The 2020 Focused Updates to the Asthma Management Guidelines: A Report from the National Asthma Education and Prevention Program Coordinating Committee Expert Panel Working Group was coordinated and supported by the National Heart, Lung, and Blood Institute (NHLBI) of the National Institutes of Health. It is designed to improve patient care and support informed decision making about asthma management in the clinical setting. This update addresses six priority topic areas as determined by the state of the science at the time of a needs assessment, and input from multiple stakeholders: Fractional Exhaled Nitric Oxide Testing Indoor Allergen Mitigation Intermittent Inhaled Corticosteroids Long-Acting Muscarinic Antagonists Immunotherapy in the Treatment of Allergic Asthma Bronchial Thermoplasty A rigorous process was undertaken to develop these evidence-based guidelines. The Agency for Healthcare Research and Quality’s (AHRQ) Evidence-Based Practice Centers conducted systematic reviews on these topics, which were used by the Expert Panel Working Group as a basis for developing recommendations and guidance. The Expert Panel used GRADE (Grading of Recommendations, Assessment, Development and Evaluation), an internationally accepted framework, in consultation with an experienced methodology team for determining the certainty of evidence and the direction and strength of recommendations based on the evidence. Practical implementation guidance for each recommendation incorporates findings from NHLBI-led patient, caregiver, and clinician focus groups. To assist clincians in implementing these recommendations into patient care, the new recommendations have been integrated into the existing Expert Panel Report-3 (EPR-3) asthma management step diagram format.

Objectives: Develop and implement a machine learning algorithm to predict severe sepsis and septic shock and evaluate the impact on clinical practice and patient outcomes. Design: Retrospective cohort for algorithm derivation and validation, pre-post impact evaluation. Setting: Tertiary teaching hospital system in Philadelphia, PA. Patients: All non-ICU admissions; algorithm derivation July 2011 to June 2014 (n = 162,212); algorithm validation October to December 2015 (n = 10,448); silent versus alert comparison January 2016 to February 2017 (silent n = 22,280; alert n = 32,184). Interventions: A random-forest classifier, derived and validated using electronic health record data, was deployed both silently and later with an alert to notify clinical teams of sepsis prediction. Measurement and Main Result: Patients identified for training the algorithm were required to have International Classification of Diseases, 9th Edition codes for severe sepsis or septic shock and a positive blood culture during their hospital encounter with either a lactate greater than 2.2 mmol/L or a systolic blood pressure less than 90 mm Hg. The algorithm demonstrated a sensitivity of 26% and specificity of 98%, with a positive predictive value of 29% and positive likelihood ratio of 13. The alert resulted in a small statistically significant increase in lactate testing and IV fluid administration. There was no significant difference in mortality, discharge disposition, or transfer to ICU, although there was a reduction in time-to-ICU transfer. Conclusions: Our machine learning algorithm can predict, with low sensitivity but high specificity, the impending occurrence of severe sepsis and septic shock. Algorithm-generated predictive alerts modestly impacted clinical measures. Next steps include describing clinical perception of this tool and optimizing algorithm design and delivery.

