Faculty Bibliography

BACKGROUND: Race, ethnicity, and language have been identified as factors impacting uptake of HIV testing. This project sought to compare testing rates between predominant and non-predominant ethnic, racial, or language populations within neighborhood FQHCs.
METHOD(S): We identified Family Health Center network locations at which more than 50% of patients served identified as the same race, and/ or had the same preferred language, and focused our analysis on these sites. We used Excel and SPSS to compare HIV testing rates between predominant and non-predominant population groups at each clinic.
RESULT(S): At 2 of 5 sites with a predominant non-English preferred language, speakers of the predominant language were more likely to receive an HIV test than speakers of other languages (p<0.001 for both sites). The other sites showed no difference by language. Of 2 clinics with a predominant racial population, there was no difference between predominant and non-predominant populations in terms of HIV testing. At all included sites, with one exception, Hispanic ethnicity was associated with a significantly higher rate of HIV testing.
CONCLUSION(S): Predominant/non-predominant race did not affect HIV testing rates, but language and ethnicity did. One mechanism for this may be increased trust associated with patient-provider language concordance, resulting in greater uptake of tests. There is a need for future research to further explore the factors associated with these findings

BACKGROUND: Payors are increasingly recognizing that social determinants of health(SDH) impact on health outcomes and healthcare costs. Z codes can be used to document and stratify patients into risk pools according to SDH. We report on the impact of SDH screening implementation in a federally qualified health center network on the use of Z codes to document SDH. In this study, we describe the prevalence of SDH screening by department, the prevalence of documented SDH, and the prevalence of documented Z codes for each SDH.
METHOD(S): In October 2017, we initiated SDH screening throughout, but focusing on the internal medicine and women's health departments of a large FQHC network (12 service delivery sites) using the OCHIN tool embedded in the elecrtonic health record. In November 2019, we retrieved the following variables from record: % of all patients who were screened, number of patients screened annually by department, % of positive screens (+ response to any question), % abnormal screens(response that triggers a best practice alert to the treating provider), and documentation of a Z code for positive or abnormal screens.
RESULT(S): There were 624,007 encounters over a 2 year study period; 2,844 patients were screened: 194 in 2017, 1068 in 2018;1644 in 2019. Overall, there were 3052 screening events (some patients received multiple screens). The majority of screening events occurred in women's health [1961(64%)], followed by adult medicine[874(29%)]. Overall, 2350(77%) of screens were "positive", of which 433 had no "abnormal" results and hence did not trigger a best practice alert. Of these 433, the most common positive items were: social isolation(63%), stress(44%), financial resource strain(8%), moved 2+ times(7%). There were 1923(63%) abnormal screens. The top 10 abnormal items in Women's Health and Adult Medicine were: Education less than high school(36% and 37%), physical activity <140 minutes(23% and 25%), hard to pay for medicine/medical care(13% and 26%), hard to pay for utilities(14% and 23%), hard to pay for food(13% and 21%), hard to pay for health insurance(11% and 22%), concerns about housing quality(3% and 9%), hard to pay child care(5% and 5%), exposure to violence(4% and 3%), never get together with family/friends(3% and 3%). Overall, encounters with an SDH screen were more likely to have a documented Z code:26% vs. 1%. Z codes were documented for the following documented needs: insufficient social insurance(53%); lack of access to health care(51%), homelessness(49%), inadequate family support(40%), lack of physical exercise(37%), underachievement in school(34%), personal history of abuse(31%), lack of assistance for care at home(29%), inadequate food supply(1%).
CONCLUSION(S): Presence of a documented SDH screen was associated with documentation of Z codes, however documentation was missing more than half the time for most documented needs. The drivers of Z code documentation deserve further exploration. Qualitative interviews and focus groups with providers may be useful

