Faculty Bibliography

OBJECTIVE. The purpose of this study is to assess the perceptions of radiologists and emergency medicine (EM) providers regarding the quality, value, and challenges associated with using outside imaging (i.e., images obtained at facilities other than their own institution). MATERIALS AND METHODS. We surveyed radiologists and EM providers at a large academic medical center regarding their perceptions of the availability and utility of outside imaging. RESULTS. Thirty-four of 101 radiologists (33.6%) and 38 of 197 EM providers (19.3%) responded. A total of 32.4% of radiologists and 55.3% of EM providers had confidence in the quality of images from outside community facilities; 20.6% and 44.7%, respectively, had confidence in the interpretations of radiologists from these outside facilities. Only 23.5% of radiologists and 5.3% of EM physicians were confident in their ability to efficiently access reports (for outside images, 47.1% and 5.3%). Very few radiologists and EM providers had accessed imaging reports from outside facilities through an available stand-alone portal. A total of 40.6% of radiologists thought that outside reports always or frequently reduced additional imaging recommendations (62.5% for outside images); 15.6% thought that reports changed interpretations of new examinations (37.5% for outside images); and 43.8% thought that reports increased confidence in interpretations of new examinations (75.0% for outside images). A total of 29.4% of EM providers thought that access to reports from outside facilities reduced repeat imaging (64.7% for outside images), 41.2% thought that they changed diagnostic or management plans (50.0% for outside images), and 50.0% thought they increased clinical confidence (67.6% for outside images). CONCLUSION. Radiologists and EM providers perceive high value in sharing images from outside facilities, despite quality concerns. Substantial challenges exist in accessing these images and reports from outside facilities, and providers are unlikely to do so using separate systems. However, even if information technology solutions for seamless image integration are adopted, providers' lack of confidence in outside studies may remain an important barrier.

The past decade has seen a growing recognition of the importance of social determinants of health for health outcomes. However, the degree to which US health systems are directly investing in community programs to address social determinants of health as opposed to screening and referral is uncertain. We searched for all public announcements of new programs involving direct financial investments in social determinants of health by US health systems from January 1, 2017, to November 30, 2019. We identified seventy-eight unique programs involving fifty-seven health systems that collectively included 917 hospitals. The programs involved at least $2.5 billion of health system funds, of which $1.6 billion in fifty-two programs was specifically committed to housing-focused interventions. Additional focus areas were employment (twenty-eight programs, $1.1 billion), education (fourteen programs, $476.4 million), food security (twenty-five programs, $294.2 million), social and community context (thirteen programs, $253.1 million), and transportation (six programs, $32 million). Health systems are making sizable investments in social determinants of health.

BACKGROUND/OBJECTIVE/OBJECTIVE:Heart failure (HF) readmission rates have plateaued despite scrutiny of hospital discharge practices. Many HF patients are discharged to skilled nursing facility (SNF) after hospitalization before returning home. Home healthcare (HHC) services received during the additional transition from SNF to home may affect readmission risk. Here, we examined whether receipt of HHC affects readmission risk during the transition from SNF to home following HF hospitalization. DESIGN/METHODS:Retrospective cohort study. SETTING/METHODS:Fee-for-service Medicare data, 2012 to 2015. PARTICIPANTS/METHODS:Beneficiaries, aged 65 years and older, hospitalized with HF who were subsequently discharged to SNF and then discharged home. MEASUREMENTS/METHODS:The primary outcome was unplanned readmission within 30 days of discharge to home from SNF. We compared time to readmission between those with and without HHC services using a Cox model. RESULTS:Of 67 585 HF hospitalizations discharged to SNFs and subsequently discharged home, 13 257 (19.6%) were discharged with HHC, and 54 328 (80.4%) were discharged without HHC. Patients discharged home from SNFs with HHC had lower 30-day readmission rates than patients discharged without HHC (22.8% vs 24.5%; P < .0001) and a longer time to readmission. In an adjusted model, the hazard for readmission was 0.91 (0.86-0.95) with receipt of HHC. CONCLUSIONS:Recipients of HHC were less likely to be readmitted within 30 days vs those discharged home without HHC. This is unexpected, as patients discharged with HHC likely have more functional impairments. Since patients requiring a SNF stay after hospital discharge may have additional needs, they may particularly benefit from restorative therapy through HHC; however, only approximately 20% received such services.

The COVID-19 pandemic has challenged front-line clinical decision-making, leading to numerous published prognostic tools. However, few models have been prospectively validated and none report implementation in practice. Here, we use 3345 retrospective and 474 prospective hospitalizations to develop and validate a parsimonious model to identify patients with favorable outcomes within 96 h of a prediction, based on real-time lab values, vital signs, and oxygen support variables. In retrospective and prospective validation, the model achieves high average precision (88.6% 95% CI: [88.4-88.7] and 90.8% [90.8-90.8]) and discrimination (95.1% [95.1-95.2] and 86.8% [86.8-86.9]) respectively. We implemented and integrated the model into the EHR, achieving a positive predictive value of 93.3% with 41% sensitivity. Preliminary results suggest clinicians are adopting these scores into their clinical workflows.

