Shiraz Emedical Journal
Volume:24 Issue: 11, Nov 2023

Context: 

Artificial intelligence (AI) Chatbots are computer programs that simulate human conversation and use artificial intelligence, including machine learning and natural language processing, to interact with users via natural language. With the outbreak of the COVID-19 pandemic, the use of digital health technologies such as chatbots has accelerated.

This study aims to investigate the application of AI chatbots in combating the COVID-19 pandemic and explore their features.

We reviewed the literature on health chatbots during the COVID-19 pandemic. PubMed, Scopus, Web of Science, and Google Scholar were searched using relevant keywords such as “chatbot”, “conversational agent,” and “artificial intelligence”. To select the relevant articles, we conducted title, abstract, and full-text screening based on inclusion and exclusion criteria. Chatbots, their applications, and design features were extracted from the selected articles.

Out of 673 articles initially identified, 17 articles were eligible for inclusion. We categorized the selected AI chatbots based on their roles, applications, and design characteristics. Around 70% of chatbots were designed to play a preventive role. Our review identified 8 key applications of the AI chatbots during the COVID-19 pandemic, which include (1) information dissemination and education, (2) self-assessment and screening, (3) connecting to health centers, (4) combating misinformation and fake news, (5) patient tracking and service delivery (6) mental health (7) monitoring exposure (8) vaccine information and scheduling. AI chatbots were deployed on various platforms, including mobile apps, web, and social media. Mobile-based chatbots were the most frequent. All chatbots used Natural Language Understanding (NLU) methods to understand natural language input and act on the user’s request. More than 50% of AI chatbots used NLU platforms, including Google Dialogflow, Rasa framework, and IBMWatson.

AI chatbots can playaneffective role in combating the COVID-19 pandemic. Increasing people’s awareness, optimizing the use of health resources, and reducing unnecessary encounters are some of the advantages of using AI chatbots during the COVID-19 outbreak. Using NLU platforms can be a suitable solution for developing AI chatbots in the healthcare domain. With advancements in the field of artificial intelligence, it seems that AI chatbots will have a promising future in healthcare, particularly in public health, chronic disease management, and mental health.

The main problems endangering patient safety are errors and accidents caused by healthcare providers, mainly due to their unfavorable patient safety attitudes.

This research aims to investigate the attitudes of healthcare professionals and internship students toward patient safety during the COVID-19 pandemic.

A cross-sectional study was conducted. Using the convenience sampling method, 232 healthcare professionals and students under training and internships were selected in intestinal care units, general wards, and operating room departments in 3 teaching hospitals affiliated withQomUniversity of Medical Sciences, Qom, Iran. Data gathering was performed during August and September 2021, when the majority of visits to the hospitals were related to patients with COVID-19. The inclusion criteria included medical staff and students with at least six months of work experience in hospitals admitting COVID-19 patients. The exclusion criteria were unwillingness to participate, withdrawal from the study, and not completing the research. The Data collection tool was the Safety Attitude Questionnaire.

Most of the study participants were nurses (73.27%), women (55.60%), married (56.47%), and with lower incomes than expenses (50%). The mean safety attitude score of the participants was 99.07± 16.31. Average scores of safety attitude in groups of nurses, nursing internship, operating room nurses, and operating room internship were 98.69, 100.26, 108.16, and 96.40, respectively. Pearson correlation test showed no significant correlation between the safety attitude scores of healthcare professionals andtheir age (P = 0.652) and workexperience (P = 0.441). Basedonthe Kruskal-Wallis test, the income status perception of the study participants was significantly correlated with their safety attitude scores (P = 0.001).

The COVID-19 pandemic had not a significant effect on the attitude of healthcare professionals in comparison with previous studies. However, in this study, the attitudes of the healthcare professionals and interns were inappropriate. It is recommended that specialized training courses on how to deal with crises such as pandemics be planned and held for healthcare providers.

One of the most common reasons for the referrals of patients to the trauma center is blunt chest injury.

