📢 Insights into early COVID-19 clinical characteristics
A Wuhan-based study explores the clinical features and epidemiology of novel coronavirus-infected pneumonia (NCIP) in
🔗 Read more: https://tnyp.me/WWGkouNh/m
pub2post
@Pub2Post
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Trade-offs in Medical AI
🤖 Lack of relevant metrics hinders AI integration.
🔬Absence of clinical trials affects validation.
👥 Need for patient and physician involvement.
T1
Examining policies for managing T2D and NCDs in Kenya and Tanzania during COVID-19:
Kenya and Tanzania updated NCD policies but gaps remain
Disasters require robust healthcare integration and resource planning
#T2D #NCD #COVID19 #Kenya #Tanzania
Policies for type 2 diabetes and non-communicable disease management during the COVID-19 pandemic in Kenya and Tanzania: a desk review and views of decision-makers
Background The COVID-19 pandemic caused disruptions in care that adversely affected the management of non-communicable diseases (NCDs) globally. Countries have responded in various ways to support people with NCDs during the pandemic. This study aimed to identify policy gaps, if any, in the management of NCDs, particularly diabetes, during COVID-19 in Kenya and Tanzania to inform recommendations for priority actions for NCD management during any future similar crises. Methods We undertook a desk review of pre-existing and newly developed national frameworks, policy models and guidelines for addressing NCDs including type 2 diabetes. This was followed by 13 key informant interviews with stakeholders involved in NCD decision-making: six in Kenya and seven in Tanzania. Thematic analysis was used to analyse the documents. Results Seventeen guidance documents were identified (Kenya=10; Tanzania=7). These included pre-existing and/or updated policies/strategic plans, guidelines, a letter, a policy brief and a report. Neither country had comprehensive policies/guidelines to ensure continuity of NCD care before the COVID-19 pandemic. However, efforts were made to update pre-existing documents and several more were developed during the pandemic to guide NCD care. Some measures were put in place during the COVID-19 period to ensure continuity of care for patients with NCDs such as longer supply of medicines. Inadequate attention was given to monitoring and evaluation and implementation issues. Conclusion Kenya and Tanzania developed and updated some policies/guidelines to include continuity of care in emergencies. However, there were gaps in the documents and between policy/guideline documents and practice. Health systems need to establish disaster preparedness plans that integrate attention to NCD care to enable them to better handle severe disruptions caused by emergencies such as pandemics. Such guidance needs to include contingency planning to enable adequate resources for NCD care and must also address evaluation of implementation effectiveness. Data are available upon reasonable request. Only the qualitative data can be made available upon reasonable request. All other relevant data are available publicly.
T 2
Impact of 3D-Printed Models in RCC with VTE:
🏥 Reduced surgical complications by 28% (OR: 5.40)
https://tnyp.me/zIbdmx0q/m
Study of NLCS Impact on ASR Performance:
📊 135,647 words analyzed, 3284 NLCS (2.4%)
❓ 76 NLCS used for key info (0.06%)
🔍 WER: Google 11.8%, Amazon 12.8%
⚠️ NLCS misrecognized at 40.8% (Google), 57.2% (Amazon)
#ASR #ClinicalDocumentation #PatientSafety https://tnyp.me/uTyoMoyV/m
📊 135,647 words analyzed, 3284 NLCS (2.4%)
❓ 76 NLCS used for key info (0.06%)
🔍 WER: Google 11.8%, Amazon 12.8%
⚠️ NLCS misrecognized at 40.8% (Google), 57.2% (Amazon)
#ASR #ClinicalDocumentation #PatientSafety https://tnyp.me/uTyoMoyV/m
Comparative trial on AI scribes easing documentation burden:
📉 38% of notes used ambient AI
🏥 Work exhaustion reduced by 0.44 points
⏰ Documentation time decreased by 0.36 hours/day
🔍 Diagnostic coding accuracy improved
#AI #Healthcare #Burnout https://tnyp.me/JrTZkSzb/m
📉 38% of notes used ambient AI
🏥 Work exhaustion reduced by 0.44 points
⏰ Documentation time decreased by 0.36 hours/day
🔍 Diagnostic coding accuracy improved
#AI #Healthcare #Burnout https://tnyp.me/JrTZkSzb/m
pub2calender test1 Ambient AI Scribes in Clinical Practice: A Randomized Trial
📉 Nabla reduced time-in-note by 9.5% compared to controls.
📈 Both AI tools showed improvements in burnouts and task load scores.
🔬 Infrequent inaccuracies observed, necessitating vigilance.
