With the development of information technology, online health communities (OHCs) are becoming an increasingly popular source of health information.
While the impact of beauty has been extensively studied in various research fields, its role in OHCs has received little attention. This study aims to evaluate the effect of physicians’ appearance, smile, and positive emotions on patients’ selection and evaluation behavior in OHCs. Additionally, it explores the difference in the beauty premium among different types of physicians.
Over 13,000 images of physicians and their relevant information were collected from the Good Doctor website, which is now China's leading OHC. We identified facial features in physicians’ photos based on deep learning and used Ordinary Least Squares (OLS) regression models to estimate the relationship between physicians’ facial expressions and patients’ behavior. We used PSM to address endogeneity issues and test the robustness of the results.
This study found that physicians’ appearance and smile positively impact patients’ selection and evaluation behavior, the results indicated that the beauty premium does exist in OHCs. In addition, heterogeneity analysis showed that the beauty of high titles, longer service duration of physicians has a greater influence on patients’ selection and evaluation behavior.
The beauty premium exists in OHCs. Therefore, this study provides new evidence on the impact of physicians’ facial attractiveness in OHCs and provides useful insights for patients, physicians, and platforms about the relationship between physician's structured or unstructured information and patients’ decision-making behaviors.
This study aims to develop a guide establishing the requirements for developing and implementing telepharmacy in Hospital Pharmacy Services (HPS). A secondary objective is providing a reference frame to promote and allocate the necessary resources.
The project was developed in 5 phases from May-October 2021: (1) constitution of a working team; (2) literature review; (3) semi-structured interviews using the nominal group technique (with direct communications between experts and face-to-face discussion); (4) development of online workshops to raise awareness and debate each of the aspects considered essential in the document; (5) preparation of the final document and validation by the working group.
As a result of this methodology, the Guide for the Efficient and Safe Provision of Telepharmacy was obtained. This Guide proposes that pharmacists follow a Strategic Plan to develop a new telepharmacy program in an HPS under quality standards. This Strategic Plan has been structured in 4 organizational phases in which the requirements or resources necessary for its implementation are established: Strategy Phase, Planning Phase, Action Phase, and Phase of monitoring results and continuous improvement.
This study provides a practical guide for hospital pharmacists and managers to develop and implement telepharmacy in an HPS with quality guarantees. The Guide provides the pharmacists collective with a reference framework to promote and allocate the necessary resources in an increasingly complex healthcare environment where telepharmacy is an essential strategy for sustainability.
To access Electronic Health Record (EHR) data, hospitals have implemented Clinical Data Warehouses (CDWs) using Extract Transform and Load (ETL) processes. While ETL performances are typically evaluated individually, our study examines the cumulative impact of ETLs on data availability.
Using a real multi-hospital CDW as a case study, we modeled EHR data processing from the software sources to the CDW's data store. We simulated a scenario where researchers aimed to reconstruct breast cancer care trajectories using EHR data. We calculated the size and characteristics of the data store population, and compared them to the original population.
EHR data are recorded in various software depending on data category, hospital, and year, each requiring specific series of ETLs for integration in the CDW. Despite acceptable transfer rates for each ETL (range 73 %-100 %), cumulative losses led to study populations in the data store being up to 90 % smaller than anticipated when researchers required data exhaustivity for patients. Population size decreased steeply with the more data categories required. No difference was found in population characteristics between the data store and the original cohorts.
Researchers should scrutinize data availability in CDWs as missing data could result from outsourced care, incomplete input, or underperforming ETLs. Integrating more data sources in CDWs increases the number of data routes, necessitating time for ETL implementation and maintenance, and increases data loss risks. Though commonly perceived as a “black box”, data transformation can significantly influence the reliability of populations studied in CDWs.
To access data generated during care, researchers build Clinical Data Warehouses (CDWs). CDWs are infrastructures composed of a series of processing steps to extract the data from the data source, transform it according to the needs and load it into a data store. Usually, the performances of these processing steps are evaluated one a time. However, each data point goes through a series of processing steps before being made available for research. In this study, we aim to evaluate the impact of the entire data processing pipeline on the availability of data points in a CDW by simulating a study on breast cancer and evaluating the impact on the size and the characteristics of the final cohort. The cumulative losses of the processing steps resulted in a population 90 % smaller than anticipated. The characteristics of the final population showed no difference to those of the original cohort.
Rapid population ageing, compounded by declines in the younger workforce that could offer timely caregiving assistance, is undermining society's ability to effectively and sufficiently protect people's health and quality of life as they grow old. This paper explores the promise of senior employment technology, a group of technological innovations that have the potential to alleviate or even eliminate the barriers faced by older individuals in the workforce. By doing so, these technologies empower older individuals to proactively safeguard their current and future living standards. This discussion delves into the potential of senior employment technologies in facilitating the continued participation of older individuals in the workforce and highlights the existing barriers preventing the widespread adoption of these technologies.
Continuously learning or adaptive artificial intelligence (AI) applications for screening, diagnostic and other clinical services are yet to be widely deployed. This is partly due to existing device regulation mechanisms that are not fit for purpose regarding the adaptive features of AI. This study aims to identify the challenges in and opportunities for the regulation of adaptive features of AI.
