What started as a cluster of patients with a mysterious respiratory illness in Wuhan, China, in December 2019, was later determined to be coronavirus disease 2019 (COVID-19). The pathogen severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), a novel Betacoronavirus , was subsequently isolated as the causative agent. SARS-CoV-2 is transmitted by respiratory droplets and fomites and presents clinically with fever, fatigue, myalgias, conjunctivitis, anosmia, dysgeusia, sore throat, nasal congestion, cough, dyspnea, nausea, vomiting, and/or diarrhea. In most critical cases, symptoms can escalate into acute respiratory distress syndrome accompanied by a runaway inflammatory cytokine response and multiorgan failure. As of this article's publication date, COVID-19 has spread to approximately 200 countries and territories, with over 4.3 million infections and more than 290,000 deaths as it has escalated into a global pandemic. Public health concerns mount as the situation evolves with an increasing number of infection hotspots around the globe. New information about the virus is emerging just as rapidly. This has led to the prompt development of clinical patient risk stratification tools to aid in determining the need for testing, isolation, monitoring, ventilator support, and disposition. COVID-19 spread is rapid, including imported cases in travelers, cases among close contacts of known infected individuals, and community-acquired cases without a readily identifiable source of infection. Critical shortages of personal protective equipment and ventilators are compounding the stress on overburdened healthcare systems. The continued challenges of social distancing, containment, isolation, and surge capacity in already stressed hospitals, clinics, and emergency departments have led to a swell in technologically-assisted care delivery strategies, such as telemedicine and web-based triage. As the race to develop an effective vaccine intensifies, several clinical trials of antivirals and immune modulators are underway, though no reliable COVID-19-specific therapeutics (inclusive of some potentially effective single and multi-drug regimens) have been identified as of yet. With many nations and regions declaring a state of emergency, unprecedented quarantine, social distancing, and border closing efforts are underway. Implementation of social and physical isolation measures has caused sudden and profound economic hardship, with marked decreases in global trade and local small business activity alike, and full ramifications likely yet to be felt. Current state-of-science, mitigation strategies, possible therapies, ethical considerations for healthcare workers and policymakers, as well as lessons learned for this evolving global threat and the eventual return to a “new normal” are discussed in this article.

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Health care is evolving and with it the need to reform medical education. As the practice of medicine enters the age of artificial intelligence (AI), the use of data to improve clinical decision making will grow, pushing the need for skillful medicine-machine interaction. As the rate of medical knowledge grows, technologies such as AI are needed to enable health care professionals to effectively use this knowledge to practice medicine. Medical professionals need to be adequately trained in this new technology, its advantages to improve cost, quality, and access to health care, and its shortfalls such as transparency and liability. AI needs to be seamlessly integrated across different aspects of the curriculum. In this paper, we have addressed the state of medical education at present and have recommended a framework on how to evolve the medical education curriculum to include AI.

IMPORTANCE Although inadequate sleep has a proven negative association with health care outcomes, to date, no large-scale studies have examined sleep in general hospital wards. OBJECTIVES To assess the subjective quantity and quality of sleep and to identify the hospital-related factors associated with sleep disturbances in hospitalized patients. DESIGN For this nationwide, single-day, multicenter, cross-sectional, observational study, which took place on February 22, 2017, all hospitals in the Netherlands were encouraged by word of mouth and conventional and social media to participate in this study. A total of 39 hospitals participated. Included patients were at least 18 years of age, were able to give informed consent, and had spent at least 1 night in a regular-care hospital ward. EXPOSURES Hospitalization in a regular-care ward. MAIN OUTCOMES AND MEASURES Quantity and quality of last night's sleep in the hospital compared with habitual sleep at home the month before hospitalization. The Consensus Sleep Diary and the Dutch-Flemish Patient-Reported Outcomes Measurement Information System (PROMIS) Sleep Disturbance item bank were used. Complementary questions assessed sleep-disturbing factors. RESULTS A total of 2005 patients were included (median age, 68 years; interquartile range, 57-77 years; 994 of 1935 [51.4%] were male [70 patients did not identify their sex]). Compared with habitual sleep at home, the total sleep time in the hospital was 83 minutes (95% CI, 75-92 minutes; P < .001) shorter. The mean number of nocturnal awakenings was 2.0 (95% CI, 1.9-2.1) times at home vs 3.3 (95% CI, 3.2-3.5) times during hospitalization (P < .001). Patients woke up 44 minutes (95% CI, 44-45 minutes; P < .001) earlier than their habitual wake-up time at home. A total of 1344 patients (70.4%) reported having been awakened by external causes, which in 718 (35.8%) concerned hospital staff. All aspects of sleep quality measured using PROMIS questions were rated worse during hospitalization than at home. The most reported sleep-disturbing factors were noise of other patients, medical devices, pain, and toilet visits. CONCLUSIONS AND RELEVANCE This study demonstrated that the duration and quality of sleep in hospitalized patients were significantly affected and revealed many potentially modifiable hospital-related factors negatively associated with sleep. Raising awareness about the importance of adequate sleep in the vulnerable hospital population and introducing interventions to target sleep-disturbing factors may improve healing.

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Background Computed tomography (CT) is thought to play a key role in COVID-19 diagnostic work-up. The possibility to compare data across different settings depends on the systematic and reproducible manner the scans are analyzed and reported. The COVID-19 Reporting and Data System (CO-RADS) and the corresponding CT severity score (CTSS) introduced by the Radiological Society of the Netherlands (NVvR) attempt to do so. However, this system has not been externally validated. Research question We aimed to prospectively validate the CO-RADS as a COVID-19 diagnostic tool at the emergency department (ED), and evaluate if the CTSS is associated with prognosis. Study Design Methods We conducted a prospective, observational study in two tertiary centers in The Netherlands, between March 19 and May 28, 2020. We consecutively included 741 adult patients at the ED with suspected COVID-19, who received a chest CT and SARS-CoV-2 PCR (PCR). Diagnostic accuracy measures were calculated for CO-RADS using PCR as reference. Logistic regression was performed for CTSS in relation to hospital admission, ICU admission and 30-day mortality. Results 741 patients were included. We found an AUC of 0.91 (CI 0.89-0.94) for CO-RADS using PCR as reference. The optimal CO-RADS cut-off was 4, with a sensitivity of 89.4% (CI 84.7-93.0) and specificity of 87.2% (CI 83.9-89.9). We found a significant association between CTSS and hospital admission, ICU admission, and 30-day mortality; adjusted odds ratios per point increase in CTSS were 1.19 (CI 1.09-1.28), 1.23 (1.15-1.32), 1.14 (1.07-1.22), respectively. Intra-class correlation coefficients for CO-RADS and CTSS were 0.94 (0.91-0.96) and 0.82 (CI 0.70-0.90). Interpretation Our findings support the use of CO-RADS and CTSS in triage, diagnosis and management decisions for patients presenting with possible COVID-19 at the ED.

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