Faculty Bibliography

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: 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: 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

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.

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

Background: The Hitech Act of 2009 led to Federal funding on EHR incentives such as Advancing Care Information within MIPs and Meaningful Use. EPIC currently has a MyChart application which allows a patient to interface with their medical records and provider. The Family Health Centers (FHC) at NYU Langone is a network of 8 Federally Qualified Health Centers (FQHC) located in Brooklyn New York. The primary service area has a large immigrant population with 47% of the population reported as being foreign born, and a diverse payor mix with 12% of patients being self-pay/uninsured.
Method(s): Retrospective analysis was performed regarding 78,168 unique patients seen within the Family Health Center Network from January 2018 to December 3rd, 2018. Patient were identified by payor class and by utilization of MyChart. Given the diversity of healthcare plans afforded within New York State, payor classes were grouped into 7 broad categories: Medicaid/Managed Medicaid, commercial, Medicare/Managed Medicare, self-pay, no insurance, Child Health Plus and Med-icaid Expansion (Affordable Care Act). Patient MyChart data abstraction within the EPIC Clarity database included whether the patient was enrolled and when the last date of activity occurred. Enrollment with activity versus enrollment without activity within the last calendar year was used to gauge whether the patient would be considered an active subject in this retrospective review.
Result(s): Regarding percentage of patient enrolled in MyChart, the patient population most likely to enroll was found to be those who have commercial health plans at 41%, with Medicaid expansion plans at 37%, followed by Medicaid tied with self-pay coverage (23%) and Medicare at 18%. Utilization tells a different story with the highest utilizers found in the Medicare enrollees at 79%, followed by Medicaid expansion at 78%, then commercial plans at 77%. The next tier of active users was found to be no coverage (67%), self-pay (66%) and Medicaid (61%). Retrospective review with enrollment data was somewhat expected; high enrollment in patients with commercial plans and lower enrollment amongst Medicare beneficiaries. What was surprising was the utilization/activity data revealed an entirely different picture. Activity usage reflected two tiers. Patients who have Medicare utilize the application as much as patients who have commercial plans and Medicaid expansion. Despite connotations on patients who are self-pay or who have no coverage at all, these patients still use the application, with greater than 50% of those enrolled, actively using MyChartwithin the last calendar year.
Conclusion(s): As medical care becomes more immersed in web-based technologies, attention and opportunities exist for patients who traditionally were viewed as not having access nor inclination to use such technologies. Continued efforts should be maintained regarding enrollment regardless of the payor class or age

Statement of Problem Or Question (One Sentence): Can a home visit complex care management program successfully identify and engage high risk patients in a FQHC? Objectives of Program/Intervention (No More Than Three Objectives): 1. Identify patients at an FQHC who are at risk for preventable hospitalization 2. Enroll and engage patients in a home visit based complex care management program. Description of Program/Intervention, Including Organizational Context (E.G. Inpatient Vs. Outpatient, Practice or Community Characteristics): The Primary Care Plus program (PCP+) is a home visit based program established to address the needs of patients at risk for preventable hospitalizations within the Family Health Centers at NYU Langone. The program staff-a physician, a nurse practitioner, a social worker and 2 community health workers-coordinate as a team to identify and address the biopsychosocial needs of high risk patients. A key intervention is the home visit lead by a physician or nurse practitioner to perform the medical assessment, medication reconciliation, and identification of both medical and social impediments to optimal health. The program is not intended to replace the patient's primary care provider, but to function as an addition to the patient's care team, identifying and mitigating risk drivers, and handing off to the primary team and care management resources once the risk drivers have been addressed. Patients are referred into the program by either their primary care doctors or care management. The program is restricted to those patients who have a continuity relationship in the Federally Qualified Health Center, and who are identified as being at risk for a preventable hospitalization. Latitude is given to the referral source in how patients are identified; guidance is given to focus on patients with a history of preventable hospitalizations (as defined by PQI) or patients with advanced disease and potential palliative care needs. Measures of Success (Discuss Qualitative And/Or Quantitative Metrics Which Will Be Used To Evaluate Program/Intervention): The primary measure of success is patient engagement. Patient engagement is defined by both consent to the program and successful home visit by the medical provider. Other outcome metrics are patient characteristics, number of emergency department visits and number of inpatient hospitalizations in the 12 months before program enrollment. Findings To Date (It Is Not Sufficient To State Findings Will Be Discussed): Since program inception in August 2018 through December 31, 2018, 75 patients have been identified by care management or primary care providers as potential candidates for the program and who met criteria as defined above. Of the 75 patients, 6 (8%) declined the program, and another 10 (13.3%) could not be found. The remaining 59 patients were seen at home and assessed. Total engagement was 78.7%. Patients identified represent a cohort of patients with an average of 2.0 inpatient admissions and 3.2 emergency department visits in the preceding 12 months prior to enrollment. Key Lessons For Dissemination (What Can Others Take Away For Implementation To Their Practice Or Community?): Identification of a high-risk patient population in a federally qualified health center and referral into a home visit based care management program is associated with high acceptance and engagement. Future study will determine if patients enrolled in the program have an impact on risk drivers and preventable hospitalizations

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