Quality of the digital gp visits and characteristics of the users: retrospective observational study.

OBJECTIVES: This study compares the demographics, diagnoses, re-admission rates, sick leaves, and prescribed medications of patients accessing digital general practitioner (GP) visits with those of patients opting for traditional face-to-face appointments in a primary health care setting. DESIGN: The study adopted a retrospective analysis of patient record data collected in 2019, comparing visits to a digital primary health center with traditional health center visits. SETTING: Primary health care. PARTICIPANTS: The data encompassed patients who utilized the digital clinic and those who visited public health centers for primary health care services. MAIN OUTCOME MEASURES: The study assessed demographics, health diagnoses, prescribed medications, sick leave recommendations, re-admission rates, and differences in costs between digital clinic and face-to-face visits. Secondary outcomes included a comparative analysis of medication categories, resolution rates for health problems, and potential impacts on health care utilization. RESULTS: Digital clinic users were typically younger, more educated, and predominantly female compared with health centre users. Digital visits were well-suited for uncomplicated infections, while health centre appointments were associated with a higher prevalence of chronic conditions. Medication patterns differed between the two modalities, with digital clinic users receiving generic over-the-counter drugs and antibiotics, whereas health centre visits commonly involved cardiac and antihypertensive medications. Sick leave recommendations were slightly higher in the digital clinic, but the difference was not significant. Approximately 70% of health problems addressed in the digital clinic were successfully resolved, and the cost of digital visits was about 50,3% of face-to-face appointments. CONCLUSION: Digital health care services offer a cost-efficient alternative for specific health problems, appealing to younger, educated individuals, when compared to the users of public health center, and may enable improvement of cost-effectiveness combined with acceptable demand management and patient segmentation practices. The results highlight the potential benefits of digital clinics, particularly for uncomplicated cases, while also emphasizing the importance of suitable referral mechanisms for in-person consultations.

Changed Digital Technology Perceptions and Influencing Factors among Older Adults during the COVID-19 Pandemic.

This correlational study aimed to identify factors that contribute to changes in perceptions of digital technology among older adults during the COVID-19 pandemic. This study utilized raw data from "The 2021 Report on the Digital Divide," a nationwide survey conducted in South Korea. Data were collected from 1171 older adults (aged ≥ 65 years) from September to December 2021. Multiple regression analyses were performed to examine the factors influencing changes in the perception of digital technology. Over one-third of the participants reported positive changes in their perceptions of digital technology during the pandemic. Key factors included self-efficacy for digital devices (ß = 0.35, p < 0.001), digital networking (ß = 0.11, p < 0.001), accessibility to digital devices (ß = 0.10, p = 0.002), and perceived health (ß = 0.08, p = 0.003). The expansion of digital technology owing to the pandemic has served as a catalyst for changes in older adults' perceptions. Healthcare providers and caregivers should consider digital technology perceptions and influencing factors when providing digital healthcare services. The results can be utilized to identify vulnerable older adults with negative perceptions of digital technology, thus minimizing disparities in access to digital healthcare services.

Usability of Electronic Health Records in Germany - An Overview of Satisfaction of University Hospital Physicians.

INTRODUCTION: EHR are a part of daily task of physicians in Germany. This study surveyed the satisfaction of a small group of physicians in German university hospitals using EHR with focus on usability. METHODS: The questioning was carried out by an online survey. Addressed were all physicians working at university hospitals in Germany. RESULTS: The study showed that users are not satisfied with EHR (Grade 3.62). They pointed out some problems in general but reflected many advantages of those systems. CONCLUSION: EHR systems have to develop and adopt to users' tasks. They have to get faster and low error rates must be realized. Existing infrastructure must be improved and rolled out to users especially in times where digital healthcare services gain importance.

Forecasting Erroneous Neural Machine Translation of Disease Symptoms: Development of Bayesian Probabilistic Classifiers for Cross-Lingual Health Translation.

BACKGROUND: Machine translation (MT) technologies have increasing applications in healthcare. Despite their convenience, cost-effectiveness, and constantly improved accuracy, research shows that the use of MT tools in medical or healthcare settings poses risks to vulnerable populations. OBJECTIVES: We aimed to develop machine learning classifiers (MNB and RVM) to forecast nuanced yet significant MT errors of clinical symptoms in Chinese neural MT outputs. METHODS: We screened human translations of MSD Manuals for information on self-diagnosis of infectious diseases and produced their matching neural MT outputs for subsequent pairwise quality assessment by trained bilingual health researchers. Different feature optimisation and normalisation techniques were used to identify the best feature set. RESULTS: The RVM classifier using optimised, normalised (L2 normalisation) semantic features achieved the highest sensitivity, specificity, AUC, and accuracy. MNB achieved similar high performance using the same optimised semantic feature set. The best probability threshold of the best performing RVM classifier was found at 0.6, with a very high positive likelihood ratio (LR+) of 27.82 (95% CI: 3.99, 193.76), and a low negative likelihood ratio (LR-) of 0.19 (95% CI: 0.08, 046), suggesting the high diagnostic utility of our model to predict the probabilities of erroneous MT of disease symptoms to help reverse potential inaccurate self-diagnosis of diseases among vulnerable people without adequate medical knowledge or an ability to ascertain the reliability of MT outputs. CONCLUSION: Our study demonstrated the viability, flexibility, and efficiency of introducing machine learning models to help promote risk-aware use of MT technologies to achieve optimal, safer digital health outcomes for vulnerable people.

Staff testing for COVID-19 via an online pre-registration form.

BACKGROUND: Healthcare workers are at increased risk of contracting severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) and potentially causing institutional outbreaks. Staff testing is critical in identifying and isolating infected individuals, whilst also reducing unnecessary workforce depletion. Tygerberg Hospital implemented an online pre-registration system to expedite staff and cluster testing. We aimed to identify specific presentations associated with a positive or negative result for SARS-CoV-2. METHODS: A retrospective descriptive study design involving all clients making use of the hospital's pre-registration system during May 2020. RESULTS: Of 799 clients, most were young and females with few comorbidities. Nurses formed the largest staff contingent in the study, followed by administrative staff, doctors and general assistants. Doctors tested earlier compared with other staff (median: 1.5 vs. 4 days). The most frequent presenting symptoms included headache, sore throat, cough and myalgia. Amongst those testing positive (n = 105), fever, altered smell, altered taste sensation, and chills were the most common symptoms. Three or more symptoms were more predictive of a positive test, but 12/145 asymptomatic clients also tested positive. CONCLUSION: Staff coronavirus testing using an online pre-registration form is a viable and acceptable strategy. Whilst some presentations are less likely to be associated with SARS-CoV-2 infection, no symptom can completely exclude it. Staff testing should form part of a bundle of strategies to protect staff, including wearing masks, regular handwashing, buddy screening, physical distancing, availability of personal protective equipment and special dispensation for coronavirus disease 2019 (COVID-19)-related leave.