Access To Medical Information Research Articles

A transformer-based deep learning model for identifying the occurrence of acute hematogenous osteomyelitis and predicting blood culture results

BackgroundAcute hematogenous osteomyelitis is the most common form of osteomyelitis in children. In recent years, the incidence of osteomyelitis has been steadily increasing. For pediatric patients, clearly describing their symptoms can be quite challenging, which often necessitates the use of complex diagnostic methods, such as radiology. For those who have been diagnosed, the ability to culture the pathogenic bacteria significantly affects their treatment plan.MethodA total of 634 patients under the age of 18 were included, and the correlation between laboratory indicators and osteomyelitis, as well as several diagnoses often confused with osteomyelitis, was analyzed. Based on this, a Transformer-based deep learning model was developed to identify osteomyelitis patients. Subsequently, the correlation between laboratory indicators and the length of hospital stay for osteomyelitis patients was examined. Finally, the correlation between the successful cultivation of pathogenic bacteria and laboratory indicators in osteomyelitis patients was analyzed, and a deep learning model was established for prediction.ResultThe laboratory indicators of patients are correlated with the presence of acute hematogenous osteomyelitis, and the deep learning model developed based on this correlation can effectively identify patients with acute hematogenous osteomyelitis. The laboratory indicators of patients with acute hematogenous osteomyelitis can partially reflect their length of hospital stay. Although most laboratory indicators lack a direct correlation with the ability to culture pathogenic bacteria in patients with acute hematogenous osteomyelitis, our model can still predict whether the bacteria can be successfully cultured.ConclusionLaboratory indicators, as easily accessible medical information, can identify osteomyelitis in pediatric patients. They can also predict whether pathogenic bacteria can be successfully cultured, regardless of whether the patient has received antibiotics beforehand. This not only simplifies the diagnostic process for pediatricians but also provides a basis for deciding whether to use empirical antibiotic therapy or discontinue treatment for blood cultures.

Comparative Analysis of Artificial Intelligence Platforms in Generating Post-Operative Instructions for Rhinologic Surgery

AbstractRecently, artificial intelligence (AI) platforms such as ChatGPT and Google Gemini have progressed at a rapid pace. To allow for optimal medical outcomes and patient safety, it is crucial that patients have clearly written post-operative instructions. Patients are increasingly turning to AI platforms for medical information. Our objective was to evaluate the ability of ChatGPT versus Gemini to develop accurate and understandable post-operative instructions for patients undergoing endoscopic sinus surgery (ESS). Postoperative instructions on ESS, balloon sinuplasty, Caldwell Luc surgery, and septoplasty were generated using ChatGPT and Gemini. Measures of readability were calculated. The Patient Education Materials Assessment Tool was utilized to determine Understandability and Actionability. ChatGPT and Gemini were capable of generating understandable post-operative instructions but were poor for actionability and readability. While AI has a promising ability to generate accessible medical information, patients should be aware of its drawbacks especially when seeking postoperative instructions.

HOSPITAL SERVICE QUALITY AND CUSTOMER TRUST TOWARDS PATIENT SATISFACTION IN GENERATION MZ

According to Law Number 17 of 2023 of the Republic of Indonesia, health efforts, health resources, and health management are required to improve public health to the highest possible standard. Indonesian hospitals should provide patients with a degree of service excellence as supplied by medical, paramedical, and medical support staff. This research aims to understand, explore, and analyze based on empirically investigating the dimensions or attributes of service quality and evaluate the importance of variance attributes for Gen MZ patients at Hospital B. This quantitative study examines the relationship between service quality and brand trust (independent variable) towards patient satisfaction (dependent variable) in Gen MZ at Hospital B. This research was conducted from January to March 7, 2024; it involved 122 Generation MZ patients using non-probability purposive sampling to represent the population. Result shows that the similar levels of satisfaction and trust between Generations M and Z at Hospital B may be due to the hospital's standardized, high-quality services. This consistency ensures that both generations have comparable experiences. Additionally, Hospital B's effective use of technology, such as hospital apps and online medical information access, meets and exceeds the expectations of both groups.