Oral cavity cancer has a low 5-y survival rate, but outcomes improve when the disease is detected early. Cytology is a less invasive method to assess oral potentially malignant disorders relative to the gold-standard scalpel biopsy and histopathology. In this report, we aimed to determine the utility of cytological signatures, including nuclear F-actin cell phenotypes, for classifying the entire spectrum of oral epithelial dysplasia and oral squamous cell carcinoma. We enrolled subjects with oral potentially malignant disorders, subjects with previously diagnosed malignant lesions, and healthy volunteers without lesions and obtained brush cytology specimens and matched scalpel biopsies from 486 subjects. Histopathological assessment of the scalpel biopsy specimens classified lesions into 6 categories. Brush cytology specimens were analyzed by machine learning classifiers trained to identify relevant cytological features. Multimodal diagnostic models were developed using cytology results, lesion characteristics, and risk factors. Squamous cells with nuclear F-actin staining were associated with early disease (i.e., lower proportions in benign lesions than in more severe lesions), whereas small round parabasal-like cells and leukocytes were associated with late disease (i.e., higher proportions in severe dysplasia and carcinoma than in less severe lesions). Lesions with the impression of oral lichen planus were unlikely to be either dysplastic or malignant. Cytological features substantially improved upon lesion appearance and risk factors in predicting squamous cell carcinoma. Diagnostic models accurately discriminated early and late disease with AUCs (95% CI) of 0.82 (0.77 to 0.87) and 0.93 (0.88 to 0.97), respectively. The cytological features identified here have the potential to improve screening and surveillance of the entire spectrum of oral potentially malignant disorders in multiple care settings.

Covid-19 is a new highly contagious RNA viral disease that has caused a global pandemic. Human-to-human transmission occurs primarily through oral and nasal droplets and possibly through the airborne route. The disease may be asymptomatic or the course may be mild with upper respiratory symptoms, moderate with non-life-threatening pneumonia, or severe with pneumonia and acute respiratory distress syndrome. The severe form is associated with significant morbidity and mortality. While patients who are unstable and in acute distress need immediate in-person attention, many patients can be evaluated at home by telemedicine or videoconferencing. The more benign manifestations of Covid-19 may be managed from home to maintain quarantine, thus avoiding spread to other patients and health care workers. This document provides an overview of the clinical presentation of Covid-19, emphasizing telemedicine strategies for assessment and triage of patients. Advantages of the virtual visit during this time of social distancing are highlighted.

BACKGROUND:The COVID-19 pandemic has resulted in significant morbidity and mortality, with large numbers of patients requiring intensive care threatening to overwhelm healthcare systems globally. There is an urgent need for a COVID-19 disease severity assessment that can assist in patient triage and resource allocation for patients at risk for severe disease. OBJECTIVE:The goal of this study was to develop, validate, and scale a clinical decision support system and mobile app to assist in COVID-19 severity assessment, management, and care. METHODS:Model training data from 701 patients with COVID-19 were collected across practices within the Family Health Centers network at New York University Langone Health. A two-tiered model was developed. Tier 1 uses easily available, non-laboratory data to help determine whether biomarker-based testing and/or hospitalization is necessary. Tier 2 predicts probability of mortality using biomarker measurements (CRP, PCT, D-dimer) and age. Both Tier 1 and Tier 2 models were validated using two external datasets from hospitals in Wuhan, China comprising 160 and 375 patients, respectively. RESULTS:All biomarkers were measured at significantly higher levels in patients that died vs. those that were not hospitalized or discharged (P < .001). The Tier 1 and Tier 2 internal validation had AUC (95% confidence interval) of 0.79 (0.74-0.84) and 0.95 (0.92-0.98), respectively. The Tier 1 and Tier 2 external validation had AUCs of 0.79 (0.74-0.84) and 0.97 (0.95-0.99), respectively. CONCLUSIONS:Our results demonstrate validity of the clinical decision support system and mobile app, which are now ready to assist healthcare providers in making evidence-based decisions in managing COVID-19 patient care. The deployment of these new capabilities has potential for immediate impact in community clinics, sites whereby application of such tools could lead to improvements in patient outcomes and cost containment. CLINICALTRIAL/UNASSIGNED/:

