scholarly journals Otolaryngologists’ Role in Redeployment During the COVID-19 Pandemic: A Commentary

2020 ◽  
Vol 163 (1) ◽  
pp. 94-95 ◽  
Author(s):  
Taha Z. Shipchandler ◽  
B. Ryan Nesemeier ◽  
Cecelia E. Schmalbach ◽  
Jonathan Y. Ting
Keyword(s):  
Health Care ◽  
Intensive Care ◽  
Intensive Care Units ◽  
Emergency Departments ◽  
Health Care Systems ◽  
Care Community ◽  
Care Systems ◽  
Hospital Wards

As otolaryngologists, we identify as subspecialists and fellowship-trained surgeons and may even identify as “super-subspecialists.” The likelihood of being redeployed and drawing from knowledge learned during our postgraduate year 1 training seemed exceedingly unlikely until physician resources became scarce in some health care systems during the COVID-19 pandemic. More now than ever, it is evident that our broad training is valuable in helping patients and allowing the otolaryngologist to meaningfully contribute to the larger health care community, especially while the majority (70%-95%) of elective care is delayed. With our skill set, otolaryngologists are poised to support various aspects of hospital wards, intensive care units, emergency departments, and beyond.

scholarly journals Clinical Predictive Models for COVID-19: Systematic Study (Preprint)

2020 ◽  
Author(s):  
Patrick Schwab ◽  
August DuMont Schütte ◽  
Benedikt Dietz ◽  
Stefan Bauer
Keyword(s):  
Machine Learning ◽  
Health Care ◽  
Intensive Care ◽  
Clinical Data ◽  
Predictive Models ◽  
Health Care Systems ◽  
Gradient Boosting ◽  
Support Vector ◽  
Care Systems ◽  
To Receive

BACKGROUND COVID-19 is a rapidly emerging respiratory disease caused by SARS-CoV-2. Due to the rapid human-to-human transmission of SARS-CoV-2, many health care systems are at risk of exceeding their health care capacities, in particular in terms of SARS-CoV-2 tests, hospital and intensive care unit (ICU) beds, and mechanical ventilators. Predictive algorithms could potentially ease the strain on health care systems by identifying those who are most likely to receive a positive SARS-CoV-2 test, be hospitalized, or admitted to the ICU. OBJECTIVE The aim of this study is to develop, study, and evaluate clinical predictive models that estimate, using machine learning and based on routinely collected clinical data, which patients are likely to receive a positive SARS-CoV-2 test or require hospitalization or intensive care. METHODS Using a systematic approach to model development and optimization, we trained and compared various types of machine learning models, including logistic regression, neural networks, support vector machines, random forests, and gradient boosting. To evaluate the developed models, we performed a retrospective evaluation on demographic, clinical, and blood analysis data from a cohort of 5644 patients. In addition, we determined which clinical features were predictive to what degree for each of the aforementioned clinical tasks using causal explanations. RESULTS Our experimental results indicate that our predictive models identified patients that test positive for SARS-CoV-2 a priori at a sensitivity of 75% (95% CI 67%-81%) and a specificity of 49% (95% CI 46%-51%), patients who are SARS-CoV-2 positive that require hospitalization with 0.92 area under the receiver operator characteristic curve (AUC; 95% CI 0.81-0.98), and patients who are SARS-CoV-2 positive that require critical care with 0.98 AUC (95% CI 0.95-1.00). CONCLUSIONS Our results indicate that predictive models trained on routinely collected clinical data could be used to predict clinical pathways for COVID-19 and, therefore, help inform care and prioritize resources.

scholarly journals Intensive Treatments in Adolescent Anorexia Nervosa

Nutrients ◽  
2021 ◽  
Vol 13 (4) ◽  
pp. 1265
Author(s):  
Beate Herpertz-Dahlmann
Keyword(s):  
Health Care ◽  
Anorexia Nervosa ◽  
Intensive Care ◽  
Health Care Systems ◽  
Intensive Treatment ◽  
Stepped Care ◽  
Patient Treatment ◽  
Care Systems ◽  
Treatment Research ◽  
Adolescent Anorexia Nervosa

Approximately one-fifth to one-third of patients with adolescent anorexia nervosa (AN) need intensive care in the course of their illness. This article provides an update and discussion on different levels of intensive care (inpatient treatment (IP), day patient treatment (DP) and home treatment (HoT)) in different health care systems based on recently published literature. Important issues discussed in this article are new recommendations for the refeeding process and the definition of target weight as well as principles of medical stabilization and psychotherapeutic approaches. The pros and cons of longer or shorter hospitalization times are discussed, and the advantages of stepped care and day patient treatment are described. A new promising intensive treatment method involving the patient, their caregivers and the direct home environment is introduced. Parents and caregivers should be included in treatment research to foster collaborative work with the attending clinicians. There is an urgent need to evaluate the mid- to long-term outcomes of various intensive treatment programs to compare their effectiveness and costs across different health care systems. This could help policy makers and other stakeholders, such as public and private insurances, to enhance the quality of eating disorder care.

scholarly journals Clinical Predictive Models for COVID-19: Systematic Study

10.2196/21439 ◽  
2020 ◽  
Vol 22 (10) ◽  
pp. e21439 ◽  
Author(s):  
Patrick Schwab ◽  
August DuMont Schütte ◽  
Benedikt Dietz ◽  
Stefan Bauer
Keyword(s):  
Machine Learning ◽  
Health Care ◽  
Intensive Care ◽  
Clinical Data ◽  
Predictive Models ◽  
Health Care Systems ◽  
Gradient Boosting ◽  
Support Vector ◽  
Care Systems ◽  
To Receive