Understanding Health Care Students' Perceptions, Beliefs, and Attitudes Toward AI-Powered Language Models: Cross-Sectional Study.

ChatGPT was not intended for use in health care, but it has potential benefits that depend on end-user understanding and acceptability, which is where health care students become crucial. There is still a limited amount of research in this area. The primary aim of our study was to assess the frequency of ChatGPT use, the perceived level of knowledge, the perceived risks associated with its use, and the ethical issues, as well as attitudes toward the use of ChatGPT in the context of education in the field of health. In addition, we aimed to examine whether there were differences across groups based on demographic variables. The second part of the study aimed to assess the association between the frequency of use, the level of perceived knowledge, the level of risk perception, and the level of perception of ethics as predictive factors for participants' attitudes toward the use of ChatGPT. A cross-sectional survey was conducted from May to June 2023 encompassing students of medicine, nursing, dentistry, nutrition, and laboratory science across the Americas. The study used descriptive analysis, chi-square tests, and ANOVA to assess statistical significance across different categories. The study used several ordinal logistic regression models to analyze the impact of predictive factors (frequency of use, perception of knowledge, perception of risk, and ethics perception scores) on attitude as the dependent variable. The models were adjusted for gender, institution type, major, and country. Stata was used to conduct all the analyses. Of 2661 health care students, 42.99% (n=1144) were unaware of ChatGPT. The median score of knowledge was "minimal" (median 2.00, IQR 1.00-3.00). Most respondents (median 2.61, IQR 2.11-3.11) regarded ChatGPT as neither ethical nor unethical. Most participants (median 3.89, IQR 3.44-4.34) "somewhat agreed" that ChatGPT (1) benefits health care settings, (2) provides trustworthy data, (3) is a helpful tool for clinical and educational medical information access, and (4) makes the work easier. In total, 70% (7/10) of people used it for homework. As the perceived knowledge of ChatGPT increased, there was a stronger tendency with regard to having a favorable attitude toward ChatGPT. Higher ethical consideration perception ratings increased the likelihood of considering ChatGPT as a source of trustworthy health care information (odds ratio [OR] 1.620, 95% CI 1.498-1.752), beneficial in medical issues (OR 1.495, 95% CI 1.452-1.539), and useful for medical literature (OR 1.494, 95% CI 1.426-1.564; P<.001 for all results). Over 40% of American health care students (1144/2661, 42.99%) were unaware of ChatGPT despite its extensive use in the health field. Our data revealed the positive attitudes toward ChatGPT and the desire to learn more about it. Medical educators must explore how chatbots may be included in undergraduate health care education programs.

A Quality and Completeness Assessment of Testicular Cancer Health Information on TikTok

TikTok has become a hub for easily accessible medical information. However, the quality and completeness of this information for testicular cancer has not been examined. Our study aims to assess the quality and completeness of testicular cancer information on TikTok. A search was performed on TikTok using the search terms “Testicular Cancer” and “Testicle Cancer”. Inclusion criteria encompassed videos about testicular cancer in English. We excluded non-English videos, irrelevant videos, and videos without audio. We evaluated these videos using the DISCERN instrument and a completeness assessment. A total of 361 videos were considered for screening and 116 videos were included. Of these, 57 were created by healthcare professionals (HCPs). The median video length was 40 s (5–277 s), with &gt;25 million cumulative views and a median of 446,400 views per video. The average DISCERN score was 29.0 ± 5.7, with HCPs providing higher-quality videos than non-HCPs (30.8 vs. 5.5, p &lt; 0.05). HCPs also had more reliable videos (21.2 vs. 18.1, p &lt; 0.05). Overall quality levels were mostly poor or very poor (97.4%), with none being good or excellent. Most HCP videos were poor (63.2%), whilst many non-HCP videos were very poor (61.0%). The most viewed video had 2,800,000 views but scored a 31 on the DISCERN tool and one on the completeness assessment. The highest DISCERN score had 11,700 views. HCP videos better defined the disease and were more complete (p &lt; 0.05). Most videos discussed self-assessment but were lacking in definitions, risk factors, symptoms, evaluation, management, and outcomes. Most of TikTok’s testicular cancer information lacks quality and completeness, whilst higher-quality videos have limited reach.