SARS-CoV-2 is the virus that causes coronavirus disease (COVID-19) which has reached pandemic levels resulting in significant morbidity and mortality affecting every inhabited continent. The large number of patients requiring intensive care threatens to overwhelm healthcare systems globally. Likewise, there is a compelling need for a COVID-19 disease severity test to prioritize care and resources for patients at elevated risk of mortality. Here, an integrated point-of-care COVID-19 Severity Score and clinical decision support system is presented using biomarker measurements of C-reactive protein (CRP), N-terminus pro B type natriuretic peptide (NT-proBNP), myoglobin (MYO), D-dimer, procalcitonin (PCT), creatine kinase-myocardial band (CK-MB), and cardiac troponin I (cTnI). The COVID-19 Severity Score combines multiplex biomarker measurements and risk factors in a statistical learning algorithm to predict mortality. The COVID-19 Severity Score was trained and evaluated using data from 160 hospitalized COVID-19 patients from Wuhan, China. Our analysis finds that COVID-19 Severity Scores were significantly higher for the group that died versus the group that was discharged with median (interquartile range) scores of 59 (40-83) and 9 (6-17), respectively, and area under the curve of 0.94 (95% CI 0.89-0.99). These promising initial models pave the way for a point-of-care COVID-19 Severity Score system to impact patient care after further validation with externally collected clinical data. Clinical decision support tools for COVID-19 have strong potential to empower healthcare providers to save lives by prioritizing critical care in patients at high risk for adverse outcomes.

SARS-CoV-2 is the virus that causes coronavirus disease (COVID-19) which has reached pandemic levels resulting in significant morbidity and mortality affecting every inhabited continent. The large number of patients requiring intensive care threatens to overwhelm healthcare systems globally. Likewise, there is a compelling need for a COVID-19 disease severity test to prioritize care and resources for patients at elevated risk of mortality. Here, an integrated point-of-care COVID-19 Severity Score and clinical decision support system is presented using biomarker measurements of C-reactive protein (CRP), N-terminus pro B type natriuretic peptide (NT-proBNP), myoglobin (MYO), D-dimer, procalcitonin (PCT), creatine kinase-myocardial band (CK-MB), and cardiac troponin I (cTnI). The COVID-19 Severity Score combines multiplex biomarker measurements and risk factors in a statistical learning algorithm to predict mortality. The COVID-19 Severity Score was trained and evaluated using data from 160 hospitalized COVID-19 patients from Wuhan, China. Our analysis finds that COVID-19 Severity Scores were significantly higher for the group that died versus the group that was discharged with median (interquartile range) scores of 59 (40-83) and 9 (6-17), respectively, and area under the curve of 0.94 (95% CI 0.89-0.99). Although this analysis represents patients with cardiac comorbidities (hypertension), the inclusion of biomarkers from other pathophysiologies implicated in COVID-19 (e.g., D-dimer for thrombotic events, CRP for infection or inflammation, and PCT for bacterial co-infection and sepsis) may improve future predictions for a more general population. These promising initial models pave the way for a point-of-care COVID-19 Severity Score system to impact patient care after further validation with externally collected clinical data. Clinical decision support tools for COVID-19 have strong potential to empower healthcare providers to save lives by prioritizing critical care in patients at high risk for adverse outcomes.

