A Qualitative Study on Using Telemedicine for Precepting and Teaching in the Academic Setting.

PURPOSE: To examine the impact of telemedicine use on precepting and teaching among preceptors and patients during the COVID-19 pandemic. METHOD: The authors conducted a secondary analysis of a qualitative study focusing on providers' and patients' experiences with and attitudes toward telemedicine at 4 academic health centers. Teaching and precepting were emergent codes from the data and organized into themes. Themes were mapped to domains from the 2009 Consolidated Framework for Implementation Research (CFIR), a framework that assists with effective implementation and consists of 5 domains: intervention characteristics, outer settings, inner settings, characteristics of individuals, and process. RESULTS: In total, 86 interviews were conducted with 65 patients and 21 providers. Nine providers and 3 patients recounted descriptions related to teaching and precepting with telemedicine. Eight themes were identified, mapping across all 5 CFIR domains, with the majority of themes (n = 6) within the domains of characteristics of individuals, processes, and intervention characteristics. Providers and patients described how a lack of prepandemic telemedicine experience and inadequate processes in place to precept and teach with telemedicine affected the learning environment and perceived quality of care. They also discussed how telemedicine exacerbated existing difficulties in maintaining resident continuity. Providers described ways communication changed with telemedicine use during the pandemic, including having to wear masks while in the same room as the trainee and sitting closely to remain within range of the camera, as well as the benefit of observing trainees with the attending's camera off. Providers expressed a lack of protected structure and time for teaching and supervising with telemedicine, and a general view that telemedicine is here to stay. CONCLUSIONS: Efforts should focus on increasing knowledge of telemedicine skills and improving processes to implement telemedicine in the teaching setting in order to best integrate it into undergraduate and graduate medical education.

The Telemedicine Experience in Primary Care Practices in the United States: Insights From Practice Leaders.

PURPOSE: The need to rapidly implement telemedicine in primary care during the coronavirus disease 2019 (COVID-19) pandemic was addressed differently by various practices. Using qualitative data from semistructured interviews with primary care practice leaders, we aimed to report commonly shared experiences and unique perspectives regarding telemedicine implementation and evolution/maturation since March 2020. METHODS: We administered a semistructured, 25-minute, virtual interview with 25 primary care practice leaders from 2 health systems in 2 states (New York and Florida) included in PCORnet, the Patient-Centered Outcomes Research Institute clinical research network. Questions were guided by 3 frameworks (health information technology evaluation, access to care, and health information technology life cycle) and involved practice leaders' perspectives on the process of telemedicine implementation in their practice, with a specific focus on the process of maturation and facilitators/barriers. Two researchers conducted inductive coding of qualitative data open-ended questions to identify common themes. Transcripts were electronically generated by virtual platform software. RESULTS: Twenty-five interviews were administered for practice leaders representing 87 primary care practices in 2 states. We identified the following 4 major themes: (1) the ease of telemedicine adoption depended on both patients' and clinicians' prior experience using virtual health platforms, (2) regulation of telemedicine varied across states and differentially affected the rollout processes, (3) visit triage rules were unclear, and (4) there were positive and negative effects of telemedicine on clinicians and patients. CONCLUSIONS: Practice leaders identified several challenges to telemedicine implementation and highlighted 2 areas, including telemedicine visit triage guidelines and telemedicine-specific staffing and scheduling protocols, for improvement.

Patient Experiences with and Preferences for Telemedicine Relative to In-Person Care During the COVID-19 Pandemic.

