The Early Impact of the COVID-19 Pandemic on Hospital Finances.

GOAL: The COVID-19 pandemic has caused both short- and long-term impacts on every aspect of society. Hospitals are among the most critical frontliners and have had to continually navigate the challenges caused by the pandemic. In this study, we examined hospitals' financial performance following the onset of the pandemic. METHODS: We used data from the Centers for Medicare & Medicaid Services Healthcare Cost Report Information System. The study sample included all general acute care and critical access hospitals that receive Medicare payments. The primary outcomes included operating margins, net patient revenues, operating expenses, and uncompensated care costs. We tested for average changes from 2019 to 2020 in hospitals' financial outcomes. We also tested for changes in financial outcomes across samples stratified by hospital characteristics: ownership type (investor-owned, nonprofit, and public), Medicaid disproportionate share hospital status, rural status, county uninsured rate quartile, and Medicaid expansion status. PRINCIPAL FINDINGS: Our sample consisted of a balanced panel of 4,059 hospitals (8,118 observations) with data spanning 2019 and 2020. Across the full sample of hospitals, operating margins declined by an average of 5.3 percentage points between 2019 and 2020, equating to a 130% reduction from 2019 levels. Underlying these margin declines, net patient revenues declined by 3.2% on average, while operating expenses increased by 1.5%. We observed no changes in uncompensated care costs despite the large number of job losses that accompanied the pandemic. When stratifying the analysis by hospital characteristics, differences were observed across ownership types. Notably, investor-owned facilities were less affected financially than nonprofit and public hospitals. Although safety-net and rural hospitals generally fared no worse than their non-safety-net and nonrural counterparts, hospitals located in Medicaid expansion states experienced steeper declines in operating margins relative to hospitals located in nonexpansion states, driven by larger relative declines in patient revenues. PRACTICAL APPLICATIONS: The operating margin declines we observed can be attributed to supply-chain issues, persistent labor shortages, and suspension of elective services. The Affordable Care Act reforms in health insurance markets likely helped to insulate hospitals from increases in uncompensated care costs. In the shifting context of the pandemic, it is important to understand hospitals' financial performance so that measures can be taken to address further financial distress that may eventually lead to increased consolidation, hospital closures, and lower quality of care. Our findings stress the need for targeted responses that are tailored to underlying hospital characteristics. Temporary and targeted increases in inpatient and outpatient service prices can help offset revenue losses from the deferment of nonurgent care. Other policies can address the ongoing workforce challenges and supply-chain issues.

Automated diagnosis and prognosis of COVID-19 pneumonia from initial ER chest X-rays using deep learning.

BACKGROUND: Airspace disease as seen on chest X-rays is an important point in triage for patients initially presenting to the emergency department with suspected COVID-19 infection. The purpose of this study is to evaluate a previously trained interpretable deep learning algorithm for the diagnosis and prognosis of COVID-19 pneumonia from chest X-rays obtained in the ED. METHODS: This retrospective study included 2456 (50% RT-PCR positive for COVID-19) adult patients who received both a chest X-ray and SARS-CoV-2 RT-PCR test from January 2020 to March of 2021 in the emergency department at a single U.S. INSTITUTION: A total of 2000 patients were included as an additional training cohort and 456 patients in the randomized internal holdout testing cohort for a previously trained Siemens AI-Radiology Companion deep learning convolutional neural network algorithm. Three cardiothoracic fellowship-trained radiologists systematically evaluated each chest X-ray and generated an airspace disease area-based severity score which was compared against the same score produced by artificial intelligence. The interobserver agreement, diagnostic accuracy, and predictive capability for inpatient outcomes were assessed. Principal statistical tests used in this study include both univariate and multivariate logistic regression. RESULTS: Overall ICC was 0.820 (95% CI 0.790-0.840). The diagnostic AUC for SARS-CoV-2 RT-PCR positivity was 0.890 (95% CI 0.861-0.920) for the neural network and 0.936 (95% CI 0.918-0.960) for radiologists. Airspace opacities score by AI alone predicted ICU admission (AUC = 0.870) and mortality (0.829) in all patients. Addition of age and BMI into a multivariate log model improved mortality prediction (AUC = 0.906). CONCLUSION: The deep learning algorithm provides an accurate and interpretable assessment of the disease burden in COVID-19 pneumonia on chest radiographs. The reported severity scores correlate with expert assessment and accurately predicts important clinical outcomes. The algorithm contributes additional prognostic information not currently incorporated into patient management.

