Faculty Bibliography

BACKGROUND:Clinicians often disregard potentially beneficial clinical decision support (CDS). OBJECTIVE:In this study, we sought to explore the psychological and behavioral barriers to the use of a CDS tool. METHODS:We conducted a qualitative study involving emergency medicine physicians and physician assistants. A semistructured interview guide was created based on the Capability, Opportunity, and Motivation-Behavior model. Interviews focused on the barriers to the use of a CDS tool built based on Wells' criteria for pulmonary embolism to assist clinicians in establishing pretest probability of pulmonary embolism before imaging. RESULTS:Interviews were conducted with 12 clinicians. Six barriers were identified, including (1) Bayesian reasoning, (2) fear of missing a pulmonary embolism, (3) time pressure or cognitive load, (4) gestalt includes Wells' criteria, (5) missed risk factors, and (6) social pressure. CONCLUSIONS:Clinicians highlighted several important psychological and behavioral barriers to CDS use. Addressing these barriers will be paramount in developing CDS that can meet its potential to transform clinical care.

Background/UNASSIGNED:Our objective was to characterize young adult patients hospitalized with coronavirus disease 2019 (COVID-19) and identify predictors of survival at 30 days. Methods/UNASSIGNED:This retrospective cohort study took place at 12 acute care hospitals in the New York City area. Patients aged 18-39 hospitalized with confirmed COVID-19 between March 1 and April 27, 2020 were included in the study. Demographic, clinical, and outcome data were extracted from electronic health record reports. Results/UNASSIGNED:= .002) were independent predictors of in-hospital 30-day mortality. Conclusions/UNASSIGNED:Obesity was identified as the strongest negative predictor of 30-day in-hospital survival in young adults with COVID-19.

BACKGROUND:Child maltreatment is a leading cause of pediatric morbidity and mortality. We previously reported on development and implementation of a child abuse clinical decision support system (CA-CDSS) in the Cerner electronic health record (EHR). Our objective was to develop a CA-CDSS in two different EHRs. METHODS:Using the CA-CDSS in Cerner as a template, CA-CDSSs were developed for use in four hospitals in the Northwell Health system who use Allscripts and two hospitals in the University of Wisconsin health system who use Epic. Each system had a combination of triggers, alerts and child abuse-specific order sets. Usability evaluation was done prior to launch of the CA-CDSS. RESULTS:Over an 18-month period, a CA-CDSS was embedded into Epic and Allscripts at two hospital systems. The CA-CDSSs vary significantly from each other in terms of the type of triggers which were able to be used, the type of alert, the ability of the alert to link directly to child abuse-specific order sets and the order sets themselves. CONCLUSIONS:Dissemination of CA-CDSS from one EHR into the EHR in other health care systems is possible but time-consuming and needs to be adapted to the strengths and limitations of the specific EHR. Site-specific usability evaluation, buy-in of multiple stakeholder groups and significant information technology support are needed. These barriers limit scalability and widespread dissemination of CA-CDSS.

There is a need to discriminate which COVID-19 inpatients are at higher risk for venous thromboembolism (VTE) to inform prophylaxis strategies. The IMPROVE-DD VTE risk assessment model (RAM) has previously demonstrated good discrimination in non-COVID populations. We aimed to externally validate the IMPROVE-DD VTE RAM in medical patients hospitalized with COVID-19. This retrospective cohort study evaluated the IMPROVE-DD VTE RAM in adult patients with COVID-19 admitted to one of thirteen Northwell Health hospitals in the New York metropolitan area between March 1, 2020 and April 27, 2020. VTE was defined as new-onset symptomatic deep venous thrombosis or pulmonary embolism. To assess the predictive value of the RAM, the receiver operating characteristic (ROC) curve was plotted and the area under the curve (AUC) was calculated. Sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) were calculated. Of 9407 patients who met study criteria, 274 patients developed VTE with a prevalence of 2.91%. The VTE rate was 0.41% for IMPROVE-DD score 0-1 (low risk), 1.21% for score 2-3 (moderate risk), and 5.30% for score ≥ 4 (high risk). Approximately 45.7% of patients were classified as high VTE risk, 33.3% moderate risk, and 21.0% low risk. Discrimination of low versus moderate-high VTE risk demonstrated sensitivity 0.971, specificity 0.215, PPV 0.036, and NPV 0.996. ROC AUC was 0.703. In this external validation study, the IMPROVE-DD VTE RAM demonstrated very good discrimination to identify hospitalized COVID-19 patients at low, moderate, and high VTE risk.