Introduction: Although telemedicine emerged during the COVID-19 pandemic as a critical mode of health care delivery, there may be differences in the perceived ease of patient-clinician communication and quality of care for telemedicine versus in-person visits, as well as variation in perceptions across patient subgroups. We examined patients' experiences with and preferences for telemedicine relative to in-person care, based on their most recent visit. Methods: We conducted a survey of 2,668 adults in a large academic health care system in November 2021. The survey captured patients' reasons for their most recent visit, perceptions on patient-clinician communication and quality of care, and attitudes toward telemedicine versus in-person care. Results: Among respondents, 552 (21%) had a telemedicine visit. Patients with telemedicine and in-person visits had similar agreement on ease of patient-clinician communication and perceived quality of the visit on average. However, for individuals 65 years of age or older, men, and those not needing urgent care, telemedicine was associated with worse perceptions of patient-clinician communication (65 years of age or older: adjusted odds ratio [aOR], 0.51; 95% confidence interval [CI], 0.31-0.85; men: aOR, 0.50; 95% CI, 0.31-0.81; urgent care: aOR 0.67; 95% CI, 0.49-0.91) and lower perceived quality (65 years of age or older, aOR 0.51; 95% CI, 0.30-0.86; men: 0.51; 95% CI, 0.32-0.83; urgent care: aOR 0.68; 95% CI, 0.49-0.93). Conclusion: Patient-perceived quality of care and patient-clinician communication were similar for telemedicine and in-person visits overall. However, among men, older adults, and those not seeking urgent care, patients using telemedicine had lower perceptions of patient-clinician communication and quality.

Data-driven analysis to understand long COVID using electronic health records from the RECOVER initiative.

Recent studies have investigated post-acute sequelae of SARS-CoV-2 infection (PASC, or long COVID) using real-world patient data such as electronic health records (EHR). Prior studies have typically been conducted on patient cohorts with specific patient populations which makes their generalizability unclear. This study aims to characterize PASC using the EHR data warehouses from two large Patient-Centered Clinical Research Networks (PCORnet), INSIGHT and OneFlorida+, which include 11 million patients in New York City (NYC) area and 16.8 million patients in Florida respectively. With a high-throughput screening pipeline based on propensity score and inverse probability of treatment weighting, we identified a broad list of diagnoses and medications which exhibited significantly higher incidence risk for patients 30-180 days after the laboratory-confirmed SARS-CoV-2 infection compared to non-infected patients. We identified more PASC diagnoses in NYC than in Florida regarding our screening criteria, and conditions including dementia, hair loss, pressure ulcers, pulmonary fibrosis, dyspnea, pulmonary embolism, chest pain, abnormal heartbeat, malaise, and fatigue, were replicated across both cohorts. Our analyses highlight potentially heterogeneous risks of PASC in different populations.

Racial/Ethnic Disparities in Post-acute Sequelae of SARS-CoV-2 Infection in New York: an EHR-Based Cohort Study from the RECOVER Program.

BACKGROUND: Compared to white individuals, Black and Hispanic individuals have higher rates of COVID-19 hospitalization and death. Less is known about racial/ethnic differences in post-acute sequelae of SARS-CoV-2 infection (PASC). OBJECTIVE: Examine racial/ethnic differences in potential PASC symptoms and conditions among hospitalized and non-hospitalized COVID-19 patients. DESIGN: Retrospective cohort study using data from electronic health records. PARTICIPANTS: 62,339 patients with COVID-19 and 247,881 patients without COVID-19 in New York City between March 2020 and October 2021. MAIN MEASURES: New symptoms and conditions 31-180 days after COVID-19 diagnosis. KEY RESULTS: The final study population included 29,331 white patients (47.1%), 12,638 Black patients (20.3%), and 20,370 Hispanic patients (32.7%) diagnosed with COVID-19. After adjusting for confounders, significant racial/ethnic differences in incident symptoms and conditions existed among both hospitalized and non-hospitalized patients. For example, 31-180 days after a positive SARS-CoV-2 test, hospitalized Black patients had higher odds of being diagnosed with diabetes (adjusted odds ratio [OR]: 1.96, 95% confidence interval [CI]: 1.50-2.56, q<0.001) and headaches (OR: 1.52, 95% CI: 1.11-2.08, q=0.02), compared to hospitalized white patients. Hospitalized Hispanic patients had higher odds of headaches (OR: 1.62, 95% CI: 1.21-2.17, q=0.003) and dyspnea (OR: 1.22, 95% CI: 1.05-1.42, q=0.02), compared to hospitalized white patients. Among non-hospitalized patients, Black patients had higher odds of being diagnosed with pulmonary embolism (OR: 1.68, 95% CI: 1.20-2.36, q=0.009) and diabetes (OR: 2.13, 95% CI: 1.75-2.58, q<0.001), but lower odds of encephalopathy (OR: 0.58, 95% CI: 0.45-0.75, q<0.001), compared to white patients. Hispanic patients had higher odds of being diagnosed with headaches (OR: 1.41, 95% CI: 1.24-1.60, q<0.001) and chest pain (OR: 1.50, 95% CI: 1.35-1.67, q < 0.001), but lower odds of encephalopathy (OR: 0.64, 95% CI: 0.51-0.80, q<0.001). CONCLUSIONS: Compared to white patients, patients from racial/ethnic minority groups had significantly different odds of developing potential PASC symptoms and conditions. Future research should examine the reasons for these differences.