A literature review of causal relationships in 21st century skills and digital leadership

The world today is constantly changing and evolving. The industry 4.0 revolution, digitisation, the arrival of the Internet of Things and the global health emergency caused by the Covid-19 crisis, are factors that have made companies have to adapt their business models and forms working to this new reality. For this reason, the need for improvement and adaptation of skills of employees and organisational capabilities is no longer a competitive advantage but necessary to survive. Digital competences, 21st century skills and digital leadership are concepts that have been widely studied theoretically in the last ten years. In addition, some empirical studies have established causal hypotheses between factors related to the skills of employees and company variables. Resilience, organisational learning capacity, agility, work engagement and motivation are the key factors that companies must promote and ensure among their employees for a correct evolution and adaptation to this new global situation. Copyright © 2022 Inderscience Enterprises Ltd.

An Interpretable Chest CT Deep Learning Algorithm for Quantification of COVID-19 Lung Disease and Prediction of Inpatient Morbidity and Mortality.

RATIONALE AND OBJECTIVES: The burden of coronavirus disease 2019 (COVID-19) airspace opacities is time consuming and challenging to quantify on computed tomography. The purpose of this study was to evaluate the ability of a deep convolutional neural network (dCNN) to predict inpatient outcomes associated with COVID-19 pneumonia. MATERIALS AND METHODS: A previously trained dCNN was tested on an external validation cohort of 241 patients who presented to the emergency department and received a chest computed tomography scan, 93 with COVID-19 and 168 without. Airspace opacity scoring systems were defined by the extent of airspace opacity in each lobe, totaled across the entire lungs. Expert and dCNN scores were concurrently evaluated for interobserver agreement, while both dCNN identified airspace opacity scoring and raw opacity values were used in the prediction of COVID-19 diagnosis and inpatient outcomes. RESULTS: Interobserver agreement for airspace opacity scoring was 0.892 (95% CI 0.834-0.930). Probability of each outcome behaved as a logistic function of the opacity scoring (25% intensive care unit admission at score of 13/25, 25% intubation at 17/25, and 25% mortality at 20/25). Length of hospitalization, intensive care unit stay, and intubation were associated with larger airspace opacity score (p = 0.032, 0.039, 0.036, respectively). CONCLUSION: The tested dCNN was highly predictive of inpatient outcomes, performs at a near expert level, and provides added value for clinicians in terms of prognostication and disease severity.

Virtual psychosocial services for the adult population in a large community oncology practice

Background: Psychological and social supports are essential to address the emotional impact of cancer. The Covid-19 pandemic exacerbated emotional distress for patients with cancer and impeded many of the traditional service delivery models for supportive services. An increase in patient reported distress from routine distress screenings highlighted the need to transition to virtual Social Work (SW) services. Methods: Patients were referred to virtual SW services three ways: Self-referral, distress screenings, and clinic staff. A virtual support group registration site was created to increase awareness which was promoted by our marketing team. Three different SW facilitated virtual support groups were offered: Stronger Together, Empower Your Recovery: A Program for Healing and Growth for Living Beyond Cancer ©∗, and Paving the Way for Your Journey: A Cancer Support Program (PTW). Of note, the PTW six-week psychoeducation support group curriculum was developed by six employed SW facilitators. Groups included closed and open formats with scheduled frequencies. In addition to virtual support groups, standard social support including, psychosocial assessments, Advance Care Planning, virtual counseling visits were offered virtually with patients via the VSee telemedicine platform. Results: Social Work referrals increased by 154% from 949 in 2019 to 2413 in 2020 due to positive distress screening. From March 2020-21, 14,948 patients received SW services which was an increase from 10,208 seen from March 2019-20. Of these, 372 received virtual psychosocial telemedicine services from March 2020-21. There were 4092 unique webpage views to the support group information and registration website. Total number of all virtual registrants in the 3 groups from May 2020 to February 2021 was 326. Conclusions: The COVID19 pandemic required us to use, technology and virtual tools to ensure continued patient access to psychosocial services and expand access to support groups, in addition to the in-person SW services that remained. Limitations of virtual support groups and telemedicine included lack of internet access felt to be from socioeconomic barriers. Further research is needed to evaluate the benefits of providing structured psychoeducational virtual support groups to patients with cancer. Virtual counseling and support groups may continue to benefit patients with cancer.

