Medical Consultation Research Articles

Diagnóstico de onicomicosis. La utilidad del test rápido de detección antigénica en comparación con el cultivo

BackgroundRingworm is highly prevalent in our setting and is frequently observed in our routine clinical practice. Diagnostic confirmation depends on techniques that are not always accessible (PCR), with highly variable sensitivity depending on the observer (direct microscopy) or delayed results (culture, histopathology). Recently, an immunochromatography-based rapid test (Diafactory®) for the antigenic detection of dermatophytes has been developed. This diagnostic tool can help diagnose ringworm, allowing early initiation of treatment and fewer consultation visits. ObjectiveTo determine the sensitivity and specificity of the rapid antigen detection test compared to conventional culture. Material and methodsFor a full year, 333 nail samples were collected from patients with suspected onychomycosis. The rapid test and the conventional culture were simultaneously performed on each sample. Those with a positive antigenic test result began treatment early. The remaining patients had appointments for serial cultures and subsequent medical consultation to evaluate the results. ResultsCompared to conventional culture, the sensitivity and specificity rates of the rapid antigen detection test are 97.2% and 80.7%, respectively. ConclusionThe effectiveness of the rapid antigen detection test is similar to that of conventional culture for the detection of dermatophytes in nail samples. It is a quick and simple diagnostic technique that reduces the number of patient visits to the hospital, and allows early treatment start.

The Integration of ChatGPT With the Metaverse for Medical Consultations

The Integration of ChatGPT With the Metaverse for Medical Consultations

Digital Transformation in Healthcare Challenges and Prospects

The following are presented, by the global community, as the main fields of Digital Transformation (DT) in Healthcare: Telemedicine Telemedicine is the remote delivery of healthcare services through Information and Communication Technologies (ICTs). It could be particularly beneficial in a range of services such as medical consultation, diagnosis remote monitoring of patients’ status and educational purposes. Patients could con- nect with healthcare providers at a distance using video calls, messaging platforms, or other communication tools. Telemedicine enhances accessibility to healthcare by overcoming geographical barriers while at the same time provides a transformative approach to healthcare services. Big Data Big data represents vast and diverse health information sets collected from several sources like Electronic Health Records (EHRs), wearable devices and social media. Big data can extract valuable insights and patterns, assisting clinical decision-making, public health management, personalised medicine and disease forecasting. Internet of Things (IoT) The Internet of Things (IoT) in healthcare refers to the interconnected network of medical devices, sensors and sys- tems that collect and exchange health data through the Inter- net. These devices offer real-time exchange of various data monitoring like patient's vital signs, and remote patient’s care, which could eventually result in the improvement of operational efficiency within healthcare systems, and patients' monitoring and care delivery. Virtual Reality (VR) in Health Virtual Reality (VR) employs immersive computer-generated environments into realistic experiences to transform medical training and education, rehabilitation, surgical planning and pain management. Employing VR tools such as headsets, users engage with three-dimensional simulation scenarios for training and skill practice in a safe environment without risking personal and patient safety. In addition, VR offers opportunities to improve outcomes, provide safer experiences to patients, while promoting the evolution of healthcare systems and care delivery. Artificial Intelligence (AI) in Health AI in healthcare is an umbrella term for the applications of Machine Learning (ML) algorithms used in medical contexts. AI algorithms analyse medical data, aiding in diagnostics, drug discovery, personalised medicine, and patient management. Virtual (personal) Assistants in Health Virtual Health Assistants (VHAs) are tools designed to engage patients, provide personalised health information and assist in various aspects of health services administration. Using Natural language Processing (NLP) and ML algorithms, VHAs offer support to remote communication between patients and healthcare providers, understand patients’ inquiries and healthcare-related issues. Also, VHAs could facil- itate appointment scheduling, symptom assessment, medication management, and health coaching, resulting at the enhancement of accessibility in healthcare and the empower- ment of the patient. Blockchain in Health Blockchain is an emerging technology, which utilises decentralised systems to record and manage healthcare information and patient data. By linking data blocks, blockchain can enhance security, interoperability and traceability, and address significant challenges such as data breaches, interoperability issues, and inefficiencies in data sharing, thus providing a stan- dardised and secure platform for data exchange across health- care systems and stakeholders. Medical Wearables Wearables emerge positive outcomes on continuing monitoring, tracking and early detecting of health issues. Wearables are devices wore on the body or putted on clothing allowing constant monitoring of health data and behaviour. They enable individuals to engage a proactive approach to their health and well-being, by empowering them to take control of their health. In addition, wearables have the potential to enhance telemedicine and population health management, while reducing healthcare costs.