Provider and Patient Experiences of Delays in Primary Care During the Early COVID-19 Pandemic.

BACKGROUND: The necessary suspension of nonacute services by healthcare systems early in the COVID-19 pandemic was predicted to cause delays in routine care in the United States, with potentially serious consequences for chronic disease management. However, limited work has examined provider or patient perspectives about care delays and their implications for care quality in future healthcare emergencies. OBJECTIVE: This study explores primary care provider (PCP) and patient experiences with healthcare delays during the COVID-19 pandemic. METHODS: PCPs and patients were recruited from four large healthcare systems in three states. Participants underwent semistructured interviews asking about their experiences with primary care and telemedicine. Data were analyzed using interpretive description. RESULTS: Twenty-one PCPs and 65 patients participated in interviews. Four main topics were identified: (1) types of care delayed, (2) causes for delays, (3) miscommunication contributing to delays, and (4) patient solutions to unmet care needs. CONCLUSIONS: Both patients and providers reported delays in preventive and routine care early in the pandemic, driven by healthcare system changes and patient concerns about infection risk. Primary care practices should develop plans for care continuity and consider new strategies for assessing care quality for effective chronic disease management in future healthcare system disruptions.

Telemedicine Impact on the Patient-Provider Relationship in Primary Care During the COVID-19 Pandemic.

BACKGROUND: The COVID-19 pandemic has necessitated a rapid uptake of telemedicine in primary care requiring both patients and providers to learn how to navigate care remotely. This change can impact the patient-provider relationship that often defines care, especially in primary care. OBJECTIVE: This study aims to provide insight into the experiences of patients and providers with telemedicine during the pandemic, and the impact it had on their relationship. RESEARCH DESIGN: A qualitative study using thematic analysis of semistructured interviews. SUBJECTS: Primary care providers (n=21) and adult patients (n=65) with chronic disease across primary care practices in 3 National Patient-centered Clinical Research Network sites in New York City, North Carolina, and Florida. MEASURES: Experiences with telemedicine during the COVID-19 pandemic in primary care. Codes related to the patient-provider relationship were analyzed for this study. RESULTS: A recurrent theme was the challenge telemedicine posed on rapport building and alliance. Patients felt that telemedicine affected provider's attentiveness in varying ways, whereas providers appreciated that telemedicine provided unique insight into patients' lives and living situations. Finally, both patients and providers described communication challenges. CONCLUSIONS: Telemedicine has altered structure and process aspects of primary health care such as the physical spaces of encounters, creating a new setting to which both patients and providers must adjust. It is important to recognize the opportunities and limits that this new technology has to help providers maintain the type of one-on-one attention that patients expect and that contributes to relationship building.

Identifying environmental risk factors for post-acute sequelae of SARS-CoV-2 infection: An EHR-based cohort study from the recover program.

