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Background: Patients' missed appointments can cause interference in the functions of the clinics and the visit of other patients. One of the most effective strategies to solve the problem of no-show rate is the use of an open access scheduling system (OA). This systematic review was conducted with the aim of investigating the impact of OA on the rate of no-show of patients in outpatient clinics. Methods: Relevant articles in English were investigated based on the keywords in title and abstract using PubMed, Scopus, and Web of Science databases and Google Scholar search engine (July 23, 2023). The articles using OA and reporting the no-show rate were included. Exclusion criteria were as follows: (1) review articles, opinion, and letters, (2) inpatient scheduling system articles, and (3) modeling or simulating OA articles. Data were extracted from the selected articles about such issues as study design, outcome measures, interventions, results, and quality score. Findings: From a total of 23,403 studies, 16 articles were selected. The specialized fields included family medicine (62.5%, 10), pediatrics (25%, four), ophthalmology, podiatric, geriatrics, internal medicine, and primary care (6.25%, one). Of 16 articles, 10 papers (62.5%) showed a significant decrease in the no-show rate. In four articles (25%), the no-show rate was not significantly reduced. In two papers (12.5%), there were no significant changes. Conclusions: According to this study results, it seems that in most outpatient clinics, the use of OA by considering some conditions such as conducting needs assessment and system design based on the patients' and providers' actual needs, and cooperating of all system stakeholders through consistent training caused a significant decrease in the no-show rate.
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Background and Aims: This systematic review aimed to evaluating the effectiveness of machine learning (ML) algorithms for the prediction and diagnosis of meningitis. Methods: On November 12, 2022, a systematic review was carried out using a keyword search in the reliable scientific databases PubMed, EMBASE, Scopus, and Web of Science. The recommendations of Preferred Reporting for Systematic Reviews and Meta-Analyses (PRISMA) were adhered to. Studies conducted in English that employed ML to predict and identify meningitis were deemed to match the inclusion criteria. The eligibility requirements were used to independently review the titles and abstracts. The whole text was then obtained and independently reviewed in accordance with the eligibility requirements. Results: After all the research matched the inclusion criteria, a total of 16 studies were added to the systematic review. Studies on the application of ML algorithms in the three categories of disease diagnosis ability (8.16) and disease prediction ability (8.16) (including cases related to identifying patients (50%), risk of death in patients (25%), the consequences of the disease in childhood (12.5%), and its etiology [12.5%]) were placed. Among the ML algorithms used in this study, logistic regression (LR) (4.16, 25%) and multiple logistic regression (MLR) (4.16, 25%) were the most used. All the included studies indicated improvements in the processes of diagnosis, prediction, and disease outbreak with the help of ML algorithms. Conclusion: The results of the study showed that in all included studies, ML algorithms were an effective approach to facilitate diagnosis, predict consequences for risk classification, and improve resource utilization by predicting the volume of patients or services as well as discovering risk factors. The role of ML algorithms in improving disease diagnosis was more significant than disease prediction and prevalence. Meanwhile, the use of combined methods can optimize differential diagnoses and facilitate the decision-making process for healthcare providers.
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Epilepsy is the most common chronic neurologic disease which is characterized by recurrent attacks of headache after seizure. Researches show that self-management is an important factor in improving the quality of life and quality of care of people affected by epilepsy. Mobile phone technologies play a potential role in patient care assistance and treatment of epilepsy. This systematic review was conducted with an aim to study the role of mobile health in the management of epilepsy. This study was conducted by searching databases such as PubMed, Scopus, Web of Science, and Google scholar search engines using the following keywords: "m-health," "mobile health," "Telemedicine," "Mobile Application," "Smartphone," "epilepsy," and "epilepsy management." Articles published from January 1, 1990 to September 1, 2021 were searched. Inclusion criteria included all articles published in English with a focus on the role of mHealth in the management of epilepsy. Review articles and studies that were not about patients were omitted. In this study, of a total of 4225 retrieved articles, 10 studies met the full-text inclusion criteria. Three types of researches (30%) were done in the USA, five studies (50%) were conducted as randomized controlled trials, and eight articles (80%) had the highest quality. Among the considered articles, three articles (30%) were engaged in training users in epilepsy management. Five articles (50%) reported improvement in seizure control in patients with epilepsy and two articles (20%) did not report any significant improvement. Mobile technologies have a promising role in providing health assessment, education, and other services for patients, and they also help in controlling seizures attack and improvement of epilepsy management. These technologies enjoy great attractiveness, and utilizing them will lead to patient satisfaction.
