Faculty Bibliography

Objectives/UNASSIGNED:To identify ICD-10-CM diagnostic codes associated with the social determinants of health (SDOH), determine frequency of use of the code for homelessness across time, and examine the frequency of interrupted periods of Medicaid eligibility (ie, Medicaid churn) for beneficiaries with and without this code. Design/UNASSIGNED:Retrospective data analyses of New York State (NYS) Medicaid claims data for years 2006-2017 to determine reliable indicators of SDOH hypothesized to affect Medicaid churn, and for years 2016-2017 to examine frequency of Medicaid churn among patients with and without an indicator for homelessness. Main Outcome Measures/UNASSIGNED:Any interruption in the eligibility for Medicaid insurance (Medicaid churn), assessed via client identification numbers (CIN) for continuity. Methods/UNASSIGNED:Analyses were conducted to assess the frequency of use and pattern of New York State Medicaid claims submission for SDOH codes. Analyses were conducted for Medicaid claims submitted for years 2016-2017 for Medicaid patients with and without a homeless code (ie, ICD-10-CM Z59.0) in 2017. Results/UNASSIGNED:ICD-9-CM / ICD-10-CM codes for lack of housing / homelessness demonstrated linear reliability over time (ie, for years 2006-2017) with increased usage. In 2016-2017, 22.9% of New York Medicaid patients with a homelessness code in 2017 experienced at least one interruption of Medicaid eligibility, while 18.8% of Medicaid patients without a homelessness code experienced Medicaid churn. Conclusions/UNASSIGNED:Medicaid policies would do well to take into consideration the barriers to continued enrollment for the Medicaid population. Measures ought to be enacted to reduce Medicaid churn, especially for individuals experiencing homelessness.

STATEMENT OF PROBLEMOR QUESTION (ONE SENTENCE): To determine the feasibility and acceptability of using a virtual-only model for initiating and maintaining patients on PrEP (pre-exposure prophylaxis) for HIV prevention. LEARNING OBJECTIVES 1: Participants will be able to identify 3 key considerations in developing a clinical workflow for virtual PrEP initiation. LEARNING OBJECTIVES 2: Participants will be able to discuss 3-5 challenges associated with virtual PrEP initiation, and identify strategies to address these challenges. DESCRIPTION OF PROGRAM/INTERVENTION, INCLUDING ORGANIZATIONAL CONTEXT (E.G. INPATIENT VS. OUTPATIENT, PRACTICE OR COMMUNITY CHARACTERISTICS): The Family Health Centers at NYU Langone (FHC) is a federally qualified health center network with 8 clinical sites in Brooklyn, NY, primarily serving a low-income, immigrant community. Since 2016, FHC has operated a focused outreach program to promote PrEP to high-risk individuals, using targeted strategies to engage those not currently in PrEP care. Our intervention sought to expand on our successful outreach model by using tele health to remove geographic barriers to participation. We developed clinical and patient navigation workflows to enable patients to initiate and continue PrEP through virtual visits. For necessary labs, patients were supported in identifying a lab collection site convenient to their home. Patient navigation staff played a key role in risk reduction education, benefits navigation, and facilitating compliance with labs and virtual care. MEASURES OF SUCCESS (DISCUSS QUALITATIVE AND/OR QUANTITATIVEMETRICSWHICHWILL BEUSEDTOEVALUATE PROGRAM/INTERVENTION): The key measure of success is PrEP uptake and continuation among the virtual visits cohort. Additional evaluation measures include the referral source of patients for virtual PrEP initiation, patient demographics, and HIV risk-these measures will enable us to assess whether we are reaching a more diverse or higher risk population through this program. FINDINGS TO DATE (IT IS NOT SUFFICIENT TO STATE FINDINGS WILL BE DISCUSSED): The pilot project launched in October 2020. In the three months since project launch, 8 patients were served through this program. Six of the patients (75%) had been initially engaged with the FHC through the HIV prevention program, while two were existing FHC patients-one of whom had previously been in standard PrEP care, but struggled to make the in-person visits. Six patients were cisgender men who have sex with men, while two were transgender women. Virtual PrEP provided an opportunity to link patients to other needed healthcare services, including vaccination and STI treatment. KEY LESSONS FOR DISSEMINATION (WHAT CAN OTHERS TAKE AWAY FOR IMPLEMENTATION TO THEIR PRACTICE OR COMMUNITY): The tele health PrEP pilot program enabled us to reach a diverse group of high-risk patients, a majority of whom had not previously been engaged in care within our health system, and we anticipate continued growth this program as we expand our outreach to additional geographic areas. Navigation staff were key in overcoming some of the barriers associated with the virtual model by building relationships with the patients and serving as a reliable source of support for patients encountering logistical barriers. PrEP initiation by tele health must account for additional logistical considerations-most notably, ensuring patient compliance with labs-but it is a feasible approach for engaging high-risk patients in HIV prevention services

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/:

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

BACKGROUND: Patient Portals(PP) allow access to medical records and interaction with providers; however activation(PPA) and use (PPU) are limited by language barriers, low health/computer literacy, and poor internet access which are prevalent issues in Federally Qualified Health Centers(FQHC). Little is known of the drivers and patterns of PPA in such settings. We aimed to describe the prevalence of PPA and PPU in adult patients of an FQHC; describe PPU activity, and test demographic, condition, and utilization-related correlates of PPA and PPU.
METHOD(S):We conducted a retrospective chart review in an FQHC that launched a PP in September 2016. We extracted demographics, PPA status(active/not) at data pull, PPU activities, presence of a chronic condition on the problem list, # emergency department, inpatient, subspecialty visits over past year (utilization summed, dichotomized >1 vs. 0-1 visit). Missing values for homelessness were coded to majority category( 0). Analyses included descriptive statistics, bivariate analyses, then logistic regression to test odds of PPA and PPU by. demographics, chronic conditions, and utilization. We report [adjusted odds ratios(confidence interval)].
RESULT(S): Data were analyzed for 62,610 adults [mean age 45(SD 17), 21% Black, 47% Hispanic, 46% Medicaid, 25% Selfpay, speaking English( 60%), Spanish (31%), Chinese(6%), Other(3%), with: hypertension( 19%), diabetes(11%), depression(8%), asthma(6%), CVD(5%); 21% had utilization>1. Overall 23,104(37%) activated the PP. PPU included viewing test results(69%), medications(62% ), immunizations( 51%), billing (38%), asking advice (29%), and scheduling appointments( 16%). PPA and PPU varied by demographics, chronic condition, and utilization, but were consistently higher for females, those who were not Medicaid recipients or Self-pay, English speakers and those with asthma, hypertension, and depression.
CONCLUSION(S): PPA was lower for non-whites and poorer patients, but higher for patients speaking the predominant languages of this FQHC, suggesting that language concordance helps engage patients. Patients with chronic conditions and more healthcare utilization had greater odds of PPA and PPU. On the other hand, Spanish-speakers were less likely to actively use the portal for functions such as scheduling appointments, suggesting that improvements in language capabilities of the platform are needed

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

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.

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.