Post-acute sequelae of SARS-CoV-2 infection (PASC) affects a wide range of organ systems among a large proportion of patients with SARS-CoV-2 infection. Although studies have identified a broad set of patient-level risk factors for PASC, little is known about the association between "exposome"-the totality of environmental exposures and the risk of PASC. Using electronic health data of patients with COVID-19 from two large clinical research networks in New York City and Florida, we identified environmental risk factors for 23 PASC symptoms and conditions from nearly 200 exposome factors. The three domains of exposome include natural environment, built environment, and social environment. We conducted a two-phase environment-wide association study. In Phase 1, we ran a mixed effects logistic regression with 5-digit ZIP Code tabulation area (ZCTA5) random intercepts for each PASC outcome and each exposome factor, adjusting for a comprehensive set of patient-level confounders. In Phase 2, we ran a mixed effects logistic regression for each PASC outcome including all significant (false positive discovery adjusted p-value < 0.05) exposome characteristics identified from Phase I and adjusting for confounders. We identified air toxicants (e.g., methyl methacrylate), particulate matter (PM2.5) compositions (e.g., ammonium), neighborhood deprivation, and built environment (e.g., food access) that were associated with increased risk of PASC conditions related to nervous, blood, circulatory, endocrine, and other organ systems. Specific environmental risk factors for each PASC condition and symptom were different across the New York City area and Florida. Future research is warranted to extend the analyses to other regions and examine more granular exposome characteristics to inform public health efforts to help patients recover from SARS-CoV-2 infection.

Data-driven identification of post-acute SARS-CoV-2 infection subphenotypes.

The post-acute sequelae of SARS-CoV-2 infection (PASC) refers to a broad spectrum of symptoms and signs that are persistent, exacerbated or newly incident in the period after acute SARS-CoV-2 infection. Most studies have examined these conditions individually without providing evidence on co-occurring conditions. In this study, we leveraged the electronic health record data of two large cohorts, INSIGHT and OneFlorida+, from the national Patient-Centered Clinical Research Network. We created a development cohort from INSIGHT and a validation cohort from OneFlorida+ including 20,881 and 13,724 patients, respectively, who were SARS-CoV-2 infected, and we investigated their newly incident diagnoses 30-180 days after a documented SARS-CoV-2 infection. Through machine learning analysis of over 137 symptoms and conditions, we identified four reproducible PASC subphenotypes, dominated by cardiac and renal (including 33.75% and 25.43% of the patients in the development and validation cohorts); respiratory, sleep and anxiety (32.75% and 38.48%); musculoskeletal and nervous system (23.37% and 23.35%); and digestive and respiratory system (10.14% and 12.74%) sequelae. These subphenotypes were associated with distinct patient demographics, underlying conditions before SARS-CoV-2 infection and acute infection phase severity. Our study provides insights into the heterogeneity of PASC and may inform stratified decision-making in the management of PASC conditions.

Influence of social deprivation index on in-hospital outcomes of COVID-19.

While it is known that social deprivation index (SDI) plays an important role on risk for acquiring Coronavirus Disease 2019 (COVID-19), the impact of SDI on in-hospital outcomes such as intubation and mortality are less well-characterized. We analyzed electronic health record data of adults hospitalized with confirmed COVID-19 between March 1, 2020 and February 8, 2021 from the INSIGHT Clinical Research Network (CRN). To compute the SDI (exposure variable), we linked clinical data using patient's residential zip-code with social data at zip-code tabulation area. SDI is a composite of seven socioeconomic characteristics determinants at the zip-code level. For this analysis, we categorized SDI into quintiles. The two outcomes of interest were in-hospital intubation and mortality. For each outcome, we examined logistic regression and random forests to determine incremental value of SDI in predicting outcomes. We studied 30,016 included COVID-19 patients. In a logistic regression model for intubation, a model including demographics, comorbidity, and vitals had an Area under the receiver operating characteristic curve (AUROC) = 0.73 (95% CI 0.70-0.75); the addition of SDI did not improve prediction [AUROC = 0.73 (95% CI 0.71-0.75)]. In a logistic regression model for in-hospital mortality, demographics, comorbidity, and vitals had an AUROC = 0.80 (95% CI 0.79-0.82); the addition of SDI in Model 2 did not improve prediction [AUROC = 0.81 (95% CI 0.79-0.82)]. Random forests revealed similar findings. SDI did not provide incremental improvement in predicting in-hospital intubation or mortality. SDI plays an important role on who acquires COVID-19 and its severity; but once hospitalized, SDI appears less important.