Read full at https://tnyp.me/DkhjcAVB/m
#AIScribes #RCT #ClinicalInnovation
AI advances medicine:
- Automated tasks ⚙️: Biomedical tasks improved.
- Fewer data needed 📉: GAI models excel with less data.
- Complex tasks support 🧩: Agents enhance healthcare.
#AI #Healthcare #Biomedical #DeepLearning https://tnyp.me/z8HpArKW/m
pub2cal AI/ML studies show a rapid increase.
🤖 Only 7.6% FDA regulated.
🔬 56.2% randomized, 58.9% prospective.
🏥 44.2% hospital/clinic-sponsored.
🌍 75.3% from high-income countries.
Studies of Artificial Intelligence/Machine Learning Registered on ClinicalTrials.gov: Cross-Sectional Study With Temporal Trends, 2010-2023
Background: The rapid growth of research in artificial intelligence (AI) and machine learning (ML) continues. However, it is unclear whether this growth reflects an increase in desirable study attributes or merely perpetuates the same issues previously raised in the literature. Objective: This study aims to evaluate temporal trends in AI/ML studies over time and identify variations that are not apparent from aggregated totals at a single point in time. Methods: We identified AI/ML studies registered on ClinicalTrials.gov with start dates between January 1, 2010, and December 31, 2023. Studies were included if AI/ML-specific terms appeared in the official title, detailed description, brief summary, intervention, primary outcome, or sponsors’ keywords. Studies registered as systematic reviews and meta-analyses were excluded. We reported trends in AI/ML studies over time, along with study characteristics that were fast-growing and those that remained unchanged during 2010-2023. Results: Of 3106 AI/ML studies, only 7.6% (n=235) were regulated by the US Food and Drug Administration. The most common study characteristics were randomized (56.2%; 670/1193; interventional) and prospective (58.9%; 1126/1913; observational) designs; a focus on diagnosis (28.2%; 335/1190) and treatment (24.4%; 290/1190); hospital/clinic (44.2%; 1373/3106) or academic (28%; 869/3106) sponsorship; and neoplasm (12.9%; 420/3245), nervous system (12.2%; 395/3245), cardiovascular (11.1%; 356/3245) or pathological conditions (10%; 325/3245; multiple counts per study possible). Enrollment data were skewed to the right: maximum 13,977,257; mean 16,962 (SD 288,155); median 255 (IQR 80-1000). The most common size category was 101-1000 (44.8%; 1372/3061; excluding withdrawn or missing), but large studies (n>1000) represented 24.1% (738/3061) of all studies: 29% (551/1898) of observational studies and 16.1% (187/1163) of trials. Study locations were predominantly in high-income countries (75.3%; 2340/3106), followed by upper-middle-income (21.7%; 675/3106), lower-middle-income (2.8%; 88/3106), and low-income countries (0.1%; 3/3106). The fastest-growing characteristics over time were high-income countries (location); Europe, Asia, and North America (location); diagnosis and treatment (primary purpose); hospital/clinic and academia (lead sponsor); randomized and prospective designs; and the 1-100 and 101-1000 size categories. Only 5.6% (47/842) of completed studies had results available on ClinicalTrials.gov, and this pattern persisted. Over time, there was an increase in not only the number of newly initiated studies, but also the number of completed studies without posted results. Conclusions: Much of the rapid growth in AI/ML studies comes from high-income countries in high-resource settings, albeit with a modest increase in upper-middle-income countries (mostly China). Lower-middle-income or low-income countries remain poorly represented. The increase in randomized or prospective designs, along with 738 large studies (n>1000), mostly ongoing, may indicate that enough studies are shifting from an in silico evaluation stage toward a prospective comparative evaluation stage. However, the ongoing limited availability of basic results on ClinicalTrials.gov contrasts with this field’s rapid advancements and the public registry’s role in reducing publication and outcome reporting biases.
RCT on Impact of Ambient AI on Practitioners' Well-being
📝 38% of 71,487 notes AI-generated
📉 -0.44 points in burnout
🕒 -0.36 hrs/day on notes
📂 Billing code quality improved
Hashtags: #RCT #AmbientAI #PractitionerWellbeing https://tnyp.me/i2GApT1k/m
📝 38% of 71,487 notes AI-generated
📉 -0.44 points in burnout
🕒 -0.36 hrs/day on notes
📂 Billing code quality improved
Hashtags: #RCT #AmbientAI #PractitionerWellbeing https://tnyp.me/i2GApT1k/m