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Background and Aims: The success of every new technology depends on numerous factors, including specialists' knowledge and perceptions of the concept, acquired attitude skills, and work environments. This systematic review aimed to examine medical students' knowledge, attitudes, and perceptions of telemedicine. Methods: Studies were obtained from the PubMed, Embase, Scopus, and Web of Science databases on June 9, 2022. We followed the Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines. Cross-sectional studies that examined medical students' knowledge, attitude, and perceptions of telemedicine approaches were considered inclusion criteria. Titles and abstracts were independently screened based on eligibility criteria. Articles that did not meet the inclusion criteria were excluded from this review. After that, the complete texts were retrieved and screened by two separate researchers based on the eligibility criteria. Disputes were resolved by discussion. The same checklist was used for data extraction. To assess the quality of the studies entering this study, the Joanna Briggs Institute Critical Appraisal Checklist for analytical cross-sectional studies was used. Results: In total, 10 eligible articles were found through this review. The sample size of the studies ranged from 60 to 3312 participants, or 6172 participants on the whole. The medical students' attitudes toward telemedicine were evaluated in eight included studies. Many of these studies (seven cases) reported positive and promising perspectives on telemedicine. However, in one study, participants revealed moderate attitudes toward online health information and online health experience sharing (p < 0.05). Students' knowledge of the telemedicine approach was evaluated in eight included studies. Many of these studies (five cases) reported that students possessed an extensively poor knowledge of telemedicine's uses. In three other studies, two reported moderate and one disclosed desirable levels of students' knowledge. All the included studies attributed medical students' poor knowledge to the lack of, and thus failure of, educational courses in this field. Conclusion: The evidence obtained from this review reveals that medical students possess positive and promising attitudes toward telemedicine technology for education, treatment, and care. However, their knowledge levels were extremely insufficient, and many had not passed any educational courses in this respect. Such results can foreground the health and education policymakers' obligations for planning, training, and empowering digital health and telemedicine literacy among medical students as the primary players in social health.
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Background and Aims: This systematic review examined healthcare students' attitudes, knowledge, and skill in Artificial Intelligence (AI). Methods: On August 3, 2022, studies were retrieved from the PubMed, Embase, Scopus, and Web of Science databases. Preferred Reporting Items for Systematic Reviews and Meta-Analyses recommendations were followed. We included cross-sectional studies that examined healthcare students' knowledge, attitudes, skills, and perceptions of AI in this review. Using the eligibility requirements as a guide, titles and abstracts were screened. Complete texts were then retrieved and independently reviewed per the eligibility requirements. To collect data, a standardized form was used. Results: Of the 38 included studies, 29 (76%) of healthcare students had a positive and promising attitude towards AI in the clinical profession and its use in he future; however, in nine of the studies (24%), students considered AI a threat to healthcare fields and had a negative attitude towards it. Furthermore, 26 studies evaluated the knowledge of healthcare students about AI. Among these, 18 studies evaluated the level of student knowledge as low (50%). On the other hand, in six of the studies, students' high knowledge of AI was reported, and two of the studies reported average student general knowledge (almost 50%). Of the six studies, four (67%) of the students had very low skills, so they stated that they had never worked with AI. Conclusion: Evidence from this review shows that healthcare students had a positive and promising attitude towards AI in medicine; however, most students had low knowledge and limited skills in working with AI. Face-to-face instruction, training manuals, and detailed instructions are therefore crucial for implementing and comprehending how AI technology works and raising students' knowledge of the advantages of AI.
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This systematic review aimed to evaluate the effect of telerehabilitation on improving physical activity, physical function, and quality of life in patients with osteoarthritis (OA). A systematic review of randomized controlled trial studies was conducted without a time limit by searching for keywords in the title, abstract, and study keywords in the scientific databases Embase, Web of Science, Scopus, and PubMed on October 20, 2021. This study was conducted according to Preferred Reporting Items for Systematic reviews and Meta-Analyses (PRISMA) guidelines. Titles and abstracts were retrieved based on the inclusion, exclusion, and quality assessment criteria. Then, full texts were retrieved and reviewed independently by two separate authors based on the eligibility criteria. Disputes were resolved through discussion. A form with the same predefined elements was used to extract data. Totally, eight eligible articles were found through this review. The studies on telerehabilitation approaches were categorized into three categories, which are: home-based exercise programs by online mobile applications, sports counseling and physiotherapist support via telephone calls, and Internet-based exercise training (IBET). In four studies (57%), telerehabilitation was effective in the short term for some months and improved the performance, self-efficacy, and quality of life of participants. On the other hand, in the long-term effects, there was no difference in participants' improvement (43%). In long-term follow-up, there was no difference between the efficiency of traditional rehabilitation and telerehabilitation in improving the physical performance and quality of life. However, telerehabilitation can be a viable alternative to traditional physiotherapy in patients with OA.