Faculty Bibliography

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.

Importance/UNASSIGNED:Hospital consolidations have been shown not to improve quality on average. Objective/UNASSIGNED:To assess a full-integration approach to hospital mergers based on quality metrics in a safety net hospital acquired by an urban academic health system. Design, Setting, and Participants/UNASSIGNED:This quality improvement study analyzed outcomes for all nonpsychiatric, nonrehabilitation, non-newborn patients discharged between September 1, 2010, and August 31, 2019, at a US safety net hospital that was acquired by an urban academic health system in January 2016. Interrupted time series and statistical process control analyses were used to assess the main outcomes and measures. Data sources included the hospital's electronic health record, Centers for Medicare & Medicaid Services Hospital Compare, and nursing quality reports. Exposures/UNASSIGNED:A full-integration approach to the merger that included: (1) early administrative and clinical leadership integration with the academic health system; (2) rapid transition to the academic health system electronic health record; (3) local ownership of quality metrics; (4) system-level goals with real-time actionable analytics through combined dashboards; and (5) implementation of value-based and other analytic-driven interventions. Main Outcomes and Measures/UNASSIGNED:The primary outcome was in-hospital mortality. Secondary outcomes included 30-day readmission, patient experience, and hospital-acquired conditions. Results/UNASSIGNED:The 122 348 patients in the premerger (September 2010 through August 2016) and the 58 904 patients in the postmerger (September 2016 through August 2019) periods had a mean (SD) age of 55.5 (22.0) years; the total sample of 181 252 patients included 112 191 women (61.9%), the payor mix was majority governmental (144 375 patients [79.7%]), and most admissions were emergent (121 469 patients [67.0%]). There was a 0.71% (95% CI, 0.57%-0.86%) absolute (27% relative) reduction in the crude mortality rate and 0.95% (95% CI, 0.83%-1.12%) absolute (33% relative) in the adjusted rate by the end of the 3-year intervention period. There was no significant improvement in readmission rates after accounting for baseline trends. There were fewer central line infections per 1000 catheter days, fewer catheter-associated urinary tract infections per 1000 discharges, and a higher likelihood of patients recommending the hospital or ranking it 9 or 10. Conclusions and Relevance/UNASSIGNED:In this quality improvement study, a hospital merger with a full-integration approach to consolidation was found to be associated with improvement in quality outcomes.

BACKGROUND:Prone positioning improves mortality in patients intubated with acute respiratory distress syndrome and has been proposed as a treatment for nonintubated patients with COVID-19 outside the ICU. However, there are substantial patient and operational barriers to prone positioning on acute floors. The objective of this project was to increase the frequency of prone positioning among acute care patients with COVID-19. METHODS:The researchers conducted a retrospective analysis of all adult patients admitted to the acute care floors with COVID-19 respiratory failure. A run chart was used to quantify the frequency of prone positioning over time. For the subset of patients assisted by a dedicated physical therapy team, oxygen before and after positioning was compared. The initiative consisted of four separate interventions: (1) nursing, physical therapy, physician, and patient education; (2) optimization of supply management and operations; (3) an acute care prone positioning team; and (4) electronic health record optimization. RESULTS:From March 9, 2020, to August 26, 2020, 176/875 (20.1%) patients were placed in prone position. Among these, 43 (24.4%) were placed in the prone position by the physical therapy team. Only 2/94 (2.1%) eligible patients admitted in the first two weeks of the pandemic were ever documented in prone position. After launching the initiative, weekly frequency peaked at 13/28 (46.4%). Mean oxygen saturation was 91% prior to prone positioning vs. 95.2% after (p < 0.001) in those positioned by physical therapy. CONCLUSION:A multidisciplinary quality improvement initiative increased frequency of prone positioning by proactively addressing barriers in knowledge, equipment, training, and information technology.

BACKGROUND:Previous work has demonstrated that patients experience functional decline at 1-3 months post-discharge after COVID-19 hospitalization. OBJECTIVE:To determine whether symptoms persist further or improve over time, we followed patients discharged after hospitalization for severe COVID-19 to characterize their overall health status and their physical and mental health at 6 months post-hospital discharge. DESIGN/METHODS:Prospective observational cohort study. PARTICIPANTS/METHODS:Patients ≥ 18 years hospitalized for COVID-19 at a single health system, who required at minimum 6 l of supplemental oxygen during admission, had intact baseline functional status, and were discharged alive. MAIN MEASURES/METHODS:Overall health status, physical health, mental health, and dyspnea were assessed with validated surveys: the PROMIS® Global Health-10 and PROMIS® Dyspnea Characteristics instruments. KEY RESULTS/RESULTS:Of 152 patients who completed the 1 month post-discharge survey, 126 (83%) completed the 6-month survey. Median age of 6-month respondents was 62; 40% were female. Ninety-three (74%) patients reported that their health had not returned to baseline at 6 months, and endorsed a mean of 7.1 symptoms. Participants' summary t-scores in both the physical health and mental health domains at 6 months (45.2, standard deviation [SD] 9.8; 47.4, SD 9.8, respectively) remained lower than their baseline (physical health 53.7, SD 9.4; mental health 54.2, SD 8.0; p<0.001). Overall, 79 (63%) patients reported shortness of breath within the prior week (median score 2 out of 10 (interquartile range [IQR] 0-5), vs 42 (33%) pre-COVID-19 infection (0, IQR 0-1)). A total of 11/124 (9%) patients without pre-COVID oxygen requirements still needed oxygen 6 months post-hospital discharge. One hundred and seven (85%) were still experiencing fatigue at 6 months post-discharge. CONCLUSIONS:Even 6 months after hospital discharge, the majority of patients report that their health has not returned to normal. Support and treatments to return these patients back to their pre-COVID baseline are urgently needed.

