IMMERSIVE TECHNOLOGIES IN HEALTHCARE TRAINING & EDUCATION: THREE PRINCIPLES FOR PROGRESS

Narrative health is a technique that healthcare professionals can use to connect with patients. The events of 2020, including the global severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), have identified that patient care is largely dependent upon relationships within the healthcare environment. Relationships in the healthcare environment are established through a trusting exchange between the patient and provider, and one technique to develop this relationship and trust is through narrative health. Narrative health provides the exchange of information between patient and provider in a discussion-like manner, or narrative health. This strategy promotes cultural competence amongst the healthcare professional team and improves communication between the patient and provider. Narrative health is an important concept for healthcare professionals to understand, and narrative health should be a part of any healthcare professional’s toolbox, especially in vulnerable times like the COVID pandemic. The inclusion of narrative health in practice has the potential to improve patient outcomes and empower healthcare professionals and patients.

The burden of non-communicable diseases, such as osteoarthritis (OA), continues to increase for individuals and society. Regrettably, in many instances, healthcare professionals fail to manage OA optimally. There is growing disparity between the strength of evidence supporting interventions for OA and the frequency of their use in practice. Physical activity and exercise, weight management and education are key management components supported by evidence yet lack appropriate implementation. Furthermore, a recognition that treatment earlier in the disease process may halt progression or reverse structural changes has not been translated into clinical practice. We have largely failed to put pathways and procedures in place that promote a proactive approach to facilitate better outcomes in OA. This paper aims to highlight areas of evidence-based practical management that could improve patient outcomes if used more effectively.

Abstract Background We evaluated the performance of a clinical algorithm (Expali™), combining two or more positive answers to SCOFF questionnaire with Body Mass Index (BMI), to identify four Broad Categories of eating disorders (ED) derived from DSM-5. Methods The clinical algorithm (Expali™) was developed from 104 combinations of BMI levels and answers to five SCOFF questions with at least two positive answers. Two senior ED physicians allocated each combination to one of the four Broad Categories of ED derived from DSM-5: restrictive disorder, bulimic disorder, hyperphagic disorder and other unspecified ED diagnosed by ED clinicians. The performance of Expali™ was evaluated on data from 206 patients with ED. Sensitivity, specificity values and Youden index were calculated for each category. Results The 206 patients were diagnosed as follows: 31.5% restrictive disorder, 18.9% bulimic disorder, 40.8% hyperphagic disorder and 8.8% other ED. The sensitivity of Expali™ for restrictive, bulimic, hyperphagic and other unspecified ED were respectively: 76.9, 69.2, 79.7 and 16.7%. The Youden index was respectively 0.73, 0.57, 0.67 and 0.07. Conclusions In a SCOFF-positive ED population (at least two positive answers), the clinical algorithm Expali™ demonstrated good suitability by correctly classifying three of the four Broad Categories of eating disorders (restrictive, bulimic and hyperphagic disorder). It could be useful both to healthcare professionals and the general population to enable earlier detection and treatment of ED and to improve patient outcomes.

Introduction: Lyme Disease (LD) is the most common tick-borne disease in North America. With the number of cases increasing yearly, Canadian healthcare professionals (HCP) rely on up-to-date and evidence-informed guidelines, instruction, and resources to effectively prevent, diagnose, and treat Lyme disease (LD). This review is the first of its kind to examine gray literature and analyze the diversity of recommendations provided to Canadian HCP about the prevention, diagnosis, and treatment of Lyme disease. Methods: A gray literature review consisting of 4 search strategies was conducted to retrieve materials targeted to Canadian HCP. Searches within targeted websites, targeted Google searches, and gray literature databases, and consultation with content experts were done to look for continuing medical education (CME) events, clinical flow charts, webinars, videos, and reference documents that discussed the prevention, diagnosis, and treatment of Lyme disease. Results: A total of 115 resources were included in this study. Recommendations surrounding prevention strategies were less varied between materials, whereas diagnosis and treatment recommendations were more varied. Our findings suggest that Canadian HCP are met with varying and sometimes contradictory recommendations for diagnosing and treating LD. Conclusions: Due to the increasing incidence of LD in Canada, there is a greater need for resource consistency. Providing this consistency may help mitigate LD burden, standardize approaches to prevention, diagnosis and treatment, and improve patient outcomes.

Inhaled corticosteroids (ICS) are the core component of asthma treatment and the only maintenance therapy known to prevent asthma death. There is currently no evidence that biologics prevent asthma death in people with asthma, and as such, biologics cannot be recommended as an alternative to ICS therapy. Taking the time to assess adherence and provide interventions and education to support patients in asthma self-management has been shown to improve patient outcomes. It is therefore our responsibility as healthcare professionals to ensure that patients are supported, educated and motivated to adhere to ICS therapy before progressing to biologic therapies.

Objectives: Predictive analysis can be used to evaluate the enormous data generated by the healthcare industry to extract information and establish relationships amongst the variables. It uses artificial intelligence to reveal associations not suspected by the healthcare professionals. Tobacco cessation is clearly beneficial; however, many tobacco users respond differently as it is based on multitude of factors.  Our objectives were to assess the data mining techniques using the WEKA tool, evaluate its role in predictive analysis, and to predict the quit status of patients using prediction algorithms in tobacco cessation.  Materials and Methods: WEKA, a data mining tool, was used to classify the data and evaluate them using 10-fold cross-validations. The various algorithms used in this tool are Naïve Bayes, SMO, Random Forest, J-48, and Decision Stump to further analyze its role in determining the quit status of patients. For this, secondary data of 655 patients from a tobacco cessation clinic were utilized and described using 20 different attributes for prediction of quit status. Results: The Decision Stump and SMO were found to be having the best prediction and accuracy for prediction of the quit status. Out of 20 attributes, previous quitting attempt, type of intervention, and number of years since the habit was initiated were found to be associated with early quitting rate. Conclusion: This study concluded that data mining and predictive analytical models like WEKA tool will not only improve patient outcomes but identify variables or a combination of variables for effective interventions in tobacco cessation.

Patients undergoing re-operative cardiac surgical procedures present a great challenge with regard to obtaining hemostasis in the surgical field. Adhesions are ever-present and these patients are often on oral anti-coagulants and platelet inhibitors. As part of a well-planned surgical intervention, a systematic approach to hemostasis should be employed to decrease blood transfusion requirement and improve patient outcomes. Topical hemostatic agents can be a great help to the surgeon in achieving surgical field hemostasis and are increasingly being employed. Our approach, to these difficult patients, includes the systematic and planned use of AristaAH, which is a novel hemostatic agent whose use has proven safe and efficacious in our patient population.