[ad_1] Discussion In this study we examined sociodemographic differences in preference for healthcare AI using a large weighted Australian sample that was calibrated to the LIA probability sample using a range of behavioural and lifestyle questions, as well as major sociodemographic variables. Overall, 56.7% (95% CI 53.8%–59.0%) of the participants were supportive of the development of AI, slightly lower than

[ad_1] Working with noise in the data is one of the challenges when dealing with free-text formatted input data. Filtering the input sentences and selecting more informative ones work as a solution to reduce the noise in the data when dealing with free-text categorisation of CFs. Finding a balance between removing the noise and keeping a sufficient number of features

[ad_1] Discussion The purpose of this study was to assess willingness of patients with DM to use mobile health applications and associated factors for self-care management. The result showed that willingness to use mHealth applications among patients with DM in the Oromia region was high (71.4%, 95% CI (66.8% to 75.9%)). This result was in line with the willingness to use

[ad_1] Discussion The purpose of this study was to assess willingness of patients with DM to use mobile health applications and associated factors for self-care management. The result showed that willingness to use mHealth applications among patients with DM in the Oromia region was high (71.4%, 95% CI (66.8% to 75.9%)). This result was in line with the willingness to use

[ad_1] Discussion In this study, we have investigated Swedish breast radiologists’ views on the use of AI in mammography screening. The respondents were, to a large extent, positive towards the integration of AI in screen reading, especially those having difficulties finding the time to perform screen-reading. This could explain the slightly more positive attitude, compared with general studies on radiologists’

[ad_1] Discussion The majority of Trusts responding reported having an EPR and over 20 different providers were identified as operational in NHS Trusts in England. Three-quarters of digital Trusts responded that they had a DSA and most these use NEWS2 as part of their sepsis alerting system. This is the approach included in the NHS National Standard Contract.16 There is

[ad_1] Discussion The majority of Trusts responding reported having an EPR and over 20 different providers were identified as operational in NHS Trusts in England. Three-quarters of digital Trusts responded that they had a DSA and most these use NEWS2 as part of their sepsis alerting system. This is the approach included in the NHS National Standard Contract.16 There is

[ad_1] Two BMJ Health & Care Informatics editors’ choice papers present insights based on case studies from real-world data and machine learning models for clinical risk prediction use cases. Seneviratne et al focus on case management to demonstrate how one might implement their proposed user-centred design toolkit consisting of process maps, storyboards and four questions.1 This toolkit was developed to

[ad_1] Discussion To the best of our knowledge, this is the first scoping review which attempts to identify the characteristics of studies in which different types of CDSSs were used to effectively support clinical decision in different settings. Previous scoping reviews have focused on CDSSs for medication review, rare-disease diagnosis, non-knowledge-based clinical decision support tools and on CDSSs to be

[ad_1] Discussion For this study, 2016 EDHS data were used that was accessed from the DHS website. Through request, permission was obtained to access the data. There are no attributes that uniquely identify individuals’ women or household addresses in the data files. This is because the geographical coordinate files are randomly displaced within a large geographical area, and it is

[ad_1] Discussion We evaluated a range of text classifiers, achieving the highest F1 score on the test set of 0.51 for conventional BERT, with recall at 56% and precision at 55%, substantially better than n-gram-based classifiers (objective 1). This classifier was trained on medical code descriptions, which outperformed standard supervision with a training set of 191 transcripts (those with no

[ad_1] Abstract Introduction Obsolete bleep/long-range pager equipment remains firmly embedded in the National Health Service (NHS). Objective To introduce a secure, chart-integrated messaging system (Epic Secure Chat) in a large NHS tertiary referral centre to replace non-emergency bleeps/long-range pagers. Methods The system was socialised in the months before go-live. Operational readiness was overseen by an implementation group with stakeholder engagement.

[ad_1] Discussion The proposed algorithm applied to the CCR Nutrition database, which is a multicente case–control study conducted in a population of 1496 pairs of Moroccan subjects with and without CRC, identified 2 dietary profiles associated with CRC: the ‘dangerous pattern’ and the ‘prudent profile’. The ‘dangerous pattern’ was characterised by a high consumption of vegetable oil, cakes, chocolate, cheese,

[ad_1] Results The optimised HURT model had a final test AUROC of 84% (95% CI 83.4% to 84.9%), while HLCC had an AUROC of 71% (95% CI 69.4% to 71.8%) (figure 1). The difference between HURT and HLCC ROC was statistically significant with Z=−22.6, p<0.001 (Delong test). ROC test performance of HLCC and HURT models predicting three or more unplanned

[ad_1] Introduction The health information gathered from sources at the facility and population level influences the quality of healthcare policy formulation, programme planning, monitoring, and evaluation, and healthcare service delivery as a whole.1 2 Alternatively, facility-level data sources can sometimes be referred to as ‘routine health information data sources’, whereas population-level data sources are known as ‘non-routine health information sources’.3

[ad_1] Results From the four units involved, data were acquired from 368 patients (table 2). Patient records used in data analysis Data from 40 type 1 (typical) patients were easily collected from each unit. Of the other types, amount collected from units over the maximum 4-year time period varied. Survival plots for patient types are shown in figure 1A. These