BACKGROUND:The environment in which a patient lives influences their health outcomes. However, the degree to which community factors are associated with readmissions is uncertain. OBJECTIVE:To estimate the influence of community factors on the Centers for Medicare & Medicaid Services risk-standardized hospital-wide readmission measure (HWR)-a quality performance measure in the U.S. RESEARCH DESIGN/METHODS:We assessed 71 community variables in 6 domains related to health outcomes: clinical care; health behaviors; social and economic factors; the physical environment; demographics; and social capital. SUBJECTS/METHODS:Medicare fee-for-service patients eligible for the HWR measure between July 2014-June 2015 (n = 6,790,723). Patients were linked to community variables using their 5-digit zip code of residence. METHODS:We used a random forest algorithm to rank variables for their importance in predicting HWR scores. Variables were entered into 6 domain-specific multivariable regression models in order of decreasing importance. Variables with P-values <0.10 were retained for a final model, after eliminating any that were collinear. RESULTS:Among 71 community variables, 19 were retained in the 6 domain models and in the final model. Domains which explained the most to least variance in HWR were: physical environment (R2 = 15%); clinical care (R2 = 12%); demographics (R2 = 11%); social and economic environment (R2 = 7%); health behaviors (R2 = 9%); and social capital (R2 = 8%). In the final model, the 19 variables explained more than a quarter of the variance in readmission rates (R2 = 27%). CONCLUSIONS:Readmissions for a wide range of clinical conditions are influenced by factors relating to the communities in which patients reside. These findings can be used to target efforts to keep patients out of the hospital.

PURPOSE/OBJECTIVE:To develop natural language processing (NLP) to identify incidental lung nodules (ILNs) in radiology reports for assessment of management recommendations. METHOD AND MATERIALS/METHODS:We searched the electronic health records for patients who underwent chest CT during 2014 and 2017, before and after implementation of a department-wide dictation macro of the Fleischner Society recommendations. We randomly selected 950 unstructured chest CT reports and reviewed manually for ILNs. An NLP tool was trained and validated against the manually reviewed set, for the task of automated detection of ILNs with exclusion of previously known or definitively benign nodules. For ILNs found in the training and validation sets, we assessed whether reported management recommendations agreed with Fleischner Society guidelines. The guideline concordance of management recommendations was compared between 2014 and 2017. RESULTS:The NLP tool identified ILNs with sensitivity and specificity of 91.1% and 82.2%, respectively, in the validation set. Positive and negative predictive values were 59.7% and 97.0%. In reports of ILNs in the training and validation sets before versus after introduction of a Fleischner reporting macro, there was no difference in the proportion of reports with ILNs (108 of 500 [21.6%] versus 101 of 450 [22.4%]; P = .8), or in the proportion of reports with ILNs containing follow-up recommendations (75 of 108 [69.4%] versus 80 of 101 [79.2%]; P = .2]. Rates of recommendation guideline concordance were not significantly different before and after implementation of the standardized macro (52 of 75 [69.3%] versus 60 of 80 [75.0%]; P = .43). CONCLUSION/CONCLUSIONS:NLP reliably automates identification of ILNs in unstructured reports, pertinent to quality improvement efforts for ILN management.

BACKGROUND:Unplanned readmissions following elective total hip (THA) and knee (TKA) arthroplasty as a result of surgical complications likely have different quality improvement targets and cost implications than those for nonsurgical readmissions. We compared payments, timing, and location of unplanned readmissions with Center for Medicare and Medicaid Services (CMS)-defined surgical complications to readmissions without such complications. METHODS:We performed a retrospective analysis on unplanned readmissions within 90 days of discharge following elective primary THA/TKA among Medicare patients discharged between April 2013 and March 2016. We categorized unplanned readmissions into groups with and without CMS-defined complications. We compared the location, timing, and payments for unplanned readmissions between both readmission categories. RESULTS:Among THA (N = 23,231) and TKA (N = 43,655) patients with unplanned 90-day readmissions, 27.1% (n = 6307) and 16.4% (n = 7173) had CMS-defined surgical complications, respectively. These readmissions with surgical complications were most commonly at the hospital of index procedure (THA: 84%; TKA: 80%) and within 30 days postdischarge (THA: 73%; TKA: 77%). In comparison, it was significantly less likely for patients without CMS-defined surgical complications to be rehospitalized at the index hospital (THA: 63%; TKA: 63%; P < .001) or within 30 days of discharge (THA: 58%; TKA: 59%; P < .001). Generally, payments associated with 90-day readmissions were higher for THA and TKA patients with CMS-defined complications than without (P < .001 for all). CONCLUSION/CONCLUSIONS:Readmissions associated with surgical complications following THA and TKA are more likely to occur at the hospital of index surgery, within 30 days of discharge, and cost more than readmissions without CMS-defined surgical complications, yet they account for only 1 in 5 readmissions.