To determine and compare the diagnostic value of point-of-care ultrasound (POCUS) and computed tomography (CT) scans in detecting rib fractures and their complications in patients with blunt chest wall trauma.

The current cross-sectional study (October 2017-March 2018) was conducted in Shahid Rajaei Hospital, Shiraz, southern Iran. Convenient non-random sampling was employed. Patients with stable vital signs underwent ultra-sonography and later were evaluated by CT scan for fractures and related complications. Sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPP), positive likelihood ratio (PLR), negative likelihood ratio (NLR), and accuracy were calculated and compared between the two procedures.

A total of 113 patients with a mean ± standard deviation (SD) age of 44.07 ± 20.07 years were enrolled, of whom 75 (66.3%) and 62 (54.9%) patients had at least one broken rib based on CT scan and sonography, respectively. The frequency of double fractures was higher than other conditions in both CT scans and ultrasound (35.53% and 37.10%). The overall sensitivity of ultrasound was calculated to be 81.58%, and with an increasing number of broken ribs, the sensitivity of ultrasound also increased (73.08% for identifying patients with one damaged rib versus 100% for detecting patients with five or more broken ribs). None of the 13 definite cases of pneumothorax were detected on ultrasound, while the sensitivity of ultrasound was appropriate for hemothorax and subperiosteal hematoma (85.71% and 80.23%, respectively).

Ultrasound offers high sensitivity, specificity, and diagnostic power in diagnosing fractures and their complications, but considering the setting of our study, care should be taken when generalizing the findings of this study.

The well-being of both the mother and her baby can be influenced by the quality of the care they receive during pregnancy, childbirth, and postpartum. It’s crucial to ascertain the quality of the healthcare provided in order to improve it. Therefore, utilizing a maternity dashboard is vital to measure key performance indicators (KPIs), improve the quality of care, and ensure high-quality care.

To identify and determine effective KPIs for developing a maternity dashboard.

This qualitative applied research was conducted in two stages to identify and determine KPIs for developing a maternity dashboard in Iran. In the first stage, a literature review was performed, followed by a qualitative comparative analysis of maternity dashboards in various countries to extract KPIs. In the second stage, 48 KPIs were identified and validated by a panel of experts using the Delphi technique. These KPIs were classified into 6 categories and finalized by the expert panel. Data analysis was conducted using content analysis and descriptive statistics.

In the initial phase of the Delphi technique, all experts confirmed three main categories of KPIs required for developing the maternity dashboard: (1) clinical activity, (2) fetal and neonatal complications, and (3) postnatal. In the second stage, an expert panel reviewed the indicators, leading to the identification of six groups of essential KPIs, including clinical activity, antenatal care, childbirth, maternal complications, fetal and neonatal complications, and postnatal care, upon which 100% consensus was reached by experts.

Maternity dashboards are vital instruments for delivering effective maternity care. These dashboards can provide valuable and practical information through KPIs, which serve as criteria for evaluating performance.

In order to analyze the relative success of countries in combating COVID-19, it is imperative to establish a set of criteria for measuring success in this domain. Subsequently, a consensus must be reached on the specific aspects and indicators that define success. Therefore this disease swiftly escalated into a global pandemic, impacting all facets of society and leaving a lasting historical imprint. This study presents a framework for assessing the performance of various countries in their battle against the COVID-19 crisis across four dimensions: healthcare system, crisis management, societal response, and historical perspective. By comparing indicators within each dimension for individual countries separately, we can assess and compare their respective crisis management capabilities while evaluating overall success. However, it is essential to acknowledge that a dichotomy often exists between health-focused solutions and those about economics and politics. Therefore, instead of pursuing an absolute solution or outcome, striving for an optimal balance point is essential. While cross-sectional assessments are necessary during the COVID-19 crisis evaluation process, comprehensive evaluations of the aforementioned dimensions can ultimately determine success levels and identify countries with superior performance.