The Use of a Mobile-Based Telehealth Service During the COVID-19 Pandemic: Provider Experience and Satisfaction

Background: Telehealth is a promising healthcare delivery model that uses telecommunication technologies to improve healthcare access by remotely offering health care services to people with limited access to these services. Due to the lockdown and restrictions caused by the COVID-19 pandemic, many healthcare organizations are now utilizing telehealth systems to remotely provide health care services and mitigate the spread of COVID-19 by minimizing physical interactions. Objective: To assess the providers’ experience and satisfaction with a telehealth technology “Sehha” being used by physicians during COVID-19, examine the challenges faced by the providers, and identify possible opportunities to improve the use of telehealth in Saudi Arabia. Method: With the collaboration of the Saudi Ministry of Health, a 30-item questionnaire consisting of quantitative and qualitative questions was distributed to 362 physicians using the Sehha telehealth app. The questionnaire items were adapted from previous studies and then tested for content validity and reliability (α = 0.88). Results: One hundred fourteen out of 362 questionnaires were analyzed with a response rate of 31%. The study showed that 67.6% of the physicians were satisfied with the work they have done through Sehha. Forty-four percent of the physicians preferred telehealth visits over traditional visits, while 35.1% did not prefer telehealth, and 21.1% reported to be neutral. However, the most commonly perceived challenge by the physicians using Sehha was difficulty in providing accurate medical assessments (73.7%), followed by overlapping of consultations (71.1%), while the most frequently cited area of the platform needed for improvement was integration with other systems (86.8%), followed by involvement of other medical specialists (81.6%). Conclusion: Telehealth is the new norm of delivering health care service, and its benefits have been realized worldwide. Telehealth can increase access to care, improve the quality of care, and reduce cost. Besides face-to-face visits, health care providers are now embracing telehealth technologies and showing interest in virtual care. Thus, telehealth should remain sustained after the era of COVID-19, and healthcare leaders should reconsider the status of telehealth

Prediction of disease comorbidity using explainable artificial intelligence and machine learning techniques: A systematic review

OBJECTIVE: Disease comorbidity is a major challenge in healthcare affecting the patient's quality of life and costs. AI-based prediction of comorbidities can overcome this issue by improving precision medicine and providing holistic care. The objective of this systematic literature review was to identify and summarise existing machine learning (ML) methods for comorbidity prediction and evaluate the interpretability and explainability of the models. MATERIALS AND METHODS: The Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) framework was used to identify articles in three databases: Ovid Medline, Web of Science and PubMed. The literature search covered a broad range of terms for the prediction of disease comorbidity and ML, including traditional predictive modelling. RESULTS: Of 829 unique articles, 58 full-text papers were assessed for eligibility. A final set of 22 articles with 61 ML models was included in this review. Of the identified ML models, 33 models achieved relatively high accuracy (80-95%) and AUC (0.80-0.89). Overall, 72% of studies had high or unclear concerns regarding the risk of bias. DISCUSSION: This systematic review is the first to examine the use of ML and explainable artificial intelligence (XAI) methods for comorbidity prediction. The chosen studies focused on a limited scope of comorbidities ranging from 1 to 34 (mean = 6), and no novel comorbidities were found due to limited phenotypic and genetic data. The lack of standard evaluation for XAI hinders fair comparisons. CONCLUSION: A broad range of ML methods has been used to predict the comorbidities of various disorders. With further development of explainable ML capacity in the field of comorbidity prediction, there is a significant possibility of identifying unmet health needs by highlighting comorbidities in patient groups that were not previously recognised to be at risk for particular comorbidities

SARS-CoV-2 vaccination modelling for safe surgery to save lives: data from an international prospective cohort study

Background: Preoperative SARS-CoV-2 vaccination could support safer elective surgery. Vaccine numbers are limited so this study aimed to inform their prioritization by modelling. Methods: The primary outcome was the number needed to vaccinate (NNV) to prevent one COVID-19-related death in 1 year. NNVs were based on postoperative SARS-CoV-2 rates and mortality in an international cohort study (surgical patients), and community SARS-CoV-2 incidence and case fatality data (general population). NNV estimates were stratified by age (18-49, 50-69, 70 or more years) and type of surgery. Best- and worst-case scenarios were used to describe uncertainty. Results: NNVs were more favourable in surgical patients than the general population. The most favourable NNVs were in patients aged 70 years or more needing cancer surgery (351; best case 196, worst case 816) or non-cancer surgery (733; best case 407, worst case 1664). Both exceeded the NNV in the general population (1840; best case 1196, worst case 3066). NNVs for surgical patients remained favourable at a range of SARS-CoV-2 incidence rates in sensitivity analysis modelling. Globally, prioritizing preoperative vaccination of patients needing elective surgery ahead of the general population could prevent an additional 58 687 (best case 115 007, worst case 20 177) COVID-19-related deaths in 1 year. Conclusion: As global roll out of SARS-CoV-2 vaccination proceeds, patients needing elective surgery should be prioritized ahead of the general population