A Pragmatic, Stepped-Wedge, Cluster-controlled Clinical Trial of Real-Time Pneumonia Clinical Decision Support

Dean, Nathan C.; Vines, Caroline; Carr, Jason; Rubin, Jenna; Webb, Brandon; Jacobs, Jason R.; Butler, Alison; Lee, Jae‐Hoon; Jephson, Al; Jenson, Nathan; Walker, Missy; Brown, Samuel M.; Irvin, Jeremy; Lungren, Matthew P.; Allen, Todd L.

doi:10.1164/rccm.202109-2092oc

Cited by 12 publications

(13 citation statements)

References 22 publications

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“…Several studies have suggested outpatient management for more patients with pneumonia may safely reduce unnecessary admissions and costs and improve patient experience 40 . Innovations that leverage decision support to assist clinicians with guideline‐based objective severity assessment has been one strategy that has shown some promise to reduce low‐risk admissions, 27,41,42 although the specific impact of these tools on older patients may need to be carefully considered. Another innovation that could support safe outpatient discharges includes hospital‐at‐home programs 29,41 .…”

Section: Discussionmentioning

confidence: 99%

Hospital admission decisions for older Veterans with community‐onset pneumonia: An analysis of 118 U.S. Veterans Affairs Medical Centers

Jones

Jin

Nevers

et al. 2023

Academic Emergency Medicine

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Objectives: Age is important for prognosis in community-onset pneumonia, but how it influences admission decisions in the emergency department (ED) is not well characterized. Using clinical data from the electronic health record in a national cohort, we examined pneumonia hospitalization patterns, variation, and relationships with mortality among older versus younger Veterans. Methods:In a retrospective cohort of patients ≥ 18 years presenting to EDs with a diagnosis of pneumonia at 118 VA Medical Centers January 1, 2006, to December 31, 2016, we compared observed, predicted, and residual hospitalization risk for Veterans < 70, 70-79, and ≥ 80 years of age using generalized estimating equations and machine learning models with 71 patient factors. We examined facility variation in residual hospitalization across facilities and explored whether facility differences in hospitalization risk correlated with differences in 30-day mortality.Results: Among 297,498 encounters, 165,003 (55%) were for Veterans < 70 years, 61,076 (21%) 70-80, and 71,419 (24%) ≥ 80. Hospitalization rates were 52%, 67%, and 76%, respectively. After other patient factors were adjusting for, age 70-79 had an odds ratio (OR) of 1.39 (95% confidence interval [CI] 1.34-1.44) and ≥ 80 had an OR of 2.1 (95% CI 2.0-2.2) compared to age < 70. There was substantial variation in hospitalization across facilities among Veterans < 70 (<35% hospitalization at the lowest decile of facilities vs. > 66% at the highest decile) that was similar but with higher risk for patients 70-79 years (54% vs. 82%) and ≥ 80 years (59% vs. 85%) and remained after accounting for patient factors, with no consistently positive or negative associations with facility-level 30-day mortality.Conclusions: Older Veterans with community-onset pneumonia experience high risk of hospitalization, with widespread facility variation that has no clear relationship to short-term mortality.

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Section: Discussionmentioning

confidence: 99%

Hospital admission decisions for older Veterans with community‐onset pneumonia: An analysis of 118 U.S. Veterans Affairs Medical Centers

Jones

Jin

Nevers

et al. 2023

Academic Emergency Medicine

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“…They can assist with a patient assessment to help predict early deterioration of their health status and even classify their types of motion or activities (Z. Liu, Zhu, et al, 2022; Huang et al, 2022). These AI algorithms can process large datasets to recognize and learn complex patterns for decision‐making (Dean et al, 2022). Recent increases in computational speed have led to the development of even more powerful artificial neural networks and deep learning algorithms that can handle and optimize very complex datasets (Bini, 2018; Kalfa et al, 2020).…”

Section: Introductionmentioning

confidence: 99%

Remote patient monitoring using artificial intelligence: Current state, applications, and challenges

Shaik

Tao

Higgins

et al. 2023

WIREs Data Min & Knowl

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The adoption of artificial intelligence (AI) in healthcare is growing rapidly.Remote patient monitoring (RPM) is one of the common healthcare applications that assist doctors to monitor patients with chronic or acute illness at remote locations, elderly people in-home care, and even hospitalized patients. The reliability of manual patient monitoring systems depends on staff time management which is dependent on their workload. Conventional patient monitoring involves invasive approaches which require skin contact to monitor health status. This study aims to do a comprehensive review of RPM systems including adopted advanced technologies, AI impact on RPM, challenges and trends in AI-enabled RPM. This review explores the benefits and challenges of patient-centric RPM architectures enabled with Internet of Things wearable devices and sensors using the cloud, fog, edge, and blockchain technologies. The role of AI in RPM ranges from physical activity classification to chronic disease monitoring and vital signs monitoring in emergency settings. This review results show that AI-enabled RPM architectures have transformed healthcare monitoring applications because of their ability to detect early deterioration in patients' health, personalize individual patient health parameter monitoring using federated learning, and learn human behavior patterns using techniques such as reinforcement learning. This review discusses the challenges and trends to adopt AI to RPM systems and implementation issues. The future directions of AI in RPM applications are analyzed based on the challenges and trends.

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“…In the United Kingdom (UK), the National Screening Committee is exploring the use of AI software in national cancer screening and has awarded £90 million to prospective multi-center trials ( 5 , 6 ). Two large, multi-hospital studies showed the mortality benefit of early detection and treatment of inpatient deterioration and pneumonia ( 7 , 8 ). However, despite advances in technology and policy, isolated success stories are not leading to efficient diffusion of validated AI software across settings.…”

Section: Introductionmentioning

confidence: 99%

Editorial: Surfacing best practices for AI software development and integration in healthcare

Sendak

Vidal

Trujillo

et al. 2023

Front. Digit. Health

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A Pragmatic, Stepped-Wedge, Cluster-controlled Clinical Trial of Real-Time Pneumonia Clinical Decision Support

Cited by 12 publications

References 22 publications

Hospital admission decisions for older Veterans with community‐onset pneumonia: An analysis of 118 U.S. Veterans Affairs Medical Centers

Hospital admission decisions for older Veterans with community‐onset pneumonia: An analysis of 118 U.S. Veterans Affairs Medical Centers

Remote patient monitoring using artificial intelligence: Current state, applications, and challenges

Editorial: Surfacing best practices for AI software development and integration in healthcare

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