Faculty Bibliography

IMPORTANCE/OBJECTIVE:Patients with poor glycaemic control have a high risk for major cardiovascular events. Improving glycaemic monitoring in patients with diabetes can improve morbidity and mortality. OBJECTIVE:To assess the effectiveness of a patient portal message in prompting patients with poorly controlled diabetes without a recent glycated haemoglobin (HbA1c) result to have their HbA1c repeated. DESIGN/METHODS:A pragmatic, randomised clinical trial. SETTING/METHODS:A large academic health system consisting of over 350 ambulatory practices. PARTICIPANTS/METHODS:Patients who had an HbA1c greater than 10% who had not had a repeat HbA1c in the prior 6 months. EXPOSURES/METHODS:A single electronic health record (EHR)-based patient portal message to prompt patients to have a repeat HbA1c test versus usual care. MAIN OUTCOMES/RESULTS:The primary outcome was a follow-up HbA1c test result within 90 days of randomisation. RESULTS:The study included 2573 patients with a mean (SD) HbA1c of 11.2%. Among 1317 patients in the intervention group, 24.2% had follow-up HbA1c tests completed within 90 days, versus 21.1% of 1256 patients in the control group (p=0.07). Patients in the intervention group were more likely to log into the patient portal within 60 days as compared with the control group (61.2% vs 52.3%, p<0.001). CONCLUSIONS:Among patients with poorly controlled diabetes and no recent HbA1c result, a brief patient portal message did not significantly increase follow-up testing but did increase patient engagement with the patient portal. Automated patient messages could be considered as a part of multipronged efforts to involve patients in their diabetes care.

PURPOSE/UNASSIGNED:We aimed to determine whether implementation of clinical decision support (CDS) tool integrated into the electronic health record of a multisite academic medical center increased the proportion of patients with AUA "high-risk" microscopic hematuria (MH) who receive guideline concordant evaluations. MATERIALS AND METHODS/UNASSIGNED:We conducted a two-arm cluster randomized quality improvement project in which 202 ambulatory sites from a large health system were randomized to either have their physicians receive at time of test results an automated CDS alert for patients with "high-risk" MH with associated recommendations for imaging and cystoscopy (intervention) or usual care (control). Primary outcome was met if a patient underwent both imaging and cystoscopy within 180 days from MH result. Secondary outcomes assessed individual completion of imaging, cystoscopy, or placement of imaging orders. RESULTS/UNASSIGNED:= .09). CONCLUSIONS/UNASSIGNED:Implementing an electronic health record-integrated CDS tool to promote evaluation of patients with high-risk MH did not lead to improvements in patient completion of a full guideline-concordant evaluation. The development of an algorithm to trigger a CDS alert was demonstrated to be feasible and effective. Further multilevel assessment of barriers to evaluation is necessary to continue to improve the approach to evaluating high-risk patients with MH.

The underlying drivers and outcomes of social determinants of health are dynamically complex, making it difficult to design effective responses. This complexity has inspired a growing number of calls to move beyond mechanistic thinking and use systems science to engage directly with complexity and highlight opportunities for methodological innovation to enhance translation of insight into real world action. This case study describes a methodological innovation combining community-based system dynamics and design thinking to understand multi-level complexity of a public health challenge: optimizing the design of a community-clinical linkage in Brooklyn, New York. In-depth description of the case illustrates methods integration and resulting insights and recommendations. Results from the case demonstrate that integrating methods generates insight at multiple levels, including connecting holistic system understanding to individual experiences of system structure and operationalizing and translating insights into action. Combining community-based system dynamics and design thinking holds value for intervention planning, strategic implementation, and sustaining change.

OBJECTIVE/UNASSIGNED:To examine the transition to telemental health within the behavioral health program of a large federally qualified health center, The Family Health Centers at NYU Langone, in the 3 months following the onset of the COVID-19 pandemic-specifically impacts on show rates and access to care. METHODS/UNASSIGNED:Demographic and clinical information for all scheduled visits was collected for two time periods: the telemental health period, March 16, 2020-July 16, 2020 (46,878 visits, 5,183 patients), and a comparison period, March 15, 2019-July 16, 2019 (47,335 visits, 5,190 patients). Data collected included modality, appointments scheduled/completed/cancelled/no-showed, age, gender, race, language, and diagnosis. Generalized estimating equations with a compound symmetry correlation structure and logit link were used for analysis. RESULTS/UNASSIGNED:= 0.01), which was eliminated by implementation of telemental health. CONCLUSIONS/UNASSIGNED:This study supports the use telemental health to increase access for all patients, including those from under-represented, lower socioeconomic status backgrounds.

