Deployment of Tele-ICU Respiratory Therapy and the Creation of an eRT Service Line

An effort at Penn Medicine to respond to expected Covid-19–related staffing challenges led to an adaptive remote care delivery model for respiratory therapy that has become part of the standard of care and is yielding several subprojects.

FLOURISHING AMONG INTERNALMEDICINE RESIDENTS DURING THE COVID-19 PANDEMIC: A CROSS-SECTIONAL MULTIINSTITUTIONAL STUDY

BACKGROUND: The epidemic of burnout in medical residents is well established. However, little is known about what helps residents flourish. The Flourish Index (FI) provides a reliable measure of well-being, but has limited data in residency contexts. The FI covers five domains: happiness & life satisfaction;physical & mental health;meaning & purpose;character & virtue;and close social relationships. This study aimed to develop validity evidence to support the FI as a measure of resident well-being and to identify factors associated with resident flourishing as measured by the FI. METHODS: Participants were recruited using convenience sampling from fourteen residency programs in three states (CT, PA, IL) to complete an online survey (April - August 2021). The primary outcome variable was the FI. The FI is reported on a scale of 0 to 10, with higher values indicating higher flourishing. The survey also included measures of quality-of-life, resilience, medicine-as-a-calling, burnout, religiosity, and residency community wellbeing (RCWB). Metadata including program size, type, and county COVID-19 daily case rates were collected. Mean FI scores between groups were compared using independent samples t test. The relationship between the FI and other variables was examined using linear regression. RESULTS: The response rate was 84% (277 of 329). Participants were 47% female, 43% white, and 32% Asian. Half were PGY1s (49%). Programs were 67% academic. The mean FI score was 6.8 (SD 1.4). Mean FI scores were higher in residents who view medicine as a calling [6.2 (SD 1.5) vs 7.0 (SD 1.3);p<.001], were satisfied with work-life balance [6.3 (SD 1.3) vs 7.7 (SD 0.9);p<.001], consider themselves at least a moderately spiritual person [6.6 (SD 1.3) vs 7.0 (SD 1.4);p=.03], and were not experiencing emotional exhaustion [6.3 (SD 1.5) vs 7.3 (SD 1.0);p<.001] or depersonalization [6.2 (SD 1.6) vs 7.2 (SD 1.1);p<.001]. There was no significant difference in mean FI scores between program types (community versus academic) or PGY year. In simple linear regression, FI scores were not significantly predicted by gender, race, relationship status, having children, PGY level, program size, type or county-level COVID-19 case rates. In multiple linear regression, high resilience (β=.28;p<.001), viewing medicine as a calling (β=.13;p=.02), intrinsic religiosity (β=.12;p=.02), high RCWB (β=.16;p=.003), low emotional exhaustion (β= -.24;p<.001) and low depersonalization (β= -.13;p=.04) were significant predictors of higher flourishing on the FI (R2=.40;p<.001). CONCLUSIONS: Both intrinsic and programmatic factors showed important associations with resident flourishing. Strategies to improve resident flourishing may include promoting resilience, cultivating meaning in medicine, supporting spirituality, improving community well-being, and identifying and responding to burnout. The FI may be a useful measure of resident well-being in future studies and interventions.

Reflections on a Health System's Telemedicine Marathon.

The coronavirus disease 2019 (COVID-19) public health emergency necessitated changes in health care delivery that will have lasting implications. The University of Pennsylvania Health System is a large multihospital system with an academic medical center at its core. To continue to care for patients with and without COVID-19, the system had to rapidly deploy telemedicine. We describe the challenges faced with the existing telemedicine infrastructures, the central mechanisms created to facilitate the necessary conversions, and the workflow changes instituted to support both inpatient and outpatient activities for thousands of providers, many of whom had little or no experience with telemedicine. We also discuss innovations that occurred as a result of this transition and the future of telemedicine at our institution.