Temporal-Spatial Correlation Attention Network for Clinical Data Analysis in Intensive Care Unit.

Recent advancements in medical information technology have enabled electronic health records (EHRs) to store comprehensive clinical data which has ushered healthcare into the era of "big data". However, medical data are rather complicated, making problem-solving in healthcare be limited in scope and comprehensiveness. The rapid development of deep learning in recent years has opened up opportunities for leveraging big data in healthcare. In this article we introduce a temporal-spatial correlation attention network (TSCAN) to address various clinical characteristic prediction problems, including mortality prediction, length of stay prediction, physiologic decline detection, and phenotype classification. Leveraging the attention mechanism model's design, our approach efficiently identifies relevant items in clinical data and temporally correlated nodes based on specific tasks, resulting in improved prediction accuracy. Additionally, our method identifies crucial clinical indicators associated with significant outcomes, which can inform and enhance treatment options. Our experiments utilize data from the publicly accessible Medical Information Mart for Intensive Care (MIMIC-IV) database. Finally, our approach demonstrates notable performance improvements of 2.0% (metric) compared to other SOTA prediction methods. Specifically, we achieved an impressive 90.7% mortality rate prediction accuracy and 45.1% accuracy in length of stay prediction.

How older Canadians access medical cannabis and information about it: A descriptive survey

How older Canadians access medical cannabis and information about it: A descriptive survey

"Lost in Translation: The Readability Discrepancy of Online Patient Educational Materials for PCL Surgery".

While the internet provides accessible medical information, often times it does not cater to the average patient's ability to understand medical text at a 6th and 8th grade reading level, per American Medical Association (AMA)/National Institute of Health (NIH) recommendations. This study looks to analyze current online materials relating to posterior cruciate ligament (PCL) surgery and their readability, understandability, and actionability. The top 100 Google searchs for "PCL surgery" were compiled. Research papers, procedural protocols, advertisements, and videos were excluded from the data collection. The readability was examined using 7 algorithms: the Flesch Reading Ease Score, Gunning Fog, Flesch-Kincaid Grade Level, Coleman-Liau Index, SMOG index, Automated Readability Index and the Linsear Write Formula. Two evaluators assessed Understandability and Actionability of the results with the Patient Educational Materials Assessment Tool (PEMAT). Outcome measures included Reading Grade Level, Reader's age minimum and maximum, Understandability, and Actionability. Of the 100 results, 16 were excluded based on the exclusion criteria. There was a statistically significant difference between the readability of the results from all algorithms and the current recommendation by AMA and NIH. Subgroup analysis demonstrated that there was no difference in readability as it pertained to which page they appeared on Google search. There was also no difference in readability between individual websites versus organizational websites (hospital and non-hospital educational websites). Three articles were at the 8th grade recommended reading level, and all three were from healthcare institutes. There is a discrepancy in readability between the recommendation of AMA/NIH and online educational materials regarding PCL surgeries, regardless of where they appear on Google and across different forums. The understandability and actionability were equally poor. Future research can focus on the readability and validity of video and social media as they are becoming increasingly popular sources of medical information.

YouTube as a Source of Information in Trauma Management for ATLS (10th Edition) Guidelines: Evaluation of Trauma Management Videos on YouTube.