Combining real data and expert knowledge to build a Bayesian Network — Application to assess multiple risk factors for fall among elderly people

Building a prediction model based on both real data and expert knowledge can be challenging. We experienced this difficulty in our context of predicting fall risk factors in the elderly, where two sources of data were available: hand-reported information from medical consultations and expert information from human specialists. As is often the case, the real data were incomplete and imbalanced, and the participation of medical experts required the selection of a kind of model they can understand. This article describes the steps followed in the construction and evaluation of the model, structured as a new framework. The proposed model is a Bayesian network (BN) built from expert knowledge and learning from a real data set, guided by both the understandability of the BN graph and the performance of the model to predict risk factors. The resulting Bayesian network model includes 90 variables to evaluate 10 actionable target risk factors. It demonstrated its potential compared to other classifiers in terms of prediction performance, whilst offering a higher degree of interpretability.

SOCIOLOGICAL PERSPECTIVES ON OSTEOARTHRITIS MANAGEMENT: INSIGHTS AND CHALLENGES

This study examines how medical sociology intersects with osteoarthritis management, with both the perspectives of individuals affected by the condition and the healthcare system, drawing insights from theoretical frameworks and empirical evidence. Two main themes were addressed: gender disparities and the living experience with osteoarthritis. It was found that the female gender is more susceptible to developing knee osteoarthritis due to lifestyle factors and other environmental influences. Additionally, women tend to report pain earlier compared to men, leading to a higher likelihood of seeking medical attention for symptoms. Regarding the experience of living with this condition, important themes such as the refusal to give up, pragmatism in treatment, staying in charge and the care from others were presented, being essential for managing this condition. Additionally, it was discovered that better communication between doctor and patient, the exchange of specific, relevant and sufficient information, contributes to a positive experience during a medical consultation and forms the foundation of a trustworthy relationship between the individual and the healthcare system. Overall, this research shows that managing osteoarthritis is complex and requires personalized healthcare approaches. Keywords: osteoarthritis, medical sociology, gender disparities, living experience, management.

Pattern of Arrhythmia among Diabetes Mellitus Patients in South-South, Nigeria: 24-Hour Ambulatory ECG (Holter ECG) Study

Background: Diabetes is known to be an independent risk factor for cardiovascular morbidity and mortality, commonly associated with rhythm disorders like atrial fibrillation (AF) and ventricular arrhythmias (VA), Diabetes Mellitus (DM) patients have silent myocardial infarctions (MI) with conduction abnormalities like ectopics and others. Regular 12 lead ECGs may not capture subtle conduction abnormalities unlike Holter ECGs which takes longer time to record heart rhythms. The study aims to determine the occurrence and pattern of cardiac arrhythmias in Diabetes Mellitus. Methods: A total of 33 diabetic subjects with age ranged from 18 years and above presented to Goodheart Medical Consultants hospital for 24-hour ambulatory ECG (Holter ECG) using Schiller type (MT-101) Holter ECG machine. Results: In this study, the population consisted of 54.5% men and 45.5% women with a mean age of 63.4 ±14.41, the most common age group was 61–80 years with 42.4%. The two most common forms of arrhythmias for Holter ECGs in this study were supraventricular ectopics 36.4% and ventricular ectopics 33.3%. Conclusion: Supraventricular ectopics is the most common arrhythmia observed among the diabetic subjects in Southern Nigeria and was notably observed more in diabetic male subjects than females. This arrhythmia exposes them to risk of stroke and mortality. Diabetes-related arrhythmias may be a sign of other underlying illnesses.