The Association Between Screening Tools Used to Assess Nutrition Status in an Acute Care Setting

Background: Screening tools to assess both the risk of malnutrition (such as the Malnutrition Screening Tool, MST) and wound development (Braden scale) are frequently utilized in the hospital setting. Hospital-acquired pressure injuries (HAPIs) result in considerable patient harm, including expensive treatments, increased length of stays, and increased mortality. Malnutrition is a significant risk factor in the development and progression of HAPIs. Therefore, identifying malnutrition risk and prevalence is an important step in preventing HAPIs. However, processes which involve multiple screening steps consume resources such as staff and time which have become scarce during the COVID-19 pandemic. While the Braden scale contains a nutrition component as a sub-score, little is known about the validity of this sub-scale to capture malnutrition risk. We aimed to explore the association between a validated malnutrition screening tool (MST) and the Braden nutrition scale to determine their use in generating nutrition consults. Methods: We conducted a retrospective chart review of adult patients who developed a HAPI during hospital admission. Baseline MST scores (0-6) and Braden nutrition sub-scale score (1-4) were collected. Higher MST scores represent increased risk of malnutrition, while lower Braden nutrition sub-scores represent poor nutrition status. Documentation of a malnutrition diagnosis using the ASPEN guidelines (yes/no) was also collected for each patient. Pearson correlation coefficients and linear regression were used to assess the association between the MST and Braden nutrition sub-score in the entire cohort. A sub-analysis was conducted in the patients with a diagnosis of malnutrition. Logistic regression was performed to evaluate the association between the Braden nutrition sub-scores and a malnutrition diagnosis. Results: The cohort included 133 patients with a mean age of 69.3 years, with 69.9% being male. 77 patients had malnutrition status recorded, with 64.9% diagnosed with malnutrition. There was a significant correlation between Braden nutrition sub-score and MST (R = -0.28;p < 0.001) in the overall cohort and in subjects with malnutrition (R = -0.35;p = 0.01). Linear regression confirmed that low Braden scale nutrition subscores (poor nutrition status) were predicted by high MST scores (risk for malnutrition) (p = < 0.001). Logistic regression modeling showed a higher Braden nutrition sub-score (better nutrition status) was associated with a diagnosis of malnutrition (OR: 0.45;p=0.057). Conclusion: The results of this study demonstrate that the MST and the Braden nutrition sub-scores are correlated in a cohort of hospitalized patients who developed a HAPI. Use of both screening tools may not be necessary for identifying those that warrant further assessment and interventions for malnutrition.

Abbott BinaxNOW Rapid Antigen Test Performance in Detecting SARSCoV-2 Infections in a COVID-19 Outbreak among Horse Racetrack Workers

Background. Rapid antigen tests (e.g., Abbott's BinaxNOW) are cheaper and faster than nucleic acid amplification tests (e.g., real-time reverse transcription polymerase chain reaction [RT-PCR]) for SARS-CoV-2 infection, with variable reported sensitivity. A horse racetrack in California experienced a COVID-19 outbreak among staff and used BinaxNOW to supplement RT-PCR. Utility of BinaxNOW in detecting SARS-CoV-2 infection in a workplace outbreak was assessed. Methods. Between November 25-December 22, 2020, anterior nasal swabs were collected from racetrack staff for six rounds of paired BinaxNOW and RT-PCR tests. BinaxNOW tests were interpreted according to manufacturer instructions. RT-PCR was performed at the state public health lab using the ThermoFisher TaqPath COVID-19 Combo Kit. Staff with positive results on either test were isolated and removed from subsequent testing. Viral cultures were attempted on specimens with cycle threshold (Ct) < 30. Results. Overall, 769 paired results from 342 staff were analyzed. Most were of Hispanic ethnicity (62.0%) and ages ranged from 18 to 92 years (median 52). BinaxNOW performance compared to RT-PCR (95% CI) was as follows: positive percent agreement (PPA) 43.3% (34.6%-52.4%);negative percent agreement (NPA) 100% (99.4%-100%);positive predictive value (PPV) 100% (93.5%-100%);negative predictive value 89.9% (87.5%-92.0%). Among 127 RT-PCR-positive specimens, those with paired BinaxNOW-positive results (n = 55) had a lower mean Ct value than those with paired BinaxNOW-negative results (n = 72) (17.8 vs. 28.5) (p < 0.001). In dual positive pairs, median time from specimen collected to RT-PCR result reported was 4 days (range 1-6), compared to the 15-minute BinaxNOW reporting time. Of 100 Ct < 30 specimens, 51 resulted in positive virus isolation, 45 (88.2%) of which were BinaxNOW-positive. Conclusion. High NPA and PPV support immediate isolation of BinaxNOWpositive individuals, while low PPA supports confirmatory testing following BinaxNOW-negative results. BinaxNOW performed better in paired specimens with lower Ct value and positive viral cultures, which could suggest that among RT-PCRpositive specimens, those that are BinaxNOW-negative may be less likely to contain infectious virus than those that are BinaxNOW-positive.