Background: Trauma is one of the leading causes of mortality worldwide, and online platforms have become essential sources of information for trauma management. YouTube can play a significant role in helping people access medical information. Methods: YouTube was searched using the keywords management of trauma and assessment of trauma to identify relevant videos. Two authors independently evaluated the videos according to the ATLS (10th edition) guidelines, the modified DISCERN (m-DISCERN) scale, and the Global Quality Scale (GQS) criteria. The videos that met the study criteria were evaluated based on the provider, video length, and view count. Results: Out of 939 videos, 667 were excluded resulting in 272 videos included in the study. According to the ATLS (10th edition) guidelines, the median score for videos was 8 (IQR 7-8). Videos uploaded by official institutions and healthcare professionals received higher scores than from uncertain sources (p = 0.003). According to the GQS, 86% of the videos were low or moderate quality; uncertain sources uploaded 78% of low-quality videos. Conclusion: YouTube is an information source about trauma management that contains videos of varying quality and has a broad audience. Official institutions and healthcare professionals should be aware of this evolving technology and publish up-to-date, accurate content to increase awareness about trauma management and help patients distinguish helpful information from misleading content.

Assessing the accuracy of an online chat-based artificial intelligence model in providing recommendations on arterial hypertension in accordance with the 2018 ESC/ESH guidelines

Abstract Background The rise of online chat-based artificial intelligence (AI) models has opened up new possibilities in the field of medicine. One of the promising contributions of AI chatbots could be their ability to provide accurate and accessible medical information to patients. This could help bridge the gap between patients and healthcare providers, especially in under-resourced areas where access to medical information and care is limited. Purpose We aimed to evaluate the ability of a commercially available AI-based online chat to provide answers to commonly asked questions related to arterial hypertension in accordance with the latest 2018 ESC/ESH guidelines (1). Methods We selected 30 commonly asked questions covering a range of arterial hypertension topics, including risk factors, symptoms, diagnosis, and treatment. These questions were submitted to the online AI interface, and the responses were recorded and evaluated by a team of four experienced cardiologists from our institution. To ensure consistency, each question was asked three times. All questions – except question 2 – were preceded by "according to the 2018 ESC/ESH Guidelines for the management of arterial hypertension". Each answer was rated as "accurate," "inaccurate," or "incomplete" based on the specialists’ clinical judgment, the 2018 ESC/ESH guidelines, and the content of the response. A response was considered "accurate" if it included all essential information, "inaccurate" if information provided was not in accordance with the guidelines, and "incomplete" if any essential information was missing. Results The AI model's responses to 24 out of the 30 questions (80%) were deemed accurate by cardiologists. Three responses were rated as incomplete (10%) and three responses were found to be inaccurate (10%). The AI responses were consistent in 28 out of the 30 questions (93%) (Tables 1-2). Conclusion We found that a popular online AI model provided largely accurate responses to commonly asked arterial hypertension questions. These answers were mostly in accordance with the 2018 ESC/ESH guidelines on the management of arterial hypertension. While the use of chat-based AI in medicine is still in its early stages and current models are not intended for medical use, the potential for such technology to improve the healthcare experience for patients is significant.Table 1:(1) Assessment of AnswersTable 2:(2) Assessment of Answers

