Try a new search

Format these results:

Searched for:

person:richas25

in-biosketch:true

Total Results:

46


Considerations for using predictive models that include race as an input variable: The case study of lung cancer screening

Stevens, Elizabeth R; Caverly, Tanner; Butler, Jorie M; Kukhareva, Polina; Richardson, Safiya; Mann, Devin M; Kawamoto, Kensaku
Indiscriminate use of predictive models incorporating race can reinforce biases present in source data and lead to an exacerbation of health disparities. In some countries, such as the United States, there is therefore a push to remove race from prediction models; however, there are still many prediction models that use race as an input. Biomedical informaticists who are given the responsibility of using these predictive models in healthcare environments are likely to be faced with questions like how to deal with race covariates in these models. Thus, there is a need for a pragmatic framework to help model users think through how to include race in their chosen model so as to avoid inadvertently exacerbating disparities. In this paper, we use the case study of lung cancer screening to propose a simple framework to guide how model users can approach the use (or non-use) of race inputs in the predictive models they are tasked with leveraging in electronic health records and clinical workflows.
PMID: 37844677
ISSN: 1532-0480
CID: 5609662

Integrating Clinical Decision Support Into Electronic Health Record Systems Using a Novel Platform (EvidencePoint): Developmental Study

Solomon, Jeffrey; Dauber-Decker, Katherine; Richardson, Safiya; Levy, Sera; Khan, Sundas; Coleman, Benjamin; Persaud, Rupert; Chelico, John; King, D'Arcy; Spyropoulos, Alex; McGinn, Thomas
BACKGROUND:Through our work, we have demonstrated how clinical decision support (CDS) tools integrated into the electronic health record (EHR) assist providers in adopting evidence-based practices. This requires confronting technical challenges that result from relying on the EHR as the foundation for tool development; for example, the individual CDS tools need to be built independently for each different EHR. OBJECTIVE:The objective of our research was to build and implement an EHR-agnostic platform for integrating CDS tools, which would remove the technical constraints inherent in relying on the EHR as the foundation and enable a single set of CDS tools that can work with any EHR. METHODS:We developed EvidencePoint, a novel, cloud-based, EHR-agnostic CDS platform, and we will describe the development of EvidencePoint and the deployment of its initial CDS tools, which include EHR-integrated applications for clinical use cases such as prediction of hospitalization survival for patients with COVID-19, venous thromboembolism prophylaxis, and pulmonary embolism diagnosis. RESULTS:The results below highlight the adoption of the CDS tools, the International Medical Prevention Registry on Venous Thromboembolism-D-Dimer, the Wells' criteria, and the Northwell COVID-19 Survival (NOCOS), following development, usability testing, and implementation. The International Medical Prevention Registry on Venous Thromboembolism-D-Dimer CDS was used in 5249 patients at the 2 clinical intervention sites. The intervention group tool adoption was 77.8% (4083/5249 possible uses). For the NOCOS tool, which was designed to assist with triaging patients with COVID-19 for hospital admission in the event of constrained hospital resources, the worst-case resourcing scenario never materialized and triaging was never required. As a result, the NOCOS tool was not frequently used, though the EvidencePoint platform's flexibility and customizability enabled the tool to be developed and deployed rapidly under the emergency conditions of the pandemic. Adoption rates for the Wells' criteria tool will be reported in a future publication. CONCLUSIONS:The EvidencePoint system successfully demonstrated that a flexible, user-friendly platform for hosting CDS tools outside of a specific EHR is feasible. The forthcoming results of our outcomes analyses will demonstrate the adoption rate of EvidencePoint tools as well as the impact of behavioral economics "nudges" on the adoption rate. Due to the EHR-agnostic nature of EvidencePoint, the development process for additional forms of CDS will be simpler than traditional and cumbersome IT integration approaches and will benefit from the capabilities provided by the core system of EvidencePoint.
PMCID:10623239
PMID: 37856193
ISSN: 2561-326x
CID: 5736162

Centering health equity in large language model deployment

Singh, Nina; Lawrence, Katharine; Richardson, Safiya; Mann, Devin M
PMCID:10597518
PMID: 37874780
ISSN: 2767-3170
CID: 5736252

Comparison of Chest Radiograph Impressions for Diagnosing Pneumonia: Accounting for Categories of Language Certainty