The Rapid Implementation of Ad Hoc Tele-Critical Care Respiratory Therapy (eRT) Service in the Wake of the COVID-19 Surge.

A 24/7 telemedicine respiratory therapist (eRT) service was set up as part of the established University of Pennsylvania teleICU (PENN E-LERT®) service during the COVID-19 pandemic, serving five hospitals and 320 critical care beds to deliver effective remote care in lieu of a unit-based RT. The eRT interventions were components of an evidence-based care bundle and included ventilator liberation protocols, low tidal volume protocols, tube patency, and an extubation checklist. In addition, the proactive rounding of patients, including ventilator checks, was included. A standardized data collection sheet was used to facilitate the review of medical records, direct audio-visual inspection, or direct interactions with staff. In May 2020, a total of 1548 interventions took place, 93.86% of which were coded as "routine" based on established workflows, 4.71% as "urgent", 0.26% "emergent", and 1.17% were missing descriptors. Based on the number of coded interventions, we tracked the number of COVID-19 patients in the system. The average intervention took 6.1 ± 3.79 min. In 16% of all the interactions, no communication with the bedside team took place. The eRT connected with the in-house respiratory therapist (RT) in 66.6% of all the interventions, followed by house staff (9.8%), advanced practice providers (APP; 2.8%), and RN (2.6%). Most of the interaction took place over the telephone (88%), secure text message (16%), or audio-video telemedicine ICU platform (1.7%). A total of 5115 minutes were spent on tasks that a bedside clinician would have otherwise executed, reducing their exposure to COVID-19. The eRT service was instrumental in several emergent and urgent critical interventions. This study shows that an eRT service can support the bedside RT providers, effectively monitor best practice bundles, and carry out patient-ventilator assessments. It was effective in certain emergent situations and reduced the exposure of RTs to COVID-19. We plan to continue the service as part of an integrated RT service and hope to provide a framework for developing similar services in other facilities.

Effect of COVID-19 Pandemic Control Measures on Diagnoses of Hematologic Cancer Among CMS Population in 2020