Accuracy and Reliability of Chatbot Responses to Physician Questions

Natural language processing tools, such as ChatGPT (generative pretrained transformer, hereafter referred to as chatbot), have the potential to radically enhance the accessibility of medical information for health professionals and patients. Assessing the safety and efficacy of these tools in answering physician-generated questions is critical to determining their suitability in clinical settings, facilitating complex decision-making, and optimizing health care efficiency. To assess the accuracy and comprehensiveness of chatbot-generated responses to physician-developed medical queries, highlighting the reliability and limitations of artificial intelligence-generated medical information. Thirty-three physicians across 17 specialties generated 284 medical questions that they subjectively classified as easy, medium, or hard with either binary (yes or no) or descriptive answers. The physicians then graded the chatbot-generated answers to these questions for accuracy (6-point Likert scale with 1 being completely incorrect and 6 being completely correct) and completeness (3-point Likert scale, with 1 being incomplete and 3 being complete plus additional context). Scores were summarized with descriptive statistics and compared using the Mann-Whitney U test or the Kruskal-Wallis test. The study (including data analysis) was conducted from January to May 2023. Accuracy, completeness, and consistency over time and between 2 different versions (GPT-3.5 and GPT-4) of chatbot-generated medical responses. Across all questions (n = 284) generated by 33 physicians (31 faculty members and 2 recent graduates from residency or fellowship programs) across 17 specialties, the median accuracy score was 5.5 (IQR, 4.0-6.0) (between almost completely and complete correct) with a mean (SD) score of 4.8 (1.6) (between mostly and almost completely correct). The median completeness score was 3.0 (IQR, 2.0-3.0) (complete and comprehensive) with a mean (SD) score of 2.5 (0.7). For questions rated easy, medium, and hard, the median accuracy scores were 6.0 (IQR, 5.0-6.0), 5.5 (IQR, 5.0-6.0), and 5.0 (IQR, 4.0-6.0), respectively (mean [SD] scores were 5.0 [1.5], 4.7 [1.7], and 4.6 [1.6], respectively; P = .05). Accuracy scores for binary and descriptive questions were similar (median score, 6.0 [IQR, 4.0-6.0] vs 5.0 [IQR, 3.4-6.0]; mean [SD] score, 4.9 [1.6] vs 4.7 [1.6]; P = .07). Of 36 questions with scores of 1.0 to 2.0, 34 were requeried or regraded 8 to 17 days later with substantial improvement (median score 2.0 [IQR, 1.0-3.0] vs 4.0 [IQR, 2.0-5.3]; P < .01). A subset of questions, regardless of initial scores (version 3.5), were regenerated and rescored using version 4 with improvement (mean accuracy [SD] score, 5.2 [1.5] vs 5.7 [0.8]; median score, 6.0 [IQR, 5.0-6.0] for original and 6.0 [IQR, 6.0-6.0] for rescored; P = .002). In this cross-sectional study, chatbot generated largely accurate information to diverse medical queries as judged by academic physician specialists with improvement over time, although it had important limitations. Further research and model development are needed to correct inaccuracies and for validation.

Information Referral System in the Health System

Information Referral System in the Health System

Open Access Medical Repositories: Status and Development Trends

The issue of reflecting the scientific achievements of individual scientists and the results of research activities of research teams in the information environment is of great importance for many branches of science and practice, including medicine, where open data can improve the provision of medical care and influence healthcare policy. The effectiveness of search queries and the further effective use of the data obtained in research and practice, especially during epidemics and the treatment of serious diseases, largely depend on accessibility of medical information. Scientific repositories allow the management of all kinds of research results, integration with information systems, and compliance with the FAIR principles. The aim of the study is to identify and analyze development, indexing (in catalogs), visibility by search engines, type-specific characteristics, etc., of open access medical repositories in Russia and abroad. As a result, of the study, the following conclusions are made: 1) the number of repositories in the world is growing, including an increase in their number in the subject area “Medicine and Health”; 2) the infrastructure of Russian open access information platforms is being formed. However, open medical repositories are being developed poorly, most part of which is not represented in global catalogs (OpenDOAR) and is incompatible with the OAI-PMN standard, which allows the collection of data from repositories by various search engines. Thus, the research results are invisible in Internet search results, and Russian medical repositories are not present in the rankings of world repositories.

Impact of the development of information society on healthcare efficiency: Empirical evidence from 31 Chinese provinces.