Super-specialty Medical Teleconsultations to Remote Areas of Ladakh, India via E-Sanjeevani under Hub-and-Spoke Model: A Pilot Study

Objectives: To assess the feasibility, spectrum, outcomes and challenges of delivering super-specialty consultations to remote areas of the Union Territory (UT) of Ladakh through telemedicine. Design: Prospective, observational (descriptive), pilot study Methodology: Super-specialty medical teleconsultations were provided to healthcare providers in UT Ladakh by specialists in the Department of Telemedicine, Postgraduate Institute of Medical Education and Research, Chandigarh from October 2021 to May 2022, employing the hub-and-spoke model of the Ayushman Bharat Digital Mission. Doctor-to-doctor teleconsultations were initiated by treating doctors at healthcare facilities in UT Ladakh on the eSanjeevani™ platform. Audio-video model was used for teleconsultation and digitally signed prescriptions were shared. Setting: India – Postgraduate Institute of Medical Education and Research (PGIMER), Chandigarh (hub) and healthcare providers in UT Ladakh (spokes) Participants: Healthcare providers in UT Ladakh and specialists in the Department of Telemedicine, PGIMER, Chandigarh Interventions: Super-specialty medical consultations using telemedicine (eSanjeevani™ platform) Main Outcome Measures: Demand and spectrum of superspecialty teleconsultations, outcome of consults (whether managed successfully at teleconsultation level, or required referral for in-person visits), and technical challenges faced. Results: A total of 26 super-specialty teleconsultations were provided; 14 (54%) were for women. Patient distribution was uniform across all age groups. The super-specialty-wise distribution of consultations was - 9 from endocrinology, 9 from neurology, 3 from hepatology, 2 from cardiology, and 3 from neonatology. Reasons for seeking consultation were assistance in diagnosis (3, 12%), optimization of therapy (10, 38%), or both diagnostic evaluation and treatment optimization (13, 50%). Five cases (19%) were referred for physical visits to a higher center for advanced diagnostic testing like liver biopsy, brain biopsy, upper GI endoscopy, etc.; other cases were managed remotely. Problems with audio-visual connectivity were encountered in three patients. Conclusion: This pilot study demonstrates that complex, super-specialty medical consultations can be effectively delivered to remote areas through telemedicine. Future research should focus on assessing long-term outcomes, patient satisfaction, and cost-effectiveness to better evaluate the impact of this model.

Prevalence and pattern of dyslipidemia among patients with ischemic stroke in a tertiary care center in south- south Nigeria

Background: Stroke is a major public health problem worldwide. Dyslipidemia is a significant risk factor for ischemic stroke, but its prevalence and characteristics in this population remain understudied. Therefore, this study aims to investigate the prevalence and patterns of dyslipidemia among individuals diagnosed with ischemic stroke in a tertiary care center in South-South Nigeria. Materials and Method: This is a retrospective hospital-based study on Ischaemic stroke patients admitted to GoodHeart Medical Consultant Hospital, Port- Harcourt, Rivers state, Nigeria, from July 2022 to June 2023. Results: The study comprised 33 subjects consisting of 18(54.5%) males and 15(45.5%) females. The mean age of the total population was 62.6 ± 12.49. The most prevalent age group was elderly (>61 years) 20 (60.6%). The females were older than the males with a mean age of 64.1 ± 2.50 and 61.3 ± 12.69 respectively. The overall prevalence of dyslipidaemia was 97%, whereas the most common form of dyslipidaemia was reduced HDL (57.6%). There was no significant difference in dyslipidaemia among male and female stroke patients, nevertheless 63.6% of the males had dyslipidaemia. Conclusion: Dyslipidaemia was common in the overall study population particularly reduced HDL and elevated LDL. The study revealed no statistically significant difference between gender and dyslipidaemia in stroke patients, nevertheless dyslipidaemia was more in the male stroke patients than females. Hence, prevention of dyslipidaemia as well as other risk factors is key to reducing the burden of stroke in our country.

Gender differences, temporal profile and outcomes of patients presenting for stress electrocardiography in southern Nigeria