Introduction Measures taken to mitigate infection spread during the 2020 COVID-19 pandemic are considered to have caused significant unintended consequences on other diseases. Large decreases in the numbers of symptomatic and asymptomatic people presenting for diagnosis of heart disease, diabetes and cancer have been observed. A recent analysis of solid tumors showed up to 70% reduction in the number of patients presenting for diagnosis. The potential exists for significantly increased morbidity and mortality for these missed or delayed presenting patients. Further, it is important to determine whether infection spread mitigation measures affected the diagnostic testing and treatment decisions for these patients. This study aimed to determine whether pandemic control measures affected presentation, testing and treatment of patients across eight different hematologic cancers. Methods CMS claims data were analyzed for the presence of diagnostic (DX) ICD 10 codes indicative of hematologic cancer. Patients with a DX code first appearing in 2019 or in 2020 were selected to provide newly diagnosed pre-COVID-19 and during COVID-19 cohorts for comparison, with unique patient counts being calculated for each month. A “COVID-19 dip” i.e. a decrease in the number of patients was calculated as the change in number of patients diagnosed in a given month relative to the number for JAN2020. Dip duration was calculated only when the decrease was >10% of the JAN2020 figure. Patients who received treatment via a “J” code Healthcare Common Procedure Coding System (HCPCS) code were extracted from the cohorts and the time taken from initial diagnosis to first treatment calculated. Results Eight hematologic cancers: AML, CLL, CML, HEME (a group of different hematologic cancers), Hodgkins (HOG), Myelodysplasia (MDS), Non-Follicular Lymphomas (NFL), and Non-Hodgkins Lymphoma (NHL) showed a decrease in the number of patients being diagnosed during the early part of 2020 (Fig.1) Fig.1. Change in new patient diagnoses for selected hematologic cancers as a proportion of their JAN2020 value There was some variation in the depth and duration of the COVID-19 dip (Table 1) with MDS having both the longest and deepest dip. Median depth and duration of the dip was 33% and 3.5 months, respectively, with all dips starting either in FEB or MAR2020. Table 1. Duration and depth of COVID-19 dips for selected hematological cancers The proportions of patients receiving therapy via J HCPCS code (JRX) are shown in Table 2 Table 2. Proportions of patients receiving J code therapy Conclusions The decline in new patient diagnoses for heme cancers during the period when COVID-19 control measures were implemented is similar to that seen with solid tumors, although the depth of the COVID-19 dip was generally larger in the latter. There is no evidence of “catch up” diagnosis occurring i.e. patients missing from Q2 2020 are not reappearing en masse in subsequent quarters. The decline for MDS patients has, except for SEP to OCT2020, remained. Collectively, (depending on the calculation method), the COVID-19 dip for these eight heme cancers represents 16,584-33,671 patients who will likely have significantly increased rates of morbidity and mortality due to delayed diagnosis. Analysis of J code treatments show little difference between the proportions of patients receiving these treatments in 2020 compared to 2019 suggesting that at least some aspects of treatment e.g. infused chemotherapy, IO drugs for these patients was relatively unchanged by pandemic control measures. It also suggests that the main cause for decreased patient numbers treated is due to decreased testing for diagnosis, rather than not being treated once diagnosed. This aligns with findings from studies in the US and UK. The results of this study indicate that there may be a “backlog” of tens of thousands of people with cancer whose diagnosis has been significantly delayed and who urgently need to be identified in order to get on proper treatment to lessen the impact of that delay. [F rmula presented] Disclosures: No relevant conflicts of interest to declare.

A call for standardised age-disaggregated health data

The 2030 Sustainable Development Goals agenda calls for health data to be disaggregated by age. However, age groupings used to record and report health data vary greatly, hindering the harmonisation, comparability, and usefulness of these data, within and across countries. This variability has become especially evident during the COVID-19 pandemic, when there was an urgent need for rapid cross-country analyses of epidemiological patterns by age to direct public health action, but such analyses were limited by the lack of standard age categories. In this Personal View, we propose a recommended set of age groupings to address this issue. These groupings are informed by age-specific patterns of morbidity, mortality, and health risks, and by opportunities for prevention and disease intervention. We recommend age groupings of 5 years for all health data, except for those younger than 5 years, during which time there are rapid biological and physiological changes that justify a finer disaggregation. Although the focus of this Personal View is on the standardisation of the analysis and display of age groups, we also outline the challenges faced in collecting data on exact age, especially for health facilities and surveillance data. The proposed age disaggregation should facilitate targeted, age-specific policies and actions for health care and disease management.

Why Is the Electronic Health Record So Challenging for Research and Clinical Care?

