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This Viewpoint proposes ethical principles to guide allocation of scarce inpatient therapies for hospitalized COVID-19 patients to maximize patient benefit, mitigate disparities, and minimize clinician burden. [ABSTRACT FROM AUTHOR]

An introduction is presented is which the authors discuss an article in the journal which deals with U.S. physician-pharmaceutical industry relations, conflicts of interest, and the drug prescribing patterns of physicians at academic medical centers.

The authors discuss aspects of telemedicine, which is the use of electronic communication by clinicians and patients to exchange health information. Topics include the incorporation of telemedicine into the existing medical education competencies by the Accreditation Council for Graduate Medical Education (ACGME), implications of telemedicine for education, and the U.S. health care industry.

A table is presented depicting commercial e-visit websites at the medical care industry in the U.S. including NowClinic.com, CallTheDoc.com and AmericanWell.com.

Importance: Accurate prediction of outcomes among patients in intensive care units (ICUs) is important for clinical research and monitoring care quality. Most existing prediction models do not take full advantage of the electronic health record, using only the single worst value of laboratory tests and vital signs and largely ignoring information present in free-text notes. Whether capturing more of the available data and applying machine learning and natural language processing (NLP) can improve and automate the prediction of outcomes among patients in the ICU remains unknown., Objectives: To evaluate the change in power for a mortality prediction model among patients in the ICU achieved by incorporating measures of clinical trajectory together with NLP of clinical text and to assess the generalizability of this approach., Design, Setting, and Participants: This retrospective cohort study included 101 196 patients with a first-time admission to the ICU and a length of stay of at least 4 hours. Twenty ICUs at 2 academic medical centers (University of California, San Francisco [UCSF], and Beth Israel Deaconess Medical Center [BIDMC], Boston, Massachusetts) and 1 community hospital (Mills-Peninsula Medical Center [MPMC], Burlingame, California) contributed data from January 1, 2001, through June 1, 2017. Data were analyzed from July 1, 2017, through August 1, 2018., Main Outcomes and Measures: In-hospital mortality and model discrimination as assessed by the area under the receiver operating characteristic curve (AUC) and model calibration as assessed by the modified Hosmer-Lemeshow statistic., Results: Among 101 196 patients included in the analysis, 51.3% (n = 51 899) were male, with a mean (SD) age of 61.3 (17.1) years; their in-hospital mortality rate was 10.4% (n = 10 505). A baseline model using only the highest and lowest observed values for each laboratory test result or vital sign achieved a cross-validated AUC of 0.831 (95% CI, 0.830-0.832). In contrast, that model augmented with measures of clinical trajectory achieved an AUC of 0.899 (95% CI, 0.896-0.902; P < .001 for AUC difference). Further augmenting this model with NLP-derived terms associated with mortality further increased the AUC to 0.922 (95% CI, 0.916-0.924; P < .001). These NLP-derived terms were associated with improved model performance even when applied across sites (AUC difference for UCSF: 0.077 to 0.021; AUC difference for MPMC: 0.071 to 0.051; AUC difference for BIDMC: 0.035 to 0.043; P < .001) when augmenting with NLP at each site., Conclusions and Relevance: Intensive care unit mortality prediction models incorporating measures of clinical trajectory and NLP-derived terms yielded excellent predictive performance and generalized well in this sample of hospitals. The role of these automated algorithms, particularly those using unstructured data from notes and other sources, in clinical research and quality improvement seems to merit additional investigation.

Importance: The association between industry payments to physicians and prescribing rates of the brand-name medications that are being promoted is controversial. In the United States, industry payment data and Medicare prescribing records recently became publicly available., Objective: To study the association between physicians' receipt of industry-sponsored meals, which account for roughly 80% of the total number of industry payments, and rates of prescribing the promoted drug to Medicare beneficiaries., Design, Setting, and Participants: Cross-sectional analysis of industry payment data from the federal Open Payments Program for August 1 through December 31, 2013, and prescribing data for individual physicians from Medicare Part D, for all of 2013. Participants were physicians who wrote Medicare prescriptions in any of 4 drug classes: statins, cardioselective β-blockers, angiotensin-converting enzyme inhibitors and angiotensin-receptor blockers (ACE inhibitors and ARBs), and selective serotonin and serotonin-norepinephrine reuptake inhibitors (SSRIs and SNRIs). We identified physicians who received industry-sponsored meals promoting the most-prescribed brand-name drug in each class (rosuvastatin, nebivolol, olmesartan, and desvenlafaxine, respectively). Data analysis was performed from August 20, 2015, to December 15, 2015., Exposures: Receipt of an industry-sponsored meal promoting the drug of interest., Main Outcomes and Measures: Prescribing rates of promoted drugs compared with alternatives in the same class, after adjustment for physician prescribing volume, demographic characteristics, specialty, and practice setting., Results: A total of 279 669 physicians received 63 524 payments associated with the 4 target drugs. Ninety-five percent of payments were meals, with a mean value of less than $20. Rosuvastatin represented 8.8% (SD, 9.9%) of statin prescriptions; nebivolol represented 3.3% (7.4%) of cardioselective β-blocker prescriptions; olmesartan represented 1.6% (3.9%) of ACE inhibitor and ARB prescriptions; and desvenlafaxine represented 0.6% (2.6%) of SSRI and SNRI prescriptions. Physicians who received a single meal promoting the drug of interest had higher rates of prescribing rosuvastatin over other statins (odds ratio [OR], 1.18; 95% CI, 1.17-1.18), nebivolol over other β-blockers (OR, 1.70; 95% CI, 1.69-1.72), olmesartan over other ACE inhibitors and ARBs (OR, 1.52; 95% CI, 1.51-1.53), and desvenlafaxine over other SSRIs and SNRIs (OR, 2.18; 95% CI, 2.13-2.23). Receipt of additional meals and receipt of meals costing more than $20 were associated with higher relative prescribing rates., Conclusions and Relevance: Receipt of industry-sponsored meals was associated with an increased rate of prescribing the brand-name medication that was being promoted. The findings represent an association, not a cause-and-effect relationship.