Faculty Bibliography

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.

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.

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.

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.

BACKGROUND:Evidence-based therapy for heart failure remains underutilized at hospital discharge, particularly for patients with heart failure who are hospitalized for another cause. We developed clinical decision support (CDS) to recommend an angiotensin converting enzyme (ACE) inhibitor during hospitalization to promote its continuation at discharge. The CDS was designed to be implemented in both interruptive and non-interruptive versions. OBJECTIVES/OBJECTIVE:To compare the effectiveness and implementation of interruptive and non-interruptive versions of a CDS to improve care for heart failure. METHODS:Hospitalizations of patients with reduced ejection fraction were pseudo-randomized to deliver interruptive or non-interruptive CDS alerts to providers based on even or odd medical record number. We compared discharge utilization of an ACE inhibitor or angiotensin receptor blocker (ARB) for these two implementation approaches. We also assessed adoption and implementation fidelity of the CDS. RESULTS:percentile) of 14 (5,32) alerts were triggered per hospitalization. CONCLUSIONS:A CDS implemented as an interruptive alert was associated with improved quality of care for heart failure. Whether the potential benefits of CDS in improving cardiovascular care were worth the high burden of interruptive alerts deserves further consideration. CLINICALTRIALS. GOV IDENTIFIER/UNASSIGNED:NCT02858674.

BACKGROUND:The Hospital Readmissions Reduction Program (HRRP) penalizes hospitals for higher-than-expected readmission rates. Almost 20% of Medicare fee-for-service (FFS) patients receive postacute care in skilled nursing facilities (SNFs) after hospitalization. SNF patients have high readmission rates. OBJECTIVE:The objective of this study was to investigate the association between changes in hospital referral patterns to SNFs and HRRP penalty pressure. DESIGN/METHODS:We examined changes in the relationship between penalty pressure and outcomes before versus after HRRP announcement among 2698 hospitals serving 6,936,393 Medicare FFS patients admitted for target conditions: acute myocardial infarction, heart failure, or pneumonia. Hospital-level penalty pressure was the expected penalty rate in the first year of the HRRP multiplied by Medicare discharge share. OUTCOMES/RESULTS:Informal integration measured by the percentage of referrals to hospitals' most referred SNF; formal integration measured by SNF acquisition; readmission-based quality index of the SNFs to which a hospital referred discharged patients; referral rate to any SNF. RESULTS:Hospitals facing the median level of penalty pressure had modest differential increases of 0.3 percentage points in the proportion of referrals to the most referred SNF and a 0.006 SD increase in the average quality index of SNFs referred to. There were no statistically significant differential increases in formal acquisition of SNFs or referral rate to SNF. CONCLUSIONS:HRRP did not prompt substantial changes in hospital referral patterns to SNFs, although readmissions for patients referred to SNF differentially decreased more than for other patients, warranting investigation of other mechanisms underlying readmissions reduction.