Makhnevich, Alexander; Sinvani, Liron; Feldhamer, Kenneth H; Zhang, Meng; Richardson, Safiya; McGinn, Thomas G; Cohen, Stuart L
OBJECTIVES/OBJECTIVE:Uncertain language in chest radiograph (CXR) reports for the diagnosis of pneumonia is prevalent. The purpose of this study is to validate an a priori stratification of CXR results for diagnosing pneumonia based on language of certainty. DESIGN/METHODS:Retrospective chart review. SETTING AND PARTICIPANTS/METHODS:CXR reports of 2,411 hospitalized patients ≥ 18 years, admitted to medicine, who received a CXR and noncontrast chest CT within 48 hours of emergency department registration at two large academic hospitals (tertiary and quaternary care) were reviewed. METHODS:test; a P value of .0031 was considered significant to account for multiple comparisons. RESULTS:CXR reports for the diagnosis of pneumonia revealed the following distribution: 61% negative, 32% uncertain, and 7% positive; CT reports were 55% negative, 22% uncertain, and 23% positive for the diagnosis of pneumonia. There were significant differences between CXR categories compared with CT categories for diagnosis of pneumonia (P < .001). Negative CXR results were not significantly different than the uncertain category with the most uncertain language (P = .030) but were significantly different from all other uncertain categories and positive results (each P < .001). Positive CXR results were not significantly different than the least uncertain category (most certain language) (P = .130) but were significantly different from all other categories (each P < .001). CONCLUSIONS AND IMPLICATIONS/CONCLUSIONS:Language used in CXR reports to diagnose pneumonia exists in categories of varying certainty and should be considered when evaluating patients for pneumonia.
PMID: 35792164
ISSN: 1558-349x
CID: 5280352

The effect of race coefficients on preemptive listing for kidney transplantation

Abate, Mersema; Jandovitz, Nicholas; Hirsch, Jamie S; Breslin, Nadine; Lau, Lawrence; Fahmy, Ahmed E; Jhaveri, Kenar D; Richardson, Safiya; Alsalmay, Yaser; Baez, Anthony; Mishra, Akash; Bolourani, Siavash; Miyara, Santiago J; Winnick, Aaron; Nair, Gayatri; Bhaskaran, Madhu C; Grodstein, Elliot; Kressel, Adam M; Teperman, Lewis W; Molmenti, Ernesto P; Nair, Vinay
BACKGROUND/UNASSIGNED:Race coefficients of estimated glomerular filtration rate (eGFR) formulas may be partially responsible for racial inequality in preemptive listing for kidney transplantation. METHODS/UNASSIGNED:We used the Scientific Registry of Transplant Recipients database to evaluate differences in racial distribution of preemptive listing before and after application of the Modification of Diet in Renal Disease (MDRD) and the Chronic Kidney Disease Epidemiology Collaboration (CKD-EPI) race coefficients to all preemptively listed non-Black kidney transplant candidates (eGFR modulation). Odds of preemptive listing were calculated by race, with Black as the reference before and after eGFR modulation. Variables known to influence preemptive listing were included in the model. RESULTS/UNASSIGNED:were removed. Compared with Black candidates, the adjusted odds of preemptive listing for White candidates decreased from 2.01 [95% confidence interval (95% CI) 1.78-2.26] before eGFR modulation to 1.18 (95% CI 1.0-1.39; P = 0.046) with the MDRD and 1.37 (95% CI 1.18-1.58) with the CKD-EPI equations after adjusting for race coefficients. CONCLUSIONS/UNASSIGNED:Removing race coefficients in GFR estimation formulas may result in a more equitable distribution of Black candidates listed earlier on a preemptive basis.
PMCID:9050544
PMID: 35498880
ISSN: 2048-8505
CID: 5394872

Automated Pulmonary Embolism Risk Assessment Using the Wells Criteria: Validation Study