Background. With over 100,000 unique lives and 600,000 visits in 2018, The Family Health Centers at NYU Langone (FHC) is one of the largest Federally Qualified Health Center network based primarily in Southwest Brooklyn New York. Within the catchment area 48% of the population report being born out of the United States, with 30% of the population describing themselves of Asian ethnicity and 42% as Latino [1]. Effective January 1, 2014 New York State law mandated hepatitis C screening to be offered to every individual born between 1945 and 1965 receiving health services. Now five years later, with the advancements in treatment options and increased access for patients where cost has become prohibitive we retrospectively reviewed how our performance has been prior to embarking on a goal of 60% screening compliance. Methods. We performed a retrospective chart review looking at a denominator of patients born between 1945 and 1965 who were seen in the FHC for a visit in 2018. Patients who were previously screened since 2016, have a diagnosis of hepatitis C, history of hepatitis C documented in either past medical history, problem list or ICD code were excluded. Data abstraction for compliance in the numerator included patients who have a resulted hepatitis C antibody or have indicated current treatment (with a hepatitis C viral load). Results. 51% of patients based on the aforementioned methodology have been screened in 2018. 11,577 patients were eligible with 650 patients having a documented refusal. 261 new diagnosis were made in 2018 and compliance for non-screened patients without any prior screening was 35%. Regarding racial/ethnic composition of the practice sites compared with patients screened, one practice site with an 87% Asian non-Hispanic population had a 35% compliance rate with screening where as the most predominate Hispanic population site (81% of total patients seen) had a 54% compliance rate. Conclusion. Overall screening rates within the network are commendable, yet more work is being done to drive provider awareness on the need for compliance. Differences in racial/ethnic backgrounds and compliance of screening completion can be seen within the FHC network. Current efforts are focused on increasing culturally appropriate awareness amongst the patient population as well as the providers

Statement of Problem Or Question (One Sentence): As healthcare delivery shifts to the value based paradigm how do you educate primary care providers on medication adherence metrics while performing a meaningful educational experience? Objectives of Program/Intervention (No More Than Three Objectives): 1.Educate Internal Medicine residents in a Primary Care residency program on NCQA HEDIS measures regarding medication adherence metrics 2.Work with a clinically integrated network (CIN) pharmacy team on identifying patients who have not refilled their medications, and how to engage patient medication adherence Description of Program/Intervention, Including Organizational Context (E.G. Inpatient Vs. Outpatient, Practice or Community Characteristics): The Family Health Centers (FHC) at NYU Langone is a network of 8 Federally Qualified Health Centers in Brooklyn New York. Primary care residents, working with the NYU CIN pharmacy team, collaborated on telephonic outreach to engage patients identified by payor contracts as nonadherent on medication refills. After initial training, which included education on how the Proportion of Days Covered (PDC) rate is a quantitative metric used to measure quality of care and scripted exercise on telephonic patient engagement, residents were tasked with identifiying challenges on medication refill as well as intervening when appropriate. At the end of the intervention period a resident focus group was conducted to determine the educational value in this quality initiative. Measures of Success (Discuss Qualitative And/Or Quantitative Metrics Which Will Be Used To Evaluate Program/Intervention): Primary endpoint was increased PDC rates based on payor data for patients who are diagnosed with either having diabetes (non gestational), hypertension or dyslipidemia. A post intervention focus group and semantical content analysis was performed regarding educational value from this exercise. Findings To Date (It Is Not Sufficient To State Findings Will Be Discussed): In a 6-month period 523 unique patients were engaged with 899 outreaches completed. 1061 barriers were identified. The top 3 patient identified barriers were: patients unaware they had not filled the prescription (31.05%), lack of clearly identified reason for non adherence (20.23%), and patients did not feel committed to taking the prescribed medication (14.97%). When comparing PDC rates from the previous year, this intervention saw a 7% increase in aggregate PDC rates for those who were prescribed medications and having diabetes. Regarding post intervention focus groups with residents, semantic content analysis revealed the highest affinity for positive descriptors in the domains of educational value, need to expand education to resident providers, and continued interest in future quality projects with the pharmacy team. Key Lessons For Dissemination (What Can Others Take Away For Implementation To Their Practice Or Community?): Key take home lessons in this intervention is that telephonic pharmacy adherence outreach has a positive impact on maintaining PDC rate compliance, particularly in patients with managed Medicare plans. On educational value, further development is needed in resident curriculum regarding medication adherence and reconciliation in the ambulatory setting. Lastly residents working with pharmacy teams find value in addressing medication adherence barriers and may impact best practices in provider prescribing habits when engaging patients