BACKGROUND: The electronic health record (EHR) has become increasingly ubiquitous. At the same time, health professionals have been turning to this resource for access to data that is needed for the delivery of health care and for clinical research. There is little doubt that the EHR has made both of these functions easier than earlier days when we relied on paper-based clinical records. Coupled with modern database and data warehouse systems, high-speed networks, and the ability to share clinical data with others are large number of challenges that arguably limit the optimal use of the EHR OBJECTIVES: Our goal was to provide an exhaustive reference for those who use the EHR in clinical and research contexts, but also for health information systems professionals as they design, implement, and maintain EHR systems. METHODS: This study includes a panel of 24 biomedical informatics researchers, information technology professionals, and clinicians, all of whom have extensive experience in design, implementation, and maintenance of EHR systems, or in using the EHR as clinicians or researchers. All members of the panel are affiliated with Penn Medicine at the University of Pennsylvania and have experience with a variety of different EHR platforms and systems and how they have evolved over time. RESULTS: Each of the authors has shared their knowledge and experience in using the EHR in a suite of 20 short essays, each representing a specific challenge and classified according to a functional hierarchy of interlocking facets such as usability and usefulness, data quality, standards, governance, data integration, clinical care, and clinical research. CONCLUSION: We provide here a set of perspectives on the challenges posed by the EHR to clinical and research users.

Insourcing and scaling a telemedicine solution in under 2 weeks: Lessons for the digital transformation of health care.

The Covid-19 pandemic required rapid scale of telemedicine as well as other digital workflows to maintain access to care while reducing infection risk. Both patients and clinicians who hadn't used telemedicine before were suddenly faced with a multi-step setup process to log into a virtual meeting. Unlike in-person examination rooms, locking a virtual meeting room was more error-prone and posed a risk of multiple patients joining the same online session. There was administrative burden on the practice staff who were generating and manually sending links to patients, and educating patients on device set up was time-consuming and unsustainable. A solution had to be deployed rapidly system-wide, without the usual roll out across months. Our answer was to design and implement a novel EHR-integrated web application called the Switchboard, in just two weeks. The Switchboard leverages a commercial, cloud-based video meeting platform and facilitates an end-to-end virtual care encounter workflow, from pre-visit reminders to post-visit SMS text message-based measurement of patient experience, with tools to extend contact-less workflows to in-person appointments. Over the first 11 months of the pandemic, the in-house platform has been adopted across 6 hospitals and >200 practices, scaled to 8,800 clinicians who at their peak conducted an average of 30,000 telemedicine appointments/week, and enabled over 10,000-20,000 text messages/day to be exchanged through the platform. Furthermore, it enabled our organization to convert from an average of 75% of telehealth visits being conducted via telephone to 75% conducted via video within weeks.

Characteristics, Outcomes, and Trends of Patients With COVID-19-Related Critical Illness at a Learning Health System in the United States.

BACKGROUND: The coronavirus disease 2019 (COVID-19) pandemic continues to surge in the United States and globally. OBJECTIVE: To describe the epidemiology of COVID-19-related critical illness, including trends in outcomes and care delivery. DESIGN: Single-health system, multihospital retrospective cohort study. SETTING: 5 hospitals within the University of Pennsylvania Health System. PATIENTS: Adults with COVID-19-related critical illness who were admitted to an intensive care unit (ICU) with acute respiratory failure or shock during the initial surge of the pandemic. MEASUREMENTS: The primary exposure for outcomes and care delivery trend analyses was longitudinal time during the pandemic. The primary outcome was all-cause 28-day in-hospital mortality. Secondary outcomes were all-cause death at any time, receipt of mechanical ventilation (MV), and readmissions. RESULTS: Among 468 patients with COVID-19-related critical illness, 319 (68.2%) were treated with MV and 121 (25.9%) with vasopressors. Outcomes were notable for an all-cause 28-day in-hospital mortality rate of 29.9%, a median ICU stay of 8 days (interquartile range [IQR], 3 to 17 days), a median hospital stay of 13 days (IQR, 7 to 25 days), and an all-cause 30-day readmission rate (among nonhospice survivors) of 10.8%. Mortality decreased over time, from 43.5% (95% CI, 31.3% to 53.8%) to 19.2% (CI, 11.6% to 26.7%) between the first and last 15-day periods in the core adjusted model, whereas patient acuity and other factors did not change. LIMITATIONS: Single-health system study; use of, or highly dynamic trends in, other clinical interventions were not evaluated, nor were complications. CONCLUSION: Among patients with COVID-19-related critical illness admitted to ICUs of a learning health system in the United States, mortality seemed to decrease over time despite stable patient characteristics. Further studies are necessary to confirm this result and to investigate causal mechanisms. PRIMARY FUNDING SOURCE: Agency for Healthcare Research and Quality.

