A Machine Learning–Based Prediction Model for the Probability of Fall Risk Among Chinese Community-Dwelling Older Adults

Fall is a common adverse event among older adults. This study aimed to identify essential fall factors and develop a machine learning–based prediction model to predict the fall risk category among community-dwelling older adults, leading to earlier intervention and better outcomes. Three prediction models (logistic regression, random forest, and naive Bayes) were constructed and evaluated. A total of 459 people were involved, including 156 participants (34.0%) with high fall risk. Seven independent predictors (frail status, age, smoking, heart attack, cerebrovascular disease, arthritis, and osteoporosis) were selected to develop the models. Among the three machine learning models, the logistic regression model had the best model fit, with the highest area under the curve (0.856) and accuracy (0.797) and sensitivity (0.735) in the test set. The logistic regression model had excellent discrimination, calibration, and clinical decision-making ability, which could aid in accurately identifying the high-risk groups and taking early intervention with the model.

Efficacy of a Telemonitoring System as a Complementary Strategy in the Treatment of Patients With Heart Failure: Randomized Clinical Trial

Episodes of decompensation are the main cause of hospital admissions in patients with heart failure. For this reason, the use of mobile apps emerges as an excellent strategy to improve coverage, real-time monitoring, and timeliness of care. ControlVit is an electronic application for early detection of complications studied within the context of a tertiary university hospital. Patients were randomized to the use of ControlVit versus placebo, during a 6-month follow-up. The primary outcome was the difference in numbers of readmissions and deaths for heart failure between both groups. One hundred forty patients were included (intervention = 71, placebo = 69), with an average age of 66 years old; 71% were men. The main etiology of heart failure was ischemic (60%), whereas the main comorbidities were arterial hypertension (44%), dyslipidemia (42%), hypothyroidism (38%), chronic kidney disease (38%), and diabetes mellitus (27%). The primary outcome occurred more frequently in the control group: readmission due to decompensation for heart failure (control group n = 14 vs intervention group n = 3; P = .0081), and death (control group n = 11 vs intervention group n = 3; P = .024). In heart failure patients, ControlVit is a useful and supplementary tool, which reduces hospital admissions due to episodes of decompensation.

Policy Recommendations to Facilitate Nurse-Driven Optimization of Clinical Artificial Intelligence Tools

No abstract available

Nursing Diagnosis Accuracy in Nursing Education: Clinical Decision Support System Compared With Paper-Based Documentation—A Before and After Study

Computer-based technologies have been widely used in nursing education, although the best educational modality to improve documentation and nursing diagnostic accuracy using electronic health records is still under investigation. It is important to address this gap and seek an effective way to address increased accuracy around nursing diagnoses identification. Nursing diagnoses are judgments that represent a synthesis of data collected by the nurse and used to guide interventions and to achieve desirable patients' outcomes. This current investigation is aimed at comparing the nursing diagnostic accuracy, satisfaction, and usability of a computerized system versus a traditional paper-based approach. A total of 66 nursing students solved three validated clinical scenarios using the NANDA-International terminologies traditional paper-based approach and then the computer-based Clinical Decision Support System. Study findings indicated a significantly higher nursing diagnostic accuracy (P

Nurse Practitioner Regulatory Assessment: Transitioning From an Onsite to a Virtual Format

The Nurse Practitioner Onsite Peer Review is an integral part of the British Columbia College of Nurses and Midwives Quality Assurance program. Traditionally an in-person assessment, Nurse Practitioner Onsite Peer Review involves a critical review of documentation by an experienced nurse practitioner assessor against regulatory standards and entry-level competencies. The onset of the COVID-19 pandemic and resulting environmental limitations required the college to rethink its approach to onsite reviews, resulting in the quality assurance program embarking on a pilot project to explore the feasibility of conducting reviews virtually. As there are many factors that can affect the transition of an onsite assessment to one that is virtual, it was important to consider the technical, workflow, and usability aspects in developing this new method of performance assessment. Therefore, including usability testing and a human factors approach to exploring this emerging method was vital to ensuring its success. In this article, we discuss our experience, including benefits, technical and administrative considerations, barriers, challenges, and lessons learned.