Background: Exercise stress testing (EST) is a safe and cost-effective screening test for coronary artery disease (CAD) that does not require invasive procedures. Careful patient selection is essential for accurate results, but not recommended for women in their reproductive years due to a higher rate of false positives. This study aims to determine the gender difference and temporal profile of patients presenting for EST in southern Nigeria. Method: This was a retrospective study of 102 subjects at GoodHeart Medical Consultant Hospital from October 2019 to January 2023 focused on adult patients with low or intermediate pretest symptoms referred for EST. Data on medication, smoking, alcohol, and presenting symptoms was collected. Results: 102 subjects were studied (mean 49±16 years, 72.5% males). Most subjects (57.8%) were aged 41-60. The average duration of the EST was 9 minutes. Chest pain was the main reason for referral, and muscle fatigue was the most common reason for stopping the EST. The highest stage recorded was stage 3 (37.3%), and the lowest was stage 5 (3.9%). Over half of the subjects had a normal result, while 6.9% had ST changes and 3.9% had T wave inversion. Stress-induced ischemia was found in 7.8% and stress-induced arrhythmia in 3.9% of subjects, the study recorded a high prevalence among the males with no significant gender difference. Conclusions: Stress ECG testing had no injuries or deaths, with over half having normal results. Ischemia and arrhythmia were rare, and muscle fatigue was the main reason for stopping.

Prevalence and Correlates of Self-Reported Erectile Dysfunction among Adult Male Patients Receiving Cardiovascular Care in Southern Nigeria

Abstract Background: The global incidence of erectile dysfunction (ED) is on the rise, with an estimated 152 million men currently affected. Cardiovascular risk factors are strongly linked to the occurrence of ED, particularly in men with cardiovascular disease (CVD). In countries like Nigeria, where it is not typically viewed as life-threatening, ED often receives less attention. Consequently, there is limited data on selfreported ED among patients seeking cardiovascular care in cardiac clinics. Therefore, this study aims to determine the prevalence of self-reported ED among adult patients with CVD visiting a cardiac center. Materials and Method: A retrospective hospital-based study of self-reported ED patients carried out in a cardiac center among males (31-82 years) in Port Harcourt, Southern Nigeria, over a one-year period (October 2022 to January 2024). Participants’ demographic data and medical history were retrieved and analyzed. Results: The study had a total of 23 male subjects. Majority of the participants were between 50-69 years old (47.8%), 65.2% took alcohol and 21.7% smokes cigarette. 82.6% had a history of hypertension and (73.9%) were on antihypertensive, (34.8%) had diabetes mellitus, (17.4%) were on antidiabetic while (60.9%) were on other forms of medication. Overweight and obesity account for 34.8% and 52.2% of the subjects respectively. Beta-blocker (23.5%) was the frequently used medication and angiotensinconverting enzyme (ACE) inhibitors was the least accounting for (5.9%). Conclusions: The increasing incidence of ED among patients with CVD risk factors and an aging population suggest that patients should be screened. ED may become a significant public health problem and closer attention needs to be paid to control these patients to reduce further complications. Keyword: Erectile dysfunction, cardiovascular risk factor, cardiac Centre, self-reported, Good Heart medical consultants’ Hospital.

The role of AI and mobile apps in patient-centric healthcare delivery

Patient-centric healthcare is an approach to healthcare that prioritizes the needs, preferences, and values of patients in the delivery of medical services. As part of a patient-centric model, patients take active part in decision-making regarding their own care, while healthcare providers tailor treatments and services to each patient's needs. The rise of artificial intelligence (AI) and mobile applications (apps) has now opened new avenues for enhancing patient engagement and improving healthcare outcomes, so by leveraging AI's sophisticated data analytics and predictive capabilities, healthcare providers are able to gain unprecedented insights into patient health patterns, thereby enabling personalized, and proactive care plans tailored to the patient’s individual needs. Furthermore, mobile apps have become potent tools, giving patients smooth access to healthcare resources, virtual medical consultations, and real-time health tracking, boosting patient independence and enabling them to manage their health with greater ease. The integration of AI and mobile apps into healthcare delivery is revolutionizing patient-centric care by enhancing patient engagement, empowerment, and self-management capabilities. However, addressing concerns around data privacy, security, and digital literacy is crucial for successful implementation. This study outlines a framework for harnessing these technologies to enhance patient outcomes and overall healthcare experiences. It explores the roles AI and mobile apps play in facilitating patient-centric healthcare and examines how the use of these technologies can promote personalized care, boost patient education and self-management, and improve communication between patients and healthcare providers.

Physician Versus Large Language Model Chatbot Responses to Web-Based Questions From Autistic Patients in Chinese: Cross-Sectional Comparative Analysis.