The development of information society has deeply changed the style of the healthcare service delivery and medical information access. This study aims to investigate how the information society development affects the efficiency of healthcare system in China, and explore provincial disparities in the impact. Based on the two-stage Data Envelopment Analysis (DEA) framework, this paper uses a panel data from 31 Chinese provinces from 2014 to 2017 to estimate efficiency of healthcare service and analyze the influence of the development of information society on efficiency. With the information society index (ISI) increased from 0.423 to 0.488 during the sample period, the healthcare efficiency experienced a slight decrease from 0.892 to 0.869. Moreover, the pure technical efficiency (PTE) is much lower than the scale efficiency (SE), and thus is the key to enhance the overall technical efficiency (TE). The time-fixed Tobit regression analysis suggests that information society development leads to a significant increase in PTE, but results in a decrease in SE, and therefore has little effect on TE. Further analysis reveals that the impact differs sharply between low-efficiency and high-efficiency provinces. For the low-efficiency provinces, the TE increases significantly with the development of the information society, mainly due to a considerable increase in PTE. In contrast, the TE decreases for the high-efficiency provinces, mainly caused by a decrease in SE. This paper highlights the importance of information infrastructure investment in healthcare system and the application of emerging information technologies to breakout the time and space boundaries of healthcare services in improving overall efficiency. In inefficient provinces, it is also necessary to properly control the growth of healthcare inputs.

Intolerance of Uncertainty and Anxiety Sensitivity as Prospective Risk Factors for Cyberchondria in Undergraduate Students

Background: Researchers have discovered a vicious loop of increased physical health concerns and online medical information seeking known as "cyberchondria," despite the benefits of widely accessible medical information on the Internet. Despite proposed theoretical models of cyberchondria, research on risk factors for the development ofcyberchondria is lacking in our setup. Anxiety sensitivity and intolerance of uncertainty are two potential risk factors.Objective: This study was conducted to determine the frequency of health related internet researches i.e cyberchondria and to locate and find association between the resultant health anxiety and intolerance of uncertainty among the medical andnon-medical students of different institutions of Islamabad and Rawalpindi. Methods: A cross-sectional study was conducted on a total of 300 students of medical and engineering universities between ages of 18 and 25 from October 2019 to March 2020 by using Cyberchondria Severity Scale, intolerance of uncertainty scale and anxiety sensitivity index scale questionnaires as data collection tool. Data collected was statistically analyzed by SPSSversion-22. Results: Out of 300 participants, 169 (56.3%) students were found positive for cyberchondria and 131 (43.7%) were found negative and majority of the positive participants were medical students. Results revealed significant association between intolerance of uncertainty (0.567, p &lt; 0.000) as well as Anxiety sensitivity (0.805, p &lt; 0.000) and cyberchondria among the medical and non-medical students of Islamabad and Rawalpindi.Conclusions: The current study's findings show that both Anxiety sensitivity and intolerance of uncertainty may have a role in the development of cyberchondria especially in medical students.

Assessment of the Readability of Web-Based Patient Education Material From Major Canadian Pediatric Associations: Cross-sectional Study.

BackgroundWeb-based patient education materials (PEMs) are frequently written above the recommended reading level in North America. Poor PEM readability limits the accessibility of medical information for individuals with average literacy levels or lower. Pediatric hospital and association websites have not only been shown to be a preferred source of information among caregivers but have also become a necessity during the COVID-19 pandemic. The readability of Canadian pediatric association websites has not yet been assessed.ObjectiveThe aim of this study is to determine if the content of PEMs from Canadian pediatric associations is written at a reading level that the majority of Canadians can understand.MethodsA total of 258 PEMs were extracted from 10 Canadian pediatric associations and evaluated for their reading level using 10 validated readability scales. The PEMs underwent a difficult word analysis and comparisons between PEMs from different associations were conducted.ResultsWeb-based PEMs were identified from 3 pediatric association websites, where the reading level (calculated as a grade level) was found to be an average of 8.8 (SD 1.8) for the Caring for Kids website, 9.5 (SD 2.2) for the Pediatric Endocrine Group website, and 13.1 (SD 2.1) for the Atlantic Pediatric Society website. The difficult word analysis identified that 19.9% (SD 6.6%) of words were unfamiliar, with 13.3% (SD 5.3%) and 31.9% (SD 6.1%) of words being considered complex (≥3 syllables) and long (≥6 letters), respectively.ConclusionsThe web-based PEMs were found to be written above the recommended seventh-grade reading level for Canadians. Consideration should be made to create PEMs at an appropriate reading level for both patients and their caregivers to encourage health literacy and ultimately promote preventative health behaviors and improve child health outcomes.