We performed in-depth qualitative, semi-structured interviews with a diverse group of 72 experts in high-income countries (Australia, Canada, New Zealand, US, and UK) who are involved in the development, acquisition, deployment and regulation of healthcare AI systems.
Our findings revealed perceived challenges in the regulation of adaptive features of machine learning (ML) systems. These challenges include the complexity of AI applications as products subject to regulation; lack of accepted definitions of adaptive changes; diverse approaches to defining significant adaptive change; and lack of clarity about regulation of adaptive change. Our findings reflect potentially competing interests among different stakeholders and diversity of approaches from regulatory bodies and legislators in different jurisdictions across the globe. In addition, our findings highlight the complex regulatory implications of adaptive AI that differ from traditional medical products, drugs or devices.
The perceived regulatory challenges raised by adaptive features of AI applications require high-level coordination within a complex regulatory ecosystem that consists of medical device regulators, professional accreditation agencies, professional medical organisations, and healthcare service providers. Regulatory approaches should complement existing safety protocols with new governance mechanisms that specifically take into account the variety of roles and responsibilities that will be required to monitor, evaluate and oversee adaptive changes.
Low back pain (LBP), a common public health problem affecting nearly 80 % of the population, causes disability and work absence, while exercise is recommended as an adjuvant treatment. YouTube is one of the most frequently used source of information, especially by patients. The aim of the study was to analyze clinically the reliability, feasibility and the content quality of exercises videos recommended for LBP on YouTube. channel.
Based on the observational study of the number of views of YouTube videos, 154 videos worth evaluation were selected using the keywords "low back pain” and “exercises",. The content quality of each video was evaluated with a) the American Medical Association Journal (JAMA), b) DISCERN, c) Global Quality Score (GQS), and d) Medical Quality Video Assessment Tool (MQ-VET) scales, while Pearson correlation analysis was performed to determine the correlation between the JAMA, GQS, DISCERN, MQ-VET scale scores.
According to the findings, the primary source for uploading exercise videos were physicians (32.5 %), with a high content quality. There was a statistically significant positive correlation between the total scores of all quality scales and the independent variables defined as view rates and number of comments (p < .05). The analysis of the video content showed that only 35 % (n = 54) of the videos explained the risks of the recommended exercises in detail
It can be concluded that the relability and feasibility of exercise videos for LBP on YouTube depend highly on the upload source. Thus digital content in health should be created with a multi-disciplinary team including healthcare professionals and also standardized to inform patients with the risks and benefits to avoid the potential harm due to the complex causes of low back pain.
Low Back Pain, which a major health problem, is the most common musculoskeletal disorder leading to increased medical expenses and work absenteeism, while the most appropriate intervention remains elusive.. The therapeutic exercise program might differ according to patient's age, physical condition and the severity of the disease, while sustainability of the exercise program depends on the preference of exercise type of each patient. From this perspective, it is an important advantage that different exercise videos are available on YouTube. However, the content quality and reliability of exercise videos are the discriminators for the expected outcomes, since good quality content will benefit patients and prevent possible injuries and worsening of pain.
Artificial intelligence (AI) technology has developed rapidly in recent years, leading to exponential growth in the AI medical industry. However, a comprehensive investigation of approved medical devices in China is needed.
We utilized a web crawler to collect data on all medical devices from the China National Medical Products Administration website since 2018. Through natural language processing techniques and manual analysis, we identified all medical devices developed by artificial intelligence medical devices (AIMD) companies and conducted a statistical analysis.
Since 2018, the number of AI-related medical devices approved in China has significantly increased. Most devices (79 %) were classified as Class II with moderate risk, whereas 21 % were classified as Class III with high risk. Most devices (74.2 %) were categorized as medical device software, and the most common application was medical image processing (63.2 %). In terms of target body areas, devices related to the heart accounted for the highest proportion (12.8 %), followed by those related to the lungs (11.3 %) and brain (6.7 %).
This study establishes a comprehensive database of medical devices developed by AIMD companies in China, enabling the public to gain a coherent understanding of their current development status.
Current transformations in the pharmaceutical sector raise pressing questions about what is considered acceptable evidence for the effects of new therapies. This article aims to identifying key challenges in clinical effectiveness evaluation of new therapies and discuss possible policy responses to these challenges.
The study builds on a systematic review of the 41 appraisals issued in 2019 by the Danish Medicines Council (DMC), which is responsible for the appraisal of new specialized therapies in Denmark.
While much political attention currently centers on the use of ‘real-world evidence’, we find that clinical effectiveness evaluation based solely on non-RCT evidence still constitutes an exception in the Danish setting (9% of the evaluations). Yet, challenges of indirectness were prevalent even when evaluations were based on RCT data (54% of the evaluations). Challenges of effect extrapolation arose in about a third of the evaluations.
As the identified challenges are likely to increase with the current trend from ‘blockbuster’ to ‘niche’ products in the pharmaceutical sector, we point to a need for regulators and health technology assessment agencies to collaborate about the development of guidance on the use of other study designs than traditional RCTs, methods that can reduce the risk of bias when conducting indirect comparisons, and principles for managed entry agreements.
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