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Background Despite widespread interest in the use of virtual (ie, telephone and video) visits for ambulatory patient care during the COVID-19 pandemic, studies examining their adoption during the pandemic by race, sex, age, or insurance are lacking. Moreover, there have been limited evaluations to date of the impact of these sociodemographic factors on the use of telephone versus video visits. Such assessments are crucial to identify, understand, and address differences in care delivery across patient populations, particularly those that could affect access to or quality of care. Objective The aim of this study was to examine changes in ambulatory visit volume and type (ie, in-person vs virtual and telephone vs video visits) by patient sociodemographics during the COVID-19 pandemic at one urban academic medical center. Methods We compared volumes and patient sociodemographics (age, sex, race, insurance) for visits during the first 11 weeks following the COVID-19 national emergency declaration (March 15 to May 31, 2020) to visits in the corresponding weeks in 2019. Additionally, for visits during the COVID-19 study period, we examined differences in visit type (ie, in-person versus virtual, and telephone versus video visits) by sociodemographics using multivariate logistic regression. Results Total visit volumes in the COVID-19 study period comprised 51.4% of the corresponding weeks in 2019 (n=80,081 vs n=155,884 visits). Although patient sociodemographics between the COVID-19 study period in 2020 and the corresponding weeks in 2019 were similar, 60.5% (n=48,475) of the visits were virtual, compared to 0% in 2019. Of the virtual visits, 61.2% (n=29,661) were video based, and 38.8% (n=18,814) were telephone based. In the COVID-19 study period, virtual (vs in-person) visits were more likely among patients with race categorized as other (vs White) and patients with Medicare (vs commercial) insurance and less likely for men, patients aged 0-17 years, 65-74 years, or ≥75 years (compared to patients aged 18-45 years), and patients with Medicaid insurance or insurance categorized as other. Among virtual visits, compared to telephone visits, video visits were more likely to be adopted by patients aged 0-17 years (vs 18-45 years), but less likely for all other age groups, men, Black (vs White) patients, and patients with Medicare or Medicaid (vs commercial) insurance. Conclusions Virtual visits comprised the majority of ambulatory visits during the COVID-19 study period, of which a majority were by video. Sociodemographic differences existed in the use of virtual versus in-person and video versus telephone visits. To ensure equitable care delivery, we present five policy recommendations to inform the further development of virtual visit programs and their reimbursement.

A panel of experts was convened by the Infectious Diseases Society of America to update the 2004 clinical practice guideline on outpatient parenteral antimicrobial therapy (OPAT) [1]. This guideline is intended to provide insight for healthcare professionals who prescribe and oversee the provision of OPAT. It considers various patient features, infusion catheter issues, monitoring questions, and antimicrobial stewardship concerns. It does not offer recommendations on the treatment of specific infections. The reader is referred to disease- or organism-specific guidelines for such support.

Objective: To assess clinician perceptions of a machine learning–based early warning system to predict severe sepsis and septic shock (Early Warning System 2.0). Design: Prospective observational study. Setting: Tertiary teaching hospital in Philadelphia, PA. Patients: Non-ICU admissions November–December 2016. Interventions: During a 6-week study period conducted 5 months after Early Warning System 2.0 alert implementation, nurses and providers were surveyed twice about their perceptions of the alert’s helpfulness and impact on care, first within 6 hours of the alert, and again 48 hours after the alert. Measurements and Main Results: For the 362 alerts triggered, 180 nurses (50% response rate) and 107 providers (30% response rate) completed the first survey. Of these, 43 nurses (24% response rate) and 44 providers (41% response rate) completed the second survey. Few (24% nurses, 13% providers) identified new clinical findings after responding to the alert. Perceptions of the presence of sepsis at the time of alert were discrepant between nurses (13%) and providers (40%). The majority of clinicians reported no change in perception of the patient’s risk for sepsis (55% nurses, 62% providers). A third of nurses (30%) but few providers (9%) reported the alert changed management. Almost half of nurses (42%) but less than a fifth of providers (16%) found the alert helpful at 6 hours. Conclusions: In general, clinical perceptions of Early Warning System 2.0 were poor. Nurses and providers differed in their perceptions of sepsis and alert benefits. These findings highlight the challenges of achieving acceptance of predictive and machine learning–based sepsis alerts.

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Few healthcare provider organizations systematically track their healthcare equity, and fewer enable direct interaction with such data by their employees. From May to August 2019, we enhanced the data architecture and reporting functionality of our existing institutional quality scorecard to allow direct comparisons of quality measure performance by gender, age, race, ethnicity, language, zip code, and payor. The Equity Lens was made available to over 4000 staff in September 2019 for 82 institutional quality measures. During the first 11 months, 235 unique individuals used the tool; users were most commonly from the quality and equity departments. Two early use cases evaluated hypertension control and readmissions by race, identifying potential inequities. This is the first description of an interactive equity lens integrated into an institutional quality scorecard made available to healthcare system employees. Early evidence suggests the tool is used and can inform quality improvement initiatives.