Development and validation of a deep neural network-based model to predict acute kidney injury following intravenous administration of iodinated contrast media in hospitalized patients with chronic kidney disease: a multicohort analysis.

Stratification of chronic kidney disease (CKD) patients [estimated glomerular filtration rate (eGFR) <60mL/min/1.73 m2] at risk for post-contrast acute kidney injury (PC-AKI) following intravenous administration of iodinated contrast media (ICM) is important for clinical decision-making and clinical trial enrollment. The derivation and internal validation cohorts originated from the Second Xiangya Hospital. The external validation cohort was generated from the Xiangya Hospital and the openly accessible database Medical Information Mart for Intensive CareIV. PC-AKI was defined based on the serum creatinine criteria of the Kidney Disease: Improving Global Outcomes (KDIGO). Six feature selection methods were used to identify the most influential predictors from 79 candidate variables. Deep neural networks (DNNs) were used to establish the model and compared with logistic regression analyses. Model discrimination was evaluated by area under the receiver operating characteristic curve (AUC). Low-risk and high-risk cutoff points were set to stratify patients. Among 4218 encounters studied, PC-AKI occurred in 10.3, 10.4 and 11.4% of encounters in the derivation, internal and external validation cohorts, respectively. The 14 variables-based DNN model had significantly better performance than the logistic regression model with AUC being 0.939 (95% confidence interval: 0.916-0.958) and 0.940 (95% confidence interval: 0.909-0.954) in the internal and external validation cohorts, respectively, and showed promising discrimination in subgroup analyses (AUC≥0.800). The observed PC-AKI risks increased significantly from the low- to intermediate- to high-risk group (<1.0 to >50%)and the accuracy of patients not developing PC-AKI was 99% in the low-risk category in both the internal and external validation cohorts. A DNN model using routinely available variables can accurately discriminate the risk of PC-AKI of hospitalized CKD patients following intravenous administration of ICM.

Semantic Information Retrieval on Medical Texts

The explosive growth and widespread accessibility of medical information on the Internet have led to a surge of research activity in a wide range of scientific communities including health informatics and information retrieval (IR). One of the common concerns of this research, across these disciplines, is how to design either clinical decision support systems or medical search engines capable of providing adequate support for both novices (e.g., patients and their next-of-kin) and experts (e.g., physicians, clinicians) tackling complex tasks (e.g., search for diagnosis, search for a treatment). However, despite the significant multi-disciplinary research advances, current medical search systems exhibit low levels of performance. This survey provides an overview of the state of the art in the disciplines of IR and health informatics, and bridging these disciplines shows how semantic search techniques can facilitate medical IR. First,we will give a broad picture of semantic search and medical IR and then highlight the major scientific challenges. Second, focusing on the semantic gap challenge, we will discuss representative state-of-the-art work related to feature-based as well as semantic-based representation and matching models that support medical search systems. In addition to seminal works, we will present recent works that rely on research advancements in deep learning. Third, we make a thorough cross-model analysis and provide some findings and lessons learned. Finally, we discuss some open issues and possible promising directions for future research trends.