Machine Learning Highlights Downtrending of COVID-19 Patients with a Distinct Laboratory Profile

Background New York City (NYC) experienced an initial surge and gradual decline in the number of SARS-CoV-2-confirmed cases in 2020. A change in the pattern of laboratory test results in COVID-19 patients over this time has not been reported or correlated with patient outcome. Methods We performed a retrospective study of routine laboratory and SARS-CoV-2 RT-PCR test results from 5,785 patients evaluated in a NYC hospital emergency department from March to June employing machine learning analysis. Results A COVID-19 high-risk laboratory test result profile (COVID19-HRP), consisting of 21 routine blood tests, was identified to characterize the SARS-CoV-2 patients. Approximately half of the SARS-CoV-2 positive patients had the distinct COVID19-HRP that separated them from SARS-CoV-2 negative patients. SARS-CoV-2 patients with the COVID19-HRP had higher SARS-CoV-2 viral loads, determined by cycle threshold values from the RT-PCR, and poorer clinical outcome compared to other positive patients without the COVID12-HRP. Furthermore, the percentage of SARS-CoV-2 patients with the COVID19-HRP has significantly decreased from March/April to May/June. Notably, viral load in the SARS-CoV-2 patients declined, and their laboratory profile became less distinguishable from SARS-CoV-2 negative patients in the later phase. Conclusions Our longitudinal analysis illustrates the temporal change of laboratory test result profile in SARS-CoV-2 patients and the COVID-19 evolvement in a US epicenter. This analysis could become an important tool in COVID-19 population disease severity tracking and prediction. In addition, this analysis may play an important role in prioritizing high-risk patients, assisting in patient triaging and optimizing the usage of resources.

Socioeconomic variation in characteristics, outcomes, and healthcare utilization of COVID-19 patients in New York City.

OBJECTIVES: There is limited evidence on how clinical outcomes differ by socioeconomic conditions among patients with coronavirus disease 2019 (COVID-19). Most studies focused on COVID-19 patients from a single hospital. Results based on patients from multiple health systems have not been reported. The objective of this study is to examine variation in patient characteristics, outcomes, and healthcare utilization by neighborhood social conditions among COVID-19 patients. METHODS: We extracted electronic health record data for 23,300 community dwelling COVID-19 patients in New York City between March 1st and June 11th, 2020 from all care settings, including hospitalized patients, patients who presented to the emergency department without hospitalization, and patients with ambulatory visits only. Zip Code Tabulation Area-level social conditions were measured by the Social Deprivation Index (SDI). Using logistic regressions and Cox proportional-hazards models, we examined the association between SDI quintiles and hospitalization and death, controlling for race, ethnicity, and other patient characteristics. RESULTS: Among 23,300 community dwelling COVID-19 patients, 60.7% were from neighborhoods with disadvantaged social conditions (top SDI quintile), although these neighborhoods only account for 34% of overall population. Compared to socially advantaged patients (bottom SDI quintile), socially disadvantaged patients (top SDI quintile) were older (median age 55 vs. 53, P<0.001), more likely to be black (23.1% vs. 6.4%, P<0.001) or Hispanic (25.4% vs. 8.5%, P<0.001), and more likely to have chronic conditions (e.g., diabetes: 21.9% vs. 10.5%, P<0.001). Logistic and Cox regressions showed that patients with disadvantaged social conditions had higher risk for hospitalization (odds ratio: 1.68; 95% confidence interval [CI]: [1.46, 1.94]; P<0.001) and mortality (hazard ratio: 1.91; 95% CI: [1.35, 2.70]; P<0.001), adjusting for other patient characteristics. CONCLUSION: Substantial socioeconomic disparities in health outcomes exist among COVID-19 patients in NYC. Disadvantaged neighborhood social conditions were associated with higher risk for hospitalization, severity of disease, and death.

