Faculty Bibliography

Importance/UNASSIGNED:Rising drug costs contribute to medication nonadherence and adverse health outcomes. Real-time prescription benefit (RTPB) systems present prescribers with patient-specific out-of-pocket cost estimates and recommend lower-cost, clinically appropriate alternatives at the point of prescribing. Objective/UNASSIGNED:To investigate whether RTPB recommendations lead to reduced patient out-of-pocket costs for medications. Design, Setting, and Participants/UNASSIGNED:In this cluster randomized trial, medical practices in a large, urban academic health system were randomly assigned to RTPB recommendations from January 13 to July 31, 2021. Participants were adult patients receiving outpatient prescriptions during the study period. The analysis was limited to prescriptions for which RTPB could recommend an available alternative. Electronic health record data were used to analyze the intervention's effects on prescribing. Data analyses were performed from August 20, 2021, to June 8, 2022. Interventions/UNASSIGNED:When a prescription was initiated in the electronic health record, the RTPB system recommended available lower-cost, clinically appropriate alternatives for a different medication, length of prescription, and/or choice of pharmacy. The prescriber could select either the initiated order or one of the recommended options. Main Outcomes and Measures/UNASSIGNED:Patient out-of-pocket cost for a prescription. Secondary outcomes were whether a mail-order prescription and a 90-day supply were ordered. Results/UNASSIGNED:Of 867 757 outpatient prescriptions at randomized practices, 36 419 (4.2%) met the inclusion criteria of having an available alternative. Out-of-pocket costs were $39.90 for a 30-day supply in the intervention group and $67.80 for a 30-day supply in the control group. The intervention led to an adjusted 11.2%; (95% CI, -15.7% to -6.4%) reduction in out-of-pocket costs. Mail-order pharmacy use was 9.6% and 7.6% in the intervention and control groups, respectively (adjusted 1.9 percentage point increase; 95% CI, 0.9 to 3.0). Rates of 90-day supply were not different. In high-cost drug classes, the intervention reduced out-of-pocket costs by 38.9%; 95% CI, -47.6% to -28.7%. Conclusions and Relevance/UNASSIGNED:This cluster randomized clinical trial showed that RTPB recommendations led to lower patient out-of-pocket costs, with the largest savings occurring for high-cost medications. However, RTPB recommendations were made for only a small percentage of prescriptions. Trial Registration/UNASSIGNED:ClinicalTrials.gov Identifier: NCT04940988; American Economic Association Registry: AEARCTR-0006909.

BACKGROUND:Male sex, elevated troponin levels, and elevated D-dimer levels are associated with more complicated COVID-19 illness and greater mortality; however, while there are known sex differences in the prognostic value of troponin and D-dimer in other disease states, it is unknown whether they exist in the setting of COVID-19. OBJECTIVE:We assessed whether sex modified the relationship between troponin, D-dimer, and severe COVID-19 illness (defined as mechanical ventilation, ICU admission or transfer, discharge to hospice, or death). METHODS:We conducted a retrospective cohort study of patients hospitalized with COVID-19 at a large, academic health system. We used multivariable regression to assess associations between sex, troponin, D-dimer, and severe COVID-19 illness, adjusting for demographic, clinical, and laboratory covariates. To test whether sex modified the relationship between severe COVID-19 illness and troponin or D-dimer, models with interaction terms were utilized. RESULTS:Among 4,574 patients hospitalized with COVID-19, male sex was associated with higher levels of troponin and greater odds of severe COVID-19 illness, but lower levels of initial D-dimer when compared with female sex. While sex did not modify the relationship between troponin level and severe COVID-19 illness, peak D-dimer level was more strongly associated with severe COVID-19 illness in male patients compared to female patients (males: OR=2.91, 95%CI=2.63-2.34, p<0.001; females: OR=2.31, 95%CI=2.04-2.63, p<0.001; p-interaction=0.005). CONCLUSION/CONCLUSIONS:Sex did not modify the association between troponin level and severe COVID-19 illness, but did modify the association between peak D-dimer and severe COVID-19 illness, suggesting greater prognostic value for D-dimer in males with COVID-19.

BACKGROUND:Little is known about the illness experience of patients' long-term emotional and physical recovery from severe COVID-19 infection. This study aimed to expand upon the recovery process of COVID-19 survivors up to 6 months after hospital discharge. METHODS:Qualitative analysis of free-response answers from a cohort study of 152 patients ≥ 18 years hospitalized with laboratory-confirmed SARS-CoV-2 surveyed at 1-month post hospital discharge and 6-months post hospital discharge. Responses were analyzed with a grounded theory approach to identify overarching themes. RESULTS:Participants described persistent complications, both physical and mental, that have affected their recovery from COVID-19. Five overarching themes of post-acute patient experiences were generated: (1) an increased awareness of a mind and body connection, (2) feelings of premature aging, (3) an overall decline in quality of life, (4) a continued fear of infection, and (5) methods of coping. CONCLUSIONS:Patients described lasting changes to their mental health and overall quality of life in connection to physical complications after severe COVID-19 infection. Patients' reports of their experience call for a greater awareness of the psychological aspects of COVID-19 recovery to provide both physical and psychological rehabilitation services. Additional resources such as education around re-infection and financial resources are needed.