THE AGE AND BY WHOM A DONOR-CONCEIVED PERSON RECEIVES INFORMATION SIGNIFICANTLY EFFECTS THEIR EXPERIENCE

The Society for Assisted Reproductive Technology (SART) recommends that the impact in children following gamete donation should be a top priority for future infertility research. We sought to identify factors that influence the experience of being a donor-conceived person (DCP). A survey was disseminated to adult DCP through the Donor Sibling Registry (DSR). The survey included questions about donor type, relationship with the donor and half-siblings and their overall satisfaction with being a DCP. Appropriate bivariate statistics were used to compare characteristics of those with a positive/neutral vs. negative DCP experience. 529 people completed the survey. Most felt “neutral” (31.6%), “positive” (26.3%) or “very positive” (20.8%) about being a DCP regardless of donor type (p=0.39). Those with a neutral/positive experience were less likely to have a college degree (66.2% vs 80.5%, p=0.003), be married (33.9% vs 53.1%, p<0.001) and have children (29.8% vs 44.2%, p=0.004). While most found out about being a DCP after age 18 (63.4%), those with a positive experience were more likely to “have always known” (40.7% vs. 21.2%, p<0.001) and be told by a family member (75.2% vs. 60.2%, p=0.004). Those with a positive experience were not more likely to consider the donor a relative (46.4% vs 37.0%, p=0.39) or have an ongoing relationship with one or more half-siblings from the same donor (24.5% vs 24.1%, p=0.94). Additionally, there was no difference in the current self-reported satisfaction with the donor relationship (77.0 vs. 64.5, p=0.09). However, a negative experience being a DCP was associated with having medical complications due to lack of a complete health history (30.0% vs. 64.6%, p<0.001). DCP are more likely to have positive feelings surrounding their conception if they are told at a young age by a family member and have regularly updated and accessible medical information.

A comprehensive literature review on the medical learning systems in the developing countries

PurposeMany people have been dying as a result of medical errors. Offering clinical learning can lead to better medical care. Clinics have conventionally incorporated direct modality to teach personnel. However, they are now starting to take electronic learning (e-learning) mechanisms to facilitate training at work or other suitable places. The objective of this study is to identify and prioritize the medical learning system in developing countries. Therefore, this paper aims at describing a line of research for developing medical learning systems.Design/methodology/approachNowadays, organizations face fast markets' changing, competition strategies, technological innovations and accessibility of medical information. However, the developing world faces a series of health crises that threaten millions of people's lives. Lack of infrastructure and trained, experienced staff are considered essential barriers to scaling up treatment for these diseases. Promoting medical learning systems in developing countries can meet these challenges. This study identifies multiple factors that influence the success of e-learning systems from the literature. The authors have presented a systematic literature review (SLR) up to 2019 on medical learning systems in developing countries. The authors have identified 109 articles and finally selected 17 of them via article choosing procedures.FindingsThe paper has shown that e-learning systems offer significant advantages for the medical sector of developing countries. The authors have found that executive, administrative and technological parameters have substantial effects on implementing e-learning in the medical field. Learning management systems offer a virtual method of augmented and quicker interactions between the learners and teachers and fast efficient instructive procedures, using computer and Internet technologies in learning procedures and presenting several teaching-learning devices.Research limitations/implicationsThe authors have limited the search to Scopus, Google Scholar, Emerald, Science Direct, IEEE, PLoS, BMC and ABI/Inform. Many academic journals probably provide a good picture of the related articles, too. This study has only reviewed the articles extracted based on some keywords such as “medical learning systems,” “medical learning environment” and “developing countries.” Medical learning systems might not have been published with those specific keywords. Also, there is a requirement for more research with the use of other methodologies. Lastly, non-English publications have been removed. There could be more potential related papers published in languages other than English.Practical implicationsThis paper helps physicians and scholars better understand the clinical learning systems in developing countries. Also, the outcomes can aid hospital managers to speed up the implementation of e-learning mechanisms. This research might also enable the authors to have a role in the body of knowledge and experience, so weakening the picture of the developing country's begging bowl is constantly requesting help. The authors hoped that their recommendations aid clinical educators, particularly in developing countries, adopt the trends in clinical education in a changing world.Originality/valueThis paper is of the pioneers systematically reviewing the adoption of medical learning, specifically in developing countries.