There is a dearth of feasibility assessments regarding using large language models (LLMs) for responding to inquiries from autistic patients within a Chinese-language context. Despite Chinese being one of the most widely spoken languages globally, the predominant research focus on applying these models in the medical field has been on English-speaking populations. This study aims to assess the effectiveness of LLM chatbots, specifically ChatGPT-4 (OpenAI) and ERNIE Bot (version 2.2.3; Baidu, Inc), one of the most advanced LLMs in China, in addressing inquiries from autistic individuals in a Chinese setting. For this study, we gathered data from DXY-a widely acknowledged, web-based, medical consultation platform in China with a user base of over 100 million individuals. A total of 100 patient consultation samples were rigorously selected from January 2018 to August 2023, amounting to 239 questions extracted from publicly available autism-related documents on the platform. To maintain objectivity, both the original questions and responses were anonymized and randomized. An evaluation team of 3 chief physicians assessed the responses across 4 dimensions: relevance, accuracy, usefulness, and empathy. The team completed 717 evaluations. The team initially identified the best response and then used a Likert scale with 5 response categories to gauge the responses, each representing a distinct level of quality. Finally, we compared the responses collected from different sources. Among the 717 evaluations conducted, 46.86% (95% CI 43.21%-50.51%) of assessors displayed varying preferences for responses from physicians, with 34.87% (95% CI 31.38%-38.36%) of assessors favoring ChatGPT and 18.27% (95% CI 15.44%-21.10%) of assessors favoring ERNIE Bot. The average relevance scores for physicians, ChatGPT, and ERNIE Bot were 3.75 (95% CI 3.69-3.82), 3.69 (95% CI 3.63-3.74), and 3.41 (95% CI 3.35-3.46), respectively. Physicians (3.66, 95% CI 3.60-3.73) and ChatGPT (3.73, 95% CI 3.69-3.77) demonstrated higher accuracy ratings compared to ERNIE Bot (3.52, 95% CI 3.47-3.57). In terms of usefulness scores, physicians (3.54, 95% CI 3.47-3.62) received higher ratings than ChatGPT (3.40, 95% CI 3.34-3.47) and ERNIE Bot (3.05, 95% CI 2.99-3.12). Finally, concerning the empathy dimension, ChatGPT (3.64, 95% CI 3.57-3.71) outperformed physicians (3.13, 95% CI 3.04-3.21) and ERNIE Bot (3.11, 95% CI 3.04-3.18). In this cross-sectional study, physicians' responses exhibited superiority in the present Chinese-language context. Nonetheless, LLMs can provide valuable medical guidance to autistic patients and may even surpass physicians in demonstrating empathy. However, it is crucial to acknowledge that further optimization and research are imperative prerequisites before the effective integration of LLMs in clinical settings across diverse linguistic environments can be realized. Chinese Clinical Trial Registry ChiCTR2300074655; https://www.chictr.org.cn/bin/project/edit?pid=199432.

The Role of User-Generated Content in the Sustainable Development of Online Healthcare Communities: Exploring the Moderating Influence of Signals

Grounded in signaling theory, this study explores the influence of user-generated content (UGC) within online healthcare communities on patient purchasing behavior, with the overarching goal of advancing the development of online medical consultation services and contributing to the sustainable evolution of the online healthcare community. Leveraging publicly available data from the “Haodf.com”, we construct an empirical model of online medical consultation purchases, integrating principles from signaling theory and trust theory. Our analysis scrutinizes the effects of various forms of UGC on patient purchasing behavior, alongside the moderating influence of associated signals. The results demonstrate that knowledge-sharing articles authored by doctors and patient ratings positively impact consultation service purchases, whereas public displays of doctors’ past consultation records impede such transactions. Furthermore, external signals were found to moderate the relationship between UGC and consultation service purchases. The implications of these findings offer actionable insights for stakeholders invested in online healthcare communities.