Clinical subphenotypes in COVID-19: derivation, validation, prediction, temporal patterns, and interaction with social determinants of health.

The coronavirus disease 2019 (COVID-19) is heterogeneous and our understanding of the biological mechanisms of host response to the viral infection remains limited. Identification of meaningful clinical subphenotypes may benefit pathophysiological study, clinical practice, and clinical trials. Here, our aim was to derive and validate COVID-19 subphenotypes using machine learning and routinely collected clinical data, assess temporal patterns of these subphenotypes during the pandemic course, and examine their interaction with social determinants of health (SDoH). We retrospectively analyzed 14418 COVID-19 patients in five major medical centers in New York City (NYC), between March 1 and June 12, 2020. Using clustering analysis, 4 biologically distinct subphenotypes were derived in the development cohort (N = 8199). Importantly, the identified subphenotypes were highly predictive of clinical outcomes (especially 60-day mortality). Sensitivity analyses in the development cohort, and rederivation and prediction in the internal (N = 3519) and external (N = 3519) validation cohorts confirmed the reproducibility and usability of the subphenotypes. Further analyses showed varying subphenotype prevalence across the peak of the outbreak in NYC. We also found that SDoH specifically influenced mortality outcome in Subphenotype IV, which is associated with older age, worse clinical manifestation, and high comorbidity burden. Our findings may lead to a better understanding of how COVID-19 causes disease in different populations and potentially benefit clinical trial development. The temporal patterns and SDoH implications of the subphenotypes may add insights to health policy to reduce social disparity in the pandemic.

A predictive model of clinical deterioration among hospitalized COVID-19 patients by harnessing hospital course trajectories.

From early March through mid-May 2020, the COVID-19 pandemic overwhelmed hospitals in New York City. In anticipation of ventilator shortages and limited ICU bed capacity, hospital operations prioritized the development of prognostic tools to predict clinical deterioration. However, early experience from frontline physicians observed that some patients developed unanticipated deterioration after having relatively stable periods, attesting to the uncertainty of clinical trajectories among hospitalized patients with COVID-19. Prediction tools that incorporate clinical variables at one time-point, usually on hospital presentation, are suboptimal for patients with dynamic changes and evolving clinical trajectories. Therefore, our study team developed a machine-learning algorithm to predict clinical deterioration among hospitalized COVID-19 patients by extracting clinically meaningful features from complex longitudinal laboratory and vital sign values during the early period of hospitalization with an emphasis on informative missing-ness. To incorporate the evolution of the disease and clinical practice over the course of the pandemic, we utilized a time-dependent cross-validation strategy for model development. Finally, we validated our prediction model on an external validation cohort of COVID-19 patients served in a demographically distinct population from the training cohort. The main finding of our study is the identification of risk profiles of early, late and no clinical deterioration during the course of hospitalization. While risk prediction models that include simple predictors at ED presentation and clinical judgement are able to identify any deterioration vs. no deterioration, our methodology is able to isolate a particular risk group that remain stable initially but deteriorate at a later stage of the course of hospitalization. We demonstrate the superior predictive performance with the utilization of laboratory and vital sign data during the early period of hospitalization compared to the utilization of data at presentation alone. Our results will allow efficient hospital resource allocation and will motivate research in understanding the late deterioration risk group.