Remote Monitoring of Critically-Ill Post-Surgical Patients: Lessons from a Biosensor Implementation Trial.

Biosensors represent one of the numerous promising technologies envisioned to extend healthcare delivery. In perioperative care, the healthcare delivery system can use biosensors to remotely supervise patients who would otherwise be admitted to a hospital. This novel technology has gained a foothold in healthcare with significant acceleration due to the COVID-19 pandemic. However, few studies have attempted to narrate, or systematically analyze, the process of their implementation. We performed an observational study of biosensor implementation. The data accuracy provided by the commercially available biosensors was compared to those offered by standard clinical monitoring on patients admitted to the intensive care unit/perioperative unit. Surveys were also conducted to examine the acceptance of technology by patients and medical staff. We demonstrated a significant difference in vital signs between sensors and standard monitoring which was very dependent on the measured variables. Sensors seemed to integrate into the workflow relatively quickly, with almost no reported problems. The acceptance of the biosensors was high by patients and slightly less by nurses directly involved in the patients' care. The staff forecast a broad implementation of biosensors in approximately three to five years, yet are eager to learn more about them. Reliability considerations proved particularly troublesome in our implementation trial. Careful evaluation of sensor readiness is most likely necessary prior to system-wide implementation by each hospital to assess for data accuracy and acceptance by the staff.

Developing the eMedical Student (eMS)-A Pilot Project Integrating Medical Students into the Tele-ICU during the COVID-19 Pandemic and beyond.

The COVID-19 pandemic has accelerated the demand for virtual healthcare delivery and highlighted the scarcity of telehealth medical student curricula, particularly tele-critical care. In partnership with the Penn E-lert program and the Department of Anesthesiology and Critical Care, the Perelman School of Medicine (PSOM) established a tele-ICU rotation to support the care of patients diagnosed with COVID-19 in the Intensive Care Unit (ICU). The four-week course had seven elements: (1) 60 h of clinical engagement; (2) multiple-choice pretest; (3) faculty-supervised, student-led case and topic presentations; (4) faculty-led debriefing sessions; (5) evidence-based-medicine discussion forum; (6) multiple-choice post-test; and (7) final reflection. Five third- and fourth-year medical students completed 300 h of supervised clinical engagement, following 16 patients over three weeks and documenting 70 clinical interventions. Knowledge of critical care and telehealth was demonstrated through improvement between pre-test and post-test scores. Professional development was demonstrated through post-course preceptor and learner feedback. This tele-ICU rotation allowed students to gain telemedicine exposure and participate in the care of COVID patients in a safe environment.

Patient Characteristics Associated With Telemedicine Access for Primary and Specialty Ambulatory Care During the COVID-19 Pandemic.