Background COVID-19 is a rapidly emerging respiratory disease caused by SARS-CoV-2. Due to the rapid human-to-human transmission of SARS-CoV-2, many health care systems are at risk of exceeding their health care capacities, in particular in terms of SARS-CoV-2 tests, hospital and intensive care unit (ICU) beds, and mechanical ventilators. Predictive algorithms could potentially ease the strain on health care systems by identifying those who are most likely to receive a positive SARS-CoV-2 test, be hospitalized, or admitted to the ICU. Objective The aim of this study is to develop, study, and evaluate clinical predictive models that estimate, using machine learning and based on routinely collected clinical data, which patients are likely to receive a positive SARS-CoV-2 test or require hospitalization or intensive care. Methods Using a systematic approach to model development and optimization, we trained and compared various types of machine learning models, including logistic regression, neural networks, support vector machines, random forests, and gradient boosting. To evaluate the developed models, we performed a retrospective evaluation on demographic, clinical, and blood analysis data from a cohort of 5644 patients. In addition, we determined which clinical features were predictive to what degree for each of the aforementioned clinical tasks using causal explanations. Results Our experimental results indicate that our predictive models identified patients that test positive for SARS-CoV-2 a priori at a sensitivity of 75% (95% CI 67%-81%) and a specificity of 49% (95% CI 46%-51%), patients who are SARS-CoV-2 positive that require hospitalization with 0.92 area under the receiver operator characteristic curve (AUC; 95% CI 0.81-0.98), and patients who are SARS-CoV-2 positive that require critical care with 0.98 AUC (95% CI 0.95-1.00). Conclusions Our results indicate that predictive models trained on routinely collected clinical data could be used to predict clinical pathways for COVID-19 and, therefore, help inform care and prioritize resources.

scholarly journals Covid-19 and infection in health-care workers: An emerging problem

2020 ◽  
Vol 88 (2) ◽  
pp. 65-66 ◽  
Author(s):  
Rosario Barranco ◽  
Francesco Ventura
Keyword(s):  
Health Care ◽  
Intensive Care ◽  
Health Care System ◽  
Health Care Workers ◽  
Exponential Growth ◽  
Health Care Systems ◽  
Care Workers ◽  
The World ◽  
Care Systems ◽  
Coronavirus Infection

The 2019 coronavirus infection (called SARS-CoV-2) began in Wuhan, spread rapidly throughout the world. In many countries the exponential growth of Covid-19 cases is overwhelming health care systems with overcrowding of hospitals and overflowing Intensive Care Units. While people must stay at home to reduce the spread of this virus health-care workers do the exact opposite. In some countries doctors are working with insufficient protection and are constantly at risk of contracting Covid-19. Health-care workers should be constantly monitored because if they are infected they may spread the virus to colleagues, hospitalized patients and even family members. Increased rates of infection in health-care workers could cause the health-care system to collapse and a further worsening of the pandemic; if there are too few doctors it will be even more difficult to manage.

Intensive care services during a pandemic: who should be driving the messaging?

2020 ◽  
Vol 22 (2) ◽  
pp. 171-172
Author(s):  
Balasubramanian Venkatesh ◽  
◽  
◽  
◽  
◽  
...  
Keyword(s):  
Health Care ◽  
Intensive Care ◽  
Health Care Services ◽  
Health Care Systems ◽  
Infection Rates ◽  
Death Rates ◽  
Admission Rates ◽  
Care Services ◽  
The World ◽  
Care Systems

To the Editor: The coronavirus disease 2019 (COVID-19) pandemic has resulted in about 2 million infections and more than 100 000 deaths worldwide to date. It has also placed an unprecedented demand on health care systems around the world and, in some countries, the surges in infection rates have overwhelmed the capacity of health care services. The admission rates to intensive care units (ICUs) — as a proportion of patients with confirmed infection — have ranged from a little over 1% in Australia to 5% in China to 16% in Italy. Death rates have also varied from less than 0.5% in Australia to as high as 10% in some countries. These rates are also influenced by the testing criteria for infection, which differ in each country.

scholarly journals Should critically ill patients with COVID-19 be managed in high-volume ICUs?

2020 ◽  
Vol 23 (4) ◽  
pp. 394-396
Author(s):  
Mahesh Ramanan ◽  
◽  
Aidan Burrell ◽  
Andrew Udy ◽  
◽  
...  
Keyword(s):  
Health Care ◽  
Intensive Care ◽  
Regional Variation ◽  
Health Care Systems ◽  
Standard Of Care ◽  
High Volume ◽  
Case Volume ◽  
Care Systems ◽  
Volume Outcome ◽  
Maximal Capacity

To the Editor: The coronavirus disease 2019 (COVID-19) pandemic has resulted in 38 394 169 cases and 1 089 047 deaths worldwide as of 15 October 2020, although to date, Australia has been relatively spared, with only 11 441 cases and 118deaths. Globally, health care systems and intensive care units (ICUs) have been under immense pressure and wide regional variation in mortality has been observed, both between and within countries. It has been suggested that a higher ICU case volume of COVID-19 may be associated with increased mortality, although this has not yet been systematically investigated. Intuitively, a negative volume–outcome association is plausible under pandemic conditions, as a stretched system running above maximal capacity may not be able to deliver its usual standard of care.

160: Cost-Effectiveness of Medical Management Strategies of Nephrolithiasis in Different Health Care Systems

2004 ◽  
Vol 171 (4S) ◽  
pp. 42-43 ◽  
Author(s):  
Yair Latan ◽  
David M. Wilhelm ◽  
David A. Duchene ◽  
Margaret S. Pearle
Keyword(s):  
Health Care ◽  
Cost Effectiveness ◽  
Medical Management ◽  
Health Care Systems ◽  
Management Strategies ◽  
Care Systems
Sign in / Sign up

Export Citation Format

Share Document