Pattern of Arrhythmia among Ischemic Stroke Patients on 24-hour Ambulatory Electrocardiography (Holter ECG) in South-South Nigeria

Background: The 24-hour Holter electrocardiogram is a valuable tool for the identification of ischaemic alterations, cardiac arrhythmias in hypertension, stroke and other cardiovascular conditions. Electrocardiographic (ECG) abnormalities and cardiac arrhythmias have been detected in 50% of stroke patients. However, literature in this area of research is limited in our environment. This study is aimed at describing the pattern of arrhythmia observed in patients with ischaemic stroke using 24-hour Holter ECG in our locality. Methods: A total of 22 stroke subjects whose age ranged from 41 and 83 years were evaluated in GoodHeart Medical Consultants hospital for 24-hour ambulatory ECG (Holter ECG) from January 2015 – December 2016. The Holter ECG data was acquired using Schiller type (MT-101) Holter ECG machine. Results: The study population consisted of 59.1% females and 40.9% males, with an average age of 66.95±12.32 years. Arrhythmias were more prevalent among females compared to males. However, bradycardia was more prevalent in males. Ventricular ectopics were the most common type of arrhythmia across both genders, with a higher prevalence in the older adult age group of 61-80 years. Conclusion: The most common arrhythmia observed among the stroke subjects in Southern Nigeria is ventricular ectopics. This could be due to increase in age and severe neurological deficit which are independent predictors of arrhythmia onset and this was notably seen among the older subjects in the study. Extended cardiac monitoring such as Holter ECG is a well-accepted method for the identification of ECG abnormalities and the implementation of prompt treatment to reduce mortality.

Superior medical resources or geographic proximity? The joint effects of regional medical resource disparity, geographic distance, and cultural differences on online medical consultation

Online medical consultation platforms enable patients to seek health advice from physicians across geographic regions. In this study, we analyze patterns of online consultation between patients and physicians. We examine the joint effects of regional medical resource disparity, geographic distance, and cultural differences between patients and physicians on patients' decisions about which physicians they consult online. Using a unique dataset of city-to-city tuples based on 813,684 online consultation records and combining it with region-level data from multiple external sources, we find that while regional medical resource disparity drives patients from medically disadvantaged regions to seek online consultations with physicians from medically advantaged regions, geographic distance and cultural differences tend to constrain these consultations. We also find that cultural differences can amplify the impact of regional medical resource disparity, whereas geographic distance may lessen this effect. Further, we discover that the constraining effect of geographic distance is partly due to the online-to-offline nature of online medical consultations. Moreover, additional analyses suggest that physicians' online reputation and information about physicians’ participation on the platform can help alleviate the negative effects of geographic distance and cultural differences. These findings hold significant implications for the allocation of medical resources and the formulation of healthcare policies.

Automatic Medical Dispatcher with Dynamic Telemonitoring System Using IoT in Rural Areas

Accessing quality healthcare in rural areas can be challenging, often requiring individuals to travel long distances to nearby towns or cities. To address this issue, the Anytime Medical Machine (AMM) project was developed, leveraging Internet of Things (IoT) technology to provide on-the-spot medical consultations. AMM, a part of the Internet of Medical Things (IoMT), is designed for primary deployment in rural areas. Through AMM, individuals can receive consultations from qualified doctors without the need to travel. Doctors can prescribe medications and provide treatment recommendations, reducing both time and expenses associated with travel. The system collects patient data using sensors for temperature, heart rate, and blood oxygen saturation (SPO2), transmitting this information to doctors via a Wi-Fi module. The doctor-patient interaction is facilitated through secure video conferencing, requiring a unique username and password for each user to prevent misuse. After analyzing the patient's health parameters, the doctor prescribes appropriate medication, which is then dispensed through AMM. Patient information is securely uploaded to a cloud server for future reference, and the system is maintained by an administrator who refills medications as needed. Keywords- Rural Healthcare, Remote Consultation, IoT Healthcare, Medical IoT, Telemedicine, Remote Monitoring, Cloud-Based Healthcare, Healthcare Accessibility

The Current Status of Utilizing a Medication Record Handbook for Evaluating Shared Medication History: A Retrospective Study Using the Japanese National Claims Database.

While the coronavirus disease 2019 (COVID-19) pandemic has impacted medication adherence and consultation patterns, its effects on the medical practice and dispensary separation system of Japan remain unclear. Thus, the utilization of the medication record handbook (MRH) in both medical and dental areas remains uncertain. This study uses the National Database of Health Insurance Claims and Specific Health Checkups of Japan (NDB); we analyzed the separation of medication prescription and dispensing in both medicine and dentistry, as well as estimated how much drug information is shared by utilizing a patient-carried MRH. The external prescription (EP) rate was used as the main indicator. We then analyzed the MRH utilization rate during outpatient medication guidance. During the pandemic, there was no distinctive change in the rate of EPs in both medicine and dentistry. Furthermore, an analysis between EPs, medical internal prescriptions (IPs), and dental IPs relative to the MRH utilization rate revealed significant correlations between EPs and medical IPs as well as medical and dental IPs. Conversely, no significant correlation was found between EPs and dental IPs. Therefore, our results suggest that active MRH implementation within healthcare facilities may lead to an increase in its utilization in dentistry.

