicaltrials.gov/ct2/show/NCT04127032.
Clinicaltrials.gov NCT04127032; https//www.clinicaltrials.gov/ct2/show/NCT04127032.
Telehealth has potential to help individuals in rural areas overcome geographical barriers and to improve access to care. The factors that influence the implementation and use of telehealth in critical access hospitals are in need of exploration.

The aim of this study is to understand the factors that influenced telehealth uptake and use in a set of frontier critical access hospitals in the United States.

This work was conducted as part of a larger evaluation of a Centers for Medicare & Medicaid Services-funded demonstration program to expand cost-based reimbursement for services for Medicare beneficiaries for frontier critical access hospitals. Our sample was 8 critical access hospitals in Montana, Nevada, and North Dakota that implemented the telehealth aspect of that demonstration. see more We reviewed applications and progress reports for the demonstration program and conducted in-person site visits. We used a semistructured discussion guide to facilitate conversations with clinical, administrative, and ce from specialty practitioners and workforce challenges. Telehealth can be used for provider-to-patient and provider-to-provider interactions to improve access to care, remove barriers, and improve quality. However, the ability of telehealth to improve outcomes is limited by factors such as workflow and infrastructure changes, practitioner acceptance and availability, and financing.
Accurate, objective pain assessment is required in the health care domain and clinical settings for appropriate pain management. Automated, objective pain detection from physiological data in patients provides valuable information to hospital staff and caregivers to better manage pain, particularly for patients who are unable to self-report. Galvanic skin response (GSR) is one of the physiologic signals that refers to the changes in sweat gland activity, which can identify features of emotional states and anxiety induced by varying pain levels. This study used different statistical features extracted from GSR data collected from postoperative patients to detect their pain intensity. To the best of our knowledge, this is the first work building pain models using postoperative adult patients instead of healthy subjects.

The goal of this study was to present an automatic pain assessment tool using GSR signals to predict different pain intensities in noncommunicative, postoperative patients.

The study was dmachine learning algorithm to classify the baseline and other pain intensities (Baseline [BL] vs Pain Level [PL] 1, BL vs PL2, BL vs PL3, and BL vs PL4). Our models achieved higher accuracy for the first 3 pain models than the BioVid paper approach despite the challenges in analyzing real patient data. For BL vs PL1, BL vs PL2, and BL vs PL4, the highest prediction accuracies were achieved when using a random forest classifier (86.0, 70.0, and 61.5, respectively). For BL vs PL3, we achieved an accuracy of 72.1 using a k-nearest-neighbor classifier.

We are the first to propose and validate a pain assessment tool to predict different pain levels in real postoperative adult patients using GSR signals. We also exploited feature selection algorithms to find the top important features related to different pain intensities.

RR2-10.2196/17783.
RR2-10.2196/17783.
Serious games can be a powerful learning tool in higher education. However, the literature indicates that the learning outcome in a serious game depends on the facilitators' competencies. Although professional facilitators in commercial game-based training have undergone specific instruction, facilitators in higher education cannot rely on such formal instruction, as game facilitation is only an occasional part of their teaching activities.

This study aimed to address the actual competencies of occasional game facilitators and their perceived competency deficits.

Having many years of experience as professional and occasional facilitators, we (n=7) defined requirements for the occasional game facilitator using individual reflection and focus discussion. Based on these results, guided interviews were conducted with additional occasional game facilitators (n=4) to check and extend the requirements. Finally, a group of occasional game facilitators (n=30) answered an online questionnaire based on the resultsthat game facilitators require and a demand for specific formal training. Thus, the study contributes to the further development of a competency model for game facilitators and enhances the efficiency of serious games.
The use of information and communication technologies (ICTs) to deliver mental health and addictions (MHA) services is a global priority, especially considering the urgent shift towards virtual delivery of care in response to the COVID-19 pandemic. It is important to monitor the evolving role of technology in MHA services. Given that MHA policy documents represent the highest level of priorities for a government's vision and strategy for mental health care, one starting point is to measure the frequency with which technology is mentioned and the terms used to describe its use in MHA policy documents (before, during, and after COVID-19). Yet, to our knowledge, no such review of the extent to which ICTs are referred to in Canadian MHA policy documents exists to date.

The objective of this systematic policy review was to examine the extent to which technology is addressed in Canadian government-based MHA policy documents prior to the COVID-19 pandemic to establish a baseline for documenting change.

We reviewed 22 government-based MHA policy documents, published between 2011 and 2019 by 13 Canadian provinces and territories.
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