Treatment Gaps in Guideline-Directed Medical Therapy for Patients Undergoing Higher-Risk Percutaneous Coronary Intervention

Cheng, Andre T; Madhavan, Mahesh V.; Kosmidou, Ioanna; Deeconda, Anurag; Karmpaliotis, Dimitri; Moses, Jeffrey W.; Kirtane, Ajay J.

doi:10.1161/circinterventions.121.011464

Cited by 2 publications

(2 citation statements)

References 5 publications

(5 reference statements)

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“…Data analytics and collaborative intelligence offer the unique opportunity to elevate the foundation of medical care and apply scientifically rigorous clinical knowledge during the clinician patient encounter. Guideline directed medical therapy (GDMT), although available, has yet to reach its full potential as well 13 . With the increasing burden and complexity of disease 14 and a declining clinical workforce 6 , 15 , operational sustainability demands engagement with AI to routinely achieve appropriate standards of care and offer novel innovative treatments for patients under our care.…”

Section: Addressing Accelerating Complexity and Exponential Informati...mentioning

confidence: 99%

Collaborative Intelligence to catalyze the digital transformation of healthcare

Bhatt,

Bae

2023

npj Digit. Med.

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Collaborative intelligence reflects the promise and limits of leveraging artificial intelligence (AI) technologies in clinical care. It involves the use of advanced analytics and computing power with an understanding that humans bear responsibility for the accuracy, completeness and any inherent bias found in the training data. Clinicians benefit from using this technology to address increased complexity and information overload, support continuous care and optimized resource allocation, and to enact efforts to eradicate disparities in health care access and quality. This requires active clinician engagement with the technology, a general understanding of how the machine produced its insight, the limitations of the algorithms, and the need to screen datasets for bias. Importantly, by interacting, the clinician and the analytics will create trust based on the clinician’s critical thinking skills leveraged to discern value of machine outputs within clinical context. Utilization of collaborative intelligence should be staged with the level of understanding and evidence. It is particularly well suited to low-complexity non-urgent care and to identifying individuals at rising risk within a population. Clinician involvement in algorithm development and the amassing of evidence to support safety and efficacy will propel adoption. Utilization of collaborative intelligence represents the natural progression of health care innovation, and if thoughtfully constructed and equitably deployed, holds the promise to decrease clinician burden and improve access to care.

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Section: Addressing Accelerating Complexity and Exponential Informati...mentioning

confidence: 99%

Collaborative Intelligence to catalyze the digital transformation of healthcare

Bhatt,

Bae

2023

npj Digit. Med.

View full text Add to dashboard Cite

show abstract

“…For patients undergoing catheterization procedures that can establish the presence of plaque, escalating aggressive secondary prevention is of paramount importance. However, even among patients undergoing higher-risk percutaneous coronary intervention (PCI) procedures, we have reported the proportion of patients with an at-goal LDL-C level at the time of PCI to be under 50% . This is despite the fact that the majority of these patients had known severe ASHD and were treated electively with time available to make adjustments to the lipid-lowering regimen.…”

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confidence: 99%