Multi-modality imaging in survivors of COVID-19

Background Widespread abnormalities of the myocardium have been reported in patients with COVID-19. However, these patients often have substantial co-morbidities and it is essential to understand whether cardiac abnormalities represent preexisting disease or are the consequence of COVID-19. Objective To determine the contribution and cardiac impact of co-morbidities in patients who have recovered from COVID-19. Methods In a prospective observational study, adult patients hospitalized with confirmed COVID-19 were recruited from the Edinburgh Heart Centre between May and November 2020 and compared to healthy and co-morbidity-matched volunteers. Patients underwent gadolinium and manganeseenhanced magnetic resonance imaging and coronary computed tomography angiography. Results Twenty-three patients (54±11 years, 20 male) who recovered from COVID-19 were recruited. Half (n=11, 48%) required admission to the intensive care unit and a third (n=7, 31%) received non-invasive or invasive ventilation. Patients had a high prevalence of known cardiovascular disease (n=18, 78%), associated risk factors (n=11, 45%) and coronary artery disease (n=8, 35%). Compared with younger healthy volunteers (n=10), myocardial native T1 values (1202 ±25 versus 1162±27 ms, P=0.008, figure 1) and extracellular volume fraction (31.9±1.7 versus 29.8±0.5 %, P=0.001, figure 1) were higher with no differences in manganese uptake. Compared to co-morbidity-matched volunteers (n=20), there were no differences in native T1 values (1202±25 versus 1196±39 ms, P=0.61, figure 1), extracellular volume fraction (31.9±1.7 versus 31.0±0.5 %, P=0.11), presence of late gadolinium enhancement or manganese uptake. These findings remained irrespective of COVID-19 disease severity, presence of concomitant myocardial injury or coronary artery disease. Conclusions Patients who have recovered following hospitalization with COVID-19 have no evidence of a major excess in myocardial injury or dysfunction compared to co-morbiditymatched volunteers. The presence of co-morbidities likely explains many of the previously reported myocardial abnormalities.

Care for Incarcerated Patients Hospitalized with COVID-19.

The COVID-19 pandemic has reshaped health care delivery for all patients but has distinctly affected the most marginalized people in society. Incarcerated patients are both more likely to be infected and more likely to die from COVID-19. There is a paucity of guidance for the care of incarcerated patients hospitalized with COVID-19. This article will discuss how patient privacy, adequate communication, and advance care planning are rights that incarcerated patients may not experience during this pandemic. We highlight the role of compassionate release and note how COVID-19 may affect this prospect. A number of pragmatic recommendations are made to attenuate the discrepancy in hospital care experienced by those admitted from prisons and jails. Physicians must be familiar with the relevant hospital policies, be prepared to adapt their practices in order to overcome barriers to care, such as continuous shackling, and advocate to change these policies when they conflict with patient care. Stigma, isolation, and concerns over staff safety are shared experiences for COVID-19 and incarcerated patients, but incarcerated patients have been experiencing this treatment long before the current pandemic. It is crucial that the internist demand the equitable care that we seek for all our patients.

Aligning SARS-CoV-2 Indicators via an Epidemic Model: Application to Hospital Admissions and RNA Detection in Sewage Sludge (preprint)

Ascertaining the state of coronavirus outbreaks is crucial for public health decision-making. Absent repeated representative viral test samples in the population, public health officials and researchers alike have relied on lagging indicators of infection to make inferences about the direction of the outbreak and attendant policy decisions. Recently researchers have shown that SARS-CoV-2 RNA can be detected in municipal sewage sludge with measured RNA concentrations rising and falling suggestively in the shape of an epidemic curve while providing an earlier signal of infection than hospital admissions data. The present paper presents a SARS-CoV-2 epidemic model to serve as a basis for estimating the incidence of infection, and shows mathematically how modeled transmission dynamics translate into infection indicators by incorporating probability distributions for indicator-specific time lags from infection. Hospital admissions and SARS-CoV-2 RNA in municipal sewage sludge are simultaneously modeled via maximum likelihood scaling to the underlying transmission model. The results demonstrate that both data series plausibly follow from the transmission model specified, provide a direct estimate of the reproductive number R0 = 2.38, and suggest that the detection of viral RNA in sewage sludge leads hospital admissions by 4.6 days on average.