Faculty Bibliography

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.

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.

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.