BACKGROUND:It is estimated that up to 40% of Alzheimer's Disease and Related Dementias cases can be prevented or delayed by addressing modifiable factors including those that influence vascular risk (hypertension, obesity, smoking, physical activity, diabetes). Prevention may be particularly important in the vascular dementia subtypes. Despite the supporting evidence, the rates of medical therapy to reduce vascular risk are not well described. METHOD/METHODS:We assessed the utilization of statins, aspirin, and blood pressure (BP) medications in adults age ≥65 years cared for at NYU Langone Health, as recorded in the electronic health record. We included two cohorts: cohort 1 included patients who were diagnosed with vascular dementia (VaD) at NYU Langone Barlow Center for Memory Evaluation between January 1, 2015 and June 24, 2019. Cohort 2 extended the inclusion to seniors with VD diagnosis by any NYU Langone physician. Definitions for vascular dementia, the covariates assessed, and medications that we included in each category are shown in Tables 1-3. RESULT/RESULTS:We included 419 and 3745 patients in cohort 1 and cohort 2, respectively. Table 4 shows the characteristics and medication adherence in cohorts 1 and 2. In cohort 1, the prescription rates for statins, aspirin, and BP medications were 66%, 66%, 70%. In cohort 2, the rates for statin, aspirin, and BP medications were 56%, 46%, and 65%, respectively. The differences between prescription rates in cohort 1 and 2 for the three medication groups were statistically significant (p<0.05). CONCLUSION/CONCLUSIONS:Our analysis of the utilization of cardiovascular medications among patients with vascular dementia illuminates potential gaps both among patients who receive care at specialty clinics, as well as the overall population with vascular dementia. The rates of medication utilization are higher for patients under the care of cognitive neurologists. Electronic health records can help identify large cohorts of patients who may benefit from improved access to preventative measures including cardiovascular medications.

BACKGROUND:As health-care spending rises internationally, policymakers have increasingly begun to look to improve health-care value. However, the precise definition of health-care value remains ambiguous. METHODS:We conducted a scoping review of the literature to understand how value has been defined in the context of health care. We searched PubMed, Embase, Google Scholar, PolicyFile and Scopus between February and March 2020 to identify articles eligible for inclusion. Publications that defined value (including high or low value) using an element of cost and an element of outcomes were included in this review. No restrictions were placed on the date of publication. Articles were limited to those published in English. RESULTS:Out of 1750 publications screened, 46 met inclusion criteria. Among the 46 included articles, 22 focused on overall value, 19 on low value and 5 on high value. We developed a framework to categorize definitions based on three core domains: components, perspective and scope. Differences across these three domains contributed to significant variations in definitions of value. CONCLUSIONS:How value is defined has the potential to influence measurement and intervention strategies in meaningful ways. To effectively improve value in health-care systems, we must understand what is meant by value and the merits of different definitions.

OBJECTIVES/OBJECTIVE:Predictive studies play important roles in the development of models informing care for patients with COVID-19. Our concern is that studies producing ill-performing models may lead to inappropriate clinical decision-making. Thus, our objective is to summarise and characterise performance of prognostic models for COVID-19 on external data. METHODS:We performed a validation of parsimonious prognostic models for patients with COVID-19 from a literature search for published and preprint articles. Ten models meeting inclusion criteria were either (a) externally validated with our data against the model variables and weights or (b) rebuilt using original features if no weights were provided. Nine studies had internally or externally validated models on cohorts of between 18 and 320 inpatients with COVID-19. One model used cross-validation. Our external validation cohort consisted of 4444 patients with COVID-19 hospitalised between 1 March and 27 May 2020. RESULTS:Most models failed validation when applied to our institution's data. Included studies reported an average validation area under the receiver-operator curve (AUROC) of 0.828. Models applied with reported features averaged an AUROC of 0.66 when validated on our data. Models rebuilt with the same features averaged an AUROC of 0.755 when validated on our data. In both cases, models did not validate against their studies' reported AUROC values. DISCUSSION/CONCLUSIONS:Published and preprint prognostic models for patients infected with COVID-19 performed substantially worse when applied to external data. Further inquiry is required to elucidate mechanisms underlying performance deviations. CONCLUSIONS:Clinicians should employ caution when applying models for clinical prediction without careful validation on local data.