BACKGROUND:National registries reveal significant gaps in medical therapy for patients with heart failure and reduced ejection fraction (HFrEF), but may not accurately (or fully) characterize the population eligible for therapy. OBJECTIVE:We developed an automated, electronic health record-based algorithm to identify HFrEF patients eligible for evidence-based therapy, and extracted treatment data to assess gaps in therapy in a large, diverse health system. METHODS:In this cross-sectional study of all NYU Langone Health outpatients with EF ≤ 40% on echocardiogram and an outpatient visit from 3/1/2019 to 2/29/2020, we assessed prescription of the following therapies: beta-blocker (BB), angiotensin converting enzyme inhibitor (ACE-I)/angiotensin receptor blocker (ARB)/angiotensin receptor neprilysin inhibitor (ARNI), and mineralocorticoid receptor antagonist (MRA). Our algorithm accounted for contraindications such as medication allergy, bradycardia, hypotension, renal dysfunction, and hyperkalemia. RESULTS:We electronically identified 2732 patients meeting inclusion criteria. Among those eligible for each medication class, 84.8% and 79.7% were appropriately prescribed BB and ACE-I/ARB/ARNI, respectively, while only 23.9% and 22.7% were appropriately prescribed MRA and ARNI, respectively. In adjusted models, younger age, cardiology visit and lower EF were associated with increased prescribing of medications. Private insurance and Medicaid were associated with increased prescribing of ARNI (OR = 1.40, 95% CI = 1.02-2.00; and OR = 1.70, 95% CI = 1.07-2.67). CONCLUSIONS:We observed substantial shortfalls in prescribing of MRA and ARNI therapy to ambulatory HFrEF patients. Subspecialty care setting, and Medicaid insurance were associated with higher rates of ARNI prescribing. Further studies are warranted to prospectively evaluate provider- and policy-level interventions to improve prescribing of these evidence-based therapies.

BACKGROUND:As health care spending reaches unsustainable levels, improving value has become an increasingly important policy priority. Relatively little research has explored factors driving value. As a first step towards filling this gap, we performed a scoping review of the literature to identify potential drivers of health care value. METHODS:Searches of PubMed, Embase, Google Scholar, Policy File, and SCOPUS were conducted between February and March 2020. Empirical studies that explored associations between any range of factors and value (loosely defined as quality or outcomes relative to cost) were eligible for inclusion. We created a template in Microsoft Excel for data extraction and evaluated the quality of included articles using the Critical Appraisal Skills Programme (CASP) quality appraisal tool. Data was synthesized using narrative methods. RESULTS:Twenty-two studies were included in analyses, of which 20 focused on low value service utilization. Independent variables represented a range of system-, hospital-, provider-, and patient-level characteristics. Although results were mixed, several consistent findings emerged. First, insurance incentive structures may affect value. For example, patients in Accountable Care Organizations had reduced rates of low value care utilization compared to patients in traditionally structured insurance plans. Second, higher intensity of care was associated with higher rates of low value care. Third, culture is likely to contribute to value. This was suggested by findings that recent medical school graduation and allopathic training were associated with reduced low value service utilization and that provider organizations had larger effects on value than did individual physicians. CONCLUSIONS:System, hospital, provider, and community characteristics influence low value care provision. To improve health care value, strategies aiming to reduce utilization of low value services and promote high value care across various levels will be essential.

BACKGROUND: We previously developed and validated a predictive model to help clinicians identify hospitalized adults with coronavirus disease 2019 (COVID-19) who may be ready for discharge given their low risk of adverse events. Whether this algorithm can prompt more timely discharge for stable patients in practice is unknown. OBJECTIVES/OBJECTIVE: The aim of the study is to estimate the effect of displaying risk scores on length of stay (LOS). METHODS: We integrated model output into the electronic health record (EHR) at four hospitals in one health system by displaying a green/orange/red score indicating low/moderate/high-risk in a patient list column and a larger COVID-19 summary report visible for each patient. Display of the score was pseudo-randomized 1:1 into intervention and control arms using a patient identifier passed to the model execution code. Intervention effect was assessed by comparing LOS between intervention and control groups. Adverse safety outcomes of death, hospice, and re-presentation were tested separately and as a composite indicator. We tracked adoption and sustained use through daily counts of score displays. RESULTS: Enrolling 1,010 patients from May 15, 2020 to December 7, 2020, the trial found no detectable difference in LOS. The intervention had no impact on safety indicators of death, hospice or re-presentation after discharge. The scores were displayed consistently throughout the study period but the study lacks a causally linked process measure of provider actions based on the score. Secondary analysis revealed complex dynamics in LOS temporally, by primary symptom, and hospital location. CONCLUSION/CONCLUSIONS: An AI-based COVID-19 risk score displayed passively to clinicians during routine care of hospitalized adults with COVID-19 was safe but had no detectable impact on LOS. Health technology challenges such as insufficient adoption, nonuniform use, and provider trust compounded with temporal factors of the COVID-19 pandemic may have contributed to the null result. TRIAL REGISTRATION/BACKGROUND: ClinicalTrials.gov identifier: NCT04570488.

