National Health Care Spending In 2017: Growth Slows To Post–Great Recession Rates; Share Of GDP Stabilizes

Martin, Anne; Hartman, Micah; Washington, Benjamin; Catlin, Aaron

doi:10.1377/hlthaff.2018.05085

Cited by 82 publications

(76 citation statements)

References 1 publication

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“…AI has become essential to the operations of large technology companies, including NVIDIA, GE, Facebook, Amazon, Microsoft, Google, and Apple, which together are valued at $3 trillion. Putting this into perspective, the US healthcare spending in 2017 was $3.5 trillion [14] and the US federal government received $3.3 trillion in tax revenue in 2017 [15]. Because AI systems are a strategic asset to company operations, these tech titans have invested large amounts of capital into developing new AI algorithms, which can subsequently be applied to the medical field.…”

Section: The Growth Of Artificial Intelligence and Its Ecosystemmentioning

confidence: 99%

Artificial intelligence in radiology: the ecosystem essential to improving patient care

et al. 2020

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Section: The Growth Of Artificial Intelligence and Its Ecosystemmentioning

confidence: 99%

Artificial intelligence in radiology: the ecosystem essential to improving patient care

et al. 2020

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“…2 As healthcare spending increased to more than 17.9% of the total US gross domestic product in 2017, lowering the cost of medicine is a rapidly growing concern. 3 Development of less costly biosimilar mAb therapeutics is a focus for both innovator and generic biopharmaceutical companies. 4,5 It is estimated biosimilars will comprise 410% of the global biologics market by 2020, with the US being the biggest markets for biosimilars.…”

Section: Introductionmentioning

confidence: 99%

A rapid method for relative quantification ofN-glycans from a therapeutic monoclonal antibody during trastuzumab biosimilar development

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Stone

Berdugo

et al. 2020

mAbs

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Glycosylation is a common post-translational modification and critical quality attribute that can modulate the efficacy of therapeutic proteins. In the production of monoclonal antibodies (mAbs), quantifying the glycoform profile is a vital characterization step. Traditional glycan analysis is time consuming and involves steps at extreme temperature or pH, which may alter glycans. Here, we describe a rapid method for glycan analysis in which glycans are released from mAb samples that are bound to protein A columns. Since host cell proteins, which may also contain glycans, were already removed, this step enables analysis of cell culture products. Glycans released from the mAb samples are then derivatized with InstantPC™ labeling agent and analyzed by HILIC-FLD-MS. To illustrate the method, the glycan profiles of six trastuzumab (Herceptin®) antibody lots and four biosimilar developmental lots were analyzed. The results derived from our novel method, which takes less than 90 min, are compared with those from a typical glycan preparation approach.

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“…Healthcare expenditures in the United States are extremely high: in 2017, the United States spent $3.5 trillion dollars on healthcare or $10,739 per person; this figure represents 17.9% of the nation's gross domestic product (GDP; Martin et al, 2019). The United States has the highest per capita healthcare expenditures of any Organization for Economic Co‐operation and Development (OECD) country; indeed, US per capita spending is 145% of the OECD median (Anderson, Hussey, & Petrosyan, 2019).…”

Section: Introductionmentioning

confidence: 99%

The effect of increased cost‐sharing on low‐value service use

et al. 2020

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We examine the effect of a value‐based insurance design (VBID) program implemented at a large public employer in the state of Oregon. The program substantially increased cost‐sharing for several healthcare services likely to be of low value for most patients: diagnostic services (e.g., imaging services) and surgeries (e.g., spinal surgeries for pain). Using a difference‐in‐differences design coupled with granular, administrative health insurance claims data over the period 2008–2012, we estimate the change in low‐value service use among beneficiaries before and after program implementation relative to a comparison group not exposed to the VBID. Our findings suggest that the VBID significantly reduced the use of targeted services, with an implied elasticity of demand of −0.22. We find no evidence that the VBID led to substitution to non‐targeted services or increased overall healthcare costs. However, we also observe no evidence that the program led to cost‐savings.

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National Health Care Spending In 2017: Growth Slows To Post–Great Recession Rates; Share Of GDP Stabilizes

Cited by 82 publications

References 1 publication

Artificial intelligence in radiology: the ecosystem essential to improving patient care

Artificial intelligence in radiology: the ecosystem essential to improving patient care

A rapid method for relative quantification ofN-glycans from a therapeutic monoclonal antibody during trastuzumab biosimilar development

The effect of increased cost‐sharing on low‐value service use

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