Objective: The rapid spread of severe acute respiratory coronavirus virus 2 (SARS-CoV-2) throughout key regions of the United States in early 2020 placed a premium on timely, national surveillance of hospital patient censuses. To meet that need, the Centers for Disease Control and Prevention's National Healthcare Safety Network (NHSN), the nation's largest hospital surveillance system, launched a module for collecting hospital coronavirus disease 2019 (COVID-19) data. We present time-series estimates of the critical hospital capacity indicators from April 1 to July 14, 2020. Design: From March 27 to July 14, 2020, the NHSN collected daily data on hospital bed occupancy, number of hospitalized patients with COVID-19, and the availability and/or use of mechanical ventilators. Time series were constructed using multiple imputation and survey weighting to allow near-real-Time daily national and state estimates to be computed. Results: During the pandemic's April peak in the United States, among an estimated 431,000 total inpatients, 84,000 (19%) had COVID-19. Although the number of inpatients with COVID-19 decreased from April to July, the proportion of occupied inpatient beds increased steadily. COVID-19 hospitalizations increased from mid-June in the South and Southwest regions after stay-At-home restrictions were eased. The proportion of inpatients with COVID-19 on ventilators decreased from April to July. Conclusions: The NHSN hospital capacity estimates served as important, near-real-Time indicators of the pandemic's magnitude, spread, and impact, providing quantitative guidance for the public health response. Use of the estimates detected the rise of hospitalizations in specific geographic regions in June after they declined from a peak in April. Patient outcomes appeared to improve from early April to mid-July.

Using data from the National Healthcare Safety Network (NHSN), we assessed changes to intensive care unit (ICU) bed capacity during the early months of the COVID-19 pandemic. Changes in capacity varied by hospital type and size. ICU beds increased by 36%, highlighting the pressure placed on hospitals during the pandemic.

Introduction: The United States federally mandated reporting of venous thromboembolism (VTE), defined by Agency for Healthcare Research & Quality Patient Safety Indicator 12 (AHRQ PSI-12), is based on administrative data, the accuracy of which has not been consistently demonstrated. We used IDEAL-X, a novel information extraction software system, to identify VTE from electronic medical records and evaluated its accuracy. Methods: Medical records for 13,248 patients admitted to an orthopedic specialty hospital from 2009 to 2014 were reviewed. Patient encounters were defined as a hospital admission where both surgery (of the spine, hip, or knee) and a radiology diagnostic study that could detect VTE was performed. Radiology reports were both manually reviewed by a physician and analyzed by IDEAL-X. Results: Among 2083 radiology reports, IDEAL-X correctly identified 176/181 VTE events, achieving a sensitivity of 97.2% [95% confidence interval (CI), 93.7%-99.1%] and specificity of 99.3% (95% CI, 98.9%-99.7%) when compared with manual review. Among 422 surgical encounters with diagnostic radiographic studies for VTE, IDEAL-X correctly identified 41 of 42 VTE events, achieving a sensitivity of 97.6% (95% CI, 87.4%-99.6%) and specificity of 99.8% (95% CI, 98.7%-100.0%). The performance surpassed that of AHRQ PSI-12, which had a sensitivity of 92.9% (95% CI, 80.5%-98.4%) and specificity of 92.9% (95% CI, 89.8%-95.3%), though only the difference in specificity was statistically significant (P<0.01). Conclusion: IDEAL-X, a novel information extraction software system, identified VTE from radiology reports with high accuracy, with specificity surpassing AHRQ PSI-12. IDEAL-X could potentially improve detection and surveillance of many medical conditions from free text of electronic medical records.

Microorganisms may colonize needleless connectors (NCs) on intravascular catheters, forming biofilms and predisposing patients to catheter-associated infection (CAI). Standard and silver-coated NCs were collected from catheterized intensive care unit patients to characterize biofilm formation using culture-dependent and culture-independent methods and to investigate the associations between NC usage and biofilm characteristics. Viable microorganisms were detected by plate counts from 46% of standard NCs and 59% of silver-coated NCs (P=0.11). There were no significant associations (P > 0.05, chi-square test) between catheter type, side of catheter placement, number of catheter lumens, site of catheter placement, or NC placement duration and positive NC findings. There was an association (P=0.04, chi-square test) between infusion type and positive findings for standard NCs. Viable microorganisms exhibiting intracellular esterase activity were detected on > 90% of both NC types (P=0.751), suggesting that a large percentage of organisms were not culturable using the conditions provided in this study. Amplification of the 16S rRNA gene from selected NCs provided a substantially larger number of operational taxonomic units per NC than did plate counts (26 to 43 versus 1 to 4 operational taxonomic units/NC, respectively), suggesting that culture-dependent methods may substantially underestimate microbial diversity on NCs. NC bacterial communities were clustered by patient and venous access type and may reflect the composition of the patient's local microbiome but also may contain organisms from the health care environment. NCs provide a portal of entry for a wide diversity of opportunistic pathogens to colonize the catheter lumen, forming a biofilm and increasing the potential for CAI, highlighting the importance of catheter maintenance practices to reduce microbial contamination.

Physical activity (PA) interventions constitute a critical component of cardiovascular disease (CVD) risk reduction programs. Objective mobile health (mHealth) software applications (apps) and wearable activity monitors (WAMs) can advance both assessment and integration of PA counseling in clinical settings and support community-based PA interventions. The use of mHealth technology for CVD risk reduction is promising, but integration into routine clinical care and population health management has proven challenging. The increasing diversity of available technologies and the lack of a comprehensive guiding framework are key barriers for standardizing data collection and integration. This paper reviews the validity, utility and feasibility of implementing mHealth technology in clinical settings and proposes an organizational framework to support PA assessment, counseling and referrals to community resources for CVD risk reduction interventions. This integration framework can be adapted to different clinical population needs. It should also be refined as technologies and regulations advance under an evolving health care system landscape in the United States and globally.