Importance: The coronavirus disease 2019 (COVID-19) pandemic has required a shift in health care delivery platforms, necessitating a new reliance on telemedicine. Objective: To evaluate whether inequities are present in telemedicine use and video visit use for telemedicine visits during the COVID-19 pandemic. Design, Setting, and Participants: In this cohort study, a retrospective medical record review was conducted from March 16 to May 11, 2020, of all patients scheduled for telemedicine visits in primary care and specialty ambulatory clinics at a large academic health system. Age, race/ethnicity, sex, language, median household income, and insurance type were all identified from the electronic medical record. Main Outcomes and Measures: A successfully completed telemedicine visit and video (vs telephone) visit for a telemedicine encounter. Multivariable models were used to assess the association between sociodemographic factors, including sex, race/ethnicity, socioeconomic status, and language, and the use of telemedicine visits, as well as video use specifically. Results: A total of 148 402 unique patients (86 055 women [58.0%]; mean [SD] age, 56.5 [17.7] years) had scheduled telemedicine visits during the study period; 80 780 patients (54.4%) completed visits. Of 78 539 patients with completed visits in which visit modality was specified, 35 824 (45.6%) were conducted via video, whereas 24 025 (56.9%) had a telephone visit. In multivariable models, older age (adjusted odds ratio [aOR], 0.85 [95% CI, 0.83-0.88] for those aged 55-64 years; aOR, 0.75 [95% CI, 0.72-0.78] for those aged 65-74 years; aOR, 0.67 [95% CI, 0.64-0.70] for those aged ≥75 years), Asian race (aOR, 0.69 [95% CI, 0.66-0.73]), non-English language as the patient's preferred language (aOR, 0.84 [95% CI, 0.78-0.90]), and Medicaid insurance (aOR, 0.93 [95% CI, 0.89-0.97]) were independently associated with fewer completed telemedicine visits. Older age (aOR, 0.79 [95% CI, 0.76-0.82] for those aged 55-64 years; aOR, 0.78 [95% CI, 0.74-0.83] for those aged 65-74 years; aOR, 0.49 [95% CI, 0.46-0.53] for those aged ≥75 years), female sex (aOR, 0.92 [95% CI, 0.90-0.95]), Black race (aOR, 0.65 [95% CI, 0.62-0.68]), Latinx ethnicity (aOR, 0.90 [95% CI, 0.83-0.97]), and lower household income (aOR, 0.57 [95% CI, 0.54-0.60] for income <$50 000; aOR, 0.89 [95% CI, 0.85-0.92], for $50 000-$100 000) were associated with less video use for telemedicine visits. These results were similar across medical specialties. Conclusions and Relevance: In this cohort study of patients scheduled for primary care and medical specialty ambulatory telemedicine visits at a large academic health system during the early phase of the COVID-19 pandemic, older patients, Asian patients, and non-English-speaking patients had lower rates of telemedicine use, while older patients, female patients, Black, Latinx, and poorer patients had less video use. Inequities in accessing telemedicine care are present, which warrant further attention.

Locally Informed Simulation to Predict Hospital Capacity Needs During the COVID-19 Pandemic.

BACKGROUND: The coronavirus disease 2019 (COVID-19) pandemic challenges hospital leaders to make time-sensitive, critical decisions about clinical operations and resource allocations. OBJECTIVE: To estimate the timing of surges in clinical demand and the best- and worst-case scenarios of local COVID-19-induced strain on hospital capacity, and thus inform clinical operations and staffing demands and identify when hospital capacity would be saturated. DESIGN: Monte Carlo simulation instantiation of a susceptible, infected, removed (SIR) model with a 1-day cycle. SETTING: 3 hospitals in an academic health system. PATIENTS: All people living in the greater Philadelphia region. MEASUREMENTS: The COVID-19 Hospital Impact Model (CHIME) (http://penn-chime.phl.io) SIR model was used to estimate the time from 23 March 2020 until hospital capacity would probably be exceeded, and the intensity of the surge, including for intensive care unit (ICU) beds and ventilators. RESULTS: Using patients with COVID-19 alone, CHIME estimated that it would be 31 to 53 days before demand exceeds existing hospital capacity. In best- and worst-case scenarios of surges in the number of patients with COVID-19, the needed total capacity for hospital beds would reach 3131 to 12 650 across the 3 hospitals, including 338 to 1608 ICU beds and 118 to 599 ventilators. LIMITATIONS: Model parameters were taken directly or derived from published data across heterogeneous populations and practice environments and from the health system's historical data. CHIME does not incorporate more transition states to model infection severity, social networks to model transmission dynamics, or geographic information to account for spatial patterns of human interaction. CONCLUSION: Publicly available and designed for hospital operations leaders, this modeling tool can inform preparations for capacity strain during the early days of a pandemic. PRIMARY FUNDING SOURCE: University of Pennsylvania Health System and the Palliative and Advanced Illness Research Center.