Impact of the Early Phase of the COVID-19 Pandemic on US Healthcare Workers: Results from the HERO Registry.

BACKGROUND: The HERO registry was established to support research on the impact of the COVID-19 pandemic on US healthcare workers. OBJECTIVE: Describe the COVID-19 pandemic experiences of and effects on individuals participating in the HERO registry. DESIGN: Cross-sectional, self-administered registry enrollment survey conducted from April 10 to July 31, 2020. SETTING: Participants worked in hospitals (74.4%), outpatient clinics (7.4%), and other settings (18.2%) located throughout the nation. PARTICIPANTS: A total of 14,600 healthcare workers. MAIN MEASURES: COVID-19 exposure, viral and antibody testing, diagnosis of COVID-19, job burnout, and physical and emotional distress. KEY RESULTS: Mean age was 42.0 years, 76.4% were female, 78.9% were White, 33.2% were nurses, 18.4% were physicians, and 30.3% worked in settings at high risk for COVID-19 exposure (e.g., ICUs, EDs, COVID-19 units). Overall, 43.7% reported a COVID-19 exposure and 91.3% were exposed at work. Just 3.8% in both high- and low-risk settings experienced COVID-19 illness. In regression analyses controlling for demographics, professional role, and work setting, the risk of COVID-19 illness was higher for Black/African-Americans (aOR 2.32, 99% CI 1.45, 3.70, p < 0.01) and Hispanic/Latinos (aOR 2.19, 99% CI 1.55, 3.08, p < 0.01) compared with Whites. Overall, 41% responded that they were experiencing job burnout. Responding about the day before they completed the survey, 53% of participants reported feeling tired a lot of the day, 51% stress, 41% trouble sleeping, 38% worry, 21% sadness, 19% physical pain, and 15% anger. On average, healthcare workers reported experiencing 2.4 of these 7 distress feelings a lot of the day. CONCLUSIONS: Healthcare workers are at high risk for COVID-19 exposure, but rates of COVID-19 illness were low. The greater risk of COVID-19 infection among race/ethnicity minorities reported in the general population is also seen in healthcare workers. The HERO registry will continue to monitor changes in healthcare worker well-being during the pandemic. TRIAL REGISTRATION: ClinicalTrials.gov identifier NCT04342806.

Routine Laboratory Blood Tests Predict SARS-CoV-2 Infection Using Machine Learning.

BACKGROUND: Accurate diagnostic strategies to identify SARS-CoV-2 positive individuals rapidly for management of patient care and protection of health care personnel are urgently needed. The predominant diagnostic test is viral RNA detection by RT-PCR from nasopharyngeal swabs specimens, however the results are not promptly obtainable in all patient care locations. Routine laboratory testing, in contrast, is readily available with a turn-around time (TAT) usually within 1-2 hours. METHOD: We developed a machine learning model incorporating patient demographic features (age, sex, race) with 27 routine laboratory tests to predict an individual's SARS-CoV-2 infection status. Laboratory testing results obtained within 2 days before the release of SARS-CoV-2 RT-PCR result were used to train a gradient boosting decision tree (GBDT) model from 3,356 SARS-CoV-2 RT-PCR tested patients (1,402 positive and 1,954 negative) evaluated at a metropolitan hospital. RESULTS: The model achieved an area under the receiver operating characteristic curve (AUC) of 0.854 (95% CI: 0.829-0.878). Application of this model to an independent patient dataset from a separate hospital resulted in a comparable AUC (0.838), validating the generalization of its use. Moreover, our model predicted initial SARS-CoV-2 RT-PCR positivity in 66% individuals whose RT-PCR result changed from negative to positive within 2 days. CONCLUSION: This model employing routine laboratory test results offers opportunities for early and rapid identification of high-risk SARS-CoV-2 infected patients before their RT-PCR results are available. It may play an important role in assisting the identification of SARS-CoV-2 infected patients in areas where RT-PCR testing is not accessible due to financial or supply constraints.