Digital Health Primer for Cardiothoracic Surgeons

Baxter, Ronald; Fann, James I.; DiMaio, J. Michael; Lobdell, Kevin W.

doi:10.1016/j.athoracsur.2020.02.072

Cited by 14 publications

(16 citation statements)

References 52 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The process of producing AI algorithms is complex, requiring experts, computing power, and large volumes of robust data structured in an organized fashion. AI algorithms can be static (unchanged over time) or dynamic, whereby data and outcomes create a virtuous cycle by continuing to inform and update the algorithms (58,59). This dynamic process and its potential are fueling rapid growth in health care AI; future investment in this area is projected to be $6.6 billion in 2021 and $36 billion by 2025 (with a compound annual growth rate of 40% to 45%) (60,61).…”

Section: Intelligent Computingmentioning

confidence: 99%

Technological advances to enhance recovery after cardiac surgery

Lobdell¹,

Appoo²,

Rose³

et al. 2021

J Hosp Manag Health Policy

View full text Add to dashboard Cite

Health care is expensive. In 2017, the global burden of disease (GBD) was estimated to be $7.8 trillion (in US dollars), or about 10% of the total global gross domestic product, which translates into approximately $1,080 per capita for the world's population. In the United States, comparable numbers for health care costs are 19% of US gross national product and $10,586 per capita (1,2). Moreover, this share of the GNP will continue to rise in the foreseeable future for several reasons, including the aging of the population and the advent of complex diagnostic and therapeutic advances. Indeed, in an International Business Machines (IBM) poll in 2012 of 480 economists, health care was determined to be the most inefficient system in the global economy, with waste estimated to be $2.5 to 4 trillion

show abstract

Section: Intelligent Computingmentioning

confidence: 99%

Technological advances to enhance recovery after cardiac surgery

Lobdell¹,

Appoo²,

Rose³

et al. 2021

J Hosp Manag Health Policy

View full text Add to dashboard Cite

show abstract

“…New evidence, updated guidelines and standards of practice, innovations in healthcare delivery, and program evolution guided by patient's feedback will challenge a mature program to either adapt or risk long-term failure. 19,33 In addition to the immediate benefits of developing an active ERP now-even if modified for COVID-19 considerations-a foundation will have been created for future growth once the bulk of the pandemic limitations have subsided. The establishment of a network of engaged team members and the introduction of ERPs within each institution's culture will benefit all stakeholders.…”

Section: Step 3: Plan and Develop The Erp Protocolmentioning

confidence: 99%

Cardiac Surgery-Enhanced Recovery Programs Modified for COVID-19: Key Steps to Preserve Resources, Manage Caseload Backlog, and Improve Patient Outcomes

Gregory

Grant²,

Boyle

et al. 2020

Journal of Cardiothoracic and Vascular Anesthesia

View full text Add to dashboard Cite

“…The rise of machine learning is driven by the ability to process “big data” and the need to deliver the best possible value- and evidence-based care. The utility of artificial intelligence (AI) coupled with machine learning, has generated much interest and many studies in clinical medicine [ 61 , 65 , 66 , 67 , 68 , 69 , 70 , 71 , 72 , 73 , 74 , 75 , 76 , 77 , 78 , 79 ]. The machine learning approach has been developed recently for advantages in performance and extensibility and has become indispensable for solving complex problems in most sciences [ 80 , 81 , 82 ].…”

Section: Predicting Csa-aki By Machine Learningmentioning

confidence: 99%

Predicting Acute Kidney Injury after Cardiac Surgery by Machine Learning Approaches

Hansrivijit

Vallabhajosyula

Mekraksakit

et al. 2020

JCM

View full text Add to dashboard Cite

Cardiac surgery-associated AKI (CSA-AKI) is common after cardiac surgery and has an adverse impact on short- and long-term mortality. Early identification of patients at high risk of CSA-AKI by applying risk prediction models allows clinicians to closely monitor these patients and initiate effective preventive and therapeutic approaches to lessen the incidence of AKI. Several risk prediction models and risk assessment scores have been developed for CSA-AKI. However, the definition of AKI and the variables utilized in these risk scores differ, making general utility complex. Recently, the utility of artificial intelligence coupled with machine learning, has generated much interest and many studies in clinical medicine, including CSA-AKI. In this article, we discussed the evolution of models established by machine learning approaches to predict CSA-AKI.

show abstract

Digital Health Primer for Cardiothoracic Surgeons

Cited by 14 publications

References 52 publications

Technological advances to enhance recovery after cardiac surgery

Technological advances to enhance recovery after cardiac surgery

Cardiac Surgery-Enhanced Recovery Programs Modified for COVID-19: Key Steps to Preserve Resources, Manage Caseload Backlog, and Improve Patient Outcomes

Predicting Acute Kidney Injury after Cardiac Surgery by Machine Learning Approaches

Contact Info

Product

Resources

About