Background - Social influencers of health (SIOH) namely race, ethnicity and structural inequities are known to affect the incidence of out of hospital sudden death (OHSD). We sought to examine the association between SIOH and the incidence of OHSD in the diverse neighborhoods of New York City (NYC) during the first wave of COVID-19 epidemic. Methods - NYC ZIP stratified data on OHSD were obtained from the Fire Department of New York during the first wave of COVID-19 epidemic (March 1 - April 10, 2019) and the same period in 2020. To assess associates of OHSD, ZIP code-specific sociodemographic characteristics for 8,491,238 NYC residents were obtained via the US Census Bureau's 2018 American Community Survey and the New York Police Department's crime statistics. Results - Between March 1 and April 10, 2020, the number of OHSD rose to 4,334 from 1,112 compared to the year prior. Of the univariate ZIP code level variables evaluated, proportions of: Black race, Hispanic/Latino ethnicity, single parent household, unemployed inhabitants, people completing less than high school education, inhabitants with no health insurance, people financially struggling or living in poverty, percent of non-citizens and population density were associated with increased rates of OHSD within ZIP codes. In multivariable analysis, ZIP codes with higher proportions of inhabitants with less than high school education (p < 0.001) and higher proportions of Black race (p = 0.04) were independent predictors for increases in ZIP code rates of OHSD. Conclusions - Educational attainment and the proportion of Black race in NYC ZIP codes remained independent predictors of increased rates of ZIP code level OHSD during the COVID-19 outbreak even after controlling for 2019 rates. To facilitate health equity, future research should focus on characterizing the impacts of structural inequities while exploring strategies to mitigate their effects.

BACKGROUND:The benefits of computed tomography pulmonary angiography (CTPA) for pulmonary embolism (PE) diagnosis must be weighed against its risks, radiation-induced malignancy, and contrast-induced nephropathy. Appropriate use of CTPA can be assessed by monitoring yield, the percentage of tests positive for PE. We identify factors that are associated low CTPA yield, which may predict overtesting. METHODS:This was a retrospective cohort study of six emergency departments between June 2014 and February 2017. The electronic health record was queried for CTPAs ordered for adult patients in the emergency department. We assessed the following patient factors: age, gender, body mass index, number of comorbidities, race, and ethnicity, provider factors: type (resident, fellow, attending, physician assistant) and environment factors: test time of day, season of visit, and crowdedness of the department. RESULTS:A total of 14,782 CTPAs were reviewed, of which 1366 were found to be positive for PE, resulting in an overall CTPA yield of 9.24%. Provider type was not associated with a difference in yield. Testing was less likely to be positive in younger patients, females, those with lower body mass indexes and those identifying as Asian or Hispanic. Testing was also less likely to be positive when ordered during the overnight shift and during the winter and spring seasons. CONCLUSION:Our study identified several patient and environmental factors associated with low CTPA yield suggesting potential targets for overtesting. Targeting education and clinical decision support to assist providers in these circumstances may meaningfully improve yields.

RATIONALE OBJECTIVES/UNASSIGNED:Excess z-axis scanning continues as an unnecessary source of radiation. This study seeks to determine patient, technologist and CT factors that affect excess scan length for chest CT. MATERIALS AND METHODS/METHODS:Retrospective evaluation of 1118 consecutive noncontrast chest CT scans, over twelve consecutive months, was performed for evaluation of scan length above and below the lung parenchyma. Scan length >2 cm was considered excessive. Bivariate analysis for mean excess scan length and presence of excess scan length analyzed technologist's exam volume during the study period, patient age, patient gender, day of week, and time of day as categorical variables. Technologists performing >100 chest CT scans during the study period were considered high-volume while all others were considered low-volume. RESULTS:Mean excess scan length was 5 mm, 29 mm, and 33 mm above the lungs, below the lungs, and total. 81% and 95% of studies had excess scanning above the lungs and below the lungs respectively. Multivariable analysis showed that high volume technologists, male patients, and patients younger than 65 had a greater amount of excess scan length and presence of excessive scanning above the lungs; high volume technologists and male patients had a greater amount of excess scan length below the lungs, and high volume technologists and patients older than 65 had greater presence of excessive scanning below the lungs, each p < 0.001. CONCLUSIONS:Excess scanning on chest CT is common, varies by patient age and gender and was significantly greater for high volume technologists.