OBJECTIVES/OBJECTIVE:High-value care is providing high quality care at low cost; we sought to define hospital value and identify the characteristics of hospitals which provide high-value care. DESIGN/METHODS:Retrospective observational study. SETTING/METHODS:Acute care hospitals in the USA. PARTICIPANTS/METHODS:All Medicare beneficiaries with claims included in Center for Medicare & Medicaid Services Overall Star Ratings or in publicly available Medicare spending per beneficiary data. PRIMARY AND SECONDARY OUTCOME MEASURES/METHODS:Our primary outcome was value defined as the difference between Star Ratings quality score and Medicare spending; the secondary outcome was classification as a 4 or 5 star hospital with lowest quintile Medicare spending ('high value') or 1 or 2 star hospital with highest quintile spending ('low value'). RESULTS:Two thousand nine hundred and fourteen hospitals had both quality and spending data, and were included. The value score had a mean (SD) of 0.58 (1.79). A total of 286 hospitals were classified as high value; these represented 28.6% of 999 4 and 5 star hospitals and 46.8% of 611 low cost hospitals. A total of 258 hospitals were classified as low value; these represented 26.6% of 970 1 and 2 star hospitals and 49.3% of 523 high cost hospitals. In regression models ownership, non-teaching status, beds, urbanity, nurse to bed ratio, percentage of dual eligible Medicare patients and percentage of disproportionate share hospital payments were associated with the primary value score. CONCLUSIONS:There are high quality hospitals that are not high value, and a number of factors are strongly associated with being low or high value. These findings can inform efforts of policymakers and hospitals to increase the value of care.

PURPOSE:The aim of the study was to describe factors that may impact pediatric trainees' willingness to disclose medical errors using clinical vignettes. METHODS:A single-center cross-sectional anonymous survey of pediatric residents and fellows at a large urban medical center in 2019 was conducted. Trainees were provided with clinical vignettes depicting an error resulting in a serious safety event (SSE), minor safety event (MSE), and near miss safety event (NMSE) and were asked to classify the type of safety event and rate and explain their agreement or disagreement with disclosure. Survey items also evaluated trainees' personal experiences with errors and disclosure. Descriptive and correlational analyses were used to characterize responses. Qualitative content from open-ended survey questions was analyzed using the constant comparative method. RESULTS:Of 126 trainees, 42 (33%) completed the survey. All agreed with disclosing the hypothetical error presented in the vignette resulting in an SSE (100%), with rates falling for the MSE (95%) and NMSE (7%). There were no significant associations between disclosure agreement for the vignettes and trainee demographic features, knowledge of safety events, prior personal experiences with errors, and disclosure. Four themes that emerged from qualitative analysis of trainees' rationales for disclosure or nondisclosure of the vignette errors are harm, parental preferences, ethical principles, and anticipatory guidance. CONCLUSIONS:Trainees had high rates of disclosure for the vignette errors cases that depicted SSEs and MSEs but lower rates for NMSEs. Trainees considered the type and level of harm caused, parental preferences, upholding ethical principles, and the need for anticipatory guidance in their rationales for disclosure or nondisclosure of the vignette errors.

OBJECTIVE:To evaluate the impact of structured recommendations on follow-up completion for incidental lung nodules (ILNs). METHODS:Patients with ILNs before and after implementation of structured Fleischner recommendations and electronic tracking were sampled randomly. The cohorts were compared for imaging follow-up. Multivariable logistic regression was used to assess appropriate follow-up and loss to follow-up, with independent variables including use of structured recommendations or tracking, age, gender, race, ethnicity, setting of the index test (inpatient, outpatient, emergency department), smoking history, and nodule features. RESULTS:In all, 1,301 patients met final inclusion criteria, including 255 patients before and 1,046 patients after structured recommendations or tracking. Baseline differences were found in the pre- and postintervention groups, with smaller ILNs and younger age after implementing structured recommendations. Comparing pre- versus postintervention outcomes, 40.0% (100 of 250) versus 29.5% (309 of 1,046) of patients had no follow-up despite Fleischner indications for imaging (P = .002), and among the remaining patients, 56.6% (82 of 145) versus 75.0% (553 of 737) followed up on time (P < .001). Delayed follow-up was more frequent before intervention. Differences postintervention were mostly accounted for by nodules ≤ 8 mm in the outpatient setting (P < .001). In multivariable analysis, younger age, White race, outpatient setting, and larger nodule size showed significant association with appropriate follow-up completion (P < .015), but structured recommendations did not. Similar results applied for loss to follow-up. DISCUSSION/CONCLUSIONS:Consistent use of structured reporting is likely key to mitigate selection bias when benchmarking rates of appropriate follow-up of ILN. Emergency department patients and inpatients are at high risk of missed or delayed follow-up despite structured recommendations.