BACKGROUND:Early initiation of prenatal care is widely accepted to improve the health outcomes of pregnancy for both mothers and their infants. Identification of the various barriers to entry into care that patients experience may inform and improve health care provision and, in turn, improve the patient's ability to receive necessary care. AIM/OBJECTIVE:This study implements a mixed-methods approach to establish methods and procedures for identifying barriers to early entry to prenatal care in a medically-vulnerable patient population and areas for future quality improvement initiatives. METHODS:An initial chart review was conducted on obstetrics patients that initiated prenatal care after their first trimester at a large federally qualified health center in Brooklyn, NY, to determine patient-specified reasons for delay. A thematic analysis of these data was implemented in combination with both parametric and non-parametric analyses to characterize the population of interest, and to identify the primary determinants of delayed entry. RESULTS:The age of patients in the population of interest (n = 169) was bimodal, with a range of 15 - 43 years and a mean of 28 years. The mean gestational age of entry into prenatal care was 19 weeks. The chart review revealed that 8% recently moved to Brooklyn from outside of NYC or the USA. Nine percent had difficulty scheduling an initial prenatal visit within their first trimester. Teenage pregnancy accounted for 7%. Provider challenges with documentation (21%) were noted. The most common themes identified (n = 155) were the patient being in transition (21%), the pregnancy being unplanned (17%), and issues with linkage to care (15%), including no shows or patient cancellations. Patients who were late to prenatal care also differed from their peers dramatically, as they were more likely to be Spanish-speaking, to be young, and to experience a relatively long delay between pregnancy confirmation and entry into care. Moreover, the greatest determinant of delayed entry into care was patient age. CONCLUSION/CONCLUSIONS:Our study provides a process for other like clinics to identify patients who are at risk for delayed entry to prenatal care and highlight common barriers to entry. Future initiatives include the introduction of a smart data element to document reasons for delay and use of community health workers for dedicated outreach after no show appointments or patient cancellations.

BACKGROUND:Research demonstrates the importance of documenting adaptations to implementation strategies that support integration of evidence-based interventions into practice. While studies have utilized the FRAME-IS [Framework for Reporting Adaptations and Modifications for Implementation Strategies] to collect structured adaptation data, they are limited by a focus on discrete implementation strategies (e.g., training), which do not reflect the complexity of multifaceted strategies like practice facilitation. In this paper, we apply the FRAME-IS to our trial evaluating the effectiveness of PF on implementation fidelity of an evidence-based technology-facilitated team care model for improved hypertension control within a federally qualified health center (FQHC). METHODS:Three data sources are used to document adaptations: (1) implementation committee meeting minutes, (2) narrative reports completed by practice facilitators, and (3) structured notes captured on root cause analysis and Plan-Do-Study-Act worksheets. Text was extracted from the data sources according to the FRAME-IS modules and inputted into a master matrix for content analysis by two authors; a third author conducted member checking and code validation. RESULTS:We modified the FRAME-IS to include part 2 of module 2 (what is modified) to add greater detail of the modified strategy, and a numbering system to track adaptations across the modules. This resulted in identification of 27 adaptations, of which 88.9% focused on supporting practices in identifying eligible patients and referring them to the intervention. About half (52.9%) of the adaptations were made to modify the context of the PF strategy to include a group-based format, add community health workers to the strategy, and to shift the implementation target to nurses. The adaptations were often widespread (83.9%), affecting all practices within the FQHC. While most adaptations were reactive (84.6%), they resulted from a systematic process of reviewing data captured by multiple sources. All adaptations included the FQHC in the decision-making process. CONCLUSION/CONCLUSIONS:With modifications, we demonstrate the ability to document our adaptation data across the FRAME-IS modules, attesting to its applicability and value for a range of implementation strategies. Based on our experiences, we recommend refinement of tracking systems to support more nimble and practical documentation of iterative, ongoing, and multifaceted adaptations. TRIAL REGISTRATION/BACKGROUND:Clinicaltrials.gov NCT03713515, Registration date: October 19, 2018.