Behavioral interventions have been shown to have powerful effects on human behavior both outside of and within the context of health care. As organizations increasingly adopt behavioral architecture, care must be taken to consider its potential negative consequences. An evidenced-based approach is best, whereby interventions that might have a significant deleterious effect on patients' health outcomes are first tested and rigorously evaluated before being systematically rolled out. In the case of clinical decision support, brief and thorough instructions should be provided for use. Physician performance when using these systems is best measured relatively, in the context of peers with similar training. Responsibility for errors must be shared with clinical team members and system designers.

BACKGROUND:Chinese studies reported predictors of severe disease and mortality associated with coronavirus disease 2019 (COVID-19). A generalizable and simple survival calculator based on data from US patients hospitalized with COVID-19 has not yet been introduced. OBJECTIVE:Develop and validate a clinical tool to predict 7-day survival in patients hospitalized with COVID-19. DESIGN/METHODS:Retrospective and prospective cohort study. SETTING/METHODS:Thirteen acute care hospitals in the New York City area. PARTICIPANTS/METHODS:Adult patients hospitalized with a confirmed diagnosis of COVID-19. The development and internal validation cohort included patients hospitalized between March 1 and May 6, 2020. The external validation cohort included patients hospitalized between March 1 and May 5, 2020. MEASUREMENTS/METHODS:Demographic, laboratory, clinical, and outcome data were extracted from the electronic health record. Optimal predictors and performance were identified using least absolute shrinkage and selection operator (LASSO) regression with receiver operating characteristic curves and measurements of area under the curve (AUC). RESULTS:The development and internal validation cohort included 11 095 patients with a median age of 65 years [interquartile range (IQR) 54-77]. Overall 7-day survival was 89%. Serum blood urea nitrogen, age, absolute neutrophil count, red cell distribution width, oxygen saturation, and serum sodium were identified as the 6 optimal of 42 possible predictors of survival. These factors constitute the NOCOS (Northwell COVID-19 Survival) Calculator. Performance in the internal validation, prospective validation, and external validation were marked by AUCs of 0.86, 0.82, and 0.82, respectively. LIMITATIONS/CONCLUSIONS:All participants were hospitalized within the New York City area. CONCLUSIONS:The NOCOS Calculator uses 6 factors routinely available at hospital admission to predict 7-day survival for patients hospitalized with COVID-19. The calculator is publicly available at https://feinstein.northwell.edu/NOCOS.

OBJECTIVE:Increased utilization of CT pulmonary angiography (CTPA) for the evaluation of pulmonary embolism has been associated with decreasing diagnostic yields and rising concerns about the harms of unnecessary testing. The objective of this study was to determine whether clinical decision support (CDS) use would be associated with increased imaging yields after controlling for selection bias. METHODS:We performed a retrospective cohort study in the emergency departments of two tertiary care hospitals of all CTPAs performed between August 2015 and September 2018. Providers ordering a CTPA are routed to an optional CDS tool, which allows them to use Wells' Criteria for pulmonary embolism. After propensity score matching, CTPA yield was calculated for the CDS-use and CDS-dismissal groups and stratified by provider type. RESULTS:A total of 7,367 CTPAs were ordered during the study period. Of those, providers used the CDS tool in 2,568 (35%) cases and did not use the tool in 4,799 (65%) of cases. After propensity score matching, CTPA yield was 11.99% in the CDS-use group and 8.70% in the CDS-dismissal group (P < .001). Attending physicians, residents, and physician assistant CDS users demonstrated a 56.5% (P = .006), 38.7% (P = .01), and 16.7% (P = .03) increased yield compared with those who dismissed the tool, respectively. DISCUSSION/CONCLUSIONS:Diagnostic yield was 38% higher for CTPAs when the provider used the CDS tool, after controlling for selection bias. Yields were higher for every provider type. Further research is needed to discover successful strategies to increase provider use of these important tools.