Zhang, Nasen Jonathan; Rameau, Philippe; Julemis, Marsophia; Liu, Yan; Solomon, Jeffrey; Khan, Sundas; McGinn, Thomas; Richardson, Safiya
BACKGROUND:Computed tomography pulmonary angiography (CTPA) is frequently used in the emergency department (ED) for the diagnosis of pulmonary embolism (PE), while posing risk for contrast-induced nephropathy and radiation-induced malignancy. OBJECTIVE:We aimed to create an automated process to calculate the Wells score for pulmonary embolism for patients in the ED, which could potentially reduce unnecessary CTPA testing. METHODS:We designed an automated process using electronic health records data elements, including using a combinatorial keyword search method to query free-text fields, and calculated automated Wells scores for a sample of all adult ED encounters that resulted in a CTPA study for PE at 2 tertiary care hospitals in New York, over a 2-month period. To validate the automated process, the scores were compared to those derived from a 2-clinician chart review. RESULTS:A total of 202 ED encounters resulted in a completed CTPA to form the retrospective study cohort. Patients classified as "PE likely" by the automated process (126/202, 62%) had a PE prevalence of 15.9%, whereas those classified as "PE unlikely" (76/202, 38%; Wells score >4) had a PE prevalence of 7.9%. With respect to classification of the patient as "PE likely," the automated process achieved an accuracy of 92.1% when compared with the chart review, with sensitivity, specificity, positive predictive value, and negative predictive value of 93%, 90.5%, 94.4%, and 88.2%, respectively. CONCLUSIONS:This was a successful development and validation of an automated process using electronic health records data elements, including free-text fields, to classify risk for PE in ED visits.
PMCID:8922138
PMID: 35225812
ISSN: 2561-326x
CID: 5435692

Association of race/ethnicity with mortality in patients hospitalized with COVID-19

Richardson, Safiya; Martinez, Johanna; Hirsch, Jamie S; Cerise, Jane; Lesser, Martin; Roswell, Robert O; Davidson, Karina W
OBJECTIVE:To evaluate racial and ethnic differences in mortality among patients hospitalized with coronavirus disease 2019 (COVID-19) after adjusting for baseline characteristics and comorbidities. METHODS:This retrospective cohort study at 13 acute care facilities in the New York City metropolitan area included sequentially hospitalized patients between March 1, 2020, and April 27, 2020. Last day of follow up was July 31, 2020. Patient demographic information, including race/ethnicity and comorbidities, were collected. The primary outcome was in-hospital mortality. RESULTS:A total of 10 869 patients were included in the study (median age, 65 years [interquartile range (IQR) 54-77; range, 18-107 years]; 40.5% female). In adjusted time-to-event analysis, increased age, male sex, insurance type (Medicare and Self-Pay), unknown smoking status, and a higher score on the Charlson Comorbidity Index were significantly associated with higher in-hospital mortality. Adjusted risk of hospital mortality for Black, Asian, Hispanic, multiracial/other, and unknown race/ethnicity patients were similar to risk for White patients. CONCLUSIONS:In a large diverse cohort of patients hospitalized with COVID-19, patients from racial/ethnic minorities experienced similar mortality risk as White patients.
PMCID:9352026
PMID: 35925973
ISSN: 1932-6203
CID: 5430812

External validation of the IMPROVE-DD risk assessment model for venous thromboembolism among inpatients with COVID-19

Goldin, Mark; Lin, Stephanie K; Kohn, Nina; Qiu, Michael; Cohen, Stuart L; Barish, Matthew A; Gianos, Eugenia; Diaz, Anise; Richardson, Safiya; Giannis, Dimitrios; Chatterjee, Saurav; Coppa, Kevin; Hirsch, Jamie S; Ngu, Sam; Firoozan, Sheila; McGinn, Thomas; Spyropoulos, Alex C
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.
PMCID:8214061
PMID: 34146235
ISSN: 1573-742x
CID: 4917922

Barriers to the Use of Clinical Decision Support for the Evaluation of Pulmonary Embolism: Qualitative Interview Study

Richardson, Safiya; Dauber-Decker, Katherine L; McGinn, Thomas; Barnaby, Douglas P; Cattamanchi, Adithya; Pekmezaris, Renee
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.
PMCID:8374661
PMID: 34346901
ISSN: 2292-9495
CID: 4996242

In-Hospital 30-Day Survival Among Young Adults With Coronavirus Disease 2019: A Cohort Study

Richardson, Safiya; Gitlin, Jordan; Kozel, Zachary; Levy, Sera; Rahman, Husneara; Hirsch, Jamie S; McGinn, Thomas; Diefenbach, Michael A
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.
PMCID:8135976
PMID: 34183983
ISSN: 2328-8957
CID: 4996232