BACKGROUND:Despite decades of evidence demonstrating the efficacy of hypertension care delivery in reducing morbidity and mortality, a majority of hypertension cases remain uncontrolled. There is an urgent need to elucidate and address multilevel facilitators and barriers clinical staff face in delivering evidence-based hypertension care, patients face in accessing it, and clinical systems face in sustaining it. Through a rigorous pre-implementation evaluation, we aimed to identify facilitators and barriers bearing the potential to affect the planned implementation of a multilevel technology-facilitated hypertension management trial across six primary care sites in a large federally qualified health center (FQHC) in New York City. METHODS:During a dedicated pre-implementation period (3-9 months/site, 2021-2022), a capacity assessment was conducted by trained practice facilitators, including (1) online anonymous surveys (n = 124; 70.5% of eligible), (2) hypertension training analytics (n = 69; 94.5% of assigned), and (3) audio-recorded semi-structured interviews (n = 67; 48.6% of eligible) with FQHC leadership and staff. Surveys measured staff sociodemographic characteristics, adaptive reserve, evidence-based practice attitudes, and implementation leadership scores via validated scales. Training analytics, derived from end-of-course quizzes, included mean score and number attempts needed to pass. Interviews assessed staff-reported facilitators and barriers to current hypertension care delivery and uptake; following audio transcription, trained qualitative researchers employed a deductive coding approach, informed by the Consolidated Framework for Implementation Research (CFIR). RESULTS:Most survey respondents reported moderate adaptive reserve (mean = 0.7, range = 0-1), evidence-based practice attitudes (mean = 2.7, range = 0-4), and implementation leadership (mean = 2.5, range = 0-4). Most staff passed training courses on first attempt and demonstrated high scores (means > 80%). Findings from interviews identified potential facilitators and barriers to implementation; specifically, staff reported that complex barriers to hypertension care, control, and clinical communication exist; there is a recognized need to improve hypertension care; in-clinic challenges with digital tool access imposes workflow delays; and despite high patient loads, staff are motivated to provide high-quality cares. CONCLUSIONS:This study serves as one of the first to apply the CFIR to a rigorous pre-implementation evaluation within the understudied context of a FQHC and can serve as a model for similar trials seeking to identify and address contextual factors known to impact implementation success. TRIAL REGISTRATION/BACKGROUND:ClinicalTrials.gov NCT03713515 , date of registration: October 19, 2018.

The Family Health Centers at New York University Langone (FHC), a federally qualified health center network in New York City, created a novel clinical decision support (CDS) tool that alerts primary health care providers to patients"™ gaps in care and triggers a dynamic, individualized order set on the basis of unique patient factors, enabling providers to readily act on each patient"™s specific gaps in care. FHC implemented this tool in 2017, starting with 15 protocols for quality measures; as of February 2024, there are 30 such protocols. During a patient visit with a provider, when there is a gap in care, a best-practice alert (BPA) fires, which includes an order set unique to the patient and visit. The provider can bypass the alert (not open it) or acknowledge the alert (open it). The provider may review the content of the order set and accept it as is or with modifications, or they can decline its recommendations if they believe it is not appropriate or plan to address the gap in care another way during the visit. To accept the dynamic order set is the intended workflow. The authors present data from September 2019 to January 2023 totaling 171,319 patient visits with at least one open gap in care among providers in pediatrics, family medicine, and internal medicine. The rate at which providers acknowledged the BPA in the first 6 months was 45% and steadily increased. In the last 6 months of the period, providers acknowledged the BPA 78% (19,281 of 24,575) of the time. Similarly, in the first 6 months, in all encounters in which a BPA was fired, 28.8% (8,585 of 29,829) had an order placed via the dynamic order set (accepted); that rate increased to 49.7% (12,210 of 24,575) during the last 6 months. This order set completion rate is notable given that most CDS use rates are low. Gap closure was higher when providers acknowledged the alert. In an analysis of all encounters with at least one open gap, spanning 2019"“2023, 46% (48,431 of 105,371) of the time, at least one gap was closed when the alert was acknowledged compared with 33% (21,993 of 65,948) when the alert was bypassed (and the recommendations of the dynamic order set were never followed). The authors show that CDS tools can be successfully implemented in a high-volume, low-resource setting if designed with efficiency in mind, ensuring provider utilization and clinical impact through closing care gaps. CDS tools that are dynamically patient specific can help improve quality of care if they are part of a broader culture of quality improvement.