Automated Generation of Clinical Reports Using Sensing Technologies with Deep Learning Techniques.

This study presents a pioneering approach that leverages advanced sensing technologies and data processing techniques to enhance the process of clinical documentation generation during medical consultations. By employing sophisticated sensors to capture and interpret various cues such as speech patterns, intonations, or pauses, the system aims to accurately perceive and understand patient-doctor interactions in real time. This sensing capability allows for the automation of transcription and summarization tasks, facilitating the creation of concise and informative clinical documents. Through the integration of automatic speech recognition sensors, spoken dialogue is seamlessly converted into text, enabling efficient data capture. Additionally, deep models such as Transformer models are utilized to extract and analyze crucial information from the dialogue, ensuring that the generated summaries encapsulate the essence of the consultations accurately. Despite encountering challenges during development, experimentation with these sensing technologies has yielded promising results. The system achieved a maximum ROUGE-1 metric score of 0.57, demonstrating its effectiveness in summarizing complex medical discussions. This sensor-based approach aims to alleviate the administrative burden on healthcare professionals by automating documentation tasks and safeguarding important patient information. Ultimately, by enhancing the efficiency and reliability of clinical documentation, this innovative method contributes to improving overall healthcare outcomes.

CONSEQUENCES OF LEADING HEALTH LIFESTYLE ON LIPID PROFILE

The activity of improving and promoting health is through leading proper health behavior which includes perfect and suitable methods for health living; through leading health lifestyle; via periodic medical consultations and attending physicians for medical checkup and advice. Beside choosing balanced diet and encourage sport exercises; cessation of smoking and alcohol intake. The other aspect, that results in promoting health, longevity and quality of living through preventing of disease occurrence should be through clinical and physical examination via screening services of diseases, such as ischemic heart disease, hypertension and renal problems. All these personal activities will lead to health lifestyle, which consequently is going to minimize the diseases might occur. On conclusion, the study shows that these behavioral aspects will reduce lipid profile; blood pressure, body weight and increase physical fitness in 42% of cases and border line effect on outcome is approximately about 58% of cases and the variable that plays a role is socio – economic status

Understanding breast cancer patient pathways and their impact on survival in Mexico

BackgroundUnderstanding patient pathways from discovery of breast symptoms to treatment start can aid in identifying ways to improve access to timely cancer care. This study aimed to describe the patient pathways experienced by uninsured women from detection to treatment initiation for breast cancer in Mexico City and estimate the potential impact of earlier treatment on patient survival. MethodsWe used process mining, a data analytics technique, to create maps of the patient pathways. We then compared the waiting times and pathways between patients who initially consulted a private service versus those who sought care at a public health service. Finally, we conducted scenario modelling to estimate the impact of early diagnosis and treatment on patient survival. ResultsOur study revealed a common pathway followed by breast cancer patients treated at the two largest public cancer centres in Mexico City. However, patients who initially sought care in private clinics experienced shorter mean wait times for their first medical consultation (66 vs 88 days), and diagnostic confirmation of cancer (57 vs 71 days) compared to those who initially utilized public clinics. Our scenario modelling indicated that improving early diagnosis to achieve at least 60% of patients starting treatment at early stages could increase mean patient survival by up to two years. ConclusionOur study highlights the potential of process mining to inform healthcare policy for improvement of breast cancer care in Mexico. Also, our findings indicate that reducing diagnostic and treatment intervals for breast cancer patients could result in substantially better patient outcomes. Policy summaryThis study revealed significant differences in time intervals along the pathways of women with breast cancer according to the type of health service first consulted by the patients: whether public primary care clinics or private doctors. Policies directed to reduce these inequities in access to timely cancer care are desperately needed to reduce socioeconomic disparities in breast cancer survival.