Development and Validation of Simple Risk Scores to Predict Hospitalization in Outpatients with COVID-19 Including the Omicron Variant.

INTRODUCTION: Outpatient physicians need guidance to support decisions regarding hospitalization of COVID-19 patients and how closely to follow outpatients. Thus, we sought to develop and validate simple risk scores to predict hospitalization for outpatients with COVID-19 that do not require laboratory testing or imaging. METHODS: We identified outpatients 12 years and older who had a positive polymerase chain reaction test for SARS-CoV-2. Logistic regression was used to derive a risk score in patients presenting before March, 2021, and it was validated in a cohort presenting from March to September 2021 and an Omicron cohort from December, 2021 to January, 2022. RESULTS: Overall, 4.0% of 5843 outpatients in the early derivation cohort (before 3/1/21), 4.2% of 3806 outpatients in the late validation cohort, and 1.2% in an Omicron cohort were hospitalized. The base risk score included age, dyspnea, and any comorbidity. Other scores added fever, respiratory rate and/or oxygen saturation. All had very good overall accuracy (AUC 0.85-0.87) and classified about half of patients into a low-risk group with < 1% hospitalization risk. Hospitalization rates in the Omicron cohort were 0.22%, 1.3% and 8.7% for the base score. Two externally derived risk scores identified more low risk patients, but with a higher overall risk of hospitalization than our novel risk scores. CONCLUSIONS: A simple risk score suitable for outpatient and telehealth settings can classify over half of COVID-19 outpatients into a very low risk group with a 0.22% hospitalization risk in the Omicron cohort. The Lehigh Outpatient COVID Hospitalization (LOCH) risk score is available online as a free app: https://ebell-projects.shinyapps.io/LehighRiskScore/.

Implementation of a Virtual Asynchronous Scribe Program to Reduce Physician Burnout.

GOAL: Administrative burden is one of many potential root causes of physician burnout. Scribe documentation assistance can reduce this burden. However, traditional in-person scribe services are challenged by consistent staffing because the model requires the physical presence of a scribe and limits the team to a single individual. In addition, in-person scribes cannot provide the flexible support required for virtual care encounters, which can now pivot geographically and temporally. To respond to these challenges, our health network implemented an asynchronous virtual scribe model and evaluated the program's impact on clinician perceptions of burnout across multiple outpatient specialties. METHODS: Using a mixed-methods, pre-/postdesign, this evaluation measured the impact of an asynchronous virtual scribe program on physician burnout. Physicians were given the Professional Fulfillment Index tool (to self-assess their mental state) and free-text comment surveys before virtual scribe initiation and again at 3-, 6-, and 12-month intervals after program implementation. Descriptive statistics of survey results and qualitative review of free-text entries were analyzed for themes of facilitation and barriers to virtual scribe use. PRINCIPAL FINDINGS: Of 50 physician participants in this study, 42 (84%) completed the preintervention survey and 15 (36%) completed all 4 surveys; 25 participants (50%) discontinued scribe use after 12 months. Burnout levels-as defined by dread, exhaustion, lack of enthusiasm, decrease in empathy, and decrease in colleague connection-all trended toward improvement during this study. Importantly, quality, time savings, burnout, and productivity moved in positive directions as well. PRACTICAL APPLICATION: The cost burden to physicians and the COVID-19 pandemic inhibited the continued use of asynchronous virtual medical scribes. Nevertheless, those who continued in the program have reported positive outcomes, which indicates that the service can be a viable and effective tool to reduce physician burnout.

Development and Validation of Simple Risk Scores to Predict Hospitalization in Outpatients with COVID-19 Including the Omicron Variant.

INTRODUCTION: Outpatient physicians need guidance to support decisions regarding hospitalization of COVID-19 patients and how closely to follow outpatients. Thus, we sought to develop and validate simple risk scores to predict hospitalization for outpatients with COVID-19 that do not require laboratory testing or imaging. METHODS: We identified outpatients 12 years and older who had a positive polymerase chain reaction test for SARS-CoV-2. Logistic regression was used to derive a risk score in patients presenting before March, 2021, and it was validated in a cohort presenting from March to September 2021 and an Omicron cohort from December, 2021 to January, 2022. RESULTS: Overall, 4.0% of 5843 outpatients in the early derivation cohort (before 3/1/21), 4.2% of 3806 outpatients in the late validation cohort, and 1.2% in an Omicron cohort were hospitalized. The base risk score included age, dyspnea, and any comorbidity. Other scores added fever, respiratory rate and/or oxygen saturation. All had very good overall accuracy (AUC 0.85-0.87) and classified about half of patients into a low-risk group with < 1% hospitalization risk. Hospitalization rates in the Omicron cohort were 0.22%, 1.3% and 8.7% for the base score. Two externally derived risk scores identified more low risk patients, but with a higher overall risk of hospitalization than our novel risk scores. CONCLUSIONS: A simple risk score suitable for outpatient and telehealth settings can classify over half of COVID-19 outpatients into a very low risk group with a 0.22% hospitalization risk in the Omicron cohort. The Lehigh Outpatient COVID Hospitalization (LOCH) risk score is available online as a free app: https://ebell-projects.shinyapps.io/LehighRiskScore/.