“Be patient so you don’t become one.”
1990 PostMedCare / MHF
During these times of increased anxieties, the aforementioned holds true as it did when first coined in the early 90s.
I understand how you may be feeling. Anxious for the most current accurate information. So many have felt and are feeling the same. What many have found to counter the negative is to stay informed by utilizing credible resources. No one has all the answers. However, the below resources can help us all to be informed and significantly reduce risk.
Get the latest public health information from the CDC: https://www.coronavirus.gov.
Get the latest research from NIH: https://www.nih.gov/coronavirus.
Medical professional resources all can tap into:
JAMA held a live interview with NIAID Director Anthony Fauci, MD, about updates on the coronavirus.
—AMA has designed a quick guide to support physicians and practices in expediting the implementation of telemedicine.
—CDC held its latest webinar: Coronavirus (COVID-19) Update and Information for Long-term Care Facilities
—The CDC updated guidelines on cruise ship travel to include additional pre-travel advice about in-cabin isolation if patients become sick during a cruise and a 14-day quarantine following completion of any cruise.
—New CDC interim guidance released on March 16 for discontinuation of home isolation for persons with COVID-19 as well as new FAQ for Healthcare Professionals released March 17 that provides additional knowledge regarding COVID-19 at this time.
—AMA issued a letter to the White House to express the urgent need to resolve the mounting problems being raised by the nation’s frontline caregivers with respect to the COVID-19 pandemic.
—New CDC guidance recommends all persons defer any travel on cruise ships and river boats worldwide and provides recommendations to clinicians and state and local health departments to provide patients with pre- and post-travel advice to reduce risk of COVID-19 transmission.
—Special edition of AMA Journal of Ethics Ethics Talk: Editor-in-chief, Audiey Kao, MD, discusses the ethical challenges, including resource scarcity and medical worker obligations, that arise during pandemics with public health expert Dr Matthew Wynia.
The estimated total annual costs of waste were $760 billion to $935 billion and savings from interventions that address waste were $191 billion to $282 billion.
Abstract / Synopsis
Importance The United States spends more on health care than any other country, with costs approaching 18% of the gross domestic product (GDP). Prior studies estimated that approximately 30% of health care spending may be considered waste. Despite efforts to reduce overtreatment, improve care, and address overpayment, it is likely that substantial waste in US health care spending remains.
Objectives To estimate current levels of waste in the US health care system in 6 previously developed domains and to report estimates of potential savings for each domain.
Evidence A search of peer-reviewed and “gray” literature from January 2012 to May 2019 focused on the 6 waste domains previously identified by the Institute of Medicine and Berwick and Hackbarth: failure of care delivery, failure of care coordination, overtreatment or low-value care, pricing failure, fraud and abuse, and administrative complexity. For each domain, available estimates of waste-related costs and data from interventions shown to reduce waste-related costs were recorded, converted to annual estimates in 2019 dollars for national populations when necessary, and combined into ranges or summed as appropriate.
Findings The review yielded 71 estimates from 54 unique peer-reviewed publications, government-based reports, and reports from the gray literature. Computations yielded the following estimated ranges of total annual cost of waste: failure of care delivery, $102.4 billion to $165.7 billion; failure of care coordination, $27.2 billion to $78.2 billion; overtreatment or low-value care, $75.7 billion to $101.2 billion; pricing failure, $230.7 billion to $240.5 billion; fraud and abuse, $58.5 billion to $83.9 billion; and administrative complexity, $265.6 billion. The estimated annual savings from measures to eliminate waste were as follows: failure of care delivery, $44.4 billion to $93.3 billion; failure of care coordination, $29.6 billion to $38.2 billion; overtreatment or low-value care, $12.8 billion to $28.6 billion; pricing failure, $81.4 billion to $91.2 billion; and fraud and abuse, $22.8 billion to $30.8 billion. No studies were identified that focused on interventions targeting administrative complexity. The estimated total annual costs of waste were $760 billion to $935 billion and savings from interventions that address waste were $191 billion to $282 billion.
Conclusions and Relevance In this review based on 6 previously identified domains of health care waste, the estimated cost of waste in the US health care system ranged from $760 billion to $935 billion, accounting for approximately 25% of total health care spending, and the projected potential savings from interventions that reduce waste, excluding savings from administrative complexity, ranged from $191 billion to $282 billion, representing a potential 25% reduction in the total cost of waste. Implementation of effective measures to eliminate waste represents an opportunity reduce the continued increases in US health care expenditures.
The answer is both.
The diagnostic performance of deep learning models is impressive.
However, we must acknowledge AI and the more advanced ML in healthcare are tools. The correct term is Augmented Intelligence (AugI).
AugI in medicine is an approach focusing on AI’s assistive role, emphasizing the fact that technology is designed to enhance human intelligence not replace it.
The aforementioned is illustrated in an excellent 09/25/19 review article in The Lancet: “A comparison of deep learning performance against health-care professionals in detecting diseases from medical imaging: a systematic review and meta-analysis”
“Our review found the diagnostic performance of deep learning models to be equivalent to that of healthcare professionals,” write Livia Faes, MD, of Cantonal Hospital Lucerne in Switzerland, and colleagues.
Diagnosis of disease using deep-learning algorithms “holds enormous potential,” they conclude. “From this exploratory meta-analysis, we cautiously state that the accuracy of deep-learning algorithms is equivalent to healthcare professionals while acknowledging that more studies considering